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How to Build an Effective Data Management Project Plan

There are a myriad of definitions of what a data management plan, or DMP, is and what it entails. These definitions often vary slightly between organizations, but the goal is the same—to create a document specifying how data is managed throughout the lifecycle of a project. It’s a necessary step to ensure that everyone throughout your organization who uses data follows established policies to ensure data integrity and security.

In essence, a comprehensive data management plan is a living document that covers the required data sources, governance, policies, access, management, security, and other components that come into play for using data. The plan also includes how data should be integrated, used, and stored for a project, use case, business application, or other purpose.

The plan is needed to ensure data meets your quality and usage requirements to deliver trusted outcomes. At a corporate level, you need to create a detailed plan to guide and govern your data usage, and have a modern data platform in place that allows you to manage your data while making it easily accessible to everyone who needs it.

Essential Components of a Data Management Plan

It’s best to think of the data management plan as a policy. A best practice is to define your goals and use cases for how you plan to utilize the data, and then create your plan based on those needs. You can always update the plan as requirements change.

Categorizing data can help inform the plan by answering questions such as:

  • What are you planning to do with the data?
  • Does the data format need to change?
  • How do you want to store the data?
  • What is the expiration date of the data?
  • Does the data set meet your usage requirements?

Based on your use cases and requirements, you may need to have a separate data policy for each project. The policies will probably be similar, and you can have a general overall data management plan that serves as the foundation for one-off plans that can be customized to meet a specific use case’s unique needs. For example, a plan may need to cover how data is managed to meet GDPR, HIPAA, or personally identifiable information (PII) requirements.

Likewise, the plan must meet the compliancy mandates of applicable countries or regions. This can get complex very quickly. That’s because some states, such as California, have their own data privacy laws that must be followed. Because policies and compliance mandates can change over time, the data management plan must be a live document that can be easily updated to meet evolving requirements.

The plan also needs to cover storage, backup, and security for the data. How and where will you store your data? In the cloud, on-premises, or a hybrid environment? How often will the data need to be backed up, and by what method? In addition, will the security methods meet your compliance requirements?

In addition, the data management plan should cover how you will monitor contextual details, such as metadata. In certain industries, such as pharmaceuticals, the data lineage is important to back up certain theories and study outcomes, so it must be part of the plan.

Keep a Strong Focus on Data Quality

Ensuring data meets your quality standard is key and, therefore, must be included in the plan. The data management plan should cover how data is ingested, integrated, updated, and profiled to ensure it meets the quality you need. The plan should also include criteria for determining when data should be deleted.

It’s up to each organization to set the quality standard for their data, but every company must share this standard with all data users—and ensure the standard is enforced to avoid data quality being compromised. At Actian, we fully understand the need for quality data that establishes trust from internal users, customers, and partners. If there is an issue, the first step is to trace the problem to the root cause to see if established policies in the data management plan were followed.

Creating a detailed plan is only part of the overall task of delivering trusted data. The other part is to educate data users about the policies, protocols, tools, and data platform to ensure everyone understands what’s required of them and how to handle any issues that arise. Training may also be required to show business analysts and others how to use the platform and data tools to maintain data quality and get the best results.

Regardless of how detailed the plan is, every data user has a responsibility to make sure they are following company protocols and that their devices that are connected to the data ecosystem meet company policies. Going outside the plan or taking shortcuts, such as creating or using data silos, can compromise data quality. At Actian, we often talk about the fact that poor data quality is a detriment to a company and its position in the marketplace, while making quality data readily available drives new and sustainable value.

Data Champions Should Own the Data Plan

Depending on the size of your company, either a person or a team will need to own the data management project plan. Generally speaking, the plan should fall under the auspices of the data and analytics team, but actual ownership is typically high up in the food chain. The CTO or CIO will usually designate a data champion—an individual or small group—who understands the current and emerging business needs and can facilitate data management policies.

This top-down approach to owning the plan helps ensure that ever-growing data volumes meet your company’s actual requirements. For example, a data engineer can put any system in place to collect data, but without a detailed understanding of how the data will be used, the engineer’s approach may not align with how the CTO or CIO plans to leverage, manage, and govern it.

The owners of the data plan will need to regularly review it to ensure it meets current needs, which often change and evolve as new data and use cases become available. The plan should also stay current on protocols for determining who can access the data, and from where. This is important in hybrid work environments when employees may need to access data remotely.

You naturally want data to be easily and readily available to everyone who needs it, but not accessible to those without proper authorization. This approach promotes a data-driven culture, but helps safeguard against unauthorized data access.

Protecting your data is an important part of the plan. This not only includes keeping it secure against potential internal breaches, but also covers incidents that are unlikely to happen, yet still possible. For instance, if someone mistakenly forgets their laptop at the airport, what’s the process for ensuring data access is not compromised? The data management plan should cover these types of scenarios.

Communicate Policies and Share the Plan

For the plan to be truly effective and have the most impact, it must be shared with everyone who uses data or is involved in data-gathering processes. The effectiveness of the plan comes down to how well it’s communicated to internal teams and data users. There’s a valid reason for creating the plan—and everyone needs to be aware of it, embrace it, adhere to it, and view it as the valuable resource it is.

Actian can help customers build and implement a comprehensive data management project plan and offer best practices for making it easily shareable across the organization. Our experts can create a plan from a data platform point of view that covers data ingestion, integration, quality, usage, security, and other key factors.

Our Actian Data Platform offers new data quality and profiling capabilities to give business analysts and others complete confidence in their data. With more data to manage and more sources to connect to, you need a scalable platform that can meet today’s data needs by providing fast query speeds at a competitive price point, which our data platform delivers.

We can help you strategically and effectively connect, govern, and manage your data to inform business decisions, automate processes, and drive other uses. Try the Actian Data Platform for free for 30 days to experience for yourself how easy it is to use and the value it offers. Have questions on creating a detailed plan for your specific needs? Talk to us. We’re here to help.

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The post How to Build an Effective Data Management Project Plan appeared first on Actian.


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Author: Scott Norris

Gen AI for ESG Reporting and Compliance

Environmental, social, and governance (ESG) initiatives assess and measure the sustainability and societal impact of a company or investment. The number of countries and even within the United States that are implementing mandatory ESG reporting is rapidly expanding. One of the most far-reaching laws is the European Union’s Corporate Sustainability Reporting Directive (CSRD), which requires companies to publish reports on the social and environmental risks they face, and on how their activities impact the rights of people and the environment. According to the Wall Street Journal, more than 50,000 EU-based companies and approximately 10,400 non-EU enterprises are subject to CSRD compliance and some of these companies will need to disclose as many as 1,000 discrete items.

Companies using manual processes for data collection will find it difficult to keep up with the breadth and depth of these mandates. This is why generative AI will begin to play a significant role to streamline data collection, automate reporting, improve accuracy and transparency, identify risks, and resolve compliance gaps.

How Generative AI Can Help with ESG Reporting and Compliance

Data Integration:  Generative AI can help address various integration challenges and streamline processes such as data mapping and transformation, data conversion, data cleansing, data standardization, data enrichment, data validation, and more. This assistance allows companies to consider a wider range of data and criteria, which can lead to more accurate assessments of a company’s ESG performance and compliance.

Natural Language Processing (NLP): Generative AI models based on NLP can extract and analyze information from regulatory texts, legal documents, and compliance guidelines. This can be valuable for understanding and adhering to complex compliance requirements.

ESG Reporting Automation: Generative AI can automate compiling ESG compliance reports, reducing the time and resources required to gather, analyze, and present data.

Data Analysis: Generative AI can process and analyze vast amounts of data to provide insights related to ESG performance and compliance. It can identify trends, patterns, and areas to help a company improve its ESG practices.

Regulatory Change Analysis: Generative AI can monitor and analyze changes in regulatory requirements. By processing and generating summaries of new regulations and regulation updates, it helps organizations stay informed and adapt their compliance practices to changes.

Compliance Chatbots: Chatbots powered by generative AI can answer compliance-related questions, guide employees and customers through compliance processes, and provide real-time compliance information. Compliance chatbots can be particularly useful in industries with strict regulatory requirements, such as banking and healthcare.

Risk Assessment: Generative AI can analyze ESG data to identify potential risks that can lead to non-compliance, such as supply chain vulnerabilities, pollution, emissions, resource usage, and improper waste disposal, helping companies proactively address these issues.

ESG Investment: Generative AI can assist in creating investment strategies that help fill ESG compliance gaps by identifying companies or assets that meet ESG criteria.

How the Actian Data Platform Can Help

You may have clear and comprehensive ESG policies, but inadequate data collection, reporting, analytics, and risk assessment can lead to non-compliance and dramatically increase the time and resources needed for meeting extensive and demanding reporting mandates. The Actian Data Platform makes it simple to connect, manage, and analyze your compliance-related data. With the unified Actian platform, you can easily integrate, transform, orchestrate, store, and analyze your data. It delivers superior price performance as demonstrated by a recent GigaOm Benchmark, enabling REAL real-time analytics with split-second response times.

The post Gen AI for ESG Reporting and Compliance appeared first on Actian.


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Author: Teresa Wingfield

How Your Peers are Experiencing their Journeys to the Cloud

According to new customer research from Actian, “Data Analytics Journey to the Cloud,” over 70% of companies are mandating that all new data analytics applications must use cloud-based platforms. Our research reveals many good reasons why the rush to the cloud is on. It also shows that organizations can run into cloud migration roadblocks, that prevent them from realizing the full potential of running their data analytics in the cloud.

Read our eBook to get insights from 450 business and technical leaders across industries and company sizes to improve your chances of a smoother cloud journey. Here are a few highlights of what these leaders shared on their cloud migration:

  • Over 60% of companies measure the impact of data analytics on their business.
  • Data privacy is the top challenge facing companies transitioning to the cloud.
  • More than half of companies say that scaling their business growth is a major challenge and are using cloud-based data analytics to address this.
  • Customer 360 customer analytics is the leading use case for companies.
  • Over 50% of companies are using cloud-based analytics to measure and improve customer experience key performance indicators (KPIs).
  • More than half of companies use data analytics to address their talent challenges.
  • Over 50% of companies use cloud-based data analytics to impact their employee experience and talent management KPIs.

Making your Cloud Migration Easier

Our research provides additional details that can help you become more confident in your cloud migration, improve planning, and better leverage cloud resources by understanding how other organizations approach their migration. If you’re already in a cloud, multi-cloud, or hybrid environment, you can use insights in our eBook to modernize applications, business processes, and data analytics in the cloud.

Register for our eBook to find out more about:

  • Leading Drivers of Cloud Transitions
  • Data Analytics Challenges and Cloud Migration Friction Points
  • Top Cloud-Native Technologies in Operation
  • Most Common Real-World Analytics Use Cases
  • How to Deliver New Capabilities.

You might also want to sign up for a free trial of the Avalanche Cloud Data Platform. You’ll discover how this modern platform simplifies how you connect, manage, and analyze your data.

The post How Your Peers are Experiencing their Journeys to the Cloud appeared first on Actian.


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Author: Teresa Wingfield

Best Practices for Using Data to Optimize Your Supply Chain

When a company is data-driven, it makes strategic decisions based on data analysis and interpretation rather than mere intuition. A data-driven approach to supply chain management is the key to building a strong supply chain, one that’s efficient, resilient, and that can easily adapt to changing business conditions.  

How exactly you can best incorporate data and analytics to optimize your supply chain depends on several factors, but these best practices should help you get started:     

#1. Build a Data-Driven Culture 

Transitioning to a data-driven approach requires a cultural change where leadership views data as valuable, creates greater awareness of what it means to be data-driven, and develops and communicates a well-defined strategy that has buy-in from all levels of the organization.  

#2. Identify Priority Business Use Cases 

The good news is that there are a lot of opportunities to use supply chain analytics to optimize your supply chain across sourcing, processing, and distribution of goods. But you’ll have to start somewhere and should prioritize opportunities that will generate the greatest benefits for your business and that are solvable with the types of data and skills available in your organization.  

#3. Define Success Criteria 

After you’ve decided which use cases will add the most value, you’ll need to define what your business hopes to achieve and the key performance indicators (KPIs) you’ll use to continuously measure your progress. Your KPIs might track things such as manufacturing downtime, labor costs, and on-time delivery.  

#4. Invest in a Data Platform  

You’ll need a solution that includes integration, management, and analytics and that supports real-time insights into what’s happening across your supply chain. The platform will also need to be highly scalable to accommodate what can be massive amounts of supply chain data.  

#5. Use Advanced Analytics 

Artificial intelligence techniques such as machine learning power predictive analytics to identify patterns and trends in data. Insights help manufacturers optimize various aspects of the supply chain, including inventory levels, procurement, transportation routes, and many other activities. Artificial intelligence uncovers insights that can allow manufacturers to improve their bottom line and provide better customer service.  

#6. Collaborate with Suppliers and Partners 

Sharing data and insights can help develop strategies aimed at improving supply chain efficiency and developing innovative products and services.  

#7. Train and Educate Employees 

The more your teams know about advanced analytics techniques, especially artificial intelligence, and how to use and interpret data, the more value you can derive from your supply chain data. Plus, with demand for analytics skills far exceeding supply, manufacturers will need to make full use of the talent pool they already have.  

Learn More 

Hopefully, you’ve found these best practices for using data to optimize your supply chain useful and actionable. Here’s my recommended reading list if you’d like to learn more about data-driven business and technologies:   

The post Best Practices for Using Data to Optimize Your Supply Chain appeared first on Actian.


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Author: Teresa Wingfield

What Makes a Great Machine Learning Platform?

Machine learning is a type of artificial intelligence that provides machines the ability to automatically learn from historical data to identify patterns and make predictions. Machine learning implementation can be complex and success hinges on using the right integration, management, and analytics foundation.

The Avalanche Cloud Data Platform is an excellent choice for deploying machine learning, enabling collaboration across the full data lifecycle with immediate access to data pipelines, scalable compute resources, and preferred tools. In addition, the Avalanche Cloud Data Platform streamlines the process of getting analytic workloads into production and intelligently managing machine learning use cases from the edge to the cloud.

With built-in data integration and data preparation for streaming, edge, and enterprise data sources, aggregation of model data has never been easier. Combined with direct support for model training, systems, and tools and the ability to execute models directly within the data platform alongside the data can capitalize on dynamic cloud scaling of analytics computing and storage resources.

The Avalanche Platform and Machine Learning

Let’s take a closer look at some of the Avalanche platform’s most impactful capabilities for making machine learning simpler, faster, accurate, and accessible:

  1. Breaking down silos: The Avalanche platform supports batch integration and real-time streaming data. Capturing and understanding real-time data streams is necessary for many of today’s machine learning use cases such as fraud detection, high-frequency trading, e-commerce, delivering personalized customer experiences, and more. Over 200 connectors and templates make it easy to source data at scale. You can load structured and semi-structured data, including event-based messages and streaming data without coding
  2. Blazing fast database: Modeling big datasets can be time-consuming. The Avalanche platform supports rapid machine learning model training and retraining on fresh data. Its columnar database with vectorized data processing is combined with optimizations such as multi-core parallelism, making it one of the world’s fastest analytics platforms. The Avalanche platform is up to 9 x faster than alternatives, according to the Enterprise Strategy Group.
  3. Granular data: One of the main keys to machine learning success is model accuracy. Large amounts of detailed data help machine learning produce more accurate results. The Avalanche platform scales to several hundred terabytes of data to analyze large data sets instead of just using data samples or subsets of data like some solutions.
  4. High-speed execution: User Defined Functions (UDFs) support scoring data on your database at break-neck speed. Having the model and data in the same place reduces the time and effort that data movement would require. And with all operations running on the Avalanche platform’s database, machine learning models will run extremely fast.
  5. Flexible tool support: Multiple machine learning tools and libraries are supported so that data scientists can choose the best tool(s) for their machine learning challenges, including DataFlow, KNIME, DataRobot, Jupyter, H2O.ai, TensorFlow, and others.

Don’t Take Our Word for It

Try our Avalanche Cloud Data Platform Free Trial to see for yourself how it can help you simplify machine learning deployment. You can also read more about the Avalanche platform.

The post What Makes a Great Machine Learning Platform? appeared first on Actian.


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Author: Teresa Wingfield

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