Data Analytics

7 Financial Analytics Strategies Every IT Leader Should Know

Teresa Wingfield

August 8, 2023

financial analytic for IT leaders

Many organizations are discovering that spreadsheets and financial reporting tools are inadequate to deliver timely insights into the underlying trends of their business operations. Perhaps this is why, according to Gartner, only 47% of decision-makers say that financial analysis adequately portrays the story of their business area and its performance. Slicing and dicing stale data from financial statements and reports don’t reveal the “why” behind the numbers that would help identify and resolve business issues before they happen and uncover hidden opportunities.

Here are 7 financial analytics strategies to help IT empower financial and line of business users to quickly get the data-driven insights they need to derive greater business value and foster innovation.

Financial Analytics Strategies for IT Leaders

Understand Strategic Goals

Your first step should be collaboration with business managers and financial staff to understand what they are trying to achieve and to determine what data, tools, and analytics techniques will help them reach those goals. You should also try to discover how they intend to measure their success.

Choose the Right Technology Infrastructure

Consider where your data needs to live, on-premises, in the cloud, or a combination of these, and choose a data platform that can support your deployment model(s). Further, you’ll need to test the data platform to ensure that it can handle the volumes of data, number of users, complex calculations, and advanced analytics to support your financial analytics use cases.

Define Data Integration Needs

Data silos can be a huge barrier to delivering financial analytics. Financial analytics often requires integrating data from diverse sources, including internal accounting and payroll systems, customer relationship management (CRM), and sales management platforms. Additionally, businesses may need access to external data sources to benchmark their company, understand changing market dynamics, and identify other factors that impact financial performance.

Ensure Data Quality

Data used for financial analytics must be complete, accurate, current, trusted, and easily accessible to everyone who needs it. To provide quality data, IT needs to have processes in place to assess and resolve data quality issues on a continuous basis.

Implement Advanced Analytics

Advanced analytics will need to be a part of the analytics portfolio that you enable and support. Your business users will need advanced analytics such as machine learning, to gain deeper real-time insights into underlying business trends than spreadsheets and financial reporting can provide. In addition, advanced analytics provide predictive insights to help businesses be more proactive and better hone their future business strategy.

Secure and Govern Data

In addition to security controls to keep your data safe, including user authentication, access control, role separation, and encryption, you’ll need data governance to adhere to regulatory guidelines when collecting, storing, using, and sharing financial information.  This requires fine-grained data governance techniques such as data masking to prevent inappropriate access while still allowing visibility to the data users need.

Address Skill and Expertise Gaps

IT organizations are facing challenges in finding and retaining talent with the technology and analytics, particularly machine learning, skills required for modern financial analytics. You will need to evaluate your in-house capabilities and determine where you fall short. Since recruiting externally can be challenging, you should consider training the staff you already have when possible.

The Actian Data Platform simplifies how you connect, manage, and analyze financial data. The Actian platform will allow you to run your analytics wherever your data lives and provides exceptional performance for large volumes of data and users and for running advanced analytics.

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About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.
Data Analytics

10 Insights Financial Analytics Provides for Enhancing Operational Efficiency

Teresa Wingfield

August 7, 2023

financial analytics

Summary

This blog explores how financial analytics can drive operational efficiency by providing insights into various aspects of business operations, enabling organizations to make data-driven decisions that optimize performance and resource utilization.

  • Financial Performance Analysis: Gain insights into revenue, expenses, liquidity, and financial ratios to understand financial health and identify areas for improvement.
  • Cost Analysis: Analyze costs across various categories to identify opportunities for cost savings and optimize resource allocation.
  • Customer Profitability Analysis: Understand revenue, costs, and customer behavior to focus resources on high-value customers and tailor marketing strategies.

Financial analytics involves the collection, interpretation, and analysis of financial data to identify patterns, trends, and relationships. These insights help businesses enhance their operational efficiency by revealing ways they can use their resources more effectively to maximize productivity and revenue and minimize costs.

Financial analytics impacts virtually all aspects of a business through the exploration of activities, revenue, budget, and other finance-related transactions to optimize performance. Below are just a few examples of how your business can leverage financial analytics. The following are a few examples of how your business can leverage financial analytics in real-time to quickly take corrective actions or act on new opportunities.

Financial Performance Analysis

Insights into revenue, expenses, liquidity, solvency, and financial ratios help organizations better understand their financial health and identify areas for improvement.

Cost Analysis

By analyzing costs for items such as raw materials, payroll, marketing, office space, inventory, research and development, utilities, and more organizations can identify opportunities to save money and optimize resource allocation.

Customer Profitability Analysis

By understanding revenue, costs, and customer behavior for different customer segments, businesses can focus their resources on high-value customers, tailor marketing strategies, and optimize customer acquisition and retention efforts.

Process Optimization

Analyzing financial data and metrics related to inventory management, supply chain, production, and distribution processes helps businesses identify bottlenecks, streamline workflows, reduce cycle times, and improve productivity.

Budgeting and Forecasting

Revenue and expense forecasts provide businesses with insights into the future that help optimize cash flow, benchmark performance, set goals, identify risks, and communicate with investors.

Cash Flow Management

By analyzing financial data, businesses can identify risks such as cash flow volatility, high debt levels, liquidity concerns, and market fluctuations. This allows organizations to develop risk mitigation strategies such as diversifying revenue streams, managing working capital, and implementing hedging strategies.

Risk Assessment

By understanding risks associated with different activities, projects, or initiatives, organizations have an early warning system for understanding and avoiding potential issues or risks.

Investment Analysis

Businesses can use financial analytics to assess the expected returns and viability of their investments. This helps businesses make informed decisions about capital expenditures, acquisitions and mergers, new ventures, and other investment opportunities.

Compliance

Financial analysis can help organizations comply with accounting standards, regulations, and reporting requirements. Companies can monitor their adherence to financial rules, improve their disclosure of accurate financial information, meet reporting obligations, and avoid penalties or reputational damage for non-compliance.

Liquidity Analysis

Financial analytics can lead to better use of working capital through analysis of accounts receivable, accounts payable, inventory, and other relevant data. Insights can help optimize liquidity and reduce cash conversion cycles by identifying working capital management inefficiencies and opportunities.

Given the far-reaching benefits of financial analytics, it is a must-have tool for success in highly competitive markets. Businesses need financial analytics to gain a competitive edge by leveraging data-driven insights to optimize their operational efficiency.

Ready to Get Started?

Successful financial analytics requires that you have the right data platform. Actian transforms your business by simplifying how you connect, manage, and analyze financial data with the Actian Data Platform. Learn how our platform helps financial services organizations improve decision-making and automation across disparate applications, data and channels.

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About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.
Data Management

6 Critical Steps in the Journey to Making Data Management Easier

Teresa Wingfield

August 3, 2023

person learning about enterprise data management and how to make it easier

A data management strategy is the process of creating a plan to handle the data created, stored, managed, and processed by an organization. This roadmap ensures that data management activities work together effectively and efficiently to meet business goals and objectives.

Here are some critical steps for creating a data management strategy along with ways to make this process easier:

1. Define Your Business Goals and Objectives

When planning these, consider the types of insights that you will need to meet your top priorities. Delivering the right information at the right time in the right context will be necessary to become a truly data-driven business.

2. Assess Current Data Practices

Organizations need to uncover barriers to data democratization and what is preventing useful insights. Try to answer the following questions. Are data analytics software, access, and user experience issues adding friction to usability? Does data lack meaning and relevance for user needs? Is data timely and presented in the right context?

3. Determine Modern Data Requirements

These should include data sources, data types, data volume, data velocity, data quality, and data security. To support modern data requirements, organizations may need to support terabytes or even petabytes of data, unstructured and structured data, and high-velocity data streams. This data must be trustworthy and protected.

4. Develop Data Management Policies and Procedures

Data governance establishes and enforces policies and processes for collecting, storing, using, and sharing information.

As you democratize data, your data governance will need to include ways to protect privacy, comply with regulations, and ensure ethical use.

5. Identify the Right Technology Requirements

It is necessary to choose the right technology to support data management goals and objectives. It’s important to take into account key capabilities the technology needs to provide, price performance, cost, and deployment criteria.

6. Continuously Monitor the Strategy

After implementing the data management strategy, continuously monitor activities such as technology adoption, how well user needs are being met, whether the organization is following data governance rules, and if costs are aligned with business value delivered.

Meet Your Data Management Objectives

The Actian Data Platform can help you meet your data management objectives by delivering these key benefits:

  • Superior Price Performance: Delivers sub-second query results at an exceptionally low cost.
  • REAL Real-Time Analytics: Provides the ability to see in real-time what is happening within the business to allow users to decide on the best courses of action at the moment. The Actian Data Platform does this in a way that doesn’t impact the performance of running queries.
  • Built-in Native Integration: The Actian Data Platform profiles and standardizes data, reduces errors by automating quality checks, and orchestrates pipelines to maximize team efficiency.
  • Flexible Deployment Options: Eliminates vendor lock-in by delivering the same set of capabilities across AWS, Google Cloud, and Microsoft Azure and enables hybrid, multi-cloud, and cross-cloud deployment of workloads.
  • Scalability: Makes it easy to analyze big data from terabytes to petabytes.
  • Role-Based Security Policies: Reduces the time and effort to comply with data and privacy regulations without compromising the usefulness of data to consumers.

Get Started Today! 

Give the Actian Data Platform a look to see how we can make your data management strategy easier. You’ll be able to set up in just minutes and gain instant access just like thousands of other businesses!

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About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.
Data Analytics

Aeriz: Unlocking the Hidden Value of SaaS Data

Jennifer Jackson

August 1, 2023

the hidden value of saas data

Getting the full value of data in your Software as a Service (SaaS) systems can be tricky. That’s because you can’t easily access the data needed for operations, supply chains, customer experiences, or other essential business functions.

Yet easy data access is possible. For example, Aeriz, a distributor of aeroponically grown cannabis, was able to optimize its supply chain by streamlining inventory management in the cloud. The company migrated and enriched data from an on-premises application to a modern cloud data platform.

As highlighted in the recent webinar, Unlocking the Hidden Value of SaaS Data to Support Operational Growth, breaking down barriers to data accessibility helped Aeriz reduce data preparation by 50%. The company collects and integrates a wide variety of data types to inform business decisions.

The ecosystem Aeriz had in place created several challenges, including relying on time-consuming processes to bring together and format data, which resulted in a time delay for insights. The company needed information in real-time for decision-making.

“If I’m looking at a 30-day or even a two-day time delay, I can’t tell if some of that data has problems with it if it’s not real-time,” points out Joe Jones, Chief Information Officer for Aeriz.

Gaining the Ability to Analyze Complete Inventory Data

Aeriz, like many other organizations, needs accurate, trustworthy reports that give insights into the business. The reports must meet a variety of business needs, be consistent, and deliver the information employees in different parts of the organization need to drive their daily activities.

Time delays or inconsistent data that isn’t trustworthy limit insights. In turn, this creates barriers to stakeholders and others making the most informed decisions on time.

By implementing the Actian Data Platform, Aeriz is now able to get the reports, insights, and data capabilities it needs, at scale and when they’re needed. Aeriz can analyze all of its inventory data, enrich the information with data from other sources such as the general ledger and Salesforce, and deliver accurate and timely reports. This gives Aeriz the ability to optimize its entire supply chain, use advanced systems for aeroponic cultivation processes, and solve business challenges.

The Actian platform allows Aeriz to easily bring together disparate data sets on a single platform for analysis, and then move the data, if needed, to other systems dedicated to the supply chain, financials, or other business areas. Actian improves efficiencies across three main areas for Aeriz:

  1. Data access and ease of use.
  2. Complete and timely data insights.
  3. Reduced time and resources spent on tasks.

“If you’re looking at this from a non-technical standpoint, the end result is we got the data that we wanted quickly, and it’s meaningful,” Jones says. “It’s getting the important data information into the appropriate hands as quickly as possible and making sure it’s correct.”

Making Data Analytics Easy

Organizations across all verticals face many of the same challenges as Aeriz. They experience difficulties extracting data from legacy SaaS systems and applications, and many also lack the skills needed to effectively analyze the data.

Actian has been managing the world’s most critical data for customers for more than 50 years—Actian was in the room when data happened. Actian offers an innovative data platform that makes it easy for users to connect, manage, and analyze data without requiring advanced skills or IT intervention.

Watch the webinar co-hosted by Actian and Aeriz to find out why Aeriz chose Actian after looking at multiple vendors. Also, find out how the platform offers visibility and accuracy to make intelligent decisions quickly, enables companies like Aeriz to get data out of SaaS systems for real-time insights, and provides performance information on supply chains and an entire product lifecycle.

Additional Resources:

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About Jennifer Jackson

Jennifer"JJ" Jackson is CMO of Actian, leading global marketing strategy with a data-driven approach. With 25 years of branding and digital marketing experience and a background in chemical engineering, JJ understands the power of analytics from both a user and marketer perspective. She's spearheaded SaaS transitions, partner ecosystem expansions, and web modernization efforts at companies like Teradata. On the Actian blog, she discusses brand strategy, digital transformation, and customer experience. Explore her recent articles for real-world lessons in data-driven marketing.
Data Integration

Introducing DataConnect 12.2

Actian Corporation

August 1, 2023

Data Integration: Actian DataConnect 12.2

We are pleased to announce the general availability of DataConnect 12.2. This release furthers our commitment to making it easy for our integration customers to connect, transform, and analyze data. With this release, customers will now have a faster path to the cloud using the Actian Data Platform to deploy existing integrations to cloud-based environments.

Highlights of the release include:

Bulk Deployment of Integrations to the Actian Data Platform

Previously, customers would need to deploy integrations to the Actian Data Platform individually. Customers can now take existing, or new .djars (projects/workflows) and deploy them to Integration Manager in bulk. This allows designers to expose integrations faster in cloud environments and applications.

EZScript Debugger Capability

The EZScript debugger makes designing and validating maps and processes that leverage EZScript much easier. Users can set breakpoints and test desired or expected behavior of EZScripts. Now all the debugging can be done in line with the DataConnect Studio editor. Engine execution pauses based on the breakpoints the user sets and values are displayed for any variable, macro, or object.

Integration Manager Client is Now Available in DataConnect Studio

This provides the ability to log into Integration Manager from the DataConnect Studio, browse existing configurations, and download the .djar and project files to the DataConnect studio if desired. Users have the option to explode the .djar files and import all the contents to a new project. This benefits clients that need to troubleshoot, edit, or extend integrations in Integration Manager.

New Troubleshooting Assistant

Provides detailed information about errors, suggestions on error resolution, as well as links to the Actian community posts and relevant help topics.

UX Enhancements

For macros, Process Designer, message objects, and browsing. Macros can be created, edited, and accessed inline using DataConnect Studio and saved for reuse in the macro library.

Improved Data Quality Metrics

Profile data to ensure data quality, including the ability to perform fuzzy matching across multiple fields in a dataset to identify duplicate records.

Direct Upgrade

For customers on version 9 or later – full backward compatibility.

Click here to access the full release notes.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.
Actian Life

National Intern Day: The Actian Summer Internship Experience

Actian Corporation

July 27, 2023

group of students celebrating national intern day

Happy National Intern Day!

As a returning intern who has spent two memorable summers at Actian, I am excited to share what sets Actian apart and how their internship program is truly exceptional. 

One aspect of the program I absolutely adore is the in-person orientation. Unlike many remote companies, Actian offers a unique opportunity for interns to come together at the Center of Excellence office in Round Rock, Texas.  

Trust me when I say this is one of the highlights of the summer! Not only do you get to bond with fellow interns and spend quality time with your manager and buddy, but you also get to explore Texas together and engage in exciting activities. A personal favorite of mine was the gameshow-style intern competition at GameOn! ATX. Despite my team losing for the second year in a row, we gave it our all, making it an exciting and fun event.  

While some worry that you forfeit feelings of community when working remotely, that is never a problem at Actian. Laughter and jokes are a regular part of our meetings, and it’s normal to see funny GIFs in our team chats. Whether it’s work anniversaries or birthdays, we celebrate the little things together. Last summer, the Actian team went above and beyond by surprising me with an edible arrangement and cupcakes to celebrate my birthday. These gestures truly exemplify the caring and supportive atmosphere within the company.  

I must also mention the remarkable Marketing team, not only because I am a marketing intern, but because the people at Actian are genuinely one of a kind. You cannot find a more collaborative, enthusiastic, and supportive group. Despite my short tenure as a summer intern, I even have my own nickname (the Sparkler) within the marketing team! Across all departments, Actian employees are eager to connect with you, help you learn, and foster your professional growth.  

Actian truly makes interns feel valued and appreciated, and I couldn’t be more grateful to be here another summer. I will carry the memories, experiences, and relationships I’ve built at Actian long after my internship ends!

-Madeline Heath – Demand Generation Marketing Intern  

Returning intern, Madeline, is pursuing majors in Marketing and International Business at Indiana University Bloomington’s Kelley School of Business. At Actian, she assists the marketing team with various projects but is primarily focused on the social gamification of the employee advocacy platform to leverage employee networks and achieve brand visibility and thought leadership. She cannot say enough about the people and culture at Actian! 

Meet more of our 2023 Summer Interns:  

Savannah Bruggeman – Conversion Rate Optimization Marketing Intern

Savannah is a recent graduate of Loyola University Chicago where she majored in in Statistics and Public Relations. At Actian, she develops and executes A/B, Multivariate, and continuous optimization tests, takes winning ideas to market and integrates them into the Actian website! Her favorite part of the internship is applying skills she learned in the classroom and learning new skills!  

 Caitlin Fuschetto – Cloud Operations Analyst Intern

Caitlin studies Information Systems at Fordham University. As a Cloud Operations Analyst intern, she works alongside her project manager to ensure efficiency of the Cloud Ops team. She enjoys collaborating with her buddy/manager on her Capstone project and the weekly intern events!  

 Ian Loo – DataConnect Engineering Intern

Ian is majoring computer science at California Polytechnic State University, San Luis Obispo. At Actian, he carries out specific tasks for the DataConnect team through investigation, brainstorming, and implementation. His favorite part of his internship is learning new technologies and industry practices!  

Alexa Cole – DevOps Intern

Alexa is majoring in computer engineering at the University of Florida. As the Actian DevOps intern, she works to perform migration from Twiki to Confluence. She has loved learning about what DevOps is truly about!   

Ethan Avila – Cloud Security Engineer Intern

Ethan is majoring in Technology Management at Texas A&M University. At Actian, Ethan is learning how to build, maintain, and enhance protection and detection on cloud-based infrastructure. He is grateful for how much he has been able to learn in just a short time!

Henry McKinney – Cloud Operations Analyst Intern

Returning intern, Henry, is a recent graduate of Nazareth University where he majored in psychology and triple-minored in math, analytics, and finance. As a member of the Customer Cloud Operations team, Henry utilizes Kubernetes to manage cloud native applications. He has loved learning about Kubernetes this summer

Sayali Dalvi – DataConnect Engineering Intern

Sayali is majoring in Information Systems at Northeastern University. As Actian’s DataCloud Engineering, she is working to build new features for the Integration of Actian’s flagship product, the Actian Data Platform. She looks forward to the weekly intern events and weekly 1:1 meeting with the employee experience team! She really appreciates the personalized guidance and feedback!

Phuong Tran – Employee Experience Intern

Phuong is pursuing a master’s in industrial and organizational Psychology at the University of Central Florida. As an Employee Experience Intern, her primary responsibility entails fostering global corporate volunteering amongst Actian employees by establishing a corporate volunteering program that reflects Actian’s values of corporate social responsibility (CSR). She loves the weekly intern events and enjoys expanding her knowledge of the industry through real-world application!  

 Gabriela Flores Diaz – Online Media Review and ESG Intern 

Gabriela is double majoring in marketing and communication at the Arizona State University W.P. Carey School of Business. In her current role, she is developing a new online media review process and implementing an ESG marketing strategy.
She has loved connecting with fellow interns and members of the Actian team, especially during in person orientation! The collaboration with professionals from various functions at Actian has inspired her with valuable insights and diverse perspectives. 

Matthew Jackson – Zen Engineering Intern  

Returning intern, Matthew Jackson is majoring in Computer Science at the Colorado School of Mines. As the Zen Engineering intern, Matthew works to create a standalone utility application to assist developers with integrating Zen and working out a sample to demonstrate a possible Zen use case. He loves learning more about the industry through hands-on applications and working with incredible people! 

 Skylar Ng – Employee Experience Intern  

Skylar is majoring in Psychology at Michigan State University. As an Employee Experience Intern, he primarily focuses on helping Actian on its path to sustainability through research and policy implementation. He loves connecting with other interns and employees across Actian and gaining insight into their roles and responsibilities. 

Aditi Purandare – Sustainability Engineering Intern  

Aditi is majoring in Economics and Urban and Environmental Policy at Occidental College. As a Sustainability intern, she researches and collaborates with the engineering team to ensure Actian’s software is environmentally friendly and works to solidify Actian’s ESG mission! She has loved getting to and working with her fellow interns!  

Sahithi Eslavath – Software Engineering Intern 

Sahithi is majoring in Data Science at Greenwich University. As an Actian Software Engineering Intern, she works to establish the connection from DBT to Ingres. During her time at Actian, she has loved developing the new adapter

Josh Reyes – Financial Planning & Analysis Intern 

Returning intern, Josh, is majoring in Finance at the University of San Francisco. As Actian’s Financial Planning & Analysis intern, he creates dashboards to track finances and company metrics. He loves the Actian intern program and really enjoys working with the finance team, learning about the technology industry, and bonding with his fellow interns! 

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.
Data Management

De-Risking The Road to Cloud: 6 Questions to Ask Along the Way

Jennifer Jackson

July 24, 2023

cloud data migration

In my career, I’ve had first-hand experience as both a user and a chooser of data analytics technology, and have also had the chance to talk with countless customers about their data analytics journey to the cloud. With some reflection, I’ve distilled the learnings down to 6 key questions that every technology and business leader should ask themselves to avoid pitfalls along the way to the cloud so they can achieve its full promise.

1. What is My Use Case?

Identifying your starting point is the critical first step of any cloud migration. The most successful cloud migrations within our customer base are associated with a specific use case. This focused approach puts boundaries around the migration, articulates the desired output, and enables you to know what success looks like. Once a single use case has been migrated to the cloud, the next one is easier and often relies on data that has already been moved.

2. How Will We Scale Over Time?

Once you’ve identified the use case, you’ll need to determine what scaling looks like for your company. The beauty of the cloud is that it’s limitless in its scalability; however, businesses do have limits. Without planning for scale, businesses run the risk of exceeding resources and timelines.

To scale quickly and maximize value, I always recommend customers evaluate use cases based on level of effort and business value: plotting each use case in a 2×2 matrix will help you identify the low-effort, high-value areas to focus on. By planning ahead for scale, you de-risk the move to the cloud because you understand what lies ahead.

3. What Moves, What Doesn’t, and What’s the Cost of Not Planning for a Hybrid Multi-Cloud Implementation?

We hear from our customers, especially those in Europe, that there is a need to be deliberate and methodical in selecting the data that moves to the cloud. Despite the availability of data masking, encryption, and other protective measures available, concerns about GDPR and privacy are still very real. These factors need to be considered as the cloud migration roadmap is developed.

Multi-cloud architectures create resiliency, address regulatory requirements, and help avoid the risk of vendor lock-in. The benefits of multi-cloud environments were emphasized in a recent meeting with one of our EMEA-based retail customers. They experienced significant lost revenue and reputation damage after an outage of one of the largest global cloud service providers. The severe impact of this singular outage made them rethink a single cloud strategy and move to multi-cloud as part of their recovery plan.

4. How Do I Control Costs?

In our research on customers’ move to the cloud, we found that half of organizations today are demanding better cost transparency, visibility, and planning capabilities. Businesses want a simple interface or console to determine which workloads are running and which need to be stopped – the easier this is to see and control, the better. Beyond visibility in the control console, our customers also use features such as idle stop, idle sleep, auto-scaling, and warehouse scheduling to manage costs. Every company should evaluate product performance and features carefully to drive the best cost model for the business. In fact, we’ve seen our health insurance customers leverage performance to control costs and increase revenue.

5. What Skills Gaps Will I Need to Plan for, and How Will I Address Them?

Our customers are battling skills gaps in key areas, including cloud, data engineering, and data science. Fifty percent of organizations lack the cloud skills to migrate effectively to the cloud, and 45 percent of organizations struggle with data integration capacity and challenges, according to our research. Instead of upskilling a team, which can often be a slow and painful process, lean on the technology and take advantage of as-a-service offerings. We’ve seen customers that engage in services agreements take advantage of platform co-management arrangements, fully managed platform services, and outsourcing to help offset skills gap challenges.

6. How Will I Measure Success?

Look beyond cost and measure success based on the performance for the business. Ask yourself: is your cloud solution solving the problem you set out to solve? One of our customers, Met Eireann, the meteorological service for Ireland, determined that query speed was a critical KPI to measure. They found after moving to the cloud that performance improved 60-600 times and reduced query result time down to less than a second. Every customer measures success differently, whether it’s operational KPIs, customer experience, or data monetization. But whatever the measure, make sure you define success early and measure it often.

Making the move to the cloud is a journey, not a single step. Following a deliberate path, guided by these key questions, can help you maximize the value of cloud, while minimizing risk and disruption. With the right technology partner and planning, you can pave a smooth road to the cloud for your organization and realize true business value from your data.

Jennifer Jackson headshot

About Jennifer Jackson

Jennifer"JJ" Jackson is CMO of Actian, leading global marketing strategy with a data-driven approach. With 25 years of branding and digital marketing experience and a background in chemical engineering, JJ understands the power of analytics from both a user and marketer perspective. She's spearheaded SaaS transitions, partner ecosystem expansions, and web modernization efforts at companies like Teradata. On the Actian blog, she discusses brand strategy, digital transformation, and customer experience. Explore her recent articles for real-world lessons in data-driven marketing.
Data Analytics

The Impact of Reconstructing Your Data for Better Business Outcomes

Actian Corporation

July 21, 2023

Data management reconstruction

Reconstructing analytical data is the essential process of restoring, recreating, or rebuilding data from partial or incomplete information. The process is needed when data has been damaged, lost, corrupted, or fragmented, yet the original information is needed for data analytics or other business processes.

Data reconstruction is necessary in various scenarios when data is lost or damaged. For example, in telecommunications, data shared over a network may become fragmented. The reconstruction process uses the pieces to rebuild the data to its original form.

Reconstructing Data for Business Processes

Techniques for rebuilding data to make it useful for business outcomes include leveraging:

Redundancy and Error Codes

If data is stored with redundancy or error correction codes, that information can help with the rebuilding process. You can leverage the codes to recover the missing or corrupted data. For instance, if you’re using a redundant array of independent disks, better known as a RAID system, data is distributed across many of those disks. If one disk fails or damages the data, information from the other disks can help you reconstruct the data.

Redundancy in Computer Networks

In distributed systems or computer networks, data can be replicated across many different nodes. If one of those nodes fails or is unavailable, that node’s data also becomes unavailable. Using the replicated copies of the data stored on other nodes lets you reconstruct the inaccessible data.

Backup Restoration Processes

You’re probably backing up your data, and that will be a key advantage when you need to restore lost or damaged information. A common approach to recovering data is to leverage your most recent backup. It’s usually a straightforward and common method for restoration—you simply use the backup to restore your data to its original state.

Data Recovery Software

This specialized software lets you restore data from your computer, mobile device, or storage media such as a hard drive, memory card, or USB drive. You can recover data that’s missing or damaged as a result of a hardware or software failure, deletion, outage, cyberattack, or someone overwriting an essential file. The software scans the storage devices, locates lost or deleted data, and then works to recover and piece the data together.

Interpolation Techniques

Interpolation reconstructs data by estimating or using approximations of missing and damaged information based on surrounding data points. These techniques are often used to reconstruct image or audio data by levering the parts of data that are available and to “smooth out” irregular data.

Database Transaction Logs

These logs do not directly reconstruct data, but they do provide critical information that allows the database to recover and rebuild data. Database transaction logs record changes to the data in database systems. When a failure occurs or data is corrupted, the database can be restored to a previous state by using the transactions that are recorded in the logs.

Manual Reconstruction

Sometimes, reconstructing data must be done manually, especially when the data is in non-standard or unique formats. The process involves piecing together data from a variety of sources to estimate the missing or corrupted data points. Manually reconstructing data can be time consuming, and the data may not be as accurate or complete as data that’s reconstructed using automated methods. Likewise, the process may require specialized tools and expertise.

Integrate Reconstructed Data to Enable Your Business

Missing or damaged data may contain important details that your business needs for decision-making, data analytics, or other uses. Reconstruction is one of many processes that help unlock the full potential of analytical data and make it ready to use for analysts and other business users. Other essential processes include data cleansing and data integration. Data management is also key to ensuring your data, including reconstructed data, is governed and stored in a way that makes it easily accessible when you need it.

When data is in the right format, integrated with other data, and managed properly, it can serve your business needs. These needs include informing business decisions, predicting business outcomes, identifying trends, improving customer experiences, and more.

Better Data Leads to Better Business Outcomes

A modern data strategy is needed to bring together and leverage all essential data for the business. This helps break down data silos while promoting a data-driven culture. A plan for reconstructing data should be part of the strategy because there’s always a likelihood that data will become lost or damaged at some point, even with strong data governance processes in place.

The Actian Data Platform can help with all of your data needs. You can use it to integrate, transform, orchestrate, and store your data in a single, easy-to-use platform that can be deployed in cloud, on-premises, or hybrid environments.

Additional Resources:

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.
Data Management

The Global Race to Responsibly Regulating AI

Actian Corporation

July 20, 2023

words spelling out AI in various heights

The campaign for responsible use of Artificial Intelligence (AI) has grown like a massive wildfire, and the magnitude of the problem is growing faster than authorities can keep up with. Agencies around the world are working to make sense of it all and provide practical solutions for change. For global business leaders, this means staying informed about compliance, ethical standards, and innovation surrounding the ethical use of AI. To date, here’s the state of AI regulation and legislation around the globe: 

While the following is not a comprehensive list, it shows the distance that needs to be traveled to adequately regulate AI. 

United States

In the U.S., progress toward regulating AI is well underway. The Federal Trade Commission has been working to join the campaign, starting with appending responsible AI to current laws. The burden of change is placed on business leaders to hold themselves accountable for mitigating bias. In April 2020, the FTC published a blog covering U.S. AI regulation to warn and guide businesses about the misuse of AI.  

“The use of AI tools should be transparent, explainable, fair and empirically sound,” Andrew Smith, Bureau of Consumer Protection at the FTC, stated. In the release, Smith highlighted some important points for businesses using AI to remember: 

  • Transparency in the collection and use of data.
  • Explain decision-making to consumers.
  • Fair decision-making.
  • Robust, empirically-sound data and modeling.
  • Accountability for compliance, ethics, fairness and nondiscrimination.

Thus far, they’ve accomplished regulating the equitable use of AI under: 

The Fair Credit Reporting Act (FCRA): Biased algorithms used in housing, employment, insurance, and credit decisions are banned. 

The FTC Act (FTCA): Bans the use of racially discriminatory bias in AI commercial use. 

The Equal Credit Opportunity Act (ECOA): Prohibits discrimination in credit decision-making based on race, color, religion, nationality, sex, marital status, age, or the use of public assistance. Discriminatory AI is banned against “protected classes.” 

In 2022, the Equal Employment Opportunity Commission (EEOC) released technical assistance guidance for algorithmic bias in employment decisions, based on the provisions under the Americans with Disabilities Act (ADA). Charlotte Burrows, Chair of the EEOC, reported that more than 80% of all employers and more than 90% of Fortune 500 companies are using such technology. Although there aren’t any federal laws that specifically target use of AI, they serve as the foundation for future legislation and regulations.  

Europe

Europe has been working on regulating the commercial use of technology since 2018. The General Data Protection Regulation (GDPR) is a resource for achieving and maintaining compliance with Europe’s laws regarding the responsible use of AI. There has been much debate amongst executives and regulators regarding the European Union’s enactment of a comprehensive set of rules for governing artificial intelligence. Executives are arguing that the rules will make it difficult to contend with international competitors. 

“Europe is the first regional bloc to significantly attempt to regulate AI, which is a huge challenge considering the wide range of systems that the broad term ‘AI’ can cover,” said Sarah Chander, senior policy adviser at digital rights group EDRi. 

China

In 2017, the Chinese State Council released the Next Generation Artificial Intelligence Development Plan as a set of guidelines surrounding the use of specific AI applications. The release was regarding currently active provisions on the management of algorithmic recommendations of Internet information services and the management of deep synthesis of Internet information services, which is still being drafted.  

In May 2023, China’s Cyberspace Administration (CAC) drafted the Administrative Measures for Generative Artificial Intelligence Services. It requires a “safety assessment” for companies desiring to develop new AI products before they can go to market. It also mentions the use of truthful, accurate data, free of discriminatory algorithms. It focuses on prevention as the major first step for responsible AI. 

Brazil

In December 2022, Brazilian Senators released a report containing studies and a draft of a regulation relating to responsible AI governance. It serves to inform future regulations that Brazil’s Senate is planning. The focal point of the regulation was the presentation of three central pillars: 

  • Guaranteeing the rights of people AI affects.
  • Classification of risk levels.
  • Predicting Governance measures.

Japan

In March 2019, Japan’s Integrated Innovation Strategy Promotion Council created the Social Principles of Human-Human-Centric AI. The two-part provision is meant to address a myriad of social issues that have come with AI innovation. One part established seven social principles to govern the public and private use of AI: 

  • Human-centricity
  • Education/literacy
  • Data protection
  • Ensuring safety
  • Fair competition
  • Fairness
  • Accountability & transparency
  • Innovation

The other part, which expounds on the 2019 provision, targets AI developers and the companies that employ them. The AI Utilisation Guidelines are meant to be an instruction manual for AI developers and companies to develop their own governance strategy. There’s also the 2021 provision, Governance Guidelines for Implementation of AI Principles, which features hypothetical examples of AI applications for them to review. While none of these regulations are legally binding, they are Japan’s first step in starting the race to regulating AI. 

Canada

In June 2022, Canada’s federal government released the Digital Charter Implementation Act. This contained Canada’s first piece of legislation to strengthen the country’s efforts to mitigate bias. The charter included the Artificial Intelligence and Data Act, which regulates international and interprovincial trade in AI. It requires that developers responsibly ensure to mitigate risk and bias. Public disclosure requirements and prohibitions on harmful use are also included. The charter is preliminary to moving toward officially enacting legislation regarding AI in Canada. 

India

Currently, there are no official regulatory requirements in India regarding the responsible use of AI. The Indian Commission NITI Aayog has released working research papers being used to begin to address the issues. The first installment of the paper, Towards Responsible #AIforAll, discusses the potential of AI for society at large and recommendations surrounding AI adoption in the public and private sectors. The next part, an Approach Document for India, established principles for responsible AI, the economic potential of AI, supporting large-scale adoption, and establishing and instilling public trust. The final paper, Adopting the Framework: A Use Case Approach on Facial Recognition Technology, is meant to be a “benchmark for future AI design, development, and deployment in India.” 

Switzerland

There are currently no specific regulations that govern the responsible use of AI. Already enacted laws are being used to inform cases as they present themselves. For example, the General Equal Treatment Act, their product liability and general civil laws address prevention of bias in the public and private sectors. 

The Future of a Global Approach

To limit or completely eradicate AI bias, there needs to be a communal effort and commitment to accuracy, trust, and compliance. Business leaders and developers should target preventive, corrective and measures for transparency, accuracy, and accountability when employing AI. Regulators must also do their due diligence in providing comprehensive, appropriate, and timely legislation that applies to the present and will be relevant in the future.  

Additional Resources:

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.
Data Management

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

Louis Grosskopf

July 19, 2023

depiction of a person learning that Actian beat Snowflake and BigQuery in 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 Data Platform offers superior performance at a fraction of the cost of these competitors.  

Actian’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. 

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About Louis Grosskopf

Louis Grosskopf is a seasoned product leader with extensive experience in software product management, global team leadership, and strategic development. Louis's background ranges from machine-level coding to delivering highly scalable cloud/SaaS offerings, earning him a patent in product technology. He has led cross-functional teams to market leadership, guiding development from feasibility to internationalization. On the Actian blog, Louis shares insights on product lifecycle management, emerging tech trends, and development best practices. Check out his posts for actionable takeaways.
Data Analytics

Looking into the Future of Data Management

Teresa Wingfield

July 15, 2023

future of data management

Data management spans the collection, storage, security, access, usage, and deprecation of data. The data management world continues to undergo substantial transformations every year. Here’s a brief look into what’s in store for the future of data management, beginning with data democratization and then delving into how it’s driving the need for easier data access, advanced analytics, and stronger data governance.

Data Democratization

Data democratization, or enabling universal access to data, is going to become an even larger priority for several reasons. Being able to deliver the right data to analysts and front-line employees who need it, in a timely basis, in the right context leads to more effective decisions in the context of their daily work. This, in turn, can help create opportunities to drive new revenue and drive operational efficiencies throughout an organization. Even more importantly, data democratization is crucial to business transformation.

Another factor driving the need for data democratization is the talent shortage for analysts and data scientists, particularly for advanced analytics requiring knowledge of artificial intelligence. 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. Businesses will 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

The use of embedded analytics and self-service will grow to support the need for 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 sales, marketing, finance, and other departments to make better decisions faster.

According to Gartner, context-driven analytics and AI models will replace 60% of existing models built on traditional data by 2025.

Artificial Intelligence

To truly democratize data, we have to democratize data analytics. Artificial intelligence allows machines to model, and even improve upon, the capabilities of human intelligence. The adoption of artificial intelligence has been growing steadily and is poised to accelerate. A report published by The AI Journal, reveals that 72% of leaders feel positive about the role that artificial intelligence will play in the future, with the number one expectation being that it will make business processes more efficient (74%). 55% believe that artificial intelligence will help to create new business models, and 54% expect it to enable the creation of new products and services.

Data Governance

How do you democratize data while protecting privacy, complying with regulations, and ensuring ethical use? These are exactly the types of challenges that are fueling the growth of data governance to establish and enforce policies and processes for collecting, storing, using and sharing information. Data governance assigns responsibility for managing data, defines who has access to data and establishes rules for using and protecting data, including compliance with regulations such as General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA), state privacy statutes and industry standards such as Payment Card Industry Data Security Standard (PCI DSS).

The future of data management is exciting, putting insights from data in the hands of everyone using embedded analytics, self-service, and artificial intelligence. Backed by strong data governance, businesses are poised to derive even greater growth and innovation using their data.

Additional Resources:

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About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.
Data Intelligence

Data Masking – The Shield to Protect Your Business

Actian Corporation

July 15, 2023

Data Masking

The chameleon changes its color to defend itself. Similarly, walking sticks mimic the appearance of twigs to deceive predators. Data masking follows the same principle! Let’s explore a methodical approach that ensures the security and usability of your data.

According to IBM’s 2022 report on the cost of data breaches, the average expense incurred by a data breach amounts to $4.35 million. The report further highlights that 83% of surveyed companies experienced multiple data breaches, with only 17% stating it was their initial incident. As sensitive data holds immense value, it becomes a desirable target and requires effective protection. Among all compromised data types, personally identifiable information (PII) is the most expensive. To safeguard this information and maintain its confidentiality, data masking has emerged as an indispensable technique.

What is Data Masking?

The purpose of data masking is to ensure the confidentiality of sensitive information. In practice, data masking entails substituting genuine data with fictional or modified data while retaining its visual representation and structure. This approach finds extensive application in test and development settings, as well as in situations where data is shared with external entities in order to avert unauthorized exposure. By employing data masking, data security is assured while preserving its usefulness and integrity, thereby mitigating the likelihood of breaches compromising confidentiality.

What are the Different Types of Data Masking?

To guarantee the effective masking of your data, data masking can employ various techniques, each with its unique advantages, allowing you to select the most suitable approach for maximizing data protection.

Static Data Masking

Static Data Masking is a data masking technique that involves modifying sensitive data within a static version of a database. The process begins with an analysis phase, where data is extracted from the production environment to create the static copy. During the masking phase, real values are substituted with fictitious ones, information is partially deleted, or data is anonymized. These modifications are permanent, and the data cannot be restored to its original state.

Format Preserving Masking

Format Preserving Masking (FPM) differs from traditional masking methods as it preserves the length, character types, and structure of the original data. By utilizing cryptographic algorithms, sensitive data is transformed into an irreversible and unidentifiable form. The masked data retains its original characteristics, allowing it to be used in systems and processes that require a specific format.

Dynamic Data Masking

Dynamic Data Masking (DDM) applies varying masking techniques each time a new user attempts to access the data. When a collaborator accesses a database, DDM enforces defined masking rules to limit the visibility of sensitive data, ensuring that only authorized users can view the actual data. Masking can be implemented by dynamically modifying query results, substituting sensitive data with fictional values, or restricting access to specific columns.

On-the-Fly Data Masking

On-the-Fly data masking, also known as real-time masking, differs from static masking by applying the masking process at the time of data access. This approach ensures enhanced confidentiality without the need to create additional data copies. However, real-time masking may result in processing overload, especially when dealing with large data volumes or complex operations, potentially causing delays or slowdowns in data access.

What are the Different Data Masking Techniques?

Random Substitution

Random substitution involves replacing sensitive data, such as names, addresses, or social security numbers, with randomly generated data. Real names can be replaced with fictitious names, addresses can be replaced with generic addresses, and telephone numbers can be substituted with random numbers.

Shuffling

Shuffling is a technique where the order of sensitive data is randomly rearranged without significant modification. This means that sensitive values within a column or set of columns are shuffled randomly. Shuffling preserves the relationships between the original data while making it virtually impossible to associate specific values with a particular entity.

Encryption

Encryption involves making sensitive data unreadable using an encryption algorithm. The data is encrypted using a specific key, rendering it unintelligible without the corresponding decryption key.

Anonymization

Anonymization is the process of removing or modifying information that could lead to the direct or indirect identification of individuals. This may involve removing names, first names, addresses, or any other identifying information.

Averaging

The averaging technique replaces a sensitive value with an aggregated average value or an approximation thereof. For example, instead of masking an individual’s salary, averaging can use the average salary of all employees in the same job category. This provides an approximation of the true value without revealing specific information about an individual.

Date Switching

Date switching involves modifying date values by retaining the year, month, and day but mixing them up or replacing them with unrelated dates. This ensures that time-sensitive information cannot be used to identify or trace specific events or individuals while maintaining a consistent date structure.

Conclusion

The significant benefit of data masking for businesses is its ability to preserve the informational richness, integrity, and representativeness of data while minimizing the risk of compromising sensitive information. With data masking, companies can successfully address compliance challenges without sacrificing their data strategy.

Data masking empowers organizations to establish secure development and testing environments without compromising the confidentiality of sensitive data. By implementing data masking, developers and testers can work with realistic datasets while avoiding the exposure of confidential information. This enhances the efficiency of development and testing processes while mitigating the risks associated with the utilization of actual sensitive data.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.