Data Management – 🔴 Red Button Data

ACTIAN Data Management

Oct 4 2024

Get to Know the Value of the Zeenea Data Discovery Platform

The Zeenea Data Discovery Platform is a cloud-native SaaS data discovery and metadata management solution that democratizes data access and accelerates your data-driven business initiatives. It is designed to help you efficiently find, understand, and trust enterprise data assets. As businesses like yours look to create and connect massive amounts of data from diverse sources, you need the ability to consolidate, govern, and make sense of that data to ensure confident decision-making and drive innovation.

The Zeenea platform is unique in the marketplace. It leverages a knowledge graph and automated processes to simplify the management of data and metadata while enhancing the overall user experience. At its core, the Zeenea Data Discovery Platform functions as a smart data catalog to deliver a sophisticated solution that goes beyond basic data inventory. By utilizing a dynamic metamodel and advanced search capabilities, the platform lets you effectively explore, curate, and manage data assets across the organization.

5 Key Capabilities of the Zeenea Data Discovery Platform

The Zeenea Data Discovery Platform solves challenges such as managing the ever-increasing volume of data assets, meeting the needs of a growing number of data producers and data consumers, and closing the knowledge gap caused by a lack of data literacy in many organizations. It can connect to all of your data sources in seconds—less time than it took you to read this.

The platform offers capabilities that include:

Automated Metadata Management and Inventory. One of the platform’s standout features is its ability to automatically gather and manage metadata from different data sources. By leveraging built-in scanners, the platform runs through various databases, applications, and data storage systems to build an accurate inventory of data assets. This approach eliminates the need for manual input, reducing the likelihood of errors and ensuring that data inventories are always up to date.

For instance, the platform can automatically connect, consolidate, and link metadata from systems such as relational databases, file systems, cloud solutions, and APIs. This approach also allows the platform to generate valuable metadata insights such as data profiling, which helps identify patterns, top values, and distributions of null values within datasets.

Metamodeling for Flexibility and Scalability. Zeenea’s metamodel is the backbone of its flexibility. Unlike static data catalogs, the Zeenea Data Discovery Platform allows you to create and evolve your metamodel based on your specific use cases. This means you can define new object classes or attributes as your data management needs grow.

As the platform scales, so does the metamodel, allowing for continuous adaptation and expansion of the data catalog. This flexibility is critical for businesses operating in fast-paced environments with ever-evolving data governance requirements.

Knowledge Graph-Driven Search and Discovery. The knowledge graph architecture is one of the most powerful features of the platform. It underpins the platform’s search engine, which allows you to navigate through complex datasets easily. Unlike traditional flat-index search engines, Zeenea’s search engine integrates natural language processing (NLP) and semantic analysis to provide more relevant and meaningful results.

This means you can quickly find the most relevant datasets, even when you aren’t exactly sure what you’re looking for. For instance, business analysts looking for customer data might not know the exact technical terms they need, but with Zeenea’s intuitive search, they can use everyday language to find the appropriate datasets.

Role-Based Interfaces: Zeenea Studio and Zeenea Explorer. These applications cater to different user needs. Zeenea offers two distinct interfaces:

- Zeenea Studio is designed for data stewards and administrators responsible for managing and curating data. The tool helps ensure the accuracy, completeness, and governance of the data within the catalog.
- Zeenea Explorer is a user-friendly interface tailored for business users or data consumers. It allows them to search, filter, and explore data assets with ease, without requiring deep technical knowledge.

This dual-interface approach ensures that each user type can interact with the platform in a way that suits their needs and role within your organization.

Security and Compliance. The platform is SOC 2 Type II certified and ISO 27001 compliant, meaning it meets the highest security standards required by industries such as banking, healthcare, and government. This makes the platform a trusted solution to manage sensitive data and for those doing business in heavily regulated sectors.

Sample Use Cases for the Zeenea Data Discovery Platform

Organizations across industries can benefit from the data discovery capabilities offered by the Zeenea platform. Use cases include:

Data Governance for Financial Services. In the financial services sector, data governance is critical to ensure regulatory compliance and maintain operational efficiency. The Zeenea Data Discovery Platform can be used to automate the documentation of data lineage, classify sensitive data, and ensure proper access controls are in place. Financial institutions can use Zeenea’s metadata management to track the flow of data across various systems, ensuring full compliance with regulations such as GDPR.
Customer 360 Insights for Retailers. Retail businesses generate vast amounts of customer data across various channels, such as in-store purchases, online transactions, or marketing interactions. With Zeenea, retailers can consolidate this data into a single source of truth, ensuring that business teams have the accurate, up-to-date data they need for customer analytics and to personalize marketing campaigns. The platform’s search and discovery capabilities allow marketing teams to easily find datasets related to customer behavior, preferences, and trends.
Improving Operational Efficiency for Healthcare. In healthcare, maintaining high data quality is essential for improving patient outcomes and complying with regulations. Hospitals and other healthcare organizations can use the Zeenea platform to govern and manage patient data, ensure data accuracy, and streamline reporting processes. Zeenea’s role-based interfaces make it easy for healthcare administrators to navigate complex datasets while ensuring sensitive information remains secure.
Scaling Data Discovery for Telecommunications. Telcos manage complex data ecosystems with data sources ranging from IoT devices to customer management systems. The Zeenea platform’s ability to automate metadata management and its scalable metamodel gives telcos the ability to effectively track, manage, and discover data across their vast infrastructure. This ensures that data teams can quickly find operational data to improve services and identify areas for innovation.

The Value of Zeenea for Modern Businesses

Your business demands a holistic view of data assets to facilitate their effective use. This requires the data lineage and metadata management capabilities enabled by the Zeenea Data Discovery Platform. The platform enables you to gain more value from your data by:

Enhancing Decision-Making. By providing a comprehensive overview of your data landscape, the Zeenea Data Discovery Platform helps you make more informed decisions. The ability to quickly find and trust data means you can act faster and with greater confidence.
Improving Data Governance. Zeenea facilitates strong data governance by enabling you to automatically track data lineage, classify assets, and manage compliance requirements. This is particularly valuable in industries like finance and healthcare where regulations demand high levels of oversight and transparency.
Increasing Operational Efficiency. The platform’s automation capabilities free up valuable time for data stewards and administrators, allowing them to focus on higher-value tasks instead of manual data cataloging. This, in turn, reduces operational bottlenecks and improves the overall efficiency of data teams.
Future-Proofing Data Management. As you grow and your data needs evolve, Zeenea’s flexible architecture ensures that you can continue to scale your data catalog without running into limitations. The dynamic metamodel allows you to adapt to new use cases, technologies, and governance requirements as they emerge.

Build Trust in Your Data Assets

The Zeenea Data Discovery Platform provides modern businesses like yours with a smart, scalable, and secure solution for data management and discovery. Its robust features, including automated metadata management, role-based interfaces, and advanced search capabilities, can give you confidence in data governance and discovery as well as your ability to fully optimize your data assets.

If you’re looking to improve operational efficiency, enhance decision-making, and ensure strong data governance, Zeenea offers a modern platform to achieve these goals. Experience it for yourself with a personalized demo.

The post Get to Know the Value of the Zeenea Data Discovery Platform appeared first on Actian.

Why Confidence in Data is Important for Business Growth

It’s no surprise to any of today’s business leaders that data technologies are experiencing unprecedented and rapid change. The rise of Artificial Intelligence (AI), its subset Generative AI (GenAI), machine learning, and other advanced technologies has enabled new and emerging opportunities at a pace never experienced before.

Yet with these opportunities comes a series of challenges such as navigating data privacy regulations, ensuring data quality and governance, and managing the increasing complexity of data integration across multiple systems. For modern organizations, staying ahead of these challenges hinges on one critical asset—data.

Data has become the lifeblood of innovation, strategy, and decision-making for forward-looking organizations. Companies that leverage data effectively can identify trends faster, make smarter decisions, and maintain a competitive edge. However, data in itself is not enough. To truly capitalize on its potential, organizations must have confidence in their data—which requires having data that’s trusted and easy to use.

What Does Data Confidence Mean?

At its core, confidence in data means trusting that the data informing decision-making is accurate, reliable, and timely. Without this assurance, data-driven insights can be flawed, leading to poor decision-making, missed opportunities, and distrust in the data.

Confidence in data comes from three key factors:

Data quality. Poor data quality can lead to disastrous results. Whether it’s incomplete data, outdated or duplicated information, or inconsistent data values, low-quality data reduces the accuracy of insights and predictions. Ensuring decisions are based on accurate information requires data to be cleansed, validated, and maintained regularly. It should also be integrated organization-wide to avoid the pervasive problem of data silos.
Data accessibility. Even if an organization has high-quality data, it’s of little use if it’s fragmented or difficult to access. For businesses to function effectively, they need a seamless flow of data across departments, systems, and processes. Ensuring data is accessible to all relevant stakeholders, applications, and systems is crucial for achieving operational efficiency and becoming a truly data-driven organization.
Data integration. Today’s businesses manage an ever-growing volume of data from numerous sources, including customer data, transaction data, and third-party data. Without technology and processes in place to integrate all these data sets into a cohesive, single source of information, businesses face a disjointed view of their operations. A well-integrated data platform provides a unified view, enabling more strategic, insightful, and confident decision-making.

An Ever-Evolving Data Management Environment

As the business landscape shifts, the way data is managed, stored, and analyzed also evolves. Traditional data management systems are no longer sufficient for handling the large volume, variety, and velocity of data bombarding modern organizations. That’s why today’s business environment demands modern, high-performance, scalable data solutions that can grow with them and meet their future needs.

The rise of cloud computing, AI, and edge computing has introduced new possibilities for businesses, but they have also added layers of complexity. To navigate this increasingly intricate ecosystem, businesses must be agile, capable of strategically adapting to new technologies while maintaining confidence in their data.

With the rapid pace of innovation, implementing new tools is not enough. Companies must also establish a strong foundation of trust in their data. This is where a modern data management solution becomes invaluable, enabling organizations to optimize the full power of their data with confidence.

Confidence in Technology: The Backbone of Innovation

Confidence isn’t just about the data—it extends to the various technologies that businesses rely on to process, analyze, and store that data. Businesses require scalable, flexible technology stacks that can handle growing workloads, perform a range of use cases, and adapt to changing demands.

Many organizations are transitioning to hybrid or multi-cloud environments to better support their data needs. These environments offer flexibility, enabling businesses to deploy data solutions that align with their unique requirements while providing the freedom to choose where data is stored and processed for various use cases.

Not surprisingly, managing these sophisticated ecosystems requires a high level of confidence in the underlying technology infrastructure. If the technology fails, data flow is disrupted, decisions are delayed, and business operations suffer. To prevent this, organizations require reliable systems that ensure seamless data management, minimize downtime, and maintain operational efficiency to keep the business running smoothly.

Confidence in technology also means investing in future-proof systems that can scale alongside the organization. As data volumes continue to grow, the ability to scale without sacrificing performance is critical for long-term success. Whether companies are processing operational data in real time or running complex analytical workloads, the technology must be robust enough to deliver consistent, high-quality results.

5 Steps to Build Confidence in Data

Ultimately, the goal of any data strategy is to drive better business outcomes. Data-driven decision-making has the power to transform how businesses operate, from improving customer experiences to optimizing supply chains to improving financial performance. Achieving these outcomes requires having confidence in the decisions themselves.

This is where analytics and real-time insights come into play. Organizations that can harness data for real-time analysis and predictions are better equipped to respond to market changes, customer needs, and internal challenges. The ability to make data-driven decisions with confidence allows businesses to innovate faster, streamline operations, and accelerate growth.

For organizations to trust their data and the systems that manage it, they need to implement a strategy focused on reliability, usability, and flexibility. Here are five ways businesses can build confidence in their data:

Invest in data quality tools. Implementing data governance policies and investing in tools to clean and maintain data help ensure that information is accurate and reliable. Performing regular audits and monitoring can prevent data integrity issues before they impact decision-making.
Ensure seamless data integration. Data from various sources must be integrated into a single, unified platform while maintaining quality. By breaking down silos and enabling smooth data flows, businesses can gain a holistic view of their operations, leading to more informed decisions.
Leverage scalable technology. Modern data platforms offer the flexibility to handle both current and future workloads. As business needs evolve, having a scalable system allows organizations to expand capacity without disrupting operations or sacrificing performance.
Empower all departments with data accessibility. Data should be easily accessible to all teams and individuals who need it, not just data scientists or those with advanced IT skills. When everyone in the organization can leverage data without barriers, it fosters a culture of collaboration and innovation.
Adapt to emerging technologies. Staying ahead of technological advancements is key to maintaining a competitive edge. Businesses should evaluate new technologies like GenAI, machine learning, and edge computing to understand how they can enhance their data strategies.

Why Choose Actian for Your Data Needs?

For businesses navigating an era of exponential change, having confidence in their data and technology is essential for success. Actian can foster that confidence. As an industry leader with more than 50 years of experience, Actian is committed to delivering trusted, easy-to-use, and flexible solutions that meet the data management needs of modern organizations in any industry.

For example, the Actian Data Platform enables businesses to connect, govern, and analyze their data with confidence, ensuring they can make informed decisions that drive growth. With a unified, high-performance data platform and a commitment to innovation, Actian helps organizations turn challenges into opportunities and confidently embrace whatever is next.

Explore how Actian can help your business achieve data-driven success today.

The post Why Confidence in Data is Important for Business Growth appeared first on Actian.

Data Crime: Cartoon Signatures

I call it a “data crime” when someone is abusing or misusing data. When we understand these stories and their implications, it can help us learn from the mistakes and prevent future data crimes. The stories can also be helpful if you must explain the importance of  data management to someone.  The Story The state of Rhode […]

Fundamentals of Edge-to-Cloud Data Management

Over the last few years edge computing has progressed significantly, both in capability and availability, continuing a progressive trend of data management at the edge. According to a recent report, the number of Internet of Things (IoT) devices worldwide is forecast to almost double from 15.9 billion in 2023 to more than 32.1 billion IoT devices in 2030. However, during that time one thing has remained constant. There has been a need for good Edge-to-Cloud data management foundations and practices.

In this blog post, we will provide an overview of edge-to-cloud data management. We will explore the main concepts, benefits, and practical applications that can help you make the most of your data.

The Edge: Where Data Meets Innovation

At the heart of edge-to-cloud data management lies the edge – the physical location where data is generated. From sensors and IoT devices to wearable technology and industrial machinery, the edge is a treasure trove of real-time insights. By processing and analyzing data closer to its source, you can reduce latency, improve efficiency, and unlock new opportunities for innovation.

The Power of Real-Time Insights

Imagine the possibilities when you can access and analyze data in real-time. Whether you’re optimizing manufacturing processes, improving customer experiences, or making critical business decisions, real-time insights provide a competitive edge.

Predictive maintenance: Prevent equipment failures and minimize downtime by analyzing sensor data to detect anomalies and predict potential issues.
Enhanced customer experiences: Personalize recommendations, optimize inventory, and provide exceptional service by leveraging real-time customer data.
Intelligent operations: Optimize fleet management, streamline supply chains, and improve energy efficiency with real-time data-driven insights.

The Benefits of Edge-to-Cloud Data Management

By implementing an effective edge-to-cloud data management strategy, you can:

Reduce latency and improve response times: Process data closer to its source to make faster decisions.
Enhance operational efficiency: Optimize processes, reduce costs, and improve productivity.
Gain a competitive advantage: Unlock new opportunities for innovation and growth.
Improve decision-making: Make data-driven decisions based on real-time insights.
Ensure data privacy and security: Protect sensitive data from unauthorized access and breaches.

Want to Learn More?

This blog post has only scratched the surface of the exciting world of edge-to-cloud data management. To dive deeper into the concepts, techniques, and best practices, be sure to download our comprehensive ebook – Edge Data Management 101.

Our eBook will cover:

The fundamentals of edge computing.
Best practices for edge data management.
Real-world use cases and success stories.
Security considerations and best practices.
The future of edge data management.

Don’t miss out on this opportunity to stay ahead of the curve. Download your free copy of our eBook today and unlock the power of real-time data at the edge.

The post Fundamentals of Edge-to-Cloud Data Management appeared first on Actian.

Data Crime: Your Phone Isn’t Here

I call it a “data crime” when someone is abusing or misusing data. When we understand these stories and their implications, it can help us learn from the mistakes and prevent future data crimes. The stories can also be helpful if you must explain the importance of data management to someone. The Story Last year, a […]

Actian’s Innovation Earns Prestigious IT4IT Award

Innovation is essential for meeting organizations’ business, IT, and technical needs. It’s why Actian invests more than 20% of our revenue in research and development. In addition to the positive responses we hear from customers for helping them solve their toughest business challenges, we also receive accolades from industry peers.

For example, we recently earned the Award of Distinction in the category “IT4IT Standard / IT Management and Planning.” The honor was decided by the jury of The Open Group India Awards 2024, which recognized our efforts to effectively employ open standards and open source. The Jury Panel called our award a testament to our outstanding work and our clear path toward the effective use of open standards and open source.

At Actian, we use the IT4IT reference architecture to manage our business and the end-to-end lifecycles of all Actian products, such as the Actian Data Platform, Vector, and Zen.

This open standard is backed by around 900 members of the Open Group that include HCLTech and almost every other industry leader as well as government institutions.

Bringing Ongoing Value to Customers

To earn the award, we provided a detailed assessment that focused on the value streams we deliver and showcased how these streams bring new and ongoing benefits to customers. The assessment included these eight key aspects of our offerings:

Modern product management practices. Our teams successfully use IT4IT, a scaled agile framework, DevOps, and site reliability engineering where appropriate for a modern, innovative approach to open standards and open source.
Continuous improvement. We ensure strong management support for optimizing the lifecycles of our digital products and services with a focus on ongoing improvement and sustainable value.
Mature product development. From gathering requirements to meet customers’ needs to releasing new products and updates, we optimize robust, value-centric processes to deliver modern, flexible, and easy-to-use products.
Ongoing customer focus. The customer is at the heart of everything we do. We maintain a strong customer focus, ensuring our products meet their business needs to build confidence in the user and data management experience.
An automation mindset. Operations are streamlined using automated order fulfillment to provide quick and easy delivery to the customer.
Accurate billing. Established mechanisms for metering and billing customers provide a quick overview of the Actian Units used in the cloud while ensuring transparent and accurate pricing.
Trusted reliability. We employ a proactive approach to system reliability using site reliability engineering.
Tool rationalization initiative. With ongoing initiatives to optimize the software landscape in engineering and throughout our organization, we drive increased efficiency and reduce costs.

What Does the Product Journey Look Like?

Delivering industry-leading products requires detailed steps to ensure success. Our journey to product delivery is represented in detail here:

This is how the four aspects work together and are implemented:

Strategy to Portfolio. In this planning process, Actian manages ISO 27001-compliant internal and external policies in Confluence. The strategic planning is handled by a dedicated team with regular reviews by the project management office and executive leadership team. This aligns the plans to our vision and governance through the executive team.

Based on these plans, the executive leadership team provides strategic funding and resource allocation for the development of projects. The development and governance of the architecture roadmap are managed by the architecture board.

Requirement to Deploy. This building process entails sprint grooming to ensure a clear understanding of user stories and to facilitate the required collection and tracking of requirements, which then benefit future products and features.

At Actian, we use efficient, automated deployments with small batch continuous integration, robust testing, version control, and seamless integrations in our development processes. This is complemented by efficient testing, extensive automation, version-controlled test cases, realistic performance testing, and integrated shift-left practices in continuous integration and continuous development pipelines with defect management.

Of course, source code version control is used to ensure traceability through testing and comments, and to promote code reuse. The code changes are traceable for build package promotion, automated validation, and centralized repository.

Request to Fulfill. In this process, during and after delivery, Actian provides a strong user engagement with self-service resources, efficient ordering and fulfillment, integrated support, effective ticket management, and collaborative issue resolution.

The external service offering is efficient, with strong contract management, knowledge sharing, and automated deployment plans along with Jira service desk and Salesforce integration. Customer instances are created via self-service with automated orchestration, deployment guidelines, Kubernetes provisioning, and continuous deployment. In addition, the billing system provides a robust usage and metering Actian Unit hour calculation system with RabbitMQ integration and usage history generation.

Detect to Correct. In this final process that involves running the product, Actian provides collaborative SLA performance reviews in tiered service contracts (Gold, Silver, and Bronze), and Salesforce integration for SLA data. Knowledge is shared through a repository.

Actian offers a site reliability engineering framework with clear lifecycle stages, along with a rich knowledge base. A robust performance and availability monitoring system is also provided.

Identifying Opportunities for Improvements and Closing Gaps

As with any major assessment, there are ongoing opportunities for improvements and identifying gaps in services or capabilities. These are evaluated and addressed to further improve Actian products and offerings.

Opportunities for improvements to our Actian processes included 12 instances for integration. These integration opportunities can benefit the development and delivery of products through increased usage and the linked exchange of data between departments and functions.

Eighteen opportunities also exist for improvements for internal processes. These include providing a more consistent approach to standardization and best practices, which is expected to improve workflows during the development and deployment of products.

In addition to these, 14 opportunities for improvement were identified that can be addressed by improving internal tools. This includes introducing new tools as well as unifying and streamlining existing heterogeneous tools.

Curious how our products and services can help your business make confident, data-driven decisions? Let’s talk.

The post Actian’s Innovation Earns Prestigious IT4IT Award appeared first on Actian.

Data Governance Best Practices and Implementation Strategies

No matter what industry you work in, you know how important it is to collect data. Retail workers rely on customer data to inform their buying decisions, healthcare workers need comprehensive and accessible data on their patients to provide treatments, and financial professionals analyze large sets of market data to make predictions for their clients. But collecting data for your organization isn’t enough — it needs to be reliable, secure, accessible, and easy for the members of your company to use. That’s where data governance comes in.

Data governance is a term for an organization’s practices and processes that help it optimize its data usage. Why is data governance important? It includes plans to protect data systems against cybersecurity threats, streamline data storage solutions, set up data democratization rules, and implement products and data platforms that support greater data efficiency throughout an organization. The specific data governance policies professionals use greatly depend on their industry, the type of data they collect, how much data they use, and other factors. However, some data governance best practices can help professionals — whether they have data roles or not — create policies that optimize and simplify their data usage.

Data Governance vs. Data Compliance

Depending on your industry, you may hear the term data compliance commonly used alongside data governance. Data compliance refers to the policies and procedures that help you meet external legal requirements surrounding your data, and data governance has more to do with optimizing and securing your data for internal use. Data compliance doesn’t include industry standards or the requirements of partner companies, just what’s legally required. Data compliance laws may influence what data governance policies you implement, but you’ll mostly work with legal teams to ensure you meet these requirements.

For example, if your organization does business in countries that belong to the European Economic Area, you must abide by the General Data Protection Regulation. This law dictates how companies collect, process, and dispose of personal data. It has a huge impact on sharing data outside of your organization, data retention timelines, and data democratization and destruction policies.

Going Beyond the Data Governance Framework

A solid data governance program requires a well-structured data governance framework that addresses data quality, collection, management, privacy and security. Organizations manage these critical components by creating company-wide policies and forming departments of data professionals who work together to support the larger data governance framework. Some of the departments that contribute to overall data stewardship include:

Data
Analytics
Engineering
IT
Legal
Compliance
Executive Management

Data stewards consistently work with these departments to create and improve their policies and strategies. A governance program with high data trust never stays stagnant, so they learn about the ever-changing needs and habits of these teams to make sure data remains the fuel of a well-oiled business.

While there may be some policies that are tailored to specific departments that use data, effective data governance requires cooperation from every team in the company. If a sales team creates a lead database outside of data governance policies, which isn’t accessible to the rest of the company, that data isn’t being used effectively. If there’s a team storing metadata in unprotected spreadsheets instead of utilizing an already-established data catalog used by the rest of the organization, it weakens the governance framework.

Data Governance Best Practices

Once you assess the needs of the department stakeholders and create a framework based on them, it’s time to create your data governance program. Here are some widely-held best practices in data governance to help you begin a new program or refine one that’s fallen behind the times.

Establish Clear Roles

For any data compliance program to succeed, data stewards must make sure that the major stakeholders know their individual and collective responsibilities. This includes who’s ultimately in charge of the data, who’s responsible for maintaining data quality, who takes charge of the data management strategy, and who’s responsible for protecting it from cyber threats. This organizational chart can get a little complex at larger organizations, but ensuring there are no gaps in responsibility is one of the most critical best practices in data governance.

Develop and Enforce Data Quality Policies

Collecting as much data as possible and sorting it out after isn’t always a good strategy for data governance. Effectively utilizing data in your industry only works if the data is accurate, reliable, and relevant. If data isn’t collected often enough or doesn’t include information that your organization relies on, then it’s not meeting its true potential.

Establishing a standard for data quality begins with learning the needs of stakeholders across your organization; collecting data that no one needs is a waste of valuable resources. Then, you must create your data quality dimensions, or what defines the data you use as high-quality. The most common data quality dimensions are:

Relevance
Completeness
Accuracy
Validity
Consistency
Uniqueness
Timeliness

Ensure Data Compliance & Security

High-quality data is a valuable commodity, and there’s no end to the bad actors and black hats developing new ways to steal it. IT and cybersecurity professionals are invaluable and should impact many of the data security best practices in your data governance plan. For example, they can make the most informed decision about what access control model to use for your data systems, which will affect how permissions to data are given. If they feel that data masking is appropriate for your data systems, they can walk you through the benefits of blurring vs. tokenization.

Plan Data Audits & Policy Checks

As we mentioned, a quality data governance program is constantly evolving and adapting to meet the changing needs of an organization — even when that feedback isn’t given directly to you. Performing regular data audits can provide insights into how well your data governance program bolsters data trust, whether there are any gaps in your procedures, who isn’t getting with the program, and more. If you notice that your strategy isn’t meeting the needs of your data governance framework, don’t worry — data governance policies should be streamlined and updated every so often, and it just means you’ve identified solid ways to improve data trust.

Strategy for Implementing Data Governance

Once you’ve developed your framework, spoken to stakeholders to assess their needs, developed strategic policies and processes based on data governance best practices, and received approval from the higher-ups, it’s time to put your plan into action. Here’s a step-by-step guide to help you get your data governance program off the ground.

1. Document Your Policies and Processes

Before you can expect members of your organization to follow your plan, they need to be made aware. Creating detailed documents that define your plan makes it easier to notify coworkers of the upcoming changes to their regular routines and creates a source of truth that everyone can refer to. Having these responsibilities outlined in a document ensures there’s no confusion and can keep you from having to frequently re-explain the finer points of your plan to critical stakeholders.

2. Discuss Roles & Responsibilities

You’ve likely talked to key members of your data governance plan about their role and responsibilities to make sure they’re able to perform their duties. However, explaining these things in-depth ensures that there’s no confusion or gaps in the plan. Encourage these members to ask questions so that they fully understand what’s required of them. It’s possible that they’ve agreed to what you’ve asked without fully understanding the processes or considering how their data governance role would conflict with other responsibilities.

3. Set Up Your Data Governance Tools

Your bold new data governance plan may require new tools — or reconfiguring existing solutions — to succeed. Suppose the level of data analysis your organization requires can only be achieved with a NoSQL database, or your plan hinges on integrating multiple data sources. Once you’ve received buy-in from management, you’ll want to implement and configure these tools to your specific needs before allowing wider access to them.

Performing this step early can help ensure that these solutions are working the way you’ve intended and that your coworkers aren’t using tools that are only partially working. Using tools yourself also provides an opportunity to streamline and automate any processes that you weren’t very familiar with before.

4. Train Your Employees

Maintaining a data governance plan doesn’t just require buy-in from managers and executives — it takes effort from every member of the organization. Training employees about their role in the company’s data governance goes beyond how to use things like a new data archiving solution that you’ve implemented. Everyone needs to be aware of their role and how they fit into the larger framework of data governance to ensure that there are no gaps in your strategy.

5. Promote a Data-Driven Culture

Regularly reminding members of your organization of how crucial data is — as well as following the data governance plan — helps ensure that they don’t lapse in their responsibilities and your program runs smoothly. For example, it’s said that the biggest cybersecurity threat these days is a company’s least-informed employee. Sending company-wide updates each time a new threat or scam becomes known to the larger cybersecurity community helps keep data governance top-of-mind and ensures that the components of your plan function properly.

While data governance plans should be fairly rigid for other members of your organization, you should think of yours as fluid and flexible to meet changing needs. Company growth and evolving organizational needs are good things, and one can’t over appreciate the link between sustainable growth and data governance growing and adapting alongside it. You can use these best practices in data governance to adapt or create new plans that make your organization more efficient, productive, and secure, no matter what changes come its way.

The post Data Governance Best Practices and Implementation Strategies appeared first on Actian.

How Integrated, Quality Data Can Make Your Business Unstoppable

Successful organizations use data in different ways for different purposes, but they have one thing in common—data is the cornerstone of their business. They use it to uncover hidden opportunities, streamline operations, and predict trends with remarkable accuracy. In other words, these companies realize the transformative potential of their data.

As noted in a recent article by KPMG, a data-driven culture differentiates companies. “For one, it enables organizations to make informed decisions, improve productivity, enhance customer experiences, and confidently respond to challenges with a factual basis,” according to the article.

That’s because the more people throughout your organization with access to timely, accurate, and trusted data, the more it improves everything from decision-making to innovation to hyper-personalized marketing. Successful organizations ensure their data is integrated, governed, and meets their high-quality standards for analytical use cases, including Gen AI.

Data is the Catalyst for Incremental Success

Data is regularly likened to something of high value, from gold that can be mined for insights to the new oil—an invaluable resource that when refined and properly utilized, drives unprecedented growth and innovation. However, unlike oil, data’s value doesn’t diminish with usage or time. Instead, it can be used repeatedly for continuous insights and ongoing improvements.

When integrated effectively with the proper preparation and quality, data becomes an unstoppable force within your organization. It enables you to make strategic decisions with confidence, giving you a competitive edge in the market.

Organizations that invest in modern data analytics and data management capabilities position themselves to identify trends, predict market shifts, and better understand every aspect of their business. Moreover, the ability to leverage data in real-time enables you to be agile, responding swiftly to emerging opportunities, and identify business, customer, and partner needs.

In addition, making data readily accessible to everyone who benefits from it amplifies the potential. Empowering employees at all skill levels with barrier-free access to relevant data and easy-to-use tools actively promotes a data-driven culture.

Solve the Challenge: Overcome Fragmented and Poor-Quality Data

Despite the clear benefits of trusted, well-managed data, many organizations continue to struggle to get the data quality needed for their use cases. Data silos, resulting from lack of data integration across systems, create barriers to delivering meaningful insights.

Likewise, poor data governance erodes trust in data and can result in decision-making based on incomplete or inaccurate information. To solve the poor data quality challenge, you must first prioritize robust data integration practices that break down silos and unify data from disparate sources. Leveraging a modern data platform that facilitates seamless integration and data flows across systems is crucial.

A unified platform helps ensure data consistency by connecting data, transforming it into a reliable asset, then making it available across the entire organization. The data can then be leveraged for timely reports, informed decision making, automated processes, and other business uses.

Implementing a strong data governance framework that enforces data quality standards will give you confidence that your data is reliable, accurate, and complete. The right framework continuously monitors your data to identify and address issues proactively. Investing in both data integration and governance removes the limitations caused by fragmented and poor-quality data, ensuring you have trusted insights to propel your business forward.

5 Surprising Wins From Modern Data Integration and Data Quality

The true value of data becomes evident when it leads to tangible business outcomes. When you have data integrated from all relevant sources and have the quality you need, every aspect of your business becomes unstoppable.

Here are five surprising wins you can gain from your data:

1. Hyper-Personalized Customer Experiences

Integrating customer data from multiple touchpoints gives you the elusive 360-degree view of your customers. This comprehensive understanding of each individual’s preferences, buying habits, spending levels, and more enables you to hyper personalize marketing. The result? Improved customer service, tailored product recommendations, increased sales, and loyal customers.

Connecting customer data on a single platform often reveals unexpected insights that can drive additional value. For example, analysis might reveal emerging trends in customer behaviors that lead to new product innovations or identify previously untapped customer segments with high growth potential. These surprise benefits can provide a competitive edge, allowing you to anticipate customer needs, optimize your inventory, and continually refine targeted marketing strategies to be more effective.

2. Ensure Ongoing Operational Efficiency

Data integration and quality management can make operations increasingly efficient by providing real-time insights into supply chain performance, inventory levels, and production processes. For instance, a manufacturer can use its data to predict potential supply chain delays or equipment breakdowns with enough time to take action, making operations more efficient and mitigating interruptions.

Plus, performing comprehensive analytics on operational data can uncover opportunities to save costs and improve efficiency. For instance, you might discover patterns that demonstrate the most optimal times for maintenance, reducing downtime even further. Likewise, you could find new ways to streamline procurement, minimize waste, or better align production schedules and forecasting with actual demand, leading to leaner operations and more agile responses to changing market conditions.

3. Mitigate Current and Emerging Risk With Precision

All businesses face some degree of risk, which must be minimized to ensure compliance, avoid penalties, and protect your business reputation. Quality data is essential to effectively identify and mitigate risk. In the financial industry, for example, integrated data can expose fraudulent activities or non-compliance with regulatory requirements.

By leveraging predictive analytics, you can anticipate potential risks and implement preventive measures, safeguarding your assets and reputation. This includes detecting subtle patterns or anomalies that could indicate emerging threats, allowing you to address them before they escalate. The surprise benefit? A more comprehensive, forward-looking risk management strategy that protects your business while positioning you to thrive in an increasingly complex business and regulatory landscape.

4. Align Innovation and Product Development With Demand

Data-driven insights can accelerate innovation by highlighting unmet customer needs and understanding emerging market trends. For example, an eCommerce company can analyze user feedback and usage patterns to develop new website features or entirely new products to meet changing demand. This iterative, data-driven approach to product development can significantly enhance competitiveness.

Aligning product development with demand is an opportunity to accelerate growth and sales. One way to do this is to closely monitor customer feedback and shifts in buying patterns to identify new or niche markets. You can also use data to create tailored products or services that resonate with target audiences. One surprise benefit is a more agile and responsive product development process that predicts and meets customer demand.

5. Get Trusted Outcomes From Gen AI

Generative AI (Gen AI) offers cutting-edge use cases, amplifying your company’s capabilities and delivering ultra-fast outcomes. With the right approach, technology, and data, you can achieve innovative breakthroughs in everything from engineering to marketing to research and development, and more.

Getting trusted results from Gen AI requires quality data. It also requires a modern data strategy that realizes the importance of using data that meets your quality standard in order to fuel the Gen AI engine, enabling it to produce reliable, actionable insights. When your data strategy aligns with your Gen AI initiatives, the potential for growth and innovation is endless.

Have Confidence That Data is Working for You

In our era where data is a critical asset, excelling in data management and analytics can deliver remarkable outcomes—if you have the right platform. Actian Data Platform is our modern and easy-to-use data management solution for data-driven organizations. It provides a powerful solution for connecting, managing, and analyzing data, making it easier than you probably thought possible to get trusted insights quickly.

Investing in robust data management practices and utilizing a modern platform with proven price performance is not just a strategic move. It’s a necessity for staying competitive in today’s fast-paced, data-driven world. With the right tools and a commitment to data quality, your company can become unstoppable. Get a custom demo of the Actian Data Platform to experience how easy data can be.

The post How Integrated, Quality Data Can Make Your Business Unstoppable appeared first on Actian.

Using a Data Platform to Power Your Data Strategy

In today’s fast-paced digital landscape, organizations are increasingly recognizing the critical role that data plays in driving business success. The ability to harness data effectively can lead to significant competitive advantages, making it essential for businesses to adopt robust data management strategies.

Understanding the Importance of Data Management

Data management involves collecting, storing, organizing, and analyzing data to inform business decisions. As the volume and complexity of data continue to grow, traditional data management methods are becoming inadequate. Organizations often find themselves dealing with data silos, where information is trapped in isolated systems, making it difficult to access and analyze. According to the McKinsey Global Institute, data-driven organizations are 23 times more likely to acquire customers, six times more likely to retain them, and 19 times more likely to be profitable than their less data-savvy counterparts. This statistic underscores the necessity for businesses to implement effective data management practices.

The Evolution of Data Platforms

Historically, data management relied heavily on on-premises solutions, often requiring significant infrastructure investment and specialized personnel. However, the advent of cloud computing has transformed the data landscape. Modern data platforms offer a unified approach that integrates various data management solutions, enabling organizations to manage their operational and analytical needs efficiently. A data platform is a comprehensive solution combining data ingestion, transformation, and analytics. It allows users across the organization to access and visualize data easily, fostering a data-driven culture.

Key Features of a Modern Data Platform

When selecting a data platform, organizations should consider several critical features:

Unified Architecture: A data platform should provide a centralized data warehouse that integrates various data sources, facilitating easier access and analysis.
Data Integration Capabilities: The ability to connect and transform data from disparate sources is essential for creating a single source of truth.
Real-Time Analytics: Modern platforms support streaming data, enabling organizations to analyze information as it arrives, which is crucial for timely decision-making.
Data Quality Management: Features that ensure data accuracy and consistency are vital to maintain trust in the insights derived from the data.
User-Friendly Analytics Tools: Built-in visualization and reporting tools allow users to generate insights without extensive technical expertise.

Overcoming Modern Data Challenges

Despite the advantages of modern data platforms, organizations still face challenges such as:

Data Overload: The exponential growth of data can overwhelm traditional systems, making it difficult to extract meaningful insights.
Cost Management: As organizations move to the cloud, managing operating costs becomes a top concern.
Skill Shortages: The demand for data professionals often exceeds supply, hindering organizations’ ability to leverage their data effectively.

To address these challenges, businesses must adopt innovative technologies that facilitate rapid insights and scalability while ensuring data quality. If you’re looking to advance your use of data to improve your competitive advantage and operational efficiency, we invite you to read our new Gorilla Guide® To… Using a Data Platform to Power Your Data Strategy for a deep dive into the benefits of a unified data platform.

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Sep 2 2024

Book of the Month: Insights from “Humanizing Data Strategy”

Welcome to our new series, “Book of the Month.” In this series, we will explore new books in the data management space, highlighting how thought leaders are driving innovation and shaping the future. This month, we’re grabbing a cup of coffee, settling into our favorite reading nook, and diving into “Humanizing Data Strategy: Leading Data […]

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Sync Your Data From Edge-to-Cloud With Actian Zen EasySync

Welcome back to the world of Actian Zen, a versatile and powerful edge data management solution designed to help you build low-latency embedded apps. This is Part 3 of the quickstart blog series that focuses on helping embedded app developers get started with Actian Zen.

Establishing consistency and consolidating data across different devices and servers are essential for most edge-to-cloud solutions. Syncing data is necessary for almost every mobile, edge, or IoT application, and developers are familiar with the basic concepts and challenges. That’s why many experienced developers value efficient solutions. The Actian Zen EasySync tool is a new utility specifically designed for this purpose.

This blog will guide you through the steps for setting up and running EasySync.

What is EasySync?

Zen EasySync is a versatile data synchronization tool that automates the synchronization of newly created or updated records from one Zen database server to another. This tool transfers data across multiple servers, whether you’re working on the edge or within a centralized network. Key features of EasySync include:

Flexible Syncing Schedule: Sync data can be scheduled to poll for changes on a defined interval or can be used as a batch transfer tool, depending on your needs.
Logging: Monitor general activity, detect errors, and troubleshoot unexpected results with logging capabilities.

Prerequisites

Before using EasySync, ensure the following in your Zen installation:

System Data: The files must have system data v2 enabled, with file format version 13 or version 16.
ZEN 16.0 installed.
Unique Key: Both source and destination files must have a user-defined unique key.

EasySync Usage Scenarios

EasySync supports various data synchronization scenarios, making it a flexible tool for different use cases. Here are some common usage scenarios depicted in the diagram below:

Push to Remote: Synchronize data from a local database to a remote database.
Pull from Remote: Synchronize data from a remote database to a local database.
Pull and Push to Remotes: Synchronize data between multiple remote databases.
Aggregate Data from Edge: Collect data from multiple edge databases and synchronize it to a central database.
Disseminate Data to Edge: Distribute data from a central database to multiple edge databases.

Getting Started With EasySync

To demonstrate how to use EasySync, we will create a Python application that simulates sensor data and synchronizes it using EasySync. This application will create a sensor table on your edge device and remote server, insert random sensor data, and sync the data with a remote database. The remote database can contain various sets of data from several edge devices.

Step 1: Create the Configuration File

First, we need to create a JSON configuration file (config.json). This file will define the synchronization settings and the files to be synchronized, where files are stored in a source (demodata) and destination (demodata) folders.

Here is an example of what the configuration file might look like:

{
  "version": 1,
  "settings": {
    "polling_interval_sec": 10,
    "log_file": " C:/ProgramData/Actian/Zen/logs/datasync.log",
    "record_err_log": " C:/ProgramData/Actian/Zen/logs/recorderrors.log",
    "resume_on_error": true
  },
  "files": [
    {
      "id": 1,
      "source_file": "btrv://localhost/demodata?dbfile= sensors.mkd",
      "source_username": "",
      "source_password": "",
      "destination_file": "btrv://<Destination Server>/demodata?dbfile= sensors.mkd",
      "destination_username": "",
      "destination_password": "",
      "unique_key": 0
    },
    {
      "id": 2,
      "source_file": "btrv://localhost/demodata?dbfile=bookstore.mkd",
      "destination_file": "btrv://<Destination Server>/demodata?dbfile=bookstore.mkd",
      "create_destination": true,
      "unique_key": 1
    }
  ]
}

Step 2: Write the Python Script

Next, we create a Python script that simulates sensor data, creates the necessary database table, and inserts records into the database.

Save the following Python code in a file named run_easysync.py. Run the script to create the sensors table on your local edge device and server, and to insert data on your edge device.

import pyodbc
import random
import time
from time import sleep
random.seed()
def CreateSensorTable(server, database):
    try:
db_connection_string = f"Driver={{Pervasive ODBC Interface}};
ServerName={server};
DBQ={database};"
        conn = pyodbc.connect(db_connection_string, autocommit=True)
        cursor = conn.cursor()
       # cursor.execute("DROP TABLE IF EXISTS sensors;")
        cursor.execute("""
            CREATE TABLE sensors SYSDATA_KEY_2(
                id IDENTITY,
                ts DATETIME NOT NULL,
                temperature INT NOT NULL,
                pressure FLOAT NOT NULL,
                humidity INT NOT NULL
            );
        """)
        print(f"Table 'sensors' created successfully on {server}")
     except pyodbc.DatabaseError as err:
         print(f"Failed to create table on {server} with error: {err}")
def GetTemperature():
     return random.randint(70, 98)
def GetPressure():
     return round(random.uniform(29.80, 30.20), 3)
def GetHumidity():
     return random.randint(40, 55)
def InsertSensorRecord(server, database):
     temp = GetTemperature()
     press = GetPressure()
     hum = GetHumidity()
     try:
      insert = 'INSERT INTO sensors (id, ts, temperature, pressure, humidity) VALUES (0, NOW(), ?, ?, ?)'
        db_connection_string = f"Driver={{Pervasive ODBC Interface}};ServerName={server};DBQ={database};"
        conn = pyodbc.connect(db_connection_string, autocommit=True)
        cursor = conn.cursor()
        cursor.execute(insert, temp, press, hum)
        print(f"Inserted record [Temperature {temp}, Pressure {press}, Humidity {hum}] on {server}")
    except pyodbc.DatabaseError as err:
        print(f"Failed to insert record on {server} with error: {err}")
# Main
local_server = "localhost"
local_database = "Demodata"
remote_server = "remote-server_name"
remote_database = "demodata"

# Create sensor table on both local and remote servers
CreateSensorTable(local_server, local_database)
CreateSensorTable(remote_server, remote_database)

while True:
    InsertSensorRecord(local_server, local_database)
    sleep(0.5)

Syncing Data from IoT Device to Remote Server

Now, let’s incorporate the data synchronization process using the EasySync tool to ensure the sensor data from the IoT device is replicated to a remote server.

Step 3: Run EasySync

To synchronize the data using EasySync, follow these steps:

Ensure the easysync utility is installed and accessible from your command line.
Run the Python script to start generating and inserting sensor data.
Execute the EasySync command to start the synchronization process.

Open your command line and navigate to the directory containing your configuration file and Python script. Then, run the following command:

easysync -o config.json

This command runs the EasySync utility with the specified configuration file and ensures that the synchronization process begins.

Conclusion

Actian Zen EasySync is a simple but effective tool for automating data synchronization across Zen database servers. By following the steps outlined in this blog, you can easily set up and run EasySync. EasySync provides the flexibility and reliability you need to manage your data on the edge. Remember to ensure your files are in the correct format, have system data v2 enabled, and possess a user-defined unique key for seamless synchronization. With EasySync, you can confidently manage data from IoT devices and synchronize it to remote servers efficiently.

For further details and visual guides, refer to the Actian Academy and the comprehensive documentation. Happy coding!

The post Sync Your Data From Edge-to-Cloud With Actian Zen EasySync appeared first on Actian.

5 Misconceptions About Data Quality and Governance

The quality and governance of data has never been more critical than it is today.

In the rapidly evolving landscape of business technology, advanced analytics and generative AI have emerged as game-changers, promising unprecedented insights and efficiencies. However, as these technologies become more sophisticated, the adage GIGO or “garbage in, garbage out” has never been more relevant. For data and IT professionals, understanding the critical role of data quality in these applications is not just important—it’s imperative for success.

Going Beyond Data Processing

Advanced analytics and generative AI don’t just process data; they amplify its value. This amplification can be a double-edged sword:

Insight Magnification: High-quality data leads to sharper insights, more accurate predictions, and more reliable AI-generated content.

Error Propagation: Poor quality data can lead to compounded errors, misleading insights, and potentially harmful AI outputs.

These technologies act as powerful lenses—magnifying both the strengths and weaknesses of your data. As the complexity of models increases, so does their sensitivity to data quality issues.

Effective Data Governance is Mandatory

Implementing robust data governance practices is equally important. Governance today is not just a regulatory checkbox—it’s a fundamental requirement for harnessing the full potential of these advanced technologies while mitigating associated risks.

As organizations rush to adopt advanced analytics and generative AI, there’s a growing realization that effective data governance is not a hindrance to innovation, but rather an enabler.

Data Reliability at Scale: Advanced analytics and AI models require vast amounts of data. Without proper governance, the reliability of these datasets becomes questionable, potentially leading to flawed insights.

Ethical AI Deployment: Generative AI in particular raises significant ethical concerns. Strong governance frameworks are essential for ensuring that AI systems are developed and deployed responsibly, with proper oversight and accountability.

Regulatory Compliance: As regulations like GDPR, CCPA, and industry-specific mandates evolve to address AI and advanced analytics, robust data governance becomes crucial for maintaining compliance and avoiding hefty penalties.

But despite the vast mines of information, many organizations still struggle with misconceptions that hinder their ability to harness the full potential of their data assets.

As data and technology leaders navigate the complex landscape of data management, it’s crucial to dispel these myths and focus on strategies that truly drive value.

For example, Gartner offers insights into the governance practices organizations typically follow, versus what they actually need:

Source: Gartner

5 Data Myths Impacting Data’s Value

Here are five common misconceptions about data quality and governance, and why addressing them is essential.

Misconception 1: The ‘Set It and Forget It’ Fallacy

Many leaders believe that implementing a data governance framework is a one-time effort. They invest heavily in initial setup but fail to recognize that data governance is an ongoing process that requires continuous attention and refinement mapped to data and analytics outcomes.

In reality, effective data governance is dynamic. As business needs evolve and new data sources emerge, governance practices must adapt. Successful organizations treat data governance as a living system, regularly reviewing and updating policies, procedures, and technologies to ensure they remain relevant and effective for all stakeholders.

Action: Establish a quarterly review process for your data governance framework, involving key stakeholders from across the organization to ensure it remains aligned with business objectives and technological advancements.

Misconception 2: The ‘Technology Will Save Us’ Trap

There’s a pervasive belief that investing in the latest data quality tools and technologies will automatically solve all data-related problems. While technology is undoubtedly crucial, it’s not a silver bullet.

The truth is, technology is only as good as the people and processes behind it. Without a strong data culture and well-defined processes, even the most advanced tools will fall short. Successful data quality and governance initiatives require a holistic approach that balances technology with human expertise and organizational alignment.

Action: Before investing in new data quality and governance tools, conduct a comprehensive assessment of your organization’s data culture and processes. Identify areas where technology can enhance existing strengths rather than trying to use it as a universal fix.

Misconception 3:. The ‘Perfect Data’ Mirage

Some leaders strive for perfect data quality across all datasets, believing that anything less is unacceptable. This pursuit of perfection can lead to analysis paralysis and a significant resource drain.

In practice, not all data needs to be perfect. The key is to identify which data elements are critical for decision-making and business operations, and focus quality efforts there. For less critical data, “good enough” quality that meets specific use case requirements may suffice.

Action: Conduct a data criticality assessment to prioritize your data assets. Develop tiered quality standards based on the importance and impact of different data elements on your business objectives.

Misconception 4: The ‘Compliance is Enough’ Complacency

With increasing regulatory pressures, some organizations view data governance primarily through the lens of compliance. They believe that meeting regulatory requirements is sufficient for good data governance.

However, true data governance goes beyond compliance. While meeting regulatory standards is crucial, effective governance should also focus on unlocking business value, improving decision-making, and fostering innovation. Compliance should be seen as a baseline, not the end goal.

Action: Expand your data governance objectives beyond compliance. Identify specific business outcomes that improved data quality and governance can drive, such as enhanced customer experienced or more accurate financial forecasting.

Misconception 5: The ‘IT Department’s Problem’ Delusion

There’s a common misconception that data quality and governance are solely the responsibility of the IT department or application owners. This siloed approach often leads to disconnects between data management efforts and business needs.

Effective data quality and governance require organization-wide commitment and collaboration. While IT plays a crucial role, business units must be actively involved in defining data quality standards, identifying critical data elements, and ensuring that governance practices align with business objectives.

Action: Establish a cross-functional data governance committee that includes representatives from IT, business units, and executive leadership. This committee should meet regularly to align data initiatives with business strategy and ensure shared responsibility for data quality.

Move From Data Myths to Data Outcomes

As we approach the complexities of data management in 2025, it’s crucial for data and technology leaders to move beyond these misconceptions. By recognizing that data quality and governance are ongoing, collaborative efforts that require a balance of technology, process, and culture, organizations can unlock the true value of their data assets.

The goal isn’t data perfection, but rather continuous improvement and alignment with business objectives. By addressing these misconceptions head-on, data and technology leaders can position their organizations for success in an increasingly competitive world.

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Understanding the Role of Data Quality in Data Governance

The ability to make informed decisions hinges on the quality and reliability of the underlying data. As organizations strive to extract maximum value from their data assets, the critical interplay between data quality and data governance has emerged as a fundamental imperative. The symbiotic relationship between these two pillars of data management can unlock unprecedented insights, drive operational efficiency, and, ultimately, position enterprises for sustained success.

Understanding Data Quality

At the heart of any data-driven initiative lies the fundamental need for accurate, complete, and timely information. Data quality encompasses a multifaceted set of attributes that determine the trustworthiness and fitness-for-purpose of data. From ensuring data integrity and consistency to minimizing errors and inconsistencies, a robust data quality framework is essential for unlocking the true potential of an organization’s data assets.

Organizations can automate data profiling, validation, and standardization by leveraging advanced data quality tools. This improves the overall quality of the information and streamlines data management processes, freeing up valuable resources for strategic initiatives.

Profiling Data With Precision

The first step in achieving data quality is understanding the underlying data structures and patterns. Automated data profiling tools, such as those offered by Actian, empower organizations to quickly and easily analyze their data, uncovering potential quality issues and identifying areas for improvement. By leveraging advanced algorithms and intelligent pattern recognition, these solutions enable businesses to tailor data quality rules to their specific requirements, ensuring that data meets the necessary standards.

Validating and Standardizing Data

With a clear understanding of data quality, the next step is implementing robust data validation and standardization processes. Data quality solutions provide a comprehensive suite of tools to cleanse, standardize, and deduplicate data, ensuring that information is consistent, accurate, and ready for analysis. Organizations can improve data insights and make more informed, data-driven decisions by integrating these capabilities.

The Importance of Data Governance

While data quality is the foundation for reliable and trustworthy information, data governance provides the overarching framework to ensure that data is effectively managed, secured, and leveraged across the enterprise. Data governance encompasses a range of policies, processes, and technologies that enable organizations to define data ownership, establish data-related roles and responsibilities, and enforce data-related controls and compliance.

Our parent company, HCLSoftware, recently announced the intent to acquire Zeenea, an innovator in data governance. Together, Zeenea and Actian will provide a highly differentiated solution for data quality and governance.

Unlocking the Power of Metadata Management

Metadata management is central to effective data governance. Solutions like Zeenea’s data discovery platform provide a centralized hub for cataloging, organizing, and managing metadata across an organization’s data ecosystem. These platforms enable enterprises to create a comprehensive, 360-degree view of their data assets and associated relationships by connecting to a wide range of data sources and leveraging advanced knowledge graph technologies.

Driving Compliance and Risk Mitigation

In today’s increasingly regulated business landscape, data governance is critical in ensuring compliance with industry standards and data privacy regulations. Robust data governance frameworks, underpinned by powerful metadata management capabilities, empower organizations to implement effective data controls, monitor data usage, and mitigate the risk of data breaches and/or non-compliance.

The Synergistic Relationship Between Data Quality and Data Governance

While data quality and data governance are distinct disciplines, they are inextricably linked and interdependent. Robust data quality underpins the effectiveness of data governance, ensuring that the policies, processes, and controls are applied to data to extract reliable, trustworthy information. Conversely, a strong data governance framework helps to maintain and continuously improve data quality, creating a virtuous cycle of data-driven excellence.

Organizations can streamline the data discovery and access process by integrating data quality and governance. Coupled with data quality assurance, this approach ensures that users can access trusted data, and use it to make informed decisions and drive business success.

As organizations embrace transformative technologies like artificial intelligence (AI) and machine learning (ML), the need for reliable, high-quality data becomes even more pronounced. Data governance and data quality work in tandem to ensure that the data feeding these advanced analytics solutions is accurate, complete, and fit-for-purpose, unlocking the full potential of these emerging technologies to drive strategic business outcomes.

In the age of data-driven transformation, the synergistic relationship between data quality and data governance is a crucial competitive advantage. By seamlessly integrating these two pillars of data management, organizations can unlock unprecedented insights, enhance operational efficiency, and position themselves for long-term success.

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AI artificial intelligence change management Data Articles Data Education Data Governance Articles Data Governance News, Articles, & Education Data Management Data Management Articles Data Management News, Articles, & Education Data Topics haskell TDAN

Aug 21 2024

Change Management in Data Projects: Why We Ignored It and Why We Can’t Afford to Anymore

For decades, we’ve heard the same refrain: “Change management is crucial for project success.” Yet leaders have nodded politely and ignored this advice, particularly in data and technology initiatives. The result? According to McKinsey, a staggering 70% of change programs fail to achieve their goals.[1] So why do we keep making the same mistake, and more importantly, […]

Data Professional Introspective: The Data Management Education Program (Part 2)

In my work with the EDM Council’s Data Management Capability Assessment Model (DCAM) 3.0 development group, we are adding a capability that has remained under the radar in our industry, that is, the responsibility of the Data Management Program to determine concept and knowledge gaps within its staff resources. The organization should then plan, organize, […]

Data Crime: A Motorcycle Is Not a Honda Civic

I call it a “data crime” when someone is abusing or misusing data. When we understand these stories and their implications, it can help us learn from the mistakes and prevent future data crimes. The stories can also be helpful if you must explain the importance of data management to someone. The Story A man registered […]

Streamlining the Chaos: Conquering Manufacturing With Data

The Complexity of Modern Manufacturing

Manufacturing today is far from the straightforward assembly lines of the past; it is chaos incarnate. Each stage in the manufacturing process comes with its own set of data points. Raw materials, production schedules, machine operations, quality control, and logistics all generate vast amounts of data, and managing this data effectively can be the difference between smooth operations and a breakdown in the process.

Data integration is a powerful way to conquer the chaos of modern manufacturing. It’s the process of combining data from diverse sources into a unified view, providing a holistic picture of the entire manufacturing process. This involves collecting data from various systems, such as Enterprise Resource Planning (ERP) systems, Manufacturing Execution Systems (MES), and Internet of Things (IoT) devices. When this data is integrated and analyzed cohesively, it can lead to significant improvements in efficiency, decision-making, and overall productivity.

The Power of a Unified Data Platform

A robust data platform is essential for effective data integration and should encompass analytics, data warehousing, and seamless integration capabilities. Let’s break down these components and see how they contribute to conquering the manufacturing chaos.

1. Analytics: Turning Data into Insights

Data without analysis is like raw material without a blueprint. Advanced analytics tools can sift through the vast amounts of data generated in manufacturing, identifying patterns and trends that might otherwise go unnoticed. Predictive analytics, for example, can forecast equipment failures before they happen, allowing for proactive maintenance and reducing downtime.

Analytics can also optimize production schedules by analyzing historical data and predicting future demand. This ensures that resources are allocated efficiently, minimizing waste and maximizing output. Additionally, quality control can be enhanced by analyzing data from different stages of the production process, identifying defects early, and implementing corrective measures.

2. Data Warehousing: A Central Repository

A data warehouse serves as a central repository where integrated data is stored. This centralized approach ensures that all relevant data is easily accessible, enabling comprehensive analysis and reporting. In manufacturing, a data warehouse can consolidate information from various departments, providing a single source of truth.

For instance, production data, inventory levels, and sales forecasts can be stored in the data warehouse. This unified view allows manufacturers to make informed decisions based on real-time data. If there’s a sudden spike in demand, the data warehouse can provide insights into inventory levels, production capacity, and lead times, enabling quick adjustments to meet the demand.

 3. Integration: Bridging the Gaps

Integration is the linchpin that holds everything together. It involves connecting various data sources and ensuring data flows seamlessly between them. In a manufacturing setting, integration can connect systems like ERP, MES, and Customer Relationship Management (CRM), creating a cohesive data ecosystem.

For example, integrating ERP and MES systems can provide a real-time view of production status, inventory levels, and order fulfillment. This integration eliminates data silos, ensuring that everyone in the organization has access to the same accurate information. It also streamlines workflows, as data doesn’t need to be manually transferred between systems, reducing the risk of errors and saving time.

Case Study: Aeriz

Aeriz is a national aeroponic cannabis brand that provides patients and enthusiasts with the purest tasting, burning, and feeling cultivated cannabis. They needed to be able to connect, manage, and analyze data from several systems, both on-premises and in the cloud, and access data that was not easy to gather from their primary tracking system.

By leveraging the Actian Data Platform, Aeriz was able to access data that wasn’t part of the canned reports provided by their third-party vendors. They were able to easily aggregate this data with Salesforce to improve inventory visibility and accelerate their order-to-cash timeline.

The result was an 80%-time savings of a full-time employee responsible for locating and aggregating data for business reporting. Aeriz can now focus resources on analyzing data to find improvements and efficiencies to accommodate rapid growth.

The Actian Data Platform for Manufacturing

Imagine having the ability to foresee equipment failures before they happen? Or being able to adjust production lines based on live demand forecasts? Enter the Actian Data Platform, a powerhouse designed to tackle the complexities of manufacturing data head-on. The Actian Data Platform transforms your raw data into actionable intelligence, empowering manufacturers to make smarter, faster decisions.

But it doesn’t stop there. The Actian Data Platform’s robust data warehousing capabilities ensure that all your critical data is centralized, accessible, and ready for deep analysis. Coupled with seamless integration features, this platform breaks down data silos and ensures a cohesive flow of information across all your systems. From the shop floor to the executive suite, everyone operates with the same up-to-date information, fostering collaboration and efficiency like never before. With Actian, chaos turns to clarity and complexity becomes a competitive advantage.

Embracing the Future of Manufacturing

Imagine analytics that predict the future, a data warehouse that’s your lone source of truth, and integration that connects it all seamlessly. This isn’t just about managing chaos—it’s about turning data into a well-choreographed dance of efficiency and productivity. By embracing the power of data, you can watch your manufacturing operations transform into a precision machine that’s ready to conquer any challenge!

The post Streamlining the Chaos: Conquering Manufacturing With Data appeared first on Actian.

How to Regain Trust in Your Data: 5 Ways to Take the Fear Out of Data Management

“May you live in interesting times” is both a curse and a blessing. It’s a curse for those who fear what could go wrong, but it’s a blessing for those who look forward to changes with confidence. The same could be said of leveraging data. To be in that latter group, organizations need to be […]

The post How to Regain Trust in Your Data: 5 Ways to Take the Fear Out of Data Management appeared first on DATAVERSITY.

Getting Started With Actian Zen and BtrievePython

Welcome to the world of Actian Zen, a versatile and powerful edge data management solution designed to help you build low-latency embedded apps. This is Part 1 of the quickstart blog series that focuses on helping embedded app developers get started with Actian Zen. In this blog, we’ll explore how to leverage BtrievePython to run Btrieve2 Python applications, using the Zen 16.0 Enterprise/Server Database Engine.

But before we dive in, let’s do a quick introduction.

What is Btrieve?

Actian Zen Btrieve interface is a high-performance, low-level, record-oriented database management system (DBMS) developed by Pervasive Software, now part of Actian Corporation. It provides efficient and reliable data storage and retrieval by focusing on record-level operations rather than complex queries. Btrieve is known for its speed, flexibility, and robustness, making it a popular choice for applications that require high-speed data access and transaction processing.

What is BtrievePython?

BtrievePython is a modern Python interface for interacting with Actian Zen databases. It allows developers to leverage the powerful features of Btrieve within Python applications, providing an easy-to-use and efficient way to manage Btrieve records. By integrating Btrieve with Python, BtrievePython enables developers to build high-performance, data-driven applications using Python’s extensive ecosystem and Btrieve’s reliable data-handling capabilities.

This comprehensive guide will walk you through the setup on both Microsoft Server 2019 and Ubuntu V20, ensuring you have all the tools you need for success.

Getting Started With Actian Zen

Actian Zen offers a range of data access solutions compatible with various operating systems, including Android, iOS, Linux, Raspbian, and Windows (including IoT and Nano Server). For this demonstration, we’ll focus on Microsoft Server 2019, though the process is similar across different platforms.

Before we dive into the setup, ensure you’ve downloaded and installed the Zen 16.0 Enterprise/Server Database Engine for Windows or Linux on Ubuntu. Detailed installation instructions can be found on Actian’s Academy channel.

Setting Up Your Environment

Installing Python and BtrievePython on Windows:

- - Download and Install Python: Visit Python’s official website and download the latest version (we’re using Python v3.12).
  - Open Command Prompt as Administrator: Ensure you have admin rights to proceed with the installation.
  - Install BtrievePython: Execute pip install btrievePython. Note that this step requires an installed ZEN 16.0 client or Engine. If the BtrievePython installation fails, ensure you have Microsoft Visual C++ 14.0 or greater by downloading the Visual C++ Build Tools.
  - Verify Installation: Run pip list to check if BtrievePython is listed.
  - Run a Btrieve2 Python Sample: Download the sample program from the Actian documentation and run it using python btr2sample.py 9 from an admin command prompt.

Installing Python and BtrievePython on Linux (Ubuntu):

- - Install PIP: Use sudo apt install python3-pip to get PIP, the Python package installer.
  - Open a terminal window as a non-“root” user and export PATH=$PATH:/usr/local/actianzen/bin
  - Install BtrievePython: Execute sudo pip install btrievePython, ensuring a ZEN 16.0 client or Engine is present.
  - Verify Installation: Run pip show btrievePython to confirm the installation.
  - Run a Btrieve2 Python Sample: After downloading the sample from the Actian documentation, run the sample with python3 btr2sample.py 9.

Visual Guide

The setup process includes several steps that are best followed with visual aids. Here are some key screenshots to help guide you through the setup:

For the Windows setup:

Downloading and setting up Python.

Python Download Site:

Command Prompt Operations: Steps to install BtrievePython.

Code snippet:

Verification and Execution: verifying the installation and running the Btrieve2 sample application.

For the Linux Setup:

Installation Commands:

Install Python3-pip

BtrievePython Setup: BtrievePython installation.

Open a terminal window as a non-“root” user and export PATH=$PATH:/usr/local/actianzen/bin

BtrievePython Installed

Sample Execution: running the Btrieve2 sample app.

Conclusion

This guide has provided a thorough walkthrough on using BtrievePython with Actian Zen to run Btrieve2 Python applications. Whether you’re working on Windows or Linux, these steps will help you set up your environment efficiently and get your applications running smoothly. Actian Zen’s compatibility with multiple platforms ensures that you can manage your data seamlessly, regardless of your operating system.

For further details and visual guides, refer to the Actian Academy and the comprehensive documentation. Happy coding!

The post Getting Started With Actian Zen and BtrievePython appeared first on Actian.

Why Effective Data Management is Key to Meeting Rising GenAI Demands

OpenAI’s ChatGPT release less than two years ago launched generative AI (GenAI) into the mainstream, with both enterprises and consumers discovering new ways to use it every day. For organizations, it’s unlocking opportunities to deliver more exceptional experiences to customers, enabling new types of applications that are adaptive, context-aware, and hyper-personalized. While the possibilities are […]

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The Costly Consequences of Poor Data Quality: Uncovering the Hidden Risks

In today’s data-driven business landscape, the quality of an organization’s data has become a critical determinant of its success. Accurate, complete, and consistent data is the foundation upon which crucial decisions, strategic planning, and operational efficiency are built. However, the reality is that poor data quality is a pervasive issue, with far-reaching implications that often go unnoticed or underestimated.

Defining Poor Data Quality

Before delving into the impacts of poor data quality, it’s essential to understand what constitutes subpar data. Inaccurate, incomplete, duplicated, or inconsistently formatted information can all be considered poor data quality. This can stem from various sources, such as data integration challenges, data capture inconsistencies, data migration pitfalls, data decay, and data duplication.

The Hidden Costs of Poor Data Quality

Loss of Revenue
Poor data quality can directly impact a business’s bottom line. Inaccurate customer information, misleading product details, and incorrect order processing can lead to lost sales, decreased customer satisfaction, and damaged brand reputation. Gartner estimates that poor data quality costs organizations an average of $15 million per year.
Reduced Operational Efficiency
When employees waste time manually correcting data errors or searching for accurate information, it significantly reduces their productivity and the overall efficiency of business processes. This can lead to delayed decision-making, missed deadlines, and increased operational costs.
Flawed Analytics and Decision-Making
Data analysis and predictive models are only as reliable as the data they are based on. Incomplete, duplicated, or inaccurate data can result in skewed insights, leading to poor strategic decisions that can have far-reaching consequences for the organization.
Compliance Risks
Stringent data privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), require organizations to maintain accurate and up-to-date personal data. Failure to comply with these regulations can result in hefty fines and reputational damage.
Missed Opportunities
Poor data quality can prevent organizations from identifying market trends, understanding customer preferences, and capitalizing on new product or service opportunities. This can allow competitors with better data management practices to gain a competitive edge.
Reputational Damage
Customers are increasingly conscious of how organizations handle their personal data. Incidents of data breaches, incorrect product information, or poor customer experiences can quickly erode trust and damage a company’s reputation, which can be challenging to rebuild.

Measuring the Financial Impact of Poor Data Quality

Annual Financial Loss: Organizations face an average annual loss of $15 million due to poor data quality. This includes direct costs like lost revenue and indirect costs such as inefficiencies and missed opportunities (Data Ladder).
GDP Impact: Poor data quality costs the US economy approximately $3.1 trillion per year. This staggering figure reflects the extensive nature of the issue across various sectors, highlighting the pervasive economic burden (Experian Data Quality) (Anodot).
Time Wasted: Employees can waste up to 27% of their time dealing with data quality issues. This includes time spent validating, correcting, or searching for accurate data, significantly reducing overall productivity (Anodot).
Missed Opportunities: Businesses can miss out on 45% of potential leads due to poor data quality, including duplicate data, invalid formatting, and other errors that hinder effective customer relationship management and sales efforts (Data Ladder).
Audit and Compliance Costs: Companies may need to spend an additional $20,000 annually on staff time to address increased audit demands caused by poor data quality. This highlights the extra operational costs that come with maintaining compliance and accuracy in financial reporting (CamSpark).

Strategies for Improving Data Quality

Addressing poor data quality requires a multi-faceted approach encompassing organizational culture, data governance, and technological solutions.

Fostering a Data-Driven Culture
Developing a workplace culture that prioritizes data quality is essential. This involves establishing clear data management policies, standardizing data formats, and assigning data ownership responsibilities to ensure accountability.
Implementing Robust Data Governance
Regularly auditing data quality, cleaning and deduplicating datasets, and maintaining data currency are crucial to maintaining high-quality data. Automated data quality monitoring and validation tools can greatly enhance these processes.
Leveraging Data Quality Solutions
Investing in specialized data quality software can automate data profiling, cleansing, matching, and deduplication tasks, significantly reducing the manual effort required to maintain data integrity.

The risks and costs associated with poor data quality are far-reaching and often underestimated. By recognizing the hidden impacts, quantifying the financial implications, and implementing comprehensive data quality strategies, organizations can unlock the true value of their data and position themselves for long-term success in the digital age.

The post The Costly Consequences of Poor Data Quality: Uncovering the Hidden Risks appeared first on Actian.

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Author: Traci Curran

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ACTIAN Data Integration Data Management data quality

Jun 23 2024

The Costly Consequences of Poor Data Quality: Uncovering the Hidden Risks

Defining Poor Data Quality

The Hidden Costs of Poor Data Quality

Loss of Revenue
Poor data quality can directly impact a business’s bottom line. Inaccurate customer information, misleading product details, and incorrect order processing can lead to lost sales, decreased customer satisfaction, and damaged brand reputation. Gartner estimates that poor data quality costs organizations an average of $15 million per year.
Reduced Operational Efficiency
When employees waste time manually correcting data errors or searching for accurate information, it significantly reduces their productivity and the overall efficiency of business processes. This can lead to delayed decision-making, missed deadlines, and increased operational costs.
Flawed Analytics and Decision-Making
Data analysis and predictive models are only as reliable as the data they are based on. Incomplete, duplicated, or inaccurate data can result in skewed insights, leading to poor strategic decisions that can have far-reaching consequences for the organization.
Compliance Risks
Stringent data privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), require organizations to maintain accurate and up-to-date personal data. Failure to comply with these regulations can result in hefty fines and reputational damage.
Missed Opportunities
Poor data quality can prevent organizations from identifying market trends, understanding customer preferences, and capitalizing on new product or service opportunities. This can allow competitors with better data management practices to gain a competitive edge.
Reputational Damage
Customers are increasingly conscious of how organizations handle their personal data. Incidents of data breaches, incorrect product information, or poor customer experiences can quickly erode trust and damage a company’s reputation, which can be challenging to rebuild.

Measuring the Financial Impact of Poor Data Quality

Annual Financial Loss: Organizations face an average annual loss of $15 million due to poor data quality. This includes direct costs like lost revenue and indirect costs such as inefficiencies and missed opportunities (Data Ladder).
GDP Impact: Poor data quality costs the US economy approximately $3.1 trillion per year. This staggering figure reflects the extensive nature of the issue across various sectors, highlighting the pervasive economic burden (Experian Data Quality) (Anodot).
Time Wasted: Employees can waste up to 27% of their time dealing with data quality issues. This includes time spent validating, correcting, or searching for accurate data, significantly reducing overall productivity (Anodot).
Missed Opportunities: Businesses can miss out on 45% of potential leads due to poor data quality, including duplicate data, invalid formatting, and other errors that hinder effective customer relationship management and sales efforts (Data Ladder).
Audit and Compliance Costs: Companies may need to spend an additional $20,000 annually on staff time to address increased audit demands caused by poor data quality. This highlights the extra operational costs that come with maintaining compliance and accuracy in financial reporting (CamSpark).

Strategies for Improving Data Quality

Addressing poor data quality requires a multi-faceted approach encompassing organizational culture, data governance, and technological solutions.

Fostering a Data-Driven Culture
Developing a workplace culture that prioritizes data quality is essential. This involves establishing clear data management policies, standardizing data formats, and assigning data ownership responsibilities to ensure accountability.
Implementing Robust Data Governance
Regularly auditing data quality, cleaning and deduplicating datasets, and maintaining data currency are crucial to maintaining high-quality data. Automated data quality monitoring and validation tools can greatly enhance these processes.
Leveraging Data Quality Solutions
Investing in specialized data quality software can automate data profiling, cleansing, matching, and deduplication tasks, significantly reducing the manual effort required to maintain data integrity.

The post The Costly Consequences of Poor Data Quality: Uncovering the Hidden Risks appeared first on Actian.

Read More
Author: Traci Curran

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Jun 20 2024

End the Tyranny of Disaggregated Data

Customer renewal rates are dropping, and your CEO is on the warpath. You need to find out why and fast. At most large companies, that is a pretty tall task. Information about customers is likely scattered across an assortment of applications and devices ranging from your customer relationship management system to logs from customer-facing applications, […]

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When Business Growth Strategy Drives Data Strategy

What are the biggest data strategy challenges facing you and your company? If you are like most, the main reason for developing a data strategy is to be capable of supporting the growth strategy of each type of business in an exclusive way – to offer competitive resilience with balance and maturity to defend and […]

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The Rising Importance of AI Governance

AI governance has become a critical topic in today’s technological landscape, especially with the rise of AI and GenAI. As CEOs express concerns regarding the potential risks with these technologies, it is important to identify and address the biggest risks. Implementing effective guardrails for AI governance has become a major point of discussion, with a […]

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Category Archives: Data Management

Data Governance Best Practices and Implementation Strategies

Data Governance vs. Data Compliance

Going Beyond the Data Governance Framework

Data Governance Best Practices

Establish Clear Roles

Develop and Enforce Data Quality Policies

Ensure Data Compliance & Security

Plan Data Audits & Policy Checks

Strategy for Implementing Data Governance

1. Document Your Policies and Processes

2. Discuss Roles & Responsibilities

3. Set Up Your Data Governance Tools

4. Train Your Employees

5. Promote a Data-Driven Culture

The Complexity of Modern Manufacturing

The Power of a Unified Data Platform

1. Analytics: Turning Data into Insights

2. Data Warehousing: A Central Repository

3. Integration: Bridging the Gaps

Case Study: Aeriz

The Actian Data Platform for Manufacturing

Embracing the Future of Manufacturing

What is Btrieve?

What is BtrievePython?

Getting Started With Actian Zen

Setting Up Your Environment

Installing Python and BtrievePython on Windows:

Installing Python and BtrievePython on Linux (Ubuntu):

Visual Guide

For the Windows setup:

For the Linux Setup:

Defining Poor Data Quality

The Hidden Costs of Poor Data Quality

Measuring the Financial Impact of Poor Data Quality

Strategies for Improving Data Quality

Defining Poor Data Quality

The Hidden Costs of Poor Data Quality

Measuring the Financial Impact of Poor Data Quality

Strategies for Improving Data Quality

 3. Integration: Bridging the Gaps