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. 

Louis Grosskopf headshot

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.
Data Analytics

How Retail Leaders Apply Data Analytics

Teresa Wingfield

July 14, 2023

retail big data analytics

Retailers can use big data to gain insights into their customers to enhance the customer experience, improve marketing and to optimize pricing, inventory and supply chain management. The collection of vast amounts of data and using advanced retail analytics to discover insights, make predictions and generate recommendations provide tremendous opportunities. Data-driven decisions can maximize sales and profit, strengthen customer loyalty, improve operational efficiency, and reduce costs.

Here’s a quick overview of some of the key uses for retail analytics:

Retail Analytics (aka “Customer 360”)

Retails analytics, also known as Customer 360 gives retailers a complete view of customers by aggregating data from the various touch points that a customer may use to contact a company to purchase products and receive service and support. This requires bringing together big data from enterprise and SaaS applications such as CRM, ERP, customer service and support, sales, social/behavioral and third-party data sources and applying advanced analytics to uncover deep insights, make predictions and generate recommendations.

Customer 360 can lead to improved business outcomes and more importantly, increased revenue. Using Customer 360, you’ll learn more about your customers, the best ways to engage with them, the targeted offers that will resonate, the likelihood to churn, and the best ways to personalize the customer experience in real-time to win more business and drive greater customer loyalty. You can also discover more about your products, including the most profitable product groups, which products benefit the most from associations with other products, optimal shelf arrangements and how to optimize promotions.

Data-Driven Pricing Decisions

By using advanced analytics to inform pricing decisions, retailers can make more informed, data-driven decisions based on customer behavior and market conditions.  Retailers need big data, including internal data such as customer behavior and sales data, and external data such as market and competitor data to drive pricing analytics for:

Price Optimization

  • Dynamic Pricing to adjust prices in real-time based on customer demand, competition, and other factors.
  • Price Gap Analysis to compare prices to those of competitors.
  • Bundle Price Analysis to set the optimal discounted price for a bundle of multiple products or services.

Inventory and Supply Chain Optimization

By analyzing big data for inventory management, retailers can identify sales trends, forecast demand, and make informed decisions about ordering, stocking, and distribution. This helps retailers optimize their inventory levels to reduce stock shortages and overstocking.

Big data analytics can also help retailers reinvent their supply chain across sourcing, processing and fulfillment of goods.  Supply chain data comes from procurement, inventory management, order management, warehouse management, fulfillment, transportation management, and more sources. Analyzing this data identifies opportunities to align supply with volatile demand, avoid supply shortages, achieve faster delivery, and decrease labor costs.

Ready to Get Started?

Retailers will need to accelerate their use of big data and advanced retail analytics to anticipate changing customer needs and dynamic market conditions.  We’ve just scratched the surface of use case opportunities, including customer 360, data-driven pricing and inventory and supply chain optimization.

Are you looking for a solution to support your big data needs? The Actian Data Platform makes it easy to analyze big data from terabytes to petabytes. The Actian Data Platform to learn how it makes it easy to connect, manage and analyze data.

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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

Getting the Most From Real-Time Financial Analytics in Healthcare

Actian Corporation

July 13, 2023

healthcare data security and healthcare financial analytics

Many organizations are implementing real-time analytics that makes data readily available to users and processes as soon as the data is available in the database. This type of analytics lets you make instantaneous decisions based on the most current data. In industries such as healthcare when seconds matter, real-time data analytics gives you the ability to execute ultra-fast queries to inform decisions in the moment.

Real-time financial analytics is one important type of analysis that can deliver results in healthcare. The analytics capabilities support data-driven outcomes across the healthcare organization, from delivering high-quality patient care to mitigating fraud to aligning processes with compliance requirements.

Improve Patient Care While Lowering Costs

Healthcare organizations, from large hospitals to healthcare provider networks, are challenged to provide the best care, improve outcomes, and lower costs. The industry shift from a fee-for-service model to value-based care emphasizes helping patients improve their overall health and live healthier lives.

Applying real-time financial analytics, along with analytics across patient and healthcare data, gives healthcare providers access to the latest information to make the most informed decisions for patient care. Quickly responding to patient needs and setting them on the path to care as quickly as possible not only improves outcomes but also leads to greater patient satisfaction. Plus, the ability to diagnose patients quickly and early in their patient journey can lower costs by avoiding unnecessary tests and treatments.

In addition, real-time financial analytics allows you to better allocate healthcare resources, including staffing, where they’re needed based on usage patterns. Having medical equipment and personnel readily available enhances the quality of patient care.

Similarly, real-time insights into finances and operations can identify areas where you can streamline operations to reduce costs, cut wasteful spending, and improve efficiency. Healthcare administrators can also use real-time financial analytics to create accurate budgets and forecasts using the very latest financial information. Accurate forecasts help you reach your financial and performance goals, ensuring your organization maintains its financial health.

Identify Healthcare Fraud Faster

Fraud is an ongoing problem in healthcare, with the Coalition Against Insurance Fraud putting the cost of fraud as high as $300 billion per year. Early detection of fraud is critical as recovering fraudulent payments is extremely difficult—if the money is recovered at all.

Real-time financial analytics can detect patterns and anomalies in data to quickly detect possible fraudulent activities in healthcare billing and insurance claims. Analytics can identify emerging fraudulent trends that otherwise may have gone unnoticed. Identifying potential fraud in real time lets you take immediate action to protect your organization.

By analyzing billing, claims, and other relevant data, you can also predict when and where medical fraud is likely to occur in the future. In addition, real-time financial analytics can identify insurance claims that are missing information, coded incorrectly, have unusual billing patterns, or show other unusual patterns that could indicate fraud.

On the flip side, analytics can also help process legitimate claims faster. This leads to higher customer satisfaction and shorter lifecycles for claims.

Meet Regulatory Compliance Mandates

Healthcare regulations are often complex and can have different requirements on the state and federal levels. Maintaining compliance is essential. Real-time financial analytics helps ensure you’re meeting regulatory requirements by spotting any deviations from established compliance protocols so you can take immediate action.

Automating the process of integrating and analyzing the large volumes of healthcare data that are available to your organization can accelerate reporting and help identify issues sooner. Automation also reduces the chance for human error.

Applying analytics to internal processes helps you monitor and audit your adherence to compliance mandates. You can ensure your processes meet requirements or identify areas where issues occur. As regulations change, performing analytics enables you to meet the latest mandates and even proactively determine where you could be at risk once new requirements are implemented. Real-time monitoring notifies you of compliance issues as they arise, preventing non-compliance issues from continuing.

Using clinical, financial, and other data can also help you better understand and track hospital readmission rates and patient satisfaction scores. Patient satisfaction is increasingly important and commonly used, along with other criteria, to measure quality of care.

A Data Platform for Real-Time Analytics

The Actian Data Platform has the ability to bring together the massive volumes of healthcare data needed for real-time insights you can trust. For example, the platform enables real-time financial analytics to monitor your financial metrics and effectively track your financial performance. You can determine the profitability of individual healthcare departments and measure the success of new initiatives.

Our platform and capabilities support healthcare providers, payers, pharmaceutical organizations, and others involved in the healthcare industry by enabling them to improve outcomes faster. With easy-to-use, trustworthy data, your organization can accelerate healthcare research, transform service delivery and payments, and optimize an outcome-centric model for care.

Watch a short video to see how we can help with healthcare billing fraud detection. Our platform and capabilities support healthcare providers, payers, pharmaceutical organizations, and others involved in the healthcare industry by enabling them to improve outcomes faster.

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 Analytics

7 Ways Financial Analytics Supports Data Security

Actian Corporation

July 12, 2023

financial analytics for data security

Financial analytics is one of many tools that when integrated on a single platform, enables organizations like yours to identify, prevent, and respond to data security threats quickly. And let’s face it—with ongoing threats against data security, you need to leverage all the robust technologies available that make sense for your business.

By optimizing financial analytics and data security best practices, you can strengthen your security posture. You’ll also be able to better protect sensitive data, assess risk, and identify possible threats quickly. Here are some more benefits of supporting your data security strategy with financial analytics.

Boost Data Security

Applying financial analytics to data security processes delivers far-reaching benefits. For example, analytical insights help with all facets of data security, including protecting against threats, creating a detailed data security strategy, assessing risk, predicting security issues, and more.

Here are seven ways that financial analytics can support data security:

Monitor Data Access and User Behavior

Data must be secure from both external and internal threats. Financial analytics can be expanded beyond providing insights into the financial well-being of the organization to also monitor who is accessing financial systems. This alerts you if there’s unauthorized access. You can also analyze user behaviors within financial systems. This helps you identify any unusual logins, any attempts to access data from unfamiliar accounts, downloads of sensitive data, or other issues that could indicate a security problem.

Automate Analytics Processes to Alert Stakeholders

Automating processes such as data pipelines and data analytics can accelerate insights and reduce the chances of human error. Fully automating data pipelines to bring financial and other data into a data warehouse or any other type of data management system lets you analyze fully integrated data. Financial analytics can identify patterns in the data or data usage—such as a spike in data downloads or data transfers outside the organization—that could point to potential security issues. Alerts can be automatically sent to stakeholders so they can take action.

Identify Potential Security Weaknesses

Your organization is probably already performing risk assessments to identify and mitigate a range of possible security problems. You can take these assessments to the next level by analyzing data from any previous breaches or security incidents. Financial analysis of issues occurring in financial systems can help identify any weak points, allowing you to mitigate risk by enhancing security. This also helps ensure better end-to-end data protection.

Proactively Predict and Prevent Fraud

Financial fraud, identity theft, and cybercrimes are becoming more common, more costly, and more sophisticated. Sensitive data that’s breached or even accidentally leaked can lead to significant issues. A comprehensive approach and advanced technologies are needed to identify and prevent data-related crimes. Financial and predictive analytics can identify trends, patterns, and anomalies that let you identify and even predict data security issues. The ability to anticipate problems is extremely helpful—the more time you have to prepare your systems, the more you can beef up your security to prevent current and emerging security threats.

Utilize Compliance Processes to Safeguard Data

Depending on your industry, you may have strict compliance requirements. Financial analytics helps you comply with various regulatory mandates and reporting. The analytics that identify non-compliance issues can also be applied to data security. Compliance processes offer a structured framework that ensures sensitive data is protected, which supports data security strategies. Likewise, compliance requirements may mandate a secure network infrastructure with firewalls, intrusion detection systems, and other security protocols that help keep data safe.

Understand Risk to Protect Sensitive Data

You will always have some degree of risk with your data. That risk may now be higher than in previous years due to remote or hybrid work environments that see more data being transmitted and shared to more locations. Data-driven risk management is needed to identify, assess, and reduce risk. Financial analytics supports risk management by delivering insights into the potential risks related to sensitive financial data and other information.

Data Security and Governance Best Practices

Data governance and data management lend themselves to keeping data secure. Financial analytics, along with other types of analysis, can improve governance, management, and security by offering insights into the levels of data security that you have in place. You can use these insights to determine if the proper levels of security are implemented or if additional measures are needed. Data governance best practices also support security policies by offering data access controls, managing data across its lifecycle, and identifying roles and responsibilities for data management and security.

Modernize Your Data Security Strategy

Making financial analytics part of your data security arsenal improves your ability to protect data and uncover threats. A comprehensive strategy that incorporates financial analytics offers data protection against unauthorized access and data misuse. Keeping large volumes of data secure for analytics and other uses requires a data platform that’s easy to use and delivers powerful insights you can trust. The Actian Data Platform does this and more by simplifying how you connect, manage, and analyze data, giving you trustworthy results. More than 10,000 customers around the world trust us with their data, and you can too.

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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 Intelligence

Actian Revolutionizes Data Discovery With NLP Search – OpenAI Integration

Actian Corporation

July 12, 2023

Zeenea NLP Capabilties

Actian is happy to announce the integration of Natural Language Processing (NLP) search capabilities in the Actian Data Intelligence Platform. This groundbreaking feature allows users to interact with the platform’s search engine using everyday language, making data exploration more intuitive and efficient.

Let’s explore how this innovation empowers users to obtain accurate and relevant results from their data searches.

How was the Actian Data Intelligence Platform’s NLP Search Integration Achieved?

To accomplish this functionality, the Actian Data Intelligence Platform leveraged the potential of OpenAI’s APIs and the advanced language processing capabilities of GPT-3.5. Actian Data Intelligence Platform’s engineers designed a prompt that effectively converts natural language questions into search queries and filters.
And voilà! As a result, users enjoy a smooth and effortless experience, as the search engine comprehends and responds to queries in a human-expert manner.

Some Examples of NLP Searches

Actian Data Intelligence Platform’s NLP search functionality opens up a world of possibilities for users to interact with their data catalog more effortlessly. Here are a few examples of the questions you can now ask in the platform’s search engine:

  • “Please find all datasets holding customer data in the central Data Lake.”
  • “Please list all duplicated datasets in the catalog.”
  • “Where can I find an analysis of our historical customer retention performance?”

These queries showcase the flexibility and convenience of communicating with the Actian Data Intelligence Platform using natural language. Whether you prefer a casual tone or a more professional approach, the platform’s search engine understands your intent and delivers accurate results.

A Feature Still in Development

Although the NLP search feature is currently in an experimental phase, the Actian Data Intelligence Platform is actively collaborating with select customers to ensure its accuracy and relevance in various contexts. Indeed, the Actian Data Intelligence Platform’s dynamic knowledge graph structure necessitates extensive real-world testing to fine-tune the system to provide the best possible experience to our users.

On the Road to AI-Driven Data Discovery

Actian Data Intelligence Platform’s dedication to innovation goes beyond NLP search. We are exploring several AI-powered features that promise to revolutionize the data discovery landscape. Some of the exciting developments include:

  • An interactive chatbot: The development of an interactive chatbot that could offer an alternative conversational search experience so users can engage in natural conversations to obtain relevant information and insights.
  • Automated generation & correction of business definitions: The platform aims to expedite catalog sourcing and enhance the quality of the glossary by automatically generating and correcting domain-specific business definitions.
  • Automatic summarization of descriptions: An automatic summarization that would enable users to grasp essential information quickly by condensing lengthy descriptions into concise summaries, ultimately saving time and improving their data comprehension.
  • Improved auto-classification and data tagging suggestions: Actian Data Intelligence Platform’s AI algorithms are being enhanced to provide more accurate auto-classification and data tagging suggestions.

…and more.

Stay tuned for more exciting developments from the Actian Data Intelligence Platform as they revolutionize the data discovery landscape.

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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 Regulate Bias in AI

Actian Corporation

July 11, 2023

bias artificial intelligence

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 non-discrimination.

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 Intelligence

What Does a Data Stack Look Like in 2023?

Actian Corporation

July 9, 2023

Data Stack

Companies are actively seeking faster and more cost-effective methods to manage their data. The advent of cloud data warehouses, which employ massively parallel processing (MPP) and SQL, has sparked a revolution in data processing. Now, we enter a new era with the Modern Data Stack (MDS), a suite of cloud-native tools that are user-friendly, scalable, and affordable. This transformative suite empowers organizations by revolutionizing data management and analysis.

The realm of digital data is experiencing explosive growth. In 2010, the annual data generation stood at 1.2 zettabytes. As per forecasts by data experts and observers, the milestone of 64 zettabytes was surpassed in 2020, and it is projected that by 2035, an almost unimaginable threshold of 2,142 zettabytes will be crossed. This exponential surge in data profoundly impacts the technical resources that companies must acquire to fully harness its potential.

In this context, it becomes necessary to redefine the parameters of the Data Stack. A Data Stack encompasses a range of tools, technologies, and platforms utilized to manage and analyze data within an organization. Typically, a Data Stack comprises various functional layers that cover all aspects of a data project, including data collection, storage, processing, analysis, and visualization.

A standard Data Stack may include components such as relational or NoSQL databases, tools for data transformation and cleansing, machine learning frameworks, solutions for data flow integration and management, as well as tools for data visualization. The selection of tools primarily depends on the specific needs and objectives of each company’s data requirements. However, as data volumes continue to soar, the traditional Data Stack must give way to the Modern Data Stack.

Differences Between Modern Data Stack and Legacy Data Stack

The primary distinctions between a Modern Data Stack and a Legacy Data Stack stem from the technologies and methodologies employed for data management and analysis.

A Legacy Data Stack refers to an older, traditional collection of technologies and tools, typically built on proprietary solutions and monolithic architectures. These systems can be expensive to maintain, challenging to adapt, and limited in terms of advanced analytical capabilities. They may also lack flexibility when it comes to integrating new data sources or working with large volumes of data.

In contrast, the Modern Data Stack embraces a more agile approach, leveraging contemporary technologies. It harnesses the power of open-source solutions, cloud computing, and service-oriented architecture (SOA) to provide enhanced flexibility, scalability, and agility. Moreover, the Modern Data Stack often incorporates tools such as cloud data warehouses, data lakes, automated data pipelines, self-service analytics platforms, data discovery platforms, and interactive visualizations. This enables businesses to delve deeper into data utilization and drive further advancements within their operations.

Promises of a Modern Data Stack in 2023

First and foremost, A Modern Data Stack offers enhanced agility for your company. It contributes to the rapid and flexible implementation of data flows, transformations, and analyses. It simplifies the process of adding or modifying data sources, allowing for easy adaptation to changing business needs. Another promise of the Modern Data Stack is scalability. Benefiting from the advantages of cloud technologies, it easily adapts to exponentially growing data volumes without requiring major investments in infrastructure.

This native scalability also translates into the seamless integration of various data sources, whether structured or unstructured, internal or external to the enterprise.

Thanks to self-service analysis platforms and interactive visualizations, a Modern Data Stack enables a greater number of users to take advantage of data, even without in-depth technical knowledge. Finally, a Modern Data Stack automates data collection, transformation, and management tasks, reducing manual effort and improving operational efficiency.

Foundations of a Modern Data Stack

When building a modern data stack, there are several fundamental components that need to be brought together.

First are the data sources, which can originate from within the company, such as transactional databases, flat files, business applications, sales tracking tools, and sensor data. Additionally, external data sources like public APIs, social networks, and market data can also play a crucial role. It is important to identify and integrate these data sources meticulously into the Modern Data Stack to ensure comprehensive coverage of data relevant to the company’s analytical needs.

Data storage is another vital element within the modern data stack. This encompasses various options such as cloud data warehouses, data lakes, relational or NoSQL databases, and distributed file systems. The primary objectives are to provide scalable, high-performance, and secure storage for the data. To facilitate data transformation, a dedicated brick within the modern data stack is required. This involves cleansing, preparing, and transforming raw data into a more structured format suitable for analysis.

Data analysis encompasses a range of techniques, including machine learning, statistical analysis, SQL queries, interactive dashboards, and data visualization. The ultimate aim is to extract actionable insights and knowledge from the data.

Finally, monitoring the data and performance of the Modern Data Stack is essential to ensure optimal operation and align with data governance efforts. This aspect plays a vital role in overseeing the functionality and effectiveness of the data stack.

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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.