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:

 

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About Jennifer Jackson

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

What is Synthetic Data?

Actian Corporation

June 4, 2023

Connection Structure

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.

 
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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 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.

Learn More

Read our data sheet to learn how our Actian Data Platform delivers core security and compliance capabilities, including single-sign on, multifactor authentication, IP allow list, role-based access control, data masking, encryption, role separation, audit logs, security alarms, regional deployment control, and more.

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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.

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About Teresa Wingfield

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

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.

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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

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:

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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 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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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:

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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 Intelligence

Business Glossary, a Data Catalog, and a Data Dictionary Differences

Actian Corporation

May 21, 2023

data dictionary vs data catalog vs business glossary

You’ve put data at the center of your company’s business strategy, but the amount of data you have to handle is exploding. You, therefore, not only need 360° visibility on your data portfolio but also a vision of the uses that can be made of it.

To do this, you can combine the actions and benefits of three essential pillars: the data catalog, the data dictionary, and the business glossary. Read this article to discover more.

Producing data is great. Gaining business knowledge from it is even better. Because the successful implementation of a data culture is a top priority of your business strategy, you need to transform the available information into operational tools for decision-making. By bridging together data and business, you will give your company (and your teams) a new impetus.

But to achieve this, you must rely on three essential pillars: a data catalog, a data dictionary, and a business glossary. Three essential tools that will help you organize and improve your data management strategy: Although they are related, these tools are actually quite different.

What is a Data Catalog and What are its Benefits?

A data catalog is a detailed inventory that lists all information from all of your organization’s data sources. Once unified in the catalog, it is more accessible, understandable, and actionable by your teams. A data catalog can collect and inventory several types of information such as datasets and their associated fields, data processes, visualizations, glossary objects (see section below), or even custom information specific to your company.

The data catalog plays a crucial role in your data strategy because it allows you to efficiently get an overview of your data, its quality, and availability, as well as its associated metadata such as its definition, associated contacts, provenance, format, etc. Another main advantage of using a data catalog is that it encourages the collaboration and sharing of data in all departments of your organization. It allows your teams to work together to identify, understand, and use data more effectively.

Finally, by centralizing the available information, a data catalog allows you to maintain a high level of data quality by ensuring that data is correctly identified, classified, documented, and maintained.

Why Implement a Business Glossary and for What Purpose

A business glossary is an essential component that helps establish a common understanding of business terms and definitions used in the organization. Its role: to facilitate communication and reduce errors or misunderstandings related to the use of your organization’s terms. It can include technical or financial definitions, procedures, or any other subject relevant to your organization.

By having a business glossary, you will almost mechanically improve data quality by ensuring that data is clearly defined and understood. It helps by reducing data entry errors, standardizing data formats, and increasing the reliability and accuracy of data.

Furthermore, the business glossary also helps you better manage regulatory compliance by standardizing the terms and definitions used in compliance reports and documents.

Finally, your business glossary contributes to faster and more reliable decision-making by providing a common knowledge base for all stakeholders in the decision-making chain.

What are the Differences With a Data Dictionary?

A data dictionary is a third tool that will help you strengthen and boost your data strategy. This data management tool provides detailed information about the data used in your business based on a set of metadata. This metadata describes the data, its structure, its format, its meaning, its owner, and its use.

This description helps your employees, those who use data on a daily basis, to understand the data and to use it appropriately. A data dictionary is also a key tool for data quality management, as it allows you to monitor data quality by identifying errors and inconsistencies. It also facilitates data reuse, providing information about existing data and its meaning, making it easy to integrate into new applications or projects.

Want to give your data strategy a boost? Combining a business glossary, a data catalog, and a data dictionary will give you a complete and consistent view of the data and business terms used in your company.

 
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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

Top Bottlenecks to Data Management Platform Adoption

Teresa Wingfield

May 19, 2023

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A data management platform (DMP) collects, manages, and analyzes data. This may sound just like a data analytics platform, but a DMP’s scope and purpose are more specific. It gathers audience data which is information about people who respond to advertisements or visit websites or other digital properties. The DMP uses this data to build anonymized customer profiles that drive targeted digital advertising and personalization.

Using a DMP helps accurately target advertising to the right audience, which results in higher response rates, increased brand recognition, and ultimately, higher conversion rates. But many factors can slow DMP adoption, including:

  1. Low Relevancy. Nothing will slow the adoption of a DMP more than data that does not meet users’ business needs. This can happen when data lacks meaning or when data isn’t timely. For example, first-party data (data your company has collected directly from its audience) often requires enrichment to be useful.
  2. Bad Data. Lack of quality data is one of the main reasons audience data isn’t used when planning campaigns for digital media. In particular, the reliability of third-party data, information collected by companies that don’t have a direct relationship with consumers, is highly variable. Digital marketers who rely on data to help them make important marketing decisions need to know that they can trust its integrity. If data isn’t accurate, complete, consistent, reliable, and up-to-date, users will lose confidence in the DMP and stop using it.
  3. Third-Party Cookies. DMPs have historically depended on third-party data. With third-party cookies going away, many are uncertain of the DMP’s future. Some businesses are implementing a zero-party data strategy where a customer intentionally and proactively shares data to fill the third-party data void.
  4. Poor Usability. Data analytics users have traditionally been technically savvy data engineers and data scientists who represent a small percentage of an organization’s employees. Organizations struggle to bring in a broader base of business users, such as marketing teams, when the DMP is hard to use.
  5. Limited Scalability. Scalability is a critical capability for DMP success, but many platforms are unable to expand with growing data volumes and users.
  6. Data Silos. It’s hard to get rid of data silos. When these can’t be integrated with the DMP, it may be difficult for organizations to deliver the complete customer profile data needed for decision-making, which can slow platform adoption.
  7. Sourcing From Multiple Vendors. Data integration, data quality, and other management workloads add more costs, and complexity when sourced from multiple vendors. This can limit further investment in the DMP if its costs exceed the business value delivered.

Learn More

To overcome these DMP bottlenecks, organizations need a scalable platform that is easy-to-use that can break down data silos. Additionally, businesses need to deliver relevant and trustworthy data. The Actian Data Platform provides data integration, data management, and data analytics in a single solution. This lowers risk, cost, and DMP complexity while allowing easier sharing and reuse across projects than cobbling together point solutions.

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About Teresa Wingfield

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

Are You Accurately Assessing Data? Here Are 7 Ways to Improve.

Actian Corporation

May 19, 2023

magnifying glass depicting how to accurately assess data

Data quality is essential for delivering reliable analytics that business users and decision-makers trust. Organizations should assess their data to ensure it meets their quality standards. Data quality management (DQM) is the practice of using data to serve an organization’s purposes with flexibility and agility. An assessment can also find gaps in data, such as missing information, that need to be filled in, to improve data quality. Here are seven ways to improve data assessments:

  1. Assess Completeness. Data completeness is the comprehensiveness or wholeness of a data set. It can be measured as a percentage of all required data that’s currently available in the data set. It’s important to note that non-essential information can be missing without making the data incomplete. For example, data that does not have a customer’s phone number will probably not impact email campaigns. Likewise, performing analytics on sales data within a certain time period will not be affected by missing information outside of those specified dates. However, for data to be complete, it must include values for all of the fields needed for the intended analytics.
  2. Ensure Consistency. Data should be the same across all uses and applications. This means that no matter where data is stored or used—on-premises, clouds, apps, or databases—it must be consistent. For example, customer data in the data warehouse needs to be the same as the customer data in a customer relationship management (CRM) system. Inconsistencies can be the result of data silos, outdated information, or information entered differently across users, such as a customer name entered with various spellings, like “John” and “Jonathan.” Testing multiple data sets helps determine consistency.
  3. Confirm Timeliness. Organizations want the most accurate data available at the time it’s being used. The right data must also be easily accessible when it’s needed, including for real-time or near-real-time use. The value and accuracy of data can depreciate over time. For example, data about buying habits prior to COVID-19 may no longer be relevant. Timely data that’s current and accurate helps stakeholders make the most informed decisions, uncovers new and emerging trends, and automates processes. This is where the right data platform delivers value—it makes integrated and timely data available to everyone who needs it.
  4. Validate Accuracy. Data must be correct, meaning it has the right information in all required fields, such as customer profile details or product specs. The fields can include everything from a customer’s date of birth and geographic location to sales numbers and corresponding sales dates. The data impacts business areas such as marketing, billing, and product design. Inaccurate data skews analysis, so it must be correct and complete. Data accuracy can be validated by confirming a data set against a verified or authentic source. Maintaining an effective data governance program helps ensure data accuracy.
  5. Determine Integrity. Data used for analysis should meet the organization’s data quality governance standards to ensure it maintains its integrity, which is the accuracy and consistency of data over its lifecycle. Each time data is duplicated or moved, the integrity can be compromised by information getting lost or attribute relationships becoming disconnected. For example, a CRM system that loses part of a customer profile, like a mobile phone number or email address, has data with compromised integrity. Data integrity allows organizations to trace and connect data. Data quality checks help verify its integrity.
  6. Measure Validity. Data must match the intended use for the data set, whether it’s for analytic insights or another purpose, and must also meet the organization’s defined rules for the data. Validated data can include information that fits into specific data types, forms, numerical ranges, or mandatory data fields, such as birth months that fall within the numbers one to 12 or zip codes that contain the correct number of digits. Data should be validated after a migration, like moving data sets from an on-premise infrastructure to the cloud. Implementing data validation rules helps ensure data meets the organization’s requirements.
  7. Evaluate Uniqueness. Uniqueness helps identify instances of data duplication by determining if the same information exists multiple times within the same data set. For example, if a list of 500 customers has data for more than 500 people, then data is duplicated. Data cleansing and de-duplication processes help resolve this problem.

Ensuring Quality Data Ensures Trustworthy Data Analytics

Assessing data is increasingly important as data volumes continue to grow and data sources expand. Having established processes in place to assess and govern data helps ensure the business can trust the results of its data analytics, including advanced analytics. Data that’s current, accurate, and complete also improves time to value. If it takes an unusually long time to get analytic results from a data set, there’s probably a data quality issue. Auditing and assessing data can identify issues and determine if a data set is fit for a specific purpose, such as advanced analytics. In addition, an audit can identify when changes were made to data, such as when a customer’s address, email, or phone number was updated.

Use a Modern Data Platform to Ensure Quality Data

One way to maintain data quality across the organization is to bring all data together on a single platform where it’s governed by established processes. Data governance ensures data meets compliance and quality standards. Data profiling also helps with data quality by identifying the structure, content, and formatting of data so it can be assessed and enhanced.

Actian offers modern, easy-to-use solutions for assessing and using data. The Actian Data Platform makes integrated data readily available to everyone who needs it. The trusted platform provides a unified experience for ingesting, transforming, analyzing, and storing data—and ensures data is complete and compliant using data quality rules.

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.
Actian Life

A Look Inside the Actian Internship Program

Actian Corporation

May 16, 2023

Actian internships and careers

The Actian internship program offers an engaging, unique, and educational opportunity for college students to learn, expand their skill sets, and make connections in the data analytics industry. The program started in 2020, but is already proving to be successful for dozens of science, technology, engineering, and mathematics (STEM) students, as well as students in other disciplines such as marketing, human resources, and finance.

The 12-week program supports up-and-coming professionals while giving Actian staff the chance to interact with students who may be a good fit to work at the company after graduating.

Rae Coffman-Bueb, Director of Employee Experience for Actian, is proud of the program’s success. “Interns are the future,” they noted. “In order for us to support that, we bring in and shepherd new talent. Giving them experience in a real-world setting is incredibly important not only for interns but also for us as an organization.”

A Learning Experience That’s Truly Unique

Actian’s internship program lets students work on actual projects, which are then shared across the company. Projects range from program implementations to fixing bugs to conducting research.

“We have a really unique structure to our internship program,” Coffman explains. “Many organizations, when they bring on interns, just give them random, day-to-day activities that an employee in that role would have. But our internship program is project-based, so each intern is brought onto a large-scale project that they work on over the course of 12 weeks.”

The hands-on work lets interns experience what real-world jobs actually entail, and the learning is mutually beneficial. While the interns learn from Actian employees, the company learns about the latest technology and business approaches taught in colleges.

“From an intern’s perspective, their project is a really great thing to showcase on a resume because they can say, ‘I was able to accomplish this giant project in 12 weeks,’” Coffman said. “And as an organization, we have the benefit of being able to learn from the students who are coming in with cutting-edge technology and information and are able to apply that to our problems and programs. That’s a huge benefit overall.”

Building Skills and Confidence

“One thing across the board that students gain is at the end of the 12 weeks, they all present in our internship showcase. It’s a way for them to show the whole company the project they worked on,” Coffman said. “One of the things that Sara and I are both passionate about is managing neurodiversity. We use a lot of those principles and apply them to presentations and public speaking.”

Coffman-Bueb and Sara Lou, Employee Experience Specialist for Actian, work with interns to help them prepare for their presentations. This includes one-on-one coaching sessions to build their skills and confidence.

Actian employees at all levels of the organization are always impressed by the work the interns do, especially their presentations.

“Every year, the entire executive team and the whole company say they want to expand the program,” Coffman notes. “We hear, ‘We need more interns!’ People really look forward to the presentations and are excited about them.”

The Path from Intern to Highly Productive Employee

Mollie Kendall is a shining example of how the internship program benefits both participants and Actian. She worked as an intern during the summer of 2021. Her manager was Kimmah Lewis, Senior Director, Digital and Demand Generation.

“During my internship, I dabbled in a lot of areas inside of demand generation, like digital, SEO, and content creation and management. I was able to sit in on a lot of meetings and learn what each entails,” she said. “I liked that I got that peek into each and every part of demand gen. That’s not something you get in school. Classes don’t go into the level of detail I experienced as an intern. Learning on the job and hands-on work is so valuable and prepares you for what comes after graduation.”

Kendall, who had 15 years of experience running her own photography business, liked the experience so much that after the internship ended, she continued to work part-time at Actian while in college. After graduating from Texas State University in San Marcos in December 2021, she accepted a full-time offer and is now Actian’s Demand Generation Specialist. She also handles the company’s social media and content management.

Other former interns are also now working at Actian. This includes three people who participated in last summer’s program and recently accepted full-time offers.

Learning, Connecting, and Engaging

“Every intern has a buddy who teaches them the ropes and day-to-day activities and is their dedicated point of contact for all questions big and small,” Coffman said. “They also have a manager who is very involved and acts as a mentor, and Sara and I meet with the students every week. We help bridge the gap between managers, buddies, and students. We make sure everything is on track. If there are any deviations, we’re able to course correct and guide them back.”

Everyone involved in the program is dedicated to setting up the interns for success and making sure what’s asked of them is achievable. This support made a big impact on Kendall.

“Working with Kimmah Lewis was what impressed me the most,” Kendall said. “I never thought I would go into tech and go into social media, but Kimmah was great. I learned so much, and I had fun doing it. It was great to be part of the program.”

She is now working with an intern as part of her job. “I mentored an intern last year and will do so again this summer,” she said. “I want to help her learn and build her skill set. It was invaluable what Kimmah did for me, and I can give interns that perspective.”

One of the highlights for Kendall and other interns is the social aspect. For example, interns typically work remotely, so Actian sent each intern a package to make s’mores. Then in a group event, the interns got together virtually, made s’mores, and told ghost stories.

“The connections through games and activities really completed the experience for me,” Kendall said.

Career Opportunities at Actian

Interested in joining an exciting team? Actian is hiring. See our openings.

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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

The Top 5 Benefits of Data Lineage

Actian Corporation

May 15, 2023

data lineage

Do you have the ambition to turn your organization into a data-driven enterprise? You cannot escape the need to accurately map all your data assets, monitor their quality, and guarantee their reliability. Data lineage can help you accomplish this mission. Here are some explanations.

To know what data you use, what it means, where it comes from, and how reliable it is throughout its life cycle, you need a holistic view of everything that is likely to transform, modify, or alter it. This is exactly the mission that data lineage fulfills, which is a data analysis technique that allows you to follow the path of data from its source to its final use. A technique that has many benefits.

Benefit 1: Improved Data Governance

Data governance is a key issue for your business and for ensuring that your data strategy can deliver its full potential. By following the path of data, from its collection to its exploitation, data lineage allows you to understand where it comes from and the transformations it has undergone over time to create a rich and contextualized data ecosystem. This 360° view of your data assets guarantees reliable and quality data governance.

Benefit 2: More Reliable, Accurate, and Quality Data

As mentioned above, one of the key strengths of data lineage is its ability to trace the origin of data. However, another great benefit is its ability to identify the errors that occur during its transformation and manipulation. Hence, you can take measures to not only correct these errors but also ensure that they do not reoccur, ultimately improving the quality of your data assets. A logic of continuous improvement that is particularly effective for the success of your data strategy.

Benefit 3: Quick Impact Analysis

Data lineage accurately identifies data flows, making sure you never stay in the wrong for too long. The first phase is based on the detailed knowledge of your business processes and your available data sources. When critical data flows are identified and mapped, it is possible to quickly analyze the potential impacts of a given transformation on data or a business process. With the impacts of each data transformation assessed in real-time, you have all the information you need to identify the ways and means to mitigate the consequences. Visibility, traceability, reactivity – data lineage saves you precious time.

Benefit 4: More Context to the Data

As you probably understood by now, data lineage continuously monitors the course of your data assets. Therefore, beyond the original source of the data, you have full visibility of the transformations that have been applied to the data throughout its journey. This visibility also extends to the use that is made of the data within your various processes or through the applications deployed in your organization. This ultra-precise tracking of the history of interactions with data allows you to give more context to data in order to improve data quality, facilitate analysis and audits, and make more informed decisions based on accurate and complete information.

Benefit 5: Build (Even More) Reliable Compliance Reports

The main expectations of successful regulatory compliance are transparency and traceability. This is the core value promise of data lineage. By using data lineage, you have all the cards in your hand to reduce compliance risks, improve data quality, facilitate audits and verifications, and reinforce stakeholders’ confidence in the compliance reports produced.

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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.