Data Intelligence

What is a Data Product – A Fundamental Concept of Data Mesh

Actian Corporation

September 4, 2023

In recent years, the evolution of data management practices has led to the emergence of a transformative concept known as Data Mesh. This paradigm shift challenges traditional centralized approaches by advocating for a distributed and domain-oriented strategy for data architecture.

At the heart of the Data Mesh philosophy lies the concept of Data Products – self-contained, domain-specific datasets curated and managed by individual teams. These Data Products are emerging as a transformative concept, enabling companies to harness the vast potential of their data.

In this article, learn everything you need to know about Data Products.

A Data Product is a digital product or service that integrates and exploits data meaningfully to deliver added value to its users. It typically combines raw data, analytics, algorithms, and interactive features to offer information, recommendations, or specific actions based on data.

Data Products can take many forms, such as interactive dashboards, applications, recommendation systems, predictive analysis tools, and many others. The Data Product is designed to facilitate decision-making, automate tasks, and improve the user experience – all by exploiting all available data as effectively as possible.

By adopting a Data Mesh architecture, organizations can improve the management and exploitation of their Data Products, encouraging a more agile, scalable, and collaborative approach to the way data is processed and used to create business value.

What are the Key Characteristics of a Data Product?

Data Products enable companies to better understand their data, make better decisions, and improve performance. The expectations linked to the use of a Data Product vary according to the company, the product, and the users. However, the main challenge is to generate value for users.. As such, the Data Product must be intuitive and accessible, but the quality of the data it contains must be at the center of all attention.

To guarantee internal trust, as well as compliance, the data used in the Data Product must be secure and well documented. In addition, a Data Product is generally autonomous, meaning that it is managed by a dedicated team, responsible for all stages of the product life cycle. Finally, a Data Product must also be scalable. It must be able to adapt to changing user needs and new technologies.

Data Product: Some Use Cases

One of the uses of a Data Product is for instance the creation of an interactive sales tracking dashboard that uses your sales data to display real-time performance, analyze trends, or visualize revenues generated by product or region, etc. It enables your sales managers to make informed decisions and identify opportunities for improvement.

Data Products can also be used to analyze customer behavior on an e-commerce platform to suggest relevant, personalized products based on their preferences and purchase history.

Similarly, a Data Product that uses sales history and other relevant data to predict future demand for a product, will enable you to better plan production and inventory. Because it monitors data from a system or process in real time, a Data Product can be used to detect anomalies or unusual behavior, enabling you to react quickly to potential problems.

What are the Benefits of Data Products for a Company?

Data Products provide relevant information and analysis, enabling you to make more informed decisions based on concrete data. The native automation associated with the Data Product concept is also your best promise of time and resource savings. Data Product also enables you to look to the future by providing information on customer needs, market trends, and gaps in existing products. You can base your innovation roadmap on the anticipated needs of your targets.

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.

Healthcare

5 Strategies for Data Migration in Healthcare

Actian Corporation

August 29, 2023

All data-driven businesses need to migrate their data at some point, whether it’s to the cloud, a sophisticated data management system like a data warehouse, or applications. In some instances, the data migration process can entail changing the data’s format or type to make it more usable, increase performance, make it easier to store, or for other reasons. This is no exception for healthcare data analytics.

In healthcare, data migration is the essential process of transferring data, including patient data, between systems in a secure way that meets compliance requirements, such as those set by the Health Insurance Portability and Accountability Act (HIPAA). Data migration can include moving information from a data platform or legacy system to a modern electronic health records (EHR) system that makes a patient’s medical information readily available to healthcare providers in any location.

Healthcare data analytics is often complex, has extensive data sets, and must be secure to protect patient privacy. Here are five ways healthcare organizations can successfully migrate data:

1. Have a Detailed Data Migration Plan

This critical first step will guide and inform the entire migration. The plan should identify where healthcare data analytics currently resides, where it needs to go, and how it’s going to get there. You’ll need to determine if this will be a full migration, which entails moving all data to a new system, like migrating on-premises data to the cloud or modernizing by moving data from a legacy patient records system to a new platform or EHR system.

Or, the migration can be done in phases over time, with the option for some data to stay in its current location or in a hybrid environment with some data in the cloud and some on-premises. The migration plan must include steps, timeframes, and responsibilities, along with identifying the tools and expertise needed to move the data. Migration tools can automate some processes for increased efficiency and to reduce the chance for manual errors.

2. Assess the Data You’ll Be Migrating

You’ll need to identify all the sources containing the data that needs to be migrated. This includes databases, files, and applications that have healthcare data. You should consider converting paper medical records to EHRs, which allows the data to be integrated for a complete patient record that’s available whenever and wherever a healthcare provider needs it. Once you know which information will be migrated and where it’s stored, the next step is to assess the data. This step determines if the data needs to be standardized or transformed to meet the new system’s requirements.

3. Understand and Follow Compliance Requirements

Healthcare is heavily regulated, which impacts data usage. You must ensure security and compliance when migrating healthcare data. This includes compliance with HIPAA and any other applicable local or state requirements. You may need to use data encryption processes and secure channels when transferring the data to ensure sensitive patient data, such as protected health information (PHI), is secure.

As part of your data migration plan, you’ll need to consider how data is protected when it’s stored, including in cloud storage. The plan may require boosting security measures to mitigate cybersecurity threats. Conducting a risk assessment can help identify any vulnerabilities or potential risks so you can resolve them before moving your data.

4. Ensure Data is in the Correct Format

Data must be in the proper format for the destination location. Some healthcare systems require data to be in a particular format or structure, which could require converting the data—without losing any of the details. Ensuring data is formatted correctly entails mapping the data, which helps you determine how information in the current system corresponds to requirements for the new system. Data mapping helps make sure different systems, apps, and databases can seamlessly share data by showing the relationships of data elements in the different systems. Mapping also helps ensure data is properly transformed before the migration, allowing it to be easily ingested and integrated with other data.

5. Check for Data Quality Issues

Any data quality problems, such as incomplete or missing information, will be migrated along with the data. That’s why it’s important to fix any problems now—correct errors, eliminate duplicate records, and make sure your data is accurate, timely, and complete before moving it. Data cleansing can give you confidence in your healthcare data. Likewise, implementing a data quality management program is one way to keep data clean and accurate. After the migration, data should be checked to ensure details were not lost or inadvertently changed in transit and to verify the data quality. Testing the data post-migration is essential to ensure it meets your usability requirements and the new system is performing properly.

Healthcare Data Requires a Comprehensive Migration Strategy

Actian can help healthcare providers and other organizations create and implement a detailed data management strategy to meet their particular needs. We can also make sure your data is secure, yet easy to use and readily available to those who need it. We’ll help you migrate data for cloud storage, data protection, healthcare data analytics, or other business goals. With the Actian Data Platform, you can easily build data pipelines to current and new data sources, and easily connect, manage, and analyze data to drive insights and prevent data silos.

Additional Resources:

About Actian Corporation

Data Analytics

9 Must-Have Financial Analytics Data Points to Look For

Teresa Wingfield

August 28, 2023

two coworkers discussing financial analytics data points

Is Financial Trouble Brewing?

There are many financial metrics that can indicate that trouble is brewing in a company. Most of them tell you what’s happening in your business, but they don’t tell you why. Let’s explore the more important metrics, revenue, profit, and contributing indicators that will help you gain a comprehensive picture of your business’s financial analytics.

Is Your Revenue Declining or Flat?

If your revenue growth is negative or slow, your investors are going to demand to know why this has happened. Here are some of the most likely answers:

1. Low Customer Demand

Have your customer’s preferences changed or is the market saturated, meaning most customers who want a product or service have already purchased it? Data sources such as transaction records, customer surveys, web analytics, market research, or social media posts can provide insights into evolving customer preferences.

2. Dissatisfied Customers

Is revenue falling because your customers are leaving or cutting back on purchases? To gauge customer satisfaction, explore customer feedback in customer complaints, and customer surveys, particularly Net Promoter Score (NPS) to assess customer loyalty. Other important metrics you should use include customer churn rate, customer lifetime value (CLV), repeat purchase rate, and average order value (AOV).

3. Increased Competition

Your revenue might be under pressure from competition with more favorable products and/or prices. Analyze competitors and changes in your own prices to see if these are impacting your revenue.

4. Weak Economic Conditions

Downturns in the economy can damper consumer and business spending. You need to monitor economic indicators such as Gross Domestic Product (GDP), unemployment, interest rates, and the consumer price index (CPI) and estimate how sensitive your company’s products and services are to these numbers.

5. Losses or Low-Profit Margins

Even if your revenue is growing at a steady clip, you can still be losing money or making very little profit. Here are some common causes.

6. High Cost of Goods Sold (COGS)

Rising input, labor, and overhead costs can quickly erode profit margins. While inflation is beyond your control, you can use financial analytics to reduce operational inefficiencies. For example, analyzing current and historical sales data and market trends helps to better predict customer demand. This can lower the COGS by helping to optimize production levels, reduce excess inventory, and avoid stockouts. And, by analyzing transportation costs, lead times, inventory holding costs, and order fulfillment, businesses can optimize the supply chain to reduce the COGS.

7. Suboptimal Pricing

By analyzing cost components associated with producing and delivering products, businesses can set prices that cover costs while ensuring profitability. Analytics can also estimate price elasticity, which measures the responsiveness of demand to price changes so that businesses can optimize pricing and promotional strategies.

8. The Wrong Product Mix

Analyzing sales data, costs, and other financial data helps businesses identify their most profitable products and prioritize these for development, marketing, and sales efforts. Analytics can also highlight low-margin products that may need price adjustments or removal from the product mix.

9. Compliance Costs

It’s expensive to comply with all the various laws and regulations and fines for violations can be exorbitant. By leveraging financial analytics in compliance management, businesses can lessen the impact on profit margins by enhancing their ability to identify, prevent, and mitigate compliance risks.

Improve Your Financial Analytics With Actian

It’s important for you to assess factors specific to your industry and business to develop strategies to overcome slow revenue growth and low-profit margins. The right data platform is also critical to your success. This is where Actian can help.

The Actian Data Platform provides a unified experience for ingesting, transforming, analyzing, and storing data for financial analytics. You can also read more about the Actian Data Platform.

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 Advanced Financial Analytics to Augment BI

Teresa Wingfield

August 21, 2023

advanced financial analytics for business intelligence

Most, if not all, organizations need to understand their financial performance to improve business processes, identify the risks they face, and focus on the right goals. Harvard Business School provides an excellent list of important financial key performance indicators (KPIs) that companies should track, measure, and analyze across a wide range of categories, including profitability, liquidity, solvency, efficiency, and valuation. However, the tools and techniques for analyzing KPIs vary greatly and reveal different insights.

Traditional spreadsheets and reporting tools provide value by showing what has happened in the past. But, they fall short when it comes to explaining why something has happened and predicting future outcomes. This is where advanced financial analytics closes the gap. This type of analysis applies sophisticated techniques such as statistics, machine learning, and data mining to fill these voids in understanding data.

Detecting Patterns, Trends, Correlations, and Relationships

Advanced financial analytics can detect hidden patterns, trends, correlations, and relationships in data that explain changes in performance. Here are just a few examples of how to use advanced financial analytics to grow revenue and profits:

Price Elasticity

If you raise the price of a product or service, will you increase revenue? Not if a drop in customer demand because of the increase causes revenue to fall. Price elasticity will help you make the right pricing decision by measuring the sensitivity of customer demand to changes in price. Developing an optimal pricing strategy is key to maximizing revenue.

Hidden Seasonality

The revenue of numerous products varies with seasons. For example, back-to-school seasons see an increase in the sale of school supplies and differences in seasonal weather influence the purchases of coats, swimsuits, and other items. Retailers understand the seasonality of many types of goods and services and maximize revenue through seasonal promotions. However, there’s also hidden seasonality when trends are not immediately obvious or easily detectable in the data. Advanced financial analytics can help identify these subtle or less obvious recurring patterns. With this knowledge, retailers can add items with sales uplifts at a particular time of the year to their promotional strategies.

Cost of Goods Sold (COGS)

The Cost of Goods Sold (COGS) is the direct costs incurred in the production or acquisition of goods that a company sells. Using advanced financial analytics is a valuable way to reduce COGS by uncovering hidden opportunities to optimize the supply chain and reduce energy consumption and fraud.

Supply Chain Costs: Advanced financial analytics can identify patterns and correlations to help businesses make better decisions when selecting suppliers, negotiating contracts, and managing supply chain data.
Energy Costs: Companies can use advanced analytics to analyze energy usage patterns to reduce utility costs, which in turn lowers COGS.
Fraud Costs: Detecting anomalies and patterns that indicate fraud in procurement, billing, and inventory management avoids financial losses that drive up COGS. In addition, advanced analytics can spot fraudulent accounting schemes such as asset misappropriation, financial statement fraud, and corruption schemes, including bribes and kickbacks.

Accurate Demand Forecasting

Accurate demand forecasts powered by advanced financial analytics help businesses make their operational and product processes more efficient. By aligning procurement, production levels, staffing, and other resources with expected revenue, businesses can improve operational efficiency, avoid overstocking or understocking, and minimize waste.

Do You Need More Than Traditional Financial Analytics?

Business intelligence (BI) and other financial reporting tools play a huge and important part in a company’s analytics portfolio. Advanced financial analytics helps to provide a deeper understanding of data, discover hidden patterns, and reveal what’s next on the horizon. Organizations that don’t have a strategy for advanced financial analytics will likely experience lower revenue and higher costs, face ongoing inefficiencies, and fall behind their forward-looking peers.

The Actian Data Platform supports the needs of traditional and advanced financial analytics. You can easily move data into the Actian Platform and leverage any analytical application to support users who have a wide range of needs and technical skills. Financial analysts, business analysts, data experts, and business users can access and query data without having to choose between performance and savings.

About Teresa Wingfield

Data Analytics

Why Data Scientists and Developers Need More Than a Data Lake

Teresa Wingfield

August 15, 2023

Graphic of mountains made from digital binary code patterns, representing data lake integration in a technological landscape.

As organizations strive to get more value from the data they collect, it has become increasingly important that data scientists and developers have easy access to information collected from multiple sources in various sizes and formats. For many businesses, creating a data lake has become the first step in this process, forming a useful repository for large amounts of data that can be analyzed and tested down the road.

However, while these repositories can create new opportunities for extracting business insights, data lakes on their own may not always be the answer. While they provide a centralized location for all of an organization’s data, they can also be challenging to manage and control.

Why are Data Lakes Useful to Organizations?

When organizations begin extracting raw, unstructured data from multiple sources, they must have a sustainable and organized format in place for storage. One of the benefits of using a data lake is that it allows organizations to keep all of their data in one place. This can be especially helpful for companies with multiple silos of information scattered across different departments or locations. But it’s also important to note that data lakes are often used for very unstructured data and can easily become a data swap since data can often lack any context or structure to be useful.

Another benefit of data lakes is that they can be used to support a variety of analytics workloads. For example, data scientists and developers can use data lakes for real-time streaming analytics, machine learning models, and AI.

Data lakes are also relatively easy and inexpensive to establish. Because they can store data in its rawest form, organizations don’t need to spend time and money on ETL (extract, transform, load) processes.

What are the Limitations of Data Lakes?

So, if data lakes are so great, why do data scientists and developers still need to look for other solutions when working with data?

One of the biggest challenges with data lakes is that they can be challenging to manage. Because data lakes store all types of data, it can be hard to keep track of everything there. It’s also challenging to control access to the data and ensure that only authorized users can view or modify it.

A predominant issue with data lakes is that they can often contain a lot of duplicate or low-quality data. This can make it time-consuming and difficult for data scientists and developers to find the specific information they need. And this can be a particular problem if the data lake has not been adequately curated.

Are Data Lakes Enough for Businesses?

Although data lakes are an excellent solution for housing unstructured data, they are often not enough for data scientists and developers when extracting all the relevant insights contained in the information. This is due to the unstructured formatting of data lakes, making the integrity of the analysis questionable and potentially inaccurate without considerable data cleansing.

Data warehouses, on the other hand, can provide a better solution for providing analysis and business insights. The information held in data warehouses is typically normalized, meaning it is cleansed, consistent, and organized into tables with well-defined relationships between them. This makes it easier to write SQL queries against the data and can be more reliable when ensuring accuracy and overall data integrity.

However, while data warehouses store data in more of a “ready” state for analysis, this doesn’t mean that data lakes are absolute for data scientists and developers. In fact, data lakes are regularly used for many experimental processes, such as data discovery and machine learning. Being able to store data in raw and unstructured formats can give data scientists much more freedom when exploring the data for insights, rather than being confined to work with normalized and structured data.

Understanding the Connection Between Data Lakes and Data Warehouses

Although data lakes and data warehouses may be different, it’s important to note that each of them is not mutually exclusive. For modern businesses, there is a convergence of these two technologies, with many organizations using both data lakes and data warehouses to manage their big data.

Data lakes and data warehouses can actually complement each other well. A data warehouse can act as the single source of truth for an organization. Meanwhile, a data lake can be used to store all of the organization’s data, including data from sources that aren’t yet well understood or trusted enough to be placed in the data warehouse. In fact, ETL (Extract, Transform, Load) tools are used for this very purpose, automatically redirecting raw, unstructured information from the data lake and organizing it efficiently in a data warehouse.

It’s important for businesses to discover how they can use data lakes and data warehouses collectively as opposed to staying focused on a particular format. While each project may have its own needs when it comes to data storage and analysis, by understanding the benefits and trade-offs of each data platform, companies can make more informed decisions about how to use them together and get the most out of their data collection efforts.

About Teresa Wingfield

Data Management

The Growing Need for AI Governance

Teresa Wingfield

August 14, 2023

It may not seem like it, but Artificial Intelligence (AI) has been around for a long time. The earliest successful AI program was written in 1951 by Christopher Strachey who later became director of the Programming Research Group at the University of Oxford.

ChatGPT Is a chatbot that uses AI to perform a variety of tasks, including engaging in conversation, generating text, and writing code. The chatbot had more than 100 million active users in January 2023, just two months after it launched, making it the fastest-growing consumer application in history. As open AI becomes more readily available to the masses, there is growing concern about unethical applications of AI.

The Need for AI Governance

The purpose of AI governance is to address various issues concerning the ethical use of AI, including transparency, bias, privacy accountability, and safety. The goals are to use AI in ways that help maximize benefits while minimizing harm and social injustice. AI Governance will also play a critical role in helping organizations comply with regulations and laws. Here’s a summary of what to expect in the United States, Europe, and Canada.

While there’s no comprehensive federal legislation on AI in the United States, municipal and state laws are zeroing in on how AI systems process personal data. For example, employers in New York City can’t use automated employment decision tools that rely on AI to screen job candidates unless the tools are audited for bias. Companies also must notify candidates if a tool is used in the hiring decision process. Colorado now requires insurance companies to prove that their AI algorithms and predicate models do not result in unfair discrimination in automated underwriting. The U.S. Chamber of Commerce provides a state-by-state artificial intelligence legislation tracker.

In Europe, the General Data Protection Regulation (GDPR) mandates that organizations should not use algorithmic systems to make significant decisions that impact legal rights without human supervision and that individuals are entitled to meaningful information about the logic and algorithmic system uses. The European Union proposed the Artificial Intelligence Act (AIA) on April 21, 2021. The proposal was passed in 2023, and creates three categories of AI systems—limited risk, high risk, and unacceptable risk—and sets different requirements for AI systems depending on which risk category they belong to.

In Canada, the proposed Artificial Intelligence and Data Act (AIDA) is designed to help ensure that AI systems are safe and non-discriminatory. The Act would hold businesses accountable for how they develop and use AI.

What Your AI Governance Strategy Should Cover

Increasingly, your organization will be under pressure from customers to demonstrate that it uses AI responsibly. Your efforts will need to increase and mature to accommodate AI regulations that are poised to expand in the not-too-distant future. It’s wise to prepare now. Here are a few steps to help you get ready.

Assign responsibility for AI governance. This could be your Chief Privacy Officer or a dedicated AI Governance Officer.
Understand AI use across your organization, including third-party solutions.
Evaluate how your organization’s use of AI impacts humanity, including employment, privacy, racism, and many other concerns.
Develop prohibited use cases.
Develop codes of conduct and ethical guidelines for data engineers, data scientists, data analysts, and front-line workers.
Make sure your company is complying with AI regulations wherever it conducts business.
Audit AI algorithms and models for bias, especially when they are used in areas that could result in racial and economic inequities.
Create key performance indicators (KPIs) to measure success, focusing on metrics that matter for bias, discrimination, fairness, and explainability.
Continuously monitor progress and take corrective actions as required.

About Actian

Customers trust Actian for their AI needs because we provide more than just a platform. We help organizations make confident, data-driven decisions for their mission-critical business needs while accelerating their business growth. Using the Actian Data Platform, companies can easily connect, manage, and analyze their data.

About Teresa Wingfield

Data Analytics

How to Build Accurate Customer Profiles Using Data

Actian Corporation

August 10, 2023

Building complete 360-degree customer profiles is difficult. It requires integrating all relevant customer data onto a single platform. The process involves a lot of data—including purchasing history, demographic information, browsing and shopping habits, and much more. Every customer interaction needs to be captured and the data integrated, and you must build data pipelines to new and emerging customer data sources. Data volumes are growing quickly, which is why a modern data platform that can easily scale is essential.

While building profiles is challenging, they are critical to elevating customer experiences. The profiles can be used across all customer engagements—delivering relevant and personalized offers, identifying a customer’s lifetime value, knowing your customers better, and more. The data-enabled profiles allow you to truly understand the dynamic nature of your customers and meet their unique needs.

Here’s how to build unified customer profiles for data-driven campaigns.

Align Data With Campaign Objectives

The purpose of building 360-degree profiles is to engage, nurture, and guide individuals along their customer journeys. You’ll need to define the goal for creating profiles—a nurture campaign for marketing, a new product launch, a customer service initiative, or something else—and then determine what data is needed to reach those goals. Building a complete profile requires a lot of data, but not all customer data is needed. For example, data from customer interactions is essential, but data that’s older than a certain number of years, like before the pandemic, or demographic information about a person’s occupation and income level, may not be relevant for a new campaign.

Aggregate All Relevant Customer Data

You’ll need a modern data platform that can scale to meet your data needs. Customer relationship management (CRM) systems have comprehensive data, but they typically cannot handle the massive data volumes and advanced analytics needed to build and optimize customer profiles. Instead, CRM systems are one type of data source to build comprehensive profiles.

Types of data often used for customer profiles include:

Social media engagement.
Purchasing history and spending patterns.
Household demographics.
Financial data.
Behavior data.
Website interactions.
Customer service engagements.
Direct feedback such as surveys.
Psychographic data.

The data must be brought together on a single, scalable platform. In addition to CRM systems, data can be integrated from customer experience platforms, data warehouses, data lakes, enterprise resource planning (ERP) systems, and other sources.

Organize, Manage, and Govern the Data

You must ensure that the data used for profiles is secure and compliant with data protection and privacy regulations as well as internal governance policies. Ideally, your organization has clear data collection policies that are public-facing, so customers know how their data is being used and have the ability to opt-out. Transparency is key to establishing customer trust. Ensuring data is accurate, current, and trustworthy is essential for building 360-degree profiles. You may have to cleanse the data and remove duplicate copies to get the data quality you need to drive your campaign. You’ll also want to regularly back up your data in case of an outage.

Segment and Analyze Data for Customer Insights

You can choose specific customer attributes, such as how much money they spend with your organization or where they are based, to segment customers. This allows you to customize offers based on common characteristics, similar buying behaviors, specific needs, or other criteria. Segmentation is a key factor in delivering targeted and personalized communications. A scalable platform with advanced analytics capabilities can deliver unique offers to customer groups of any size, including a single individual.

Analyzing integrated customer data lets you create segmented groups to meet campaign goals. This includes performing behavior analytics to understand customer actions and patterns, predictive analytics to predict future customer behaviors, or sentiment analysis to engage customers based on their feelings about your company.

Continually Enrich Customer Profiles With Fresh Data

Customer preferences, behaviors, and demographics change over time. Having confidence in your profiles requires you to integrate new data and insights on an ongoing basis. Keeping each profile up-to-date and accurate with the latest information ensures your campaigns are making the biggest impact with the most optimal offers. You also need to add data pipelines as new data sources become available. Bringing in fresh data and keeping customer profiles current helps ensure you don’t miss marketing opportunities while enabling you to better understand any changes in customer wants and needs. This allows you to predict and meet customer preferences while alerting you to customers at risk of churn.

Make Data Easy for Building Customer Profiles

Once you’ve built customer profiles, they can be leveraged to deliver better customer experiences, create lifelong customers, increase customer satisfaction, and improve sales. The profiles do not have to be limited to marketing. You can optimize them across product development, customer strategies, and other business priorities.

The Actian Data Platform makes data easy to use with advanced capabilities needed to build and utilize customer profiles. You can quickly and easily add data pipelines to new data sources, simplify how you connect, manage, and analyze data, and have complete confidence in your insights to ensure customer campaigns are successful. The easy-to-use platform enables you to construct complete and accurate profiles while giving you real-time insights into customer interactions, preferences, and behaviors. We also offer pre-built templates to help you gain faster time to value.

Additional Resources:

About Actian Corporation

Data Analytics

Does Using Data Analytics Improve Price Points in Retail?

Teresa Wingfield

August 8, 2023

Dollar sign depicting retail price points and data analytics

The Impact of Data Analytics on Retail Pricing

How can your business stay competitive in retail? Pricing analytics is a valuable tool to help optimize pricing strategies to maximize revenue and profits. By using advanced analytics to inform pricing decisions, retailers can make more informed, data-driven decisions based on customer behavior and market conditions.

Data, including internal data such as customer behavior and sales data, and external data such as market and competitor data drive pricing analytics. There are many techniques in the pricing analytics arsenal that retailers can use to analyze this data to gain insights into how customers will respond to different prices. Here’s a summary of some of the top methods.

Price Optimization

Price optimization analyzes customer and market data to find the ideal price point for a product or service. This data analytics method is based on price sensitivity, how changes in the price of products/services affect customer demand, and how much profit the retailer can earn from selling the product or service at a certain price.

Price sensitivity differs greatly across different consumers. For this reason, it’s useful to use segmentation to divide a market into distinct groups of consumers with different price sensitivities. Businesses can charge more and increase profits for some segments, and for others, businesses can offer discounts and price promotions to increase conversion.

Dynamic Pricing

Dynamic pricing automatically adjusts the price of a product or service in real-time based on customer demand, competition and other factors. By using real-time analytics to set prices that reflect current conditions, retailers can better optimize their revenue or provide than with traditional pricing strategies that set prices based on fixed costs and profit margins.

Dynamic pricing is especially valuable in dynamic markets where factors such as supply and demand, seasonal patterns and competitive pricing change quickly. This method is useful across any selling channel, but real-time analytics can be essential for competing in e-commerce. For example, Amazon dominates the e-commerce market with its ability to rapidly and frequently change prices to undercut competitors.

Price Gap Analysis

Price gap analysis compares the price of a company’s product or service to those of its competitors. Using price gap analysis shows if a product/service is priced above the market, below the market or on par with the market.

“Minding the gap” depends on the company’s price strategy for the product/service. For example, a company usually prices national brands a certain percentage higher than private labels or generics. Price gaps may vary at different retailers and a retailer may want to adjust price since some brands prefer a more consistent price because most consumers shop at multiple retailers.

Bundle Price Analysis

Bundle price analytics involves setting the optimal discounted price for a bundle of multiple products or services, taking into account the price of individual prices in the bundle, production costs, desired profit margin and expected customer demand. The goal of bundle price analytics is to encourage customers to purchase more items with a cost-effective way to buy the products together. This type of data analytics helps retailers increase their revenue and improve customer loyalty and retention.

Getting Started

Businesses need to make informed decisions about pricing to be successful in retail using data analytics. Pricing analytics can help transform pricing through better data science if its built on the right platform where data can be collected, cleansed, managed, and analyzed in a centralized location. See how the Actian Data Platform makes price analytics easy.

About Teresa Wingfield

Data Analytics

7 Financial Analytics Strategies Every IT Leader Should Know

Teresa Wingfield

August 8, 2023

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

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

Financial Analytics Strategies for IT Leaders

Understand Strategic Goals

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

Choose the Right Technology Infrastructure

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

Define Data Integration Needs

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

Ensure Data Quality

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

Implement Advanced Analytics

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

Secure and Govern Data

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

Address Skill and Expertise Gaps

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

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

About Teresa Wingfield

Data Analytics

10 Insights Financial Analytics Provides for Enhancing Operational Efficiency

Teresa Wingfield

August 7, 2023

Summary

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

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

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

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

Financial Performance Analysis

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

Cost Analysis

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

Customer Profitability Analysis

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

Process Optimization

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

Budgeting and Forecasting

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

Cash Flow Management

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

Risk Assessment

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

Investment Analysis

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

Compliance

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

Liquidity Analysis

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

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

Ready to Get Started?

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

About Teresa Wingfield

Data Management

6 Critical Steps in the Journey to Making Data Management Easier

Teresa Wingfield

August 3, 2023

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

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

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

1. Define Your Business Goals and Objectives

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

2. Assess Current Data Practices

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

3. Determine Modern Data Requirements

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

4. Develop Data Management Policies and Procedures

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

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

5. Identify the Right Technology Requirements

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

6. Continuously Monitor the Strategy

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

Meet Your Data Management Objectives

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

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

Get Started Today!

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

About Teresa Wingfield

Data Analytics

Aeriz: Unlocking the Hidden Value of SaaS Data

Jennifer Jackson

August 1, 2023

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

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

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

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

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

Gaining the Ability to Analyze Complete Inventory Data

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

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

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

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

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

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

Making Data Analytics Easy

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

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

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

Additional Resources:

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.

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