Data Intelligence

What is Edge Analytics?

Actian Corporation

June 20, 2023

Edge Analytics enables data-driven companies to go straight to analyzing their data after it has been collected by IoT devices. It helps eliminate data processing bottlenecks.

Learn more about Edge Analytics, its benefits, and concrete use cases to better understand this new data trend.

Speed up data processing and analysis, and reduce the number of steps between collecting and using your data assets: That’s the promise of Edge Analytics. This method of data processing is all about proximity to the data source. It avoids all the steps involved in sending data to a data processing center.

How Edge Analytics Works

Edge Analytics responds to a very different logic than traditional data analysis, with which data is generally transferred to a remote processing center, such as a server or cloud, and the analysis is performed. In the case of Edge Analytics, connected devices or sensors located at the edge of the network collect data in real-time from various sources such as industrial machines, vehicles, surveillance equipment, IoT sensors, etc.

The raw data collected is pre-processed locally – it is then filtered and prepared for immediate analysis. The purpose of this local pre-processing is to clean, sample, normalize and compress the data, in order to reduce the quantity of data to be transferred and guarantee its quality, prior to analysis. Once this preliminary phase has been completed, data analysis is also carried out on-site, at the edge of the network, using algorithms and models previously deployed on local devices or servers.

With Edge Analytics, you can fine-tune your data analysis strategy by transferring only essential data or exceptional events to a remote processing center. The objective? Reduce network bandwidth requirements and save storage resources.

What are the Benefits of Edge Analytics

If the proximity between the source of the data and the means of processing and analyzing it appears to be the main advantage of Edge Analytics, you’ll be able to reap five main benefits:

Accelerate Real-Time Decision-Making

Less distance between the place where data is collected and the place where it is processed and analyzed means the prospect of time savings on two levels. As Edge Analytics processes data at the network edge, where the data is generated, this enables real-time analysis, eliminating the latency associated with sending data to a remote location. Another advantage of this real-time dimension is that it enables autonomous data analysis.

Reduce Latency Between Data Collection and Analysis

Edge Analytics is a promise of real-time exploitation of your data assets because data processing is done locally. In the case of applications requiring rapid responses, such as the Internet of Things (IoT) or industrial control systems (production or predictive maintenance, for example), proximity data processing drastically reduces latency and optimizes processing times.

Limit Network Bandwidth Requirements

Traditional data analysis almost always relies on the transfer of large quantities of data to a remote data processing center. The result: intensive use of network bandwidth. This is particularly true when your business generates large volumes of data at high speed. Edge Analytics has the advantage of reducing the amount of data that needs to be transferred, as part of the analysis is carried out locally. Only essential information or relevant analysis results are transmitted, reducing the load on the network.

Optimize Data Security and Confidentiality

As you know, not all data have the same level of criticality. Some sensitive data cannot be transferred outside the local network for security or confidentiality reasons. Edge Analytics enables this data to be processed locally, which can enhance security and confidentiality by avoiding transfers of sensitive data to external locations.

Embark on the Road to Scalability

Because Edge Analytics enables part of the data analysis to be carried out locally, it enables a significant reduction in network load. In so doing, Edge Analytics facilitates scalability by avoiding bandwidth bottlenecks and paves the way for the multiplication of IoT devices without the risk of network overload.

Data analysis can be distributed across several processing nodes, facilitating horizontal scalability. Adding new devices or servers at the edge of the network increases overall processing capacity and enables you to cope with growing demand without having to reconfigure the centralized processing architecture.

What are the Main Use Cases for Edge Analytics

While the Edge Analytics phenomenon is relatively recent, it’s already being used massively in many business sectors.

Manufacturing

Edge is already widely used in manufacturing and industrial automation. In particular, it helps to monitor production tools in real-time, in order to detect breakdowns, optimize production, plan maintenance, or even improve the overall efficiency of equipment and processes.

Healthcare

In the healthcare and telemedicine sector, Edge Analytics is used in connected medical devices to monitor patients’ vital signs, detect anomalies, and alert healthcare professionals in real-time.

Smart Cities and Mobility

Edge Analytics is also well suited to the urban mobility and smart cities sector. In the development of autonomous urban transport, for example, real-time analytics can detect obstacles, interpret the road environment, and make autonomous driving decisions.

Security and Surveillance

The surveillance and security sector has also seized on Edge Analytics, enabling real-time analysis of video streams to detect movement or facial recognition.

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 to Build a Growth-Focused Data Analytics Tech Stack

Teresa Wingfield

June 19, 2023

chart showing price index and earnings with growth-focused data analytics

Building a growth-focused data analytics tech stack is all about cloud deployment flexibility and cloud-native support. According to Gartner, more than 85% of organizations will embrace a cloud-first principle by 2025, but they will not be able to fully execute their digital strategies unless they use cloud-native architectures and technologies. Cloud-native technologies empower organizations to build and run scalable data analytics in modern, dynamic environments such as public, private, and hybrid clouds.

Cloud Deployment Models

Your data analytics solution should support multi-cloud and hybrid cloud deployment models for greater flexibility, efficiency, and data protection. Here’s a brief overview of each model and its benefits:

Multi-Cloud simply means that a business is using several different public clouds such as AWS, Microsoft Azure, and Google Cloud, instead of just one. Why multi-cloud? Below are some of the compelling reasons:

Being able to choose the best-fit technology for a cloud project.
Getting the best value by choosing providers with the lowest cost and having leverage during price negotiations.
Obtaining different geographic choices for cloud data center locations.

A hybrid cloud model uses a combination of public clouds, on-premises computing, and private clouds in your data center with orchestration among these platforms. Hybrid cloud deployment is useful for companies who can’t or do not want to make the shift to cloud-only architectures. For example, companies in highly regulated industries such as finance and healthcare may want to store sensitive data on-premises but still leverage elastic clouds for their advanced analytics. Other businesses may have applications that would require too much expensive movement of data to and from the cloud, making on-premises a more attractive option.

Cloud-Native Technologies

Beware; even though most analytics databases today run in the cloud, there are huge and significant differences between cloud-ready and cloud-native. Let’s explore what cloud-native means and its benefits.

The Cloud Native Computing Foundation defines cloud native as:

“Cloud native technologies empower organizations to build and run scalable applications in modern, dynamic environments such as public, private, and hybrid clouds. Containers, service meshes, microservices, immutable infrastructure, and declarative APIs exemplify this approach.”

“These techniques enable loosely coupled systems that are resilient, manageable, and observable. Combined with robust automation, they allow engineers to make high-impact changes frequently and predictably with minimal toil.”

Below are some of the key benefits of a cloud-native analytics database versus a cloud-ready analytics database.

Scalability

On-demand elastic scaling offers near-limitless scaling of computing, storage, and other resources.

Resiliency

A cloud-native approach makes it possible for the cloud-native database to survive a system failure without losing data.

Accessibility

Cloud-native uses distributed database technology to make the database easily accessible.

Avoid Vendor Lock-In

Standards-based cloud-native services support portability across clouds.

Business Agility

Small-footprint cloud-native applications are easier to develop, deploy, and iterate.

Automation

Cloud-native databases support DevOps processes to enable automation and collaboration.

Reduced Cost

A cloud native database allows you to pay-as-you-go and pay for only resources that you need.

Get Started With the Actian Data Platform

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

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

Data-Driven Analytics Use Cases Powered by the Actian Data Platform

Teresa Wingfield

June 15, 2023

data analytics being pointed to on a large screen

Use Cases for Data-Driven Analytics

Our new eBook “Data-Driven Analytics Use Cases Powered by the Actian Data Platform” is designed for users and application builders looking to address a wide range of data analytics, integration, and edge use cases. We have included the following examples from real-world customer experiences and deployments to serve as a guide to help you understand what is possible with the Actian Data Platform (formerly Avalanche).

Customer 360

With the Actian Data Platform powering Customer 360, organizations can rapidly personalize the customer experience through micro-segmentation, next-best-action, and market basket analysis while improving customer acquisition and retention through campaign optimization, and churn analysis to increase customer loyalty.

Healthcare Analytics

The Actian Data Platform helps healthcare payer and provider organizations leverage analytics to protect their businesses against fraud, and increase care delivery, provider efficiency, and accuracy while accelerating the transformation to an outcome-centric model.

IoT-Powered Edge-to-Cloud Analytics

Edge applications and devices rely on complex data processing and analytics to improve automation and end-user decision support. The underlying cloud and edge data management solutions must leverage a variety of hardware architectures, operating systems, communications interfaces, and languages. The platform and its Zen Edge data management option provide broad, high-performing, and cost-effective capabilities for this demanding set of requirements.

ITOps Health and Security Analytics

With the explosion of ITOps, DevOps, AIOps, and SecOps data streaming from multiple clouds, applications, and on-premises platforms, many vendors are working to provide data visibility in their domains. However, they fall short of creating a holistic view to predictively identify trouble spots, security risks, and bottlenecks. How can businesses gain real-time actionable insights with a holistic IT analytics approach? The platform makes it easy to combine data from thousands of data sources into a unified hybrid-cloud data platform capable of real-time analysis of applications, infrastructure, and security posture.

Supply Chain Analytics

Manufacturing is a far more complex process, compared with just a few decades ago, with subcomponents required to assemble a single final product sourced from several places around the globe. Along with this complexity is a massive amount of data that needs to be analyzed to optimize supply chains, manage procurement, address distribution challenges, and predict needs. The Actian Data Platform helps companies easily aggregate and analyze massive amounts of supply chain data to gain data-driven insights for optimizing supply chain efficiency, reducing disruptions, and increasing operating margins.

Machine Learning and Data Science

The Actian Data Platform enables data science teams to collaborate across the full data lifecycle with immediate access to data pipelines, scalable compute resources, and preferred tools. In addition, the platform streamlines the process of getting analytic workloads into production and intelligently managing machine learning use cases from the edge to the cloud. With built-in data integration and data preparation for any streaming, edge, or enterprise data source, aggregation of model data has never been easier. Combined with direct support for model training systems and tools and the ability to execute models directly within the data platform alongside the data, companies can capitalize on dynamic cloud scaling of analytics, compute, and storage resources.

Why Actian?

Customers trust Actian because we provide more than just a platform. We help organizations make confident, data-driven decisions to reduce costs and enhance performance. Using our Actian Data Platform, companies can easily connect, manage, and analyze their data for a wide range of use cases. You can trust that your teams are making the best decisions that address today’s challenges and anticipate future needs.

Read the eBook to learn more.

About Teresa Wingfield

Data Analytics

Leveraging Supply Chain Data to Inform Predictive Analytics

Teresa Wingfield

June 13, 2023

boats representing supply chain data and analytics

Predictive analytics is a powerful tool to help use supply chain data to make more informed decisions about the future. This might involve analyzing data about inventory, order fulfillment, delivery times, manufacturing equipment and processes, suppliers, customers, and other factors that impact your supply chain. Predictive analytics can help you deal with some of your supply chain challenges more effectively, including demand volatility, supply shortages, manufacturing downtime, and high warehouse labor costs.

Six Steps to Inform Predictive Analytics

Knowing what’s going to happen in the future can help you transform your supply chain, but you’ll need to first understand how to leverage your supply chain data to inform predictive analytics. Here are some foundational steps to help you get started:

1. Collect Data

Predictive analytics relies on historical data to predict future events. How much data you’ll need depends on the type of problem you’re trying to solve, model complexity, data accuracy, and many other things. The types of data required depend on what you are trying to forecast. For instance, to forecast demand, you would need to gather data on past sales, customer orders, market research, planned promotions, and more.

2. Clean and Pre-Process Data

Data quality is key for predictive analytics to make accurate forecasts. Your data collection process needs to ensure that data is accurate, complete, unique, valid, consistent, and from the right time period.

3. Select a Predictive Analytics Technique

Machine learning uses algorithms and statistical models to identify patterns in data and make predictions. You need to select the appropriate machine-learning technique based on your data and the nature of your use case. Here are the major ones to choose from:

Regression Analysis: Finds a relationship between one or more independent variables and a dependent variable.
Decision Tree: Type of machine learning used to make predictions based on how a previous set of questions were answered.
Neural Networks: Simulates the functioning of the human brain to analyze complex data sets. It creates an adaptive system that computers use to learn from their mistakes and improve continuously.
Time-Series Analysis: Analyzes time-based data to predict future values.
Classification: Prediction technique that uses machine learning to calculate the probability that an item belongs to a particular category.
Clustering: Uses machine learning to group objects into categories based on their similarities, thereby splitting a large dataset into smaller subsets.

4. Train the Model

Training a machine learning model is a process in which a machine learning algorithm is fed with data from which it can learn.

5. Validate the Model

After training, you need to validate the model to ensure that it can accurately predict the future. This involves comparing the model’s predictions with actual data from a test period.

6. Use the Model to Forecast the Future

Once you have validated your model, you are ready to start using it to forecast data for future periods.

You’ll also need the right machine learning platform to execute these six predictive analytics steps successfully. Our blog “What Makes a Great Machine Learning Platform” helps you to discover how to evaluate a solution and learn about the Actian Data Platform’s capabilities.

About Teresa Wingfield

Data Management

7 Ways to Stop Data Quality Issues in Their Tracks

Traci Curran

June 8, 2023

footprint showing data quality issues being stopped in their tracks

Data quality is one of the most important aspects of any successful data strategy, and it’s essential to ensure that the data you collect and store is accurate and reliable. Poor data quality can lead to costly mistakes in decision-making, inaccurate predictions, and ineffective strategies. Data quality is essential in any organization, and there are a few key strategies you can use to instantly improve your data quality. Here are seven strategies to improve data quality:

1. Automation of Data Entry

Automating data entry is one of the most effective strategies for improving data quality. Automation helps ensure that data is entered accurately and quickly, reducing the risk of human error. Automation also allows you to quickly identify any errors or inconsistencies in the data, which allows you to trust the data you use to make decisions. Automation can help reduce the time spent manually entering data, freeing up more time for other tasks.

2. Data Standardization

Data standardization is another key strategy for improving data quality. Data standardization helps to ensure that data is consistent and reliable, and that data is entered in the same format across the organization. This helps to ensure that data is comparable and easy to analyze. Standardizing data also helps to reduce the risk of errors due to different formats and versions.

3. Data Verification

Data verification is another essential strategy for improving data quality. Data verification helps to ensure that the data is accurate, and it helps to detect any discrepancies or errors in the data. Data verification can also help you identify any patterns or anomalies that could indicate a problem with the data or your data pipelines. This allows staff to diagnose and resolve issues faster.

4. Use Data Integration Tools

Data integration tools are a great way to improve data quality. Data integration solutions, like Actian Data Platform, allow you to quickly and easily combine data from multiple sources, which helps to ensure that the data is accurate and up-to-date. Data integration tools can also help you automate the process of combining data and transformation, which can help to reduce the amount of time spent manually entering data.

5. Encourage Self-Service Data Quality

Encouraging self-service data quality is another excellent strategy. Self-service data quality empowers users to take ownership of the data they enter. By providing users with easy-to-use tools, training, and support, you can help ensure that data is entered correctly and quickly.

6. Implement Data Profiling

Data profiling helps to identify any patterns or anomalies in the data, which can help you identify any potential issues with the data. Implement tools or processes that can easily identify and segregate data that doesn’t adhere to your organization’s data standards.

7. Integrate Data Quality into your Pipelines

Create profiling and quality rules that can be integrated into your pipelines. Data management tools vary wildly in capabilities, so look for products that can provide a quick “at-a-glance” view of data quality based on the rules you’ve established. This can make it easier for staff to determine if there are expected results in data quality anomalies or something that could single a more significant problem at an earlier stage in the pipeline.

Benefits of Improving Data Quality

Improving data quality can have a number of benefits for any organization. Here are a few of the key benefits of improving data quality:

Improved Decision-Making: When data is accurate and reliable, it can help improve decision-making by ensuring that decisions are based on accurate and up-to-date data.
Enhanced Efficiency: Improved data quality can also help to improve efficiency, as it reduces the amount of time spent manually entering and verifying data, freeing up more time for other tasks.
Better Customer Service: Improved data quality can also help to improve customer service, as it helps to ensure that customer data is accurate and up-to-date.
Cost Savings: Improved data quality can also help save costs, as it reduces the time and resources spent manually entering and verifying data.

Get Started!

Automation of data entry, data standardization, data verification, data integration tools, and data quality processes are great strategies for improving data quality. Data governance is also essential for ensuring data accuracy and reliability. By following these strategies, you can ensure that your data is accurate and reliable, which can help to improve decision-making, enhance efficiency, and improve customer service. It can also help save costs, as it reduces the time and resources spent manually entering and verifying data. Actian’s Data Platform can support you in implementing these strategies to get the most out of your data.

About Traci Curran

Traci Curran is Director of Product Marketing at Actian, focusing on the Actian Data Platform. With 20+ years in tech marketing, Traci has led launches at startups and established enterprises like CloudBolt Software. She specializes in communicating how digital transformation and cloud technologies drive competitive advantage. Traci's articles on the Actian blog demonstrate how to leverage the Data Platform for agile innovation. Explore her posts to accelerate your data initiatives.

Data Analytics

Business Analytics vs. Financial Analytics: What’s the Difference?

Jennifer Jackson

June 6, 2023

presentation of business analytics versus financial analytics

There’s a saying that data is just data until it’s analyzed. It’s the analytics that turns data sets into insights to guide businesses. Data users and decision-makers need to know which type of analysis will deliver the answers needed. Two common types of data analytics are business analytics and financial analytics. While business and financial analytics can overlap in the data they use and even have common goals—business and finance are often intertwined—they also have distinct differences and drive different use cases. These analytics inform business decisions, drive organization-wide improvements, and identify solutions to ongoing and emerging challenges. By contrast, financial analytics offer insights into current and future financial operations, allowing organizations to take actions that improve financial performance and boost profitability.

It’s best to think of business analytics and financial analytics as complementary rather than working against each other. For example, analyzing sales data benefits both the business and finance. Let’s look at how business and financial analytics are different—and why those differences are important:

Business vs. Financial Analytics

The most obvious difference between business and financial analytics is the areas of focus. Business analytics looks at overall business performance and daily operations to inform decisions on strategies, processes, problem-solving, and other business-centric areas. These analytics enable a range of improvements and benefits, such as charting an accelerated path to reaching business goals and measuring progress along the way. Financial analytics focuses on all financial aspects of the business, which can range from determining profitability to measuring top and bottom-line performance to informing budget decisions. Applying these analytics also helps organizations predict cash flow, measure business value, and determine how changes, such as launching a new product or improving sales by a certain percentage, will affect profitability. Knowing the type of insights that are needed will determine which analytics need to be performed.

Business analytics are generally more widely used throughout an organization than financial analytics. A business analyst is a general term for anyone who performs business analytics. Other positions using business analysis can include data scientists, citizen data scientists, machine learning and AI developers, operations teams, chief data officers, and others across the business. Financial analytics falls under the domain of CFOs and their departments. They perform analytics to build financial forecasts, identify potential risks, predict future financial performance, and provide other financial information.

Business analytics helps with workflows, process improvements, and organization-wide decision-making. For example, analytics can identify inefficient business processes, such as bottlenecks that slow down operations, and determine the best avenues for improvement. With financial analytics, organizations can make more accurate financial forecasts and investment decisions. In conjunction with predictive financial models, the analytics can answer a variety of fiscal-related questions, such as determining a customer’s lifetime value, understanding how churn and net new customers impact revenue, and measuring ways that initiatives like implementing environment, social, and governance (ESG) best practices influence profit margins.

Each type of analytics has specific questions it answers for what/if scenarios as well as providing insights into business or financial areas. Business analytics typically informs overall business strategies, such as determining if there’s a gap in the marketplace where the company can introduce a new product line, and help the business prioritize goals. Financial analytics also helps inform strategies, but those strategies are tied to goals for the chief financial officer (CFO) and the broader financial team. These analytics uncover insights related to business expenses, the organization’s overall financial health, and investments, including investments in research and development.

For the best analytic results, all relevant data should be integrated and made available to analysts. This means business and financial data can be brought together for insights. Specific business insights can be uncovered by analyzing data related to operations, customers, supply chains, products, sales, marketing, employees, sales, and other business areas. Financial analytics looks at financial and economic data, which is needed for any fiscal planning. Current, accurate, and appropriate data is required for each type of analytics to deliver relevant and trustworthy insights.

Simplifying Data Analytics

In addition to business and financial analytics, there are other types such as sales analytics, compliance analytics, and risk analytics. They all have several things in common—they use data to inform decision-making, predict outcomes, identify and mitigate problems, and drive improvements. Regardless of the analytics being performed, organizations need a modern platform that can scale to meet growing data volumes, make integrated data readily accessible to everyone who needs it, and is easy to use for all analysts. The Actian Data Platform delivers these capabilities and more. Whether analysts want a deeper understanding of the business or are taking a deep dive into finances, the Actian Data platform makes it easy to connect, manage, and analyze data. The easy-to-use platform brings together all data from all sources to deliver the analytic insights decision-makers and stakeholders need.

Additional Resources:

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 Intelligence

What is Synthetic Data?

Actian Corporation

June 4, 2023

Synthetic data can be defined as artificially annotated information. They are generated by algorithms or computer simulations and are widely used in the healthcare, industrial, and financial sectors. A look back at a key trend in the world of data.

The Key Differences Between Real and Synthetic Data

Synthetic data, also known as artificial data, is computer-generated rather than collected from real sources. While they are intended to represent patterns and characteristics similar to those of real data, they are not derived directly from real observations or events. There are therefore three main differences between conventional data and artificial data.

Representativeness

The first distinction between real data and synthetic data concerns the notion of representativeness. Real data comes from sources, measurements, or observations made in the real world. They reflect the characteristics and variations of a tangible, observed reality. They are therefore as representative as possible. Synthetic data, on the other hand, is generated in a programmed way. Although they are designed to reproduce patterns and characteristics similar to real data, they do not always capture all the complexity and variability of real data.

Confidentiality

Real data is likely to contain sensitive information about individuals. They are governed by strong confidentiality principles, due to personally identifiable information (PII) or compliance risks. Synthetic data, on the other hand, is generated in such a way as not to contain any real or identifiable information. As such, they provide a workaround for data confidentiality issues, offering a safer alternative for sharing, analysis, and application development.

Availability

Synthetic data can be generated in unlimited quantities and tailored to the specific needs of an application. This frees you from the limitations of real data in terms of quantity and availability, giving you greater flexibility when testing, experimenting, or developing data-intensive applications.

How are Synthetic Data Generated?

Synthetic data can be created using statistical models that reproduce the distributions, correlations, and characteristics of real data. They can also be generated via simulation. This involves creating simulated scenarios and processes that mimic real-life behavior. Machine learning can be used to generate synthetic data by learning from existing real data.

Finally, real data can sometimes be used as the basis for generating synthetic data. In this case, a number of elements are modified to preserve the confidentiality or sensitivity of the information. In all cases, synthetic data generation is always based on a thorough understanding of the characteristics and structures of your real data, in order to maximize its realism and representativeness.

What are the Main Advantages of Synthetic Data?

More flexible, more available, and often richer, there are many reasons to be interested in the generation of synthetic data, as they offer four major advantages:

Limiting Data Confidentiality Issues

Generating dummy data that contains no personally identifiable information means that data can be shared, analyzed, and processed without ever risking individual privacy or data protection regulations.

Improve Data Accuracy

In many cases, real data can have information gaps. Synthetic data helps to fill these gaps by generating additional data for areas where real data is incomplete. This provides a more complete and accurate representation of the entire dataset. They can also be used to correct imbalances in data classes or to detect and compensate for outliers.

Guarantee Data Availability

Real data can often be scarce and difficult to access. With synthetic data, there are no quantitative constraints or dependence on limited real-world resources. They can be produced at will, allowing greater flexibility in project realization and scenario exploration.

Control Costs Linked to Data Collection and Storage

Collecting real data can be costly in terms of financial, human, and material resources. By using synthetic data, it is possible to generate data at a lower cost. What’s more, synthetic data can be generated on demand, reducing storage capacity requirements and optimizing costs.

Some Examples of Uses for Synthetic Data

Synthetic data already meets a number of uses. When it comes to synthetic location data, for example, routes, and movements of people, or vehicles can be easily simulated, saving considerable time in urban planning or logistics.

Synthetic image and video data are used to simulate scenes, objects, and movements, and are commonplace in the world of virtual reality, video analysis, and object recognition model training. Synthetic text data is used to simulate documents, conversations, and even sentiment analysis.

Finally, synthetic financial data can be created to simulate transactions, investment portfolios, price variations, trading volumes, and so on. They are therefore very common in the analysis of financial markets or the development of trading algorithms.

About Actian Corporation

Data Security

Actian Achieves ISO 27001 Certification

Bryan Batty

June 1, 2023

persons hands showing ISO 27001 certification

I am pleased to share that Actian has successfully achieved International Organization for Standardization (ISO) 27001 certification in April 2023. Our certification scope includes all of Actian’s worldwide office and data center locations and covers the design, development, testing, support, and sale of all Actian products.

What is ISO 27001?

ISO 27000 is a set of internationally recognized standards that outlines best practices for building Information Security Management Systems (ISMS). The standards are designed to help organizations establish, implement, maintain, and continually improve their information security practices to protect against potential threats and vulnerabilities. One of these standards, ISO 27001, is perhaps the best-known standard in the industry for ISMS.

What is ISO 27001 Certification?

The ISO 27001 standard lists the requirements for building an Information Security Management System. The requirements cover such domains as information security policy, asset management, cryptography, physical security, incident management, and more. In total, there are 114 controls grouped into 14 domains. During the certification process, an independent auditor examines an organization’s adherence to all 114 of these controls.

Why is ISO 27001 Certification Important?

Data breaches are increasing in frequency and cost. In 2022, the average cost of a data breach reached a record high of US $4.35 million, according to the “Cost of a Data Breach Report 2022” by IBM and the Ponemon Institute. This report reveals that 83% of organizations studied have had more than one data breach. Fraudulent use of stolen or compromised credentials was the most common cause of data breaches (19% of breaches), followed by phishing (16% of breaches) and ransomware (11% of breaches).

By following ISO 27000 as a guideline for effective security, organizations can reduce the risk of data breaches and other security incidents, better protect their information assets, and improve compliance with applicable legal and regulatory requirements.

Although an organization can follow the guidance issued in the standard, Actian has chosen to go through the certification process with an independent accreditation body. This certification gives us confidence in the fact that we have built and are operating our ISMS properly, and also assures customers and business partners of our commitment to handling their information safely and securely.

You Can Trust Your Data With Actian

Whether your organization is required to comply with General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), Sarbanes-Oxley Act (SOX), Federal Information Security Management (FISMA), Payment Card Data Security Standard (PCI DSS), or the California Consumer Privacy Act (CCPA), the products you select to manage your data is critical to your success.

Actian’s commitment to providing the highest level of security and protection for our products and processes drove our decision to pursue ISO 27001 certification. By achieving this certification, we have demonstrated our ability to effectively manage information security risks and ensure confidentiality, integrity, and availability of systems and services.

Moving forward, we will continue to invest in our information security practices to maintain our ISO 27001 certification and to provide the highest level of security and protection. We look forward to building on this achievement and continually refining and improving Actian data security.

About Bryan Batty

Bryan Batty is Senior Director of Solution Security at Actian, with over two decades of security and engineering experience. He has led key security initiatives, guiding both customers and partners in addressing pressing cybersecurity questions and compliance requirements. Before Actian, Bryan directed global product security for HCL Software. Bryan has delivered talks at security conferences like RSA and OWASP meetups. He often publishes insights on emerging threats and secure development life cycle (SDLC) best practices. Bryan's blog posts on the Actian site focus on security leadership, encryption methods, and compliance. Explore his latest articles for practical guidance on protecting your data assets.

Data Management

Using Data to Improve Your ROI Just Got Easier

Teresa Wingfield

June 1, 2023

depiction of using data to improve your rio

Are your data analytics providing a positive return on investment (ROI) for your organization? Unfortunately, the answer may be no because the data isn’t offering enough value, meaning that it isn’t positively impacting business outcomes. Too often, data platforms are information graveyards. This may sound harsh, but Forrester estimates that less than 0.5% of all data is ever analyzed and used. It also estimates that if the typical Fortune 1000 business were able to increase data accessibility by 10%, it would generate more than $65 million in additional net income.

You should and can turn this around. Delivering the right data, at the right time and in the right context will make it easier to use data to improve your ROI. Here are some pointers to help you get started.

Deliver the Right Data

You can’t improve business outcomes unless you ask your users what data they really need. You’re likely to get an extensive list of requests, so you should also find out what key performance indicators (KPIs) and other methods users apply to measure their success. This will provide a way for you to prioritize data that will help users meet their goals. Also, try to understand issues that are preventing users from getting the insights they need, including factors such as usability, data quality, and accessibility.

Deliver Data at the Right Time

Organizations with traditional data analytics, data warehousing, business intelligence, and data management processes often take weeks to respond to requests for the right data. As a result, current data isn’t available when users need it for decision-making. Real-time data analytics helps organizations deliver data in a manner that improves situational awareness as change is happening and thus empowers them to decide on the best courses of action at the moment.

Deliver Data in the Right Context

Analytics embedded within day-to-day tools and applications delivers data in the right context, allowing users in sales, marketing, finance, and other departments to make better decisions faster. According to Gartner, context-driven analytics, and Artificial Intelligence (AI) models will replace 60% of existing models built on traditional data, by 2025.

The Actian Data Platform Improves ROI

The Actian Data Platform is the ideal solution for making it easy to deliver the right data, at the right time and in the right context.

REAL-Real Time Analytics: The Actian Data Platform is able to not only update data in the instant that it changes but does so in a way that does not impact the performance of other workloads or queries. While other technologies claim real-time analytics, their data updates always impact query performance. Thus, they deliver “near” real-time or “human” real-time…but never REAL real-time. If you need TRUE real-time insights –at the moment they matter– you need the Actian Data Platform.

Embedded Analytics: The Actian Data Platform includes a scalable connectivity framework, a lightweight embeddable runtime engine, a low-code development environment, and ready-to-use APIs to deliver embedded analytics quickly.

Native Integration: In addition, the Actian Data Platform includes integration. This means you can work with one vendor to solve multiple problems: integrating data from any source to any target, transforming it along the way with profiling and cleansing via automation and orchestration, and delivering real-time analytics. One-stop shopping with the Actian Data Platform saves you headaches with procurement, simplifies your ecosystem and gets you results faster.

About Teresa Wingfield

Data Management

Real-Time Data Analytics During Uncertain Times

Teresa Wingfield

May 30, 2023

downward trend of data analytics during uncertain times

Are we in a recession? Not in the U.S., according to some economists, a recession is defined as two consecutive quarters of negative gross domestic product (GDP) growth. But most will agree that we are living in uncertain times with the recent failure of two large banks, inflation, widespread layoffs in the technology sector, and geopolitical uncertainty. As a result, the top worry for most CEOs in 2023 is a recession or an economic downturn, according to a recent survey from The Conference Board.

In response to economic pressures, many companies are examining their technology spending more closely, and data analytics is no exception. However, analytics provides the opportunity to deliver more business value than what it costs, and this becomes even more important when an organization’s bottom line is under pressure. Here are just a few areas where data analytics has a huge impact by providing real-time insights that help businesses optimize their operations to increase revenue and cut costs.

Optimizing Pricing and Promotions: By analyzing customer behavior, purchasing patterns, market trends, and competitor pricing, businesses can identify the best pricing strategies and promotional offers to increase sales.

Acquiring and Retaining Customers: Analyzing data can help businesses know their customers better to develop targeted strategies and deliver personalized customer experiences that win new business and prevent customer churn.

Identifying Process Inefficiencies: Data analytics can help businesses detect areas where processes need to be optimized by identifying bottlenecks, and areas where resources are being wasted or where the business is overspending.

Improving Forecasting and Planning: Businesses can use analytics to predict future sales, which leads to better production planning.

Detecting Fraud: Detecting fraud with analytics helps avoid financial losses and reduces the costs of investigating and resolving fraud cases.

Reducing Energy Spend: Businesses can analyze energy consumption to reduce energy waste, lowering energy bills.

Increase Employee Productivity: Analyzing employee data can help identify where employees are over or under-utilized to reduce costs and improve productivity.

Assessing and Managing Risks: Risk management analytics helps spot trends and weaknesses and provide insights into the best way to resolve them proactively.

Connect Business Value With the Cost of Business Analytics

Cost does matter. In today’s uncertain times, data analytics initiatives must align costs with business value more than ever before. However, you need to focus on cost optimization rather than cost-cutting. A cost-optimal solution should not only process analytics workloads cost-effectively, but also include data integration, data quality, and other management workloads that add more costs and complexity when sourced from multiple vendors.

The Actian Data Platform provides high business value at low cost. It’s built to maximize resource utilization to deliver unmatched performance and an unbeatable total cost of ownership. Plus, it’s a single platform for data integration, data management, and data analytics. This translates into lower risk, cost, and complexity than cobbling together point solutions.

About Teresa Wingfield

Data Analytics

How to Use Cloud Migration to Modernize Data and Analytics

Actian Corporation

May 25, 2023

cloud migration showing uploads and downloads

Ensuring a hassle-free cloud migration takes a lot of planning and working with the right vendor. While you have specific goals that you want to achieve by moving to the cloud, you can also benefit the business by thinking about how you want to expand and optimize the cloud once you’ve migrated. For example, the cloud journey can be the optimal time to modernize your data and analytics.

Organizations are turning to the cloud for a variety of reasons, such as gaining scalability, accelerating innovation, and integrating data from traditional and new sources. While there’s a lot of talk about the benefits of the cloud—and there are certainly many advantages—it’s also important to realize that challenges can occur both during and after migration.

Identify and Solve Cloud Migration Challenges

New research based on surveys of 450 business and IT leaders identified some of the common data and analytics challenges organizations face when migrating to the cloud. They include data privacy, regulatory compliance, ethical data use concerns, and the ability to scale.

One way you can solve these challenges is to deploy a modern cloud data platform that can deliver data integration, scalability, and advanced analytics capabilities. The right platform can also solve another common problem you might experience in your cloud migration—operationalizing as you add more data sources, data pipelines, and analytics use cases.

You need the ability to quickly add new data sources, build pipelines with or without using code, perform analytics at scale, and meet other business needs in a cloud or hybrid environment. A cloud data platform can deliver these capabilities, along with enabling you to easily manage, access, and use data—without ongoing IT assistance.

Use the Cloud for Real-Time Analytics

Yesterday’s analytics approaches won’t deliver the rapid insights you need for today’s advanced automation, most informed decision-making, and the ability to identify emerging trends as they happen to shape product and service offerings. That’s one reason why real-time data analytics is becoming more mainstream.

According to research conducted for Actian, common technologies operational in the cloud include data streaming and real-time analytics, data security and privacy, and data integration. Deploying these capabilities with an experienced cloud data platform vendor can help you avoid problems that other organizations routinely face, such as cloud migrations that don’t meet established objectives or not having transparency into costs, resulting in budget overruns.

Vendor assessments are also important. Companies evaluating vendors often look at the functionality and capabilities offered, the business understanding and personalization of the sales process, and IT efficiency and user experience. A vendor handling your cloud migration should help you deploy the environment that’s best for your business, such as a multi-cloud or hybrid approach, without being locked into a specific cloud service provider.

Once organizations are in the cloud, they are implementing a variety of use cases. The most popular ones, according to research for Actian, include customer 360 and customer analytics, financial risk management, and supply chain and inventory optimization. With a modern cloud data platform, you can bring almost any use case to the cloud.

Drive Transformational Insights Using a Cloud Data Platform

Moving to the cloud can help you modernize both the business and IT. As highlighted in our new eBook “The Top Data and Analytics Capabilities Every Modern Business Should Have,” your cloud migration journey is an opportunity to optimize and expand the use of data and analytics in the cloud. The Actian Data Platform can help. The platform makes it easy for you to connect, manage, and analyze data in the cloud. It also offers superior price performance, and you can use your preferred tools and languages to get answers from your data. Read the eBook to find out more about our research, the top challenges organizations face with cloud migrations, and how to eliminate IT bottlenecks. You’ll also find out how your peers are using cloud platforms for analytics and the best practices for smooth cloud migration.

Additional Resources:

About Actian Corporation

Data Analytics

How Supply Chain Analytics Measures Your Company’s Health

Traci Curran

May 23, 2023

person learning about shipping and transporting and supply chain analytics

In today’s highly competitive business world, companies are constantly looking for ways to improve their supply chain operations. One of the most effective ways to do this is by measuring supply chain performance using real-time analytics. By understanding the performance of each aspect of the supply chain, companies can identify bottlenecks, reduce lead times, and improve customer satisfaction. By implementing real-time supply chain analytics, you can gain valuable insights into your company’s health and identify areas for improvement.

Key Performance Indicators in Supply Chain Analytics

Before diving into the benefits of supply chain analytics, it’s essential to understand the key performance indicators (KPIs) that are typically used to measure supply chain performance. These metrics are vast, but three that are common examples are:

Inventory Turnover: This KPI measures how quickly you are selling your inventory. A low inventory turnover rate can indicate that you are carrying too much inventory, while a high rate can suggest that you are not keeping enough stock on hand.

Order Cycle Time: This KPI measures the time it takes from when a customer places an order to when the order is fulfilled. A longer order cycle time can lead to dissatisfied customers, while a shorter cycle time can improve customer satisfaction.

Perfect Order Rate: This KPI measures the percentage of orders that are delivered on time, in full, and without any errors. A low perfect order rate can indicate that you have issues with your order fulfillment process, which can lead to lost sales and dissatisfied customers.

Using Data Analytics to Improve Supply Chain Performance

One of the most effective ways to improve supply chain performance is using data analytics. By collecting and analyzing data from various aspects of the supply chain, companies can identify patterns and trends that can be used to optimize operations. Data analytics can be used to identify areas where supply chain operations are inefficient or ineffective, such as high inventory levels or long lead times. It can also be used to identify opportunities for improvement, like reducing transportation costs or improving manufacturing efficiency. Some specific areas where supply chain analytics can improve performance include:

Improved Forecasting Accuracy: By analyzing historical data and trends, you can improve your forecasting accuracy. This can help you better anticipate demand for your products and avoid overstocking or understocking.
Better Inventory Management: By analyzing inventory turnover and other metrics, you can optimize your inventory levels to reduce carrying costs while still meeting customer demand.
Increased Supply Chain Visibility: By using analytics tools, you can gain more visibility into your supply chain operations. This can help you identify bottlenecks or inefficiencies and make data-driven decisions to improve your supply chain.
Faster Order Fulfillment: By analyzing order cycle times and perfect order rates, you can identify areas where you can streamline your order fulfillment process. This can help you deliver products to customers faster and improve customer satisfaction.
Reduced Risk: By analyzing your supply chain, you can identify potential risks and take steps to mitigate them. For example, you may identify a supplier who is at risk of going out of business, and you can take steps to find a new supplier before a disruption occurs.

Best Practices for Implementing Supply Chain KPIs

Implementing KPIs in a supply chain can be a complex process, but there are several best practices that companies can follow to ensure success. These include:

Defining Clear Objectives: Before implementing KPIs, it’s important to define clear objectives that align with overall business goals. This ensures that KPIs are relevant and meaningful.
Choosing the Right KPIs: Not all KPIs are created equal, and it’s important to choose KPIs that are relevant to specific aspects of the supply chain. This ensures that KPIs provide meaningful insights.
Collecting Accurate, Data: KPIs are only as good as the data that is used to measure them, so it’s important to collect accurate and reliable data. That means that the data must be consistent, complete, and correct, and that data must be available in a timeframe that allows your business to react to changes.
Communicating Results: KPIs should be communicated to all stakeholders in a clear and concise manner. This ensures that everyone understands the importance of KPIs and how they contribute to overall business success.
Continuously Improving: Supply chain operations are constantly evolving, so it’s important to continuously review and improve KPIs to ensure they remain relevant and effective.

By analyzing key performance indicators, businesses can identify inefficiencies, improve customer satisfaction, and reduce costs. Supply chain analytics can provide valuable insights into overall business health when they are built using KPI’s that are directly tied to overall business objectives.

Use these resources to learn how the Actian Data Platform is helping to deliver real-time data for supply chain analytics:

What is Edge Analytics?

How Edge Analytics Works

What are the Benefits of Edge Analytics

Accelerate Real-Time Decision-Making

Reduce Latency Between Data Collection and Analysis

Limit Network Bandwidth Requirements

Optimize Data Security and Confidentiality

Embark on the Road to Scalability

What are the Main Use Cases for Edge Analytics

Manufacturing

Healthcare

Smart Cities and Mobility

Security and Surveillance

About Actian Corporation

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

How to Build a Growth-Focused Data Analytics Tech Stack

Cloud Deployment Models

Cloud-Native Technologies

Scalability

Resiliency

Accessibility

Avoid Vendor Lock-In

Business Agility

Automation

Reduced Cost

Get Started With the Actian Data Platform

About Teresa Wingfield

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

Data-Driven Analytics Use Cases Powered by the Actian Data Platform

Use Cases for Data-Driven Analytics

Customer 360

Healthcare Analytics

IoT-Powered Edge-to-Cloud Analytics

ITOps Health and Security Analytics

Supply Chain Analytics

Machine Learning and Data Science

Why Actian?

About Teresa Wingfield

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

Leveraging Supply Chain Data to Inform Predictive Analytics

Six Steps to Inform Predictive Analytics

1. Collect Data

2. Clean and Pre-Process Data

3. Select a Predictive Analytics Technique

4. Train the Model

5. Validate the Model

6. Use the Model to Forecast the Future

About Teresa Wingfield

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

7 Ways to Stop Data Quality Issues in Their Tracks

1. Automation of Data Entry

2. Data Standardization

3. Data Verification

4. Use Data Integration Tools

5. Encourage Self-Service Data Quality

6. Implement Data Profiling

7. Integrate Data Quality into your Pipelines

Benefits of Improving Data Quality

Get Started!

About Traci Curran

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

Business Analytics vs. Financial Analytics: What’s the Difference?