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Mar 22 2024
Strategies for Midsize Enterprises to Overcome Cloud Adoption Challenges

While moving to the cloud is transformative for businesses, the reality is that midsize enterprise CIOs and CDOs must consider a number of challenges associated with cloud adoption. Here are the three most pressing challenges we hear about – and how you can work to solve them.

  • Leveraging existing data infrastructure investments
  • Closing technical skills gap
  • Cloud cost visibility and control

Recommendations

  • Innovate with secure hybrid cloud solutions
  • Choose managed services that align with the technical ability of your data team
  • Maintain cost control with a more streamlined data stack

Innovate With Secure Hybrid Cloud Solutions

There is no denying that cloud is cheaper in the long run. The elimination of CapExcosts enables CIOs to allocate resources strategically, enhance financial predictability, and align IT spending with business goals. This shift toward OpEx-based models is integral to modernizing IT operations and supporting organizational growth and agility in today’s digital economy.

Data pyramid on the data cloud in 2028

But migrating all workloads to the cloud in a single step carries inherent risks including potential disruptions. Moreover, companies with strict data sovereignty requirements or regulatory obligations may need to retain certain data on-premises due to legal, security, or privacy considerations. Hybrid cloud mitigates these risks by enabling companies to migrate gradually, validate deployments, and address issues iteratively, without impacting critical business operations. It offers a pragmatic approach for midsize enterprises seeking to migrate to the cloud while leveraging their existing data infrastructure investments.

How Actian Hybrid Data Integration Can Help

The Actian Data Platform combines the benefits of on-premises infrastructure with the scalability and elasticity of the cloud for analytic workloads. Facilitating seamless integration between on-premises data sources and the cloud data warehouse, the platform enables companies to build hybrid cloud data pipelines that span both environments. This integration simplifies data movement, storage and analysis, enabling organizations to extend the lifespan of existing assets and deliver a cohesive, unified and resilient data infrastructure. To learn more read the ebook 8 Key Reasons to Consider a Hybrid Data Integration Solution

Choose Managed Services That Align With the Technical Ability of Your Data Team

Cloud brings an array of new opportunities to the table, but the cloud skills gap remains a problem. High demand means there’s fierce market competition for skilled technical workers. Midsize enterprises across industries and geos are struggling to hire and retain top talent in the areas of cloud architecture, operations, security, and governance, which in turn severely delays their cloud adoption, migration, and maturity. This carries the potential greater risk of falling behind competitors.

Data Analytics on cloud skills

Bridging this skills gap requires strategic investments in HR and Learning and Development (L&D), but the long-term solution has to go simply beyond upskilling employees. One such answer is managed services that are typically low- or no-code, thus enabling even non-IT users to automate key BI, reporting, and analytic workloads with proper oversight and accountability. Managed solutions are typically designed to handle large volumes of data and scale seamlessly as data volumes grow—perfect for midsize enterprises. They often leverage distributed processing frameworks and cloud infrastructure to ensure high performance and reliability, even with complex data pipelines.

Actian’s Low-Code Solutions

The Actian Data Platform was built for collaboration and governance midsize enterprises demand. The platform comes with more than 200 fully managed pre-built connectors to popular data sources such as databases, cloud storage, APIs, and applications. These connectors eliminate the need for manual coding to interact with different data systems, speeding up the integration process and reducing the likelihood of errors. The platform also includes built-in tools for data transformation, cleansing, and enrichment. Citizen integrators and business analysts can apply various transformations to the data as it flows through the pipeline, such as filtering, aggregating and cleansing, ensuring data quality and reliability—all without code.

Maintain Cost Control with a More Streamlined Data Stack

Midsize enterprises are rethinking their data landscape to reduce cloud modernization complexity and drive clear accountability for costs across their technology stack. This complexity arises due to various factors, including the need to refactor legacy applications, integrate with existing on-premises systems, manage hybrid cloud environments, address security and compliance requirements, and ensure minimal disruption to business operations.

Point solutions, while helpful for specific problems, can lead to increased operational overhead, reduced data quality, and potential points of failure, increasing the risk of data breaches and regulatory violations. Although the cost of entry is low, the ongoing support, maintenance, and interoperability cost of these solutions are almost always high.

Data Analytics on Top Cloud Challenges

A successful journey to cloud requires organizations to adopt a more holistic approach to data management, with a focus on leveraging data across the entire organization’s ecosystem. Data platforms can simplify data infrastructure, thus enabling organizations to migrate and modernize their data systems faster and more effectively in cloud-native environments all while reducing licensing costs and streamlining maintenance and support.

How Actian’s Unified Platform Can Help

The Actian Data Platform can unlock the full potential of the cloud and offers several advantages over multiple point solutions with its centralized and unified environment for managing all aspects of the data journey from collection through to analysis. The platform reduces the learning curve for users, enabling them to derive greater value from their data assets while reducing complexity, improving governance, and driving efficiency and cost savings.

Getting Started

The best way for data teams to get started is with a free trial of the Actian Data Platform. From there, you can load your own data and explore what’s possible within the platform. Alternatively, book a demo to see how Actian can accelerate your journey to the cloud in a governed, scalable, and price-performant way.

The post Strategies for Midsize Enterprises to Overcome Cloud Adoption Challenges appeared first on Actian.


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Author: Dee Radh

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Nov 7 2023
Data Monetization: How You Can Deliver More Value

Because data is one of an organization’s most critical assets, monetizing it should be a top priority for business leaders. Data monetization is the act of generating economic benefits from data, either internally or externally through selling data to third parties.  Benefits typically fall within three broad categories, increase revenue, lower costs, and manage risks. A previous blog post Real-Time Data Analytics During Uncertain Times lists some examples of each of these categories and delves into the business value of data.

Although many organizations use data analytics to achieve these types of benefits, many leaders are constantly seeking even greater opportunities to monetize their data. So, how can you do more?  Here are a few tips to realize additional monetization advantages:

#1. Assess the Data You Have

Your first step should be to identify what data you have that can generate value by looking at its relevance to use cases and its potential impact on a business.  You should also assess data quality and fix known issues as this is key to successful data monetization.

#2. Define Your Business Objectives

Once you understand your data assets, you can decide on your monetization goals, defining new ways you or your potential customers can use it to create more revenue, lower costs, and manage risks. This is often a balancing act, weighing quick wins versus long-term strategy, costs versus benefits, and risks versus potential impact. Ultimately, the prioritization process should be based on expected business value.

#3. Develop a Data Monetization Strategy

Internal data monetization involves understanding users and how they view or measure success. Your strategy will need to include data that will help users meet their goals. Selecting the right data often depends on which key performance indicators (KPIs) they use to measure performance.

External data monetization strategies involve many factors.  You will need to determine:

  • The best market – Research buyers, their requirements, and their willingness to pay for data.
  • The most appropriate monetization model – Choose among options such as data licensing, data subscriptions, one-time data sales, or others.
  • The optimal packaging and pricing – Understand market dynamics such as supply and demand and consumption preferences.
  • The right channel(s) – Determine third-party platforms and data marketplaces that can broaden customer reach.

#4. Ensure Privacy, Compliance, and Ethical Use

Whether you are pursuing internal or external data monetization, you will need to ensure that data sharing and usage: a) comply with data privacy regulations, b) protect sensitive information, and c) does not harm individuals or society. If you are selling data, you will need to be transparent about where and how you collect data.

#5. Invest in Data Infrastructure

Data monetization is enabled through ingesting, transforming, analyzing, and storing data using a trusted, flexible, and easy-to-use data platform. Having the ability to run the platform in the cloud will:

  • Make it easier to facilitate connectivity among locations spread across the world. ​
  • Provide the ability to grow or shrink CPU, memory, and storage resources to adapt to the changing demands of data monetization workloads​.
  • Help you stay current with the latest technologies to support innovation.

Additionally, data monetization is most effective when a data platform provides real-time data analytics so that users are empowered to make important decisions in the moment.

#6. Promote Data Monetization Efforts

Internally, gaining leadership support, training, and education, communicating benefits and successes, and creating data champions, can all help to encourage data monetization adoption.  Externally, data is a product that will require appropriate sales and marketing strategies. And, just like most new initiatives, you will need to continuously evaluate and optimize the performance and impact of your data monetization efforts.

Get Started with Actian

For your data monetization initiative to succeed, you will need data that everyone has confidence in and can easily use. Learn how the Actian Data Platform delivers easy-to-use data you can trust.

The post Data Monetization: How You Can Deliver More Value appeared first on Actian.


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Author: Teresa Wingfield

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Sep 19 2023
When Do I Need an Object-Oriented Database Management System?

What is an Object-Oriented Database Management System?

An object-oriented database management system (OODBMS) is based on the principles of object-oriented programming. Data is created, modeled, and stored as objects, which are self-contained units that contain both data and the operations or methods that can be performed on that data.

Should your organization’s enterprise data management include an OODBMS? Here’s a quick look at where it excels and the types of applications that can benefit from these advantages.

What is an OODBMS used for?

An OODBMS is most valuable for applications with complex data relationships that require persistence, support for diverse data types, and frequent schema changes.

Complex data structures and relationships: An OODBMS is especially useful for applications with complex data structures and relationships since this type of database accommodates a more flexible and dynamic data model than relational databases. An object can store relationships that it has with other objects, including many-to-many relationships, and objects can be formed into more complex objects than traditional data models.

Performance: An OODBMS can provide improved performance compared to relational databases, especially for applications with complex data structures.

Persistence: Object databases bring permanent persistence to object storage.

Highly Extensible: Because objects can be easily modified and extended, it can be easier to evolve the data model over time.

Capable of handling diverse data types: OODBMS can store different types of data such as pictures, audio, video, text, and more.

Schema Evolution Support: The tight coupling between data and applications in an OODBMS makes schema evolution more feasible.

What are some common applications built on top of Object-Oriented Databases?

Here are some examples of applications that commonly use an OODBMS as part of enterprise data management:

Computer-aided design (CAD)/Computer-aided manufacturing (CAM): An OODBMS helps to store and manipulate complex 3D models of buildings, machine parts, etc.

Content management/digital asset management: An OODBMS handles complex schemas and structured, semi-structured, and unstructured data types, including text, images, audio, and videos.

Financial applications: An OODBMS can be useful for financial applications that need to store complex data structures such as portfolios of stocks and bonds.

E-commerce applications: An OODBMS can handle complex data such as customer orders, product catalogs, and transaction histories.

Healthcare applications: An OODBMS can provide efficient storage and retrieval of elector health records (EHRs) and medical imaging such as X-rays, MRIs, and CT scans.

Gaming applications: An OODBMS helps store and access data about game objects, such as characters and weapons, and game events such as player interactions and game state changes.

Why NoSQL?

While an OODBMS provides a more efficient way to store and access complex data structures, many of these databases lack enterprise features required for mission-critical business applications.

NoSQL from Actian is an OODBMS that doesn’t require making these tradeoffs. It provides performance, scalability, availability, and reliability. NoSQL features ACID and distributed transaction support, two-phase commit, and online schema evolution. Its two-level cache and multi-session/multi-threaded architecture are optimized for next-generation multi-core server architectures to deliver linear scalability to handle growth in data volume and concurrent user access.

The post When Do I Need an Object-Oriented Database Management System? appeared first on Actian.


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Author: Teresa Wingfield

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Aug 21 2023
How to Build Accurate Customer Profiles Using Data Analytics

A comprehensive data analytics strategy gives financial firms a competitive edge, helping them inform decision making, drive overall financial performance, and improve business outcomes. The fact is, all types of financial firms, from banks to investment companies, are finding new uses for analytics while optimizing proven use cases.

You’re probably leveraging analytics for some use cases, but there’s more you can do. Embedding analytics processes across your organization can deliver more value—and deliver value faster. Here are 10 ways to benefit from data analytics at your financial organization:

1. Deliver personalized financial services

Tailored offerings are mandatory for success in financial services. Connecting customer and financial data for analytics gives you a better understanding of each customer’s financial goals, risk profile, and financial status. You can then deliver personalized offerings to meet customers’ unique needs. Offerings can include cash back on credit cards, or personal or business loans at a favorable interest rate. Meeting each individual’s financial needs improves customer experiences while enabling cross-selling opportunities that improve revenues.

2. Gain real-time insights

Real-time insights position your firm to seize opportunities or enable you to take action if you spot a potential problem. For example, you can deliver special terms on a loan or make a limited time debit card offer while someone is browsing your site, or take immediate action if you suspect fraud on an account. For example, credit card companies use real-time analytics to approve transactions exceptionally fast and also analyze purchases for fraud. Likewise, in stock trading, every millisecond can make a difference when buying or selling at market prices, making real-time insights invaluable.

3. Improve operational efficiency

Analytics let you automate processes to improve operations. Manual and repetitive tasks can be automated to minimize human intervention and errors while speeding up processes. For instance, onboarding customers, approving loans, and processing transactions are common tasks ripe for automation. Data analytics can also play a key role in digital transformations, enabling digital processes and workflows that make operations more efficient. For example, Academy Bank transformed operations in a hybrid environment, saving more than four hours of manual data entry per day and developing new online services to improve the customer experience.

4. Manage risk across the enterprise

The financial industry is constantly exposed to risk—market risk, credit risk, operational risk, and more. Data analytics offers early insights into risk, giving you time to proactively mitigate issues before they become full-blown problems. Applying analytics lets you better understand and manage risk to protect your organization against potential losses while supporting financial stability. For example, analyzing customer data, historical data, credit scores, and other information predicts the likelihood of a person defaulting on a loan.

5. Inform financial investment decisions

In an industry as complex as financial services, you need the ability to analyze vast amounts of data quickly to understand trends, market changes, and performance goals to guide investment strategies. Sophisticated data models and analytic techniques offer granular insights and answers to what/if scenarios to inform investments. In addition, financial analytics can help you strategically build a diversified investment portfolio based on your risk tolerance and objectives.

6. Ensure accurate regulatory reporting

In your heavily regulated industry, timely, trustworthy reporting is critical. Compliance with myriad rules that are constantly changing requires analytics for visibility into adherence and to create accurate compliance and regulatory reports. Data analytics also helps monitor compliance to identify potential issues, helping you avoid penalties by ensuring operations follow legal protocols. Plus, analytics processes offer an audit trail in reporting, giving your stakeholders and auditors visibility into how the reports were created.

7. Enhance fraud detection and prevention capabilities

Fraud is ever-present in the financial sector—and fraudulent tactics are becoming increasingly sophisticated and harder to detect. Your business must be able to identify fraud before financial losses occur. Analytics, including advanced fraud detection models that use machine learning capabilities, help identify patterns and anomalies that could indicate fraud. Analytics must also prevent false positives. For example, analysis must be able to distinguish between a customer’s legitimate purchases and fraud to avoid suspending a valid customer’s account.

8. Create accurate financial forecasts

Forecasts directly impact profitability, so they must be trustworthy. Data analytics can deliver accurate forecasts to help with budgeting and investments. The forecasts predict revenue, expenses, and organization-wide financial performance. Having a detailed understanding of finances enables you to make informed decisions that increase profitability. Data-driven predictions also inform scenario analysis, which lets you evaluate potential business outcomes and risks based on assumptions you make about the future.

9. Determine customers’ credit scores

Credit scoring is essential in finance, allowing banks and other lenders to evaluate a customer’s creditworthiness based on their credit history, income, and other factors. Analytics can determine if the person is a good credit risk, meaning the customer will repay the loan on time and manage their credit responsibly. Analytics can be used for any sort of financing, from offering a loan to raising credit card limits.

10. Understand customer sentiment

Like other industries, financial services firms want to understand the perception customers and the public have about their business. That’s where sentiment analysis helps. It interprets the emotions, attitudes, and opinions behind social media posts, reviews, survey responses, and other customer feedback. This lets you better understand customer feelings about your brand and services. You can determine if your customer and business strategies are working, and make improvements accordingly. Customer sentiment also serves as an economic indicator, giving you insights into how optimistic customers are about their personal finances and the overall economy.

Unify Data for Financial Services Use Cases

Data analytics has become an essential part of decision making, automated processes, and forecasting for financial services. The insights help firms like yours stay competitive and proactively adjust to changing market conditions and customer needs. New analytics use cases are constantly emerging. One way to capitalize on these use cases is to have all data unified on a single, easy-to-use cloud data platform that makes data readily available for analysts and anyone else who needs it. The Actian Data Platform does this and more. It connects all your data so you can drive financial services use cases with confidence and enable enterprise data management for financial services.

Related resources you may find useful:

The post How to Build Accurate Customer Profiles Using Data Analytics appeared first on Actian.


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Author: Saquondria Burris

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Jul 19 2023
Actian Beats Snowflake and BigQuery in GigaOm TPC-H Benchmark Test

Driven by the desire for organizations to get better business insights, data systems are becoming more specialized, and data stacks are increasing in complexity. As companies continue their quest toward data-driven operations, they must balance speed and cost. This is why we recently engaged with GigaOm Research to conduct a TPC-H Benchmark Test against Snowflake and BigQuery – the results were clear, the Actian cloud data platform offers superior performance at a fraction of the cost of these competitors.  

The Actian platform’s operational data warehouse is designed to support real-time data analytics so customers can maintain a competitive advantage. The TPC-H benchmark consists of a series of ad-hoc analytical queries that involve complex joins, aggregations, and sorting operations. These queries represent common decision support tasks to generate sales reports, analyze trends, and perform advanced data analytics. In today’s rapidly changing business climate, there is no room for delays when it comes to accessing data to support business decisions.  

Our data analytics engine ensures that the warehouse capability in the Actian platform delivers on the promise of performance without runaway costs. The GigaOm field test, informed by TPC-H spec validation queries, highlights the price and performance ratio and cost-effectiveness of the Actian platform, providing independent validation of the Actian data platform in terms of both performance and cost. 

The Results 

In the GigaOm benchmark, the Actian Data Platform outperformed both Snowflake and BigQuery in 20 of the 22 queries, clearly illustrating Actian’s powerful decision support capabilities. Leveraging decades of data management experience, the Actian platform provides data warehouse technology that uses in-memory computing along with optimized data storage, vector processing, and query execution that exploits powerful CPU features. These capabilities significantly improve the speed and efficiency of real-time analytics. 

The benchmark results reveal query execution and price efficiencies that outperform competitor solutions, lowering the total cost of ownership without sacrificing speed. Overall, the Actian platform delivered query results that were 3x faster than Snowflake and 9x faster than BigQuery. Performance improved with additional users, highlighting the platform’s ability to scale with concurrency to meet the demands of all business users. 

In terms of cost, the GigaOm field tests further prove the value of the Actian data platform over the competition. Snowflake’s costs were nearly 4x higher than Actian’s, and BigQuery ranged from 11x to 16x more expensive based on concurrency. 

The post Actian Beats Snowflake and BigQuery in GigaOm TPC-H Benchmark Test appeared first on Actian.


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Author: Louis Grosskopf

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Mar 28 2023
Are you Building Your Data Strategy to Scale?

A data strategy is a long-term plan that defines the infrastructure, people, tools, organization, and processes to manage information assets. The goal of a data strategy is to help a business leverage its data to support decision making. To make the plan a reality, the data strategy must scale. Here are a few pointers on how to achieve this:

Infrastructure

The right infrastructure is necessary to give an organization the foundation it needs to scale and manage data and analytics across the enterprise. A modern cloud data platform will make it easy to scale with data volumes, reuse data pipelines and ensure privacy and regulations are met while also making sure that data is accessible to analysts and business users. The platform should use cloud native technologies that allow an organization to build and run scalable data analytics in public, private, and hybrid clouds.

People

The talent shortage for analysts and data scientists, particularly for advanced analytics requiring knowledge of artificial intelligence, is a big challenge. With the U.S. Bureau of Labor Statistics projecting a growth rate of nearly 28% in the number of jobs requiring data science skills by 2026, the shortage will continue to grow.

To cope with the shortage, businesses will need to invest more in training and education. The more teams know about advanced data analytics techniques and how to use and interpret data, the more value an organization can derive from its data. Also, with demand for analytics skills far exceeding supply, organizations will need to make of the talent pool they already have.

Tools

A cost-optimal solution should not only process data analytics workloads cost effectively, but also include data integration, data quality, and data management that add more costs, and complexity when sourced from multiple vendors. However, there is no such thing as a one-size fits-all tool when it comes to analytics. Increasingly, organizations are adding many types of advanced analytics such as machine learning to their analytics tool portfolio to identify patterns and trends in data that help optimize various aspects of the business.

Businesses will also need to devise strategies for users to easily access data on their own so that limited technical staff doesn’t bottleneck data analytics. Embedded analytics and self-service help support the needs of data democratization. Self-service gives users insights faster so businesses can realize the value of data faster. Analytics embedded within day-to-day tools and applications deliver data in the right context, allowing users to make better decisions faster

Organization

For a data strategy to scale, an organization needs to build a data driven culture. Transitioning to a data driven approach requires a corporate 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.

Processes

There are many processes involved in a scalable data strategy. Data governance is particularly critical to democratizing data while protecting privacy, complying with regulations, and ensuring ethical use. Data governance establishes and enforces policies and processes for collecting, storing, using, and sharing information. These include assigning responsibility for managing data, defining who has access to data and establishing rules for usage and protection.

Get Started with the Avalanche Cloud Data Platform

The Avalanche Cloud Data Platform provides data integration, data management, and data analytics services in a single platform that offers customers the full benefits of cloud native technologies. It can quickly shrink or grow CPU capacity, memory, and storage resources as workload demands change. As user load increases, containerized servers are provisioned to match demand. Storage is provisioned independently from compute resources to support compute or storage-centric analytic workloads. Integration services can be scaled in line with the number of data sources and data volumes.

Contact our enterprise data management experts for a free trial of the Avalanche Cloud Data Platform.

The post Are you Building Your Data Strategy to Scale? appeared first on Actian.


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Author: Teresa Wingfield

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Mar 20 2023
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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