Data Analytics

How to Optimize Customer Analytics to Improve Customer Experience

Teresa Wingfield

September 26, 2023

person reacting to social media and customer analytics

In a recent Martechcube survey, only 18% of retail leaders believe that they could significantly improve the post-purchase customer experience. In contrast, a whopping 80% of consumers feel otherwise. Providing a poor post-purchase customer experience can prevent you from building customer loyalty. Customer analytics can provide valuable insights and data-driven strategies to help you get to know your customers, personalize customer experiences, and improve customer satisfaction.

Over-Reliance on Customer Segmentation

One of the biggest culprits underlying a poor post-purchase customer experience is segmentation. Analytics allows you to segment your customers into similar groups with similar characteristics such as income, gender, age, etc., or behaviors such as purchases, path-to-purchase, and promotional responses.

Marketers use segmentation to help them tailor their campaigns, promotions, and communication to each segment, hoping that these will resonate with customers in the same segment. But do they? Not always. People falling within a segment often have different needs, values, and motivations, and, even if they have the same behaviors, their reasons or motivations for that behavior can be very different.

Insufficient Personalization

By analyzing a customer’s purchase history, browsing behavior, demographics, and other customer activities, you can deliver targeted content, product recommendations, and offers that are more likely to resonate with the customers. More savvy retailers are bringing zero-party data into the personalization mix. Zero-party data is information from customers that they voluntarily and deliberately share with you. The use of zero-party data has risen in popularity after Google announced its intended phase-out of support for third-party tracking cookies in Chrome back in early 2020. Since this time, marketers have realized that zero-party data is more than a replacement strategy for cookie data and now understand that one of the best ways to know what a customer wants is to simply ask the customer.

Predictive Analytics Can’t Always Forecast Churn

There’s no doubt that predictive analytics is a valuable tool that can help you predict customer behavior, such as their likelihood of churning or making a repeat purchase. Insights can assist you in proactively addressing issues and engaging at-risk customers.

On the downside, there are tons of factors that cause predictive analytics to fail to predict customer churn. Insufficient or poor-quality data will impact the accuracy of results for any type of modeling. Predictive models base their predictions on trends in historical data. As such, they might fail to predict that a customer has decided to churn abruptly due to a recent negative experience. This is a big shortfall for predictive accuracy because 76% of shoppers will stop doing business with a company after just one negative experience. In addition, the competitive landscape is constantly evolving, and historical data may not reveal this.

These shortcomings have several implications for users of predictive analytics. It’s important to regularly update predictive analytics models, validate results, and incorporate a variety of data sources, both internal and external. Also, predictive analytics needs to be part of a comprehensive data analytics approach that includes adaptive analytics strategies. For example, analyzing current data from customer support interactions, including call logs, chat transcripts, and email can quickly identify if a customer is experiencing an issue. And keeping track of new social media mentions and conversations can help you spot unhappy customers faster.

Let’s Make CX Easy Together

Customer analytics provide valuable insights to help you know your customers better to help you deliver a more engaging customer experience. But more is needed than traditional segmentation. You’re going to need to focus more on individual customers and engage with them directly to understand their needs. Advanced analytics such as predictive modeling are useful for understanding future customer behavior, but you’ll still need adaptive analytics to identify sudden changes in the customer experience or market dynamics.

According to a recent GigaOm TPC-H Benchmark Test, the Actian Data Platform operational data warehouse is 9x faster and 16x cheaper than alternatives. The Actian Data Platform makes it easy to track, manage, and analyze customer analytics to better identify areas that need improvement and help improve business outcomes. Contact us to start your journey to improving CX.

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

Revolutionize Customer Experience With Customer Analytics

Teresa Wingfield

September 21, 2023

Customer Journey Analytics: Seeing the Bigger Picture

Customer journey analytics interprets data from various touch points and interactions that a prospect or customer has with a company from end to end. Insights into the customer experience, behavior, and points of friction in sales and service help optimize the customer journey to improve customer satisfaction and drive better business outcomes. However, to derive these benefits you’re going to need a tool to give you a holistic understanding of customer engagement: the customer journey map.

What is a Customer Journey Map?

A customer journey map is a visual representation of stages, touch points, actions, and experiences that a customer goes through while engaging with a company’s products, services, or brand.

Stages of the Journey

The customer journey map reveals the stages that a customer progresses through as they engage with a company both before and after becoming a customer. Common stages include:

Awareness

The customer recognizes a need.

Consideration

The customer compares options to meet the need.

Decision

The customer chooses the best solution.

Retention

A company’s ongoing marketing, service, sales, and communications with a customer post-purchase to promote loyalty and encourage additional purchases.

Advocacy

A satisfied customer becomes a vocal supporter of the brand.

Touch Points

Touch points are the interactions or points of contact that a customer has with the company, from start to finish. These can include advertising, website visits, social media interactions, customer service calls, virtual and in-person events, emails, texts, physical store visits, and more.

Customer Actions and Experiences

Alongside each touchpoint, the customer journey map highlights the actions a customer takes and customer satisfaction or dissatisfaction during each interaction.

What Insight Does a Customer Journey Map Provide?

Analyzing data from the customer journey map helps uncover patterns, trends, and correlations within the customer journey that may otherwise be overlooked. You’ll get a better understanding of how customers behave, where they engage and convert, why they might leave, and what’s next on their wish list. This data-driven approach helps you make informed decisions that optimize the customer journey.

Know Your Customers Better

By understanding customer behavior, preferences, and the factors that influence their decisions, businesses can craft more effective messages and marketing and sales campaigns.

Assess Touchpoint Effectiveness

Identifying which touch points are most influential in driving conversions, engagement, and customer satisfaction can lower customer acquisition costs, optimize revenue, and increase customer loyalty.

Prevent Customer Churn

With knowledge of customers who have had poor experiences, a company can identify customers who might leave and take proactive measures to retain them. The company can also take steps to ensure that customer-facing processes work better in the future.

Anticipate Future Needs

Businesses can apply predictive analytics to their customer journey data so they can anticipate what their customers might need in the future and create proactive solutions or strategies to meet those needs.

Getting Started on Your Customer Analytics Journey

The Actian Data Platform can help you get started on your customer analytics journey. It provides everything you need to unify data across customer interaction touch points, bringing together data from call center and website logs, social media interactions, customer relationship management, customer service applications, third-party data, and more. Using the Actian platform, you’ll have greater confidence in the data you use to inform your customer journey.

About Teresa Wingfield

Data Management

Do I Need an Object-Oriented Database Management System?

Teresa Wingfield

September 19, 2023

What is an Object-Oriented Database Management System?

An object-oriented database management system (OODBMS) is based on the principles of object-oriented programming. Data is created, modeled, and stored as objects, which are self-contained units that contain both data and the operations or methods that can be performed on that data.

Should your organization’s enterprise data management include an OODBMS? Here’s a quick look at where it excels and the types of applications that can benefit from these advantages.

What is an OODBMS Used For?

An OODBMS is most valuable for applications with complex data relationships that require persistence, support for diverse data types, and frequent schema changes.

Complex Data Structures and Relationships

An OODBMS is especially useful for applications with complex data structures and relationships since this type of database accommodates a more flexible and dynamic data model than relational databases. An object can store relationships that it has with other objects, including many-to-many relationships, and objects can be formed into more complex objects than traditional data models.

Performance

An OODBMS can provide improved performance compared to relational databases, especially for applications with complex data structures.

Persistence

Object databases bring permanent persistence to object storage.

Highly Extensible

Because objects can be easily modified and extended, it can be easier to evolve the data model over time.

Capable of Handling Diverse Data Types

OODBMS can store different types of data such as pictures, audio, video, text, and more.

Schema Evolution Support

The tight coupling between data and applications in an OODBMS makes schema evolution more feasible.

What Are Some Common Applications Built on Top of Object-Oriented Databases?

Here are some examples of applications that commonly use an OODBMS as part of enterprise data management:

Computer-Aided Design (CAD)/Computer-Aided Manufacturing (CAM)

An OODBMS helps to store and manipulate complex 3D models of buildings, machine parts, etc.

Content Management/Digital Asset Management

An OODBMS handles complex schemas and structured, semi-structured, and unstructured data types, including text, images, audio, and videos.

Financial Applications

An OODBMS can be useful for financial applications that need to store complex data structures such as portfolios of stocks and bonds.

E-Commerce Applications

An OODBMS can handle complex data such as customer orders, product catalogs, and transaction histories.

Healthcare Applications

An OODBMS can provide efficient storage and retrieval of elector health records (EHRs) and medical imaging such as X-rays, MRIs, and CT scans.

Gaming Applications

An OODBMS helps store and access data about game objects, such as characters and weapons, and game events such as player interactions and game state changes.

Why NoSQL?

While an OODBMS provides a more efficient way to store and access complex data structures, many of these databases lack enterprise features required for mission-critical business applications.

NoSQL from Actian is an OODBMS that doesn’t require making these tradeoffs. It provides performance, scalability, availability, and reliability. NoSQL features ACID and distributed transaction support, two-phase commit, and online schema evolution. Its two-level cache and multi-session/multi-threaded architecture are optimized for next-generation multi-core server architectures to deliver linear scalability to handle growth in data volume and concurrent user access.

About Teresa Wingfield

Data Intelligence

What is Generative AI?

Actian Corporation

September 19, 2023

Automation Data Analytic With Robot And Digital Visualization For Big Data Scientist

Generative AI is a type of artificial intelligence technology that can produce various kinds of content, including text, images, sound, and synthetic data. For several months now, generative AI has been a hot subject of conversation. The cause? The fact that it’s accessible to everyone, simply and, more often than not, free of charge. In this article, discover everything you ever needed to know about generative AI.

The history of generative AI goes back to the early 50s, with the first attempts at generating language via machines. However, it’s only very recently that deep neural networks have given rise to significant advances. In 2014, GANs (generative adversarial networks) emerged, enabling the generation of realistic data. In 2018, OpenAI introduced the GPT model, capable of creating coherent text. Since then, generative AI has conquered art, music, and literature, demonstrating its creative potential, but also raising questions about the ethics and relevance of machine-generated creations.

How Generative AI Works

Generative AI is a branch of artificial intelligence. It aims to create realistic content, such as text, images, or music, using machine learning models. The most common model is the generative adversarial network (GAN).

A GAN consists of two distinct parts: the generator and the discriminator. The generator creates data (e.g. images) from random noise. The discriminator evaluates whether the data is real or generated. The strength of generative AI? Both parts (generator and discriminator) improve continuously via an iterative process. The generator adjusts its output to challenge the discriminator, while the discriminator improves its ability to distinguish reality from generation. Over time, the generator succeeds in creating increasingly convincing data. GANs have evolved to generate text (like GPT) or other forms of content, revolutionizing creativity, art, design, and simulation.

How Can You Benefit From Generative AI?

Companies can benefit from generative AI in a variety of ways. It can stimulate creativity by producing innovative ideas, designs, and concepts. Generative AI improves personalization by tailoring products and services to individual customer preferences. It accelerates design by automating the creation of prototypes and visual elements. In addition, it optimizes decision-making by generating simulations and scenarios to inform strategic choices. It can also facilitate the automation of creative tasks, increasing operational efficiency. However, ethical and quality considerations must be taken into account to ensure that the results generated are relevant and comply with established standards.

What are the Risks of Using Generative AI?

Concerns about Artificial Intelligence have their roots in science fiction. But should we deny the dangers associated with the indiscriminate use of generative AI? Certainly not, because the risks are very real! Indeed, the quality of the content (images, texts, videos…) generated by generative AI is such that it can lead to incorrect… but credible information. In addition, there are risks associated with copyright infringement or plagiarism, as generative models can reproduce existing content without authorization and, above all, without mentioning their sources. But the main threat associated with the use of generative AI is linked to the disclosure of misleading information (also known as Fake news), to the production of deepfakes. Finally, strong ethical concerns are emerging around the automation of artistic creation, questioning the authenticity and artistic value of creations generated in this way.

What are the Most Inspiring Use Cases for Generative AI?

Improving Customer Experience

Generative AI has revolutionized the customer experience in a variety of industries. This is notably the case in e-commerce, which leverages generative AI to make product recommendations based on past preferences. In healthcare, it proposes personalized care models based on medical data, thereby improving patient care. In customer relations, it generates automated, contextual responses for fast, efficient customer service based on autonomy.

Personalizing Customer Paths

As the notion of the customer journey becomes ever more central in a digitalized world, generative AI makes it possible to deliver relevant answers more quickly. In marketing, it generates targeted messages tailored to targets’ interests to boost engagement. On e-learning platforms, for example, generative AI can tailor study plans to the skills and needs of each learner. Tourism can also benefit from generative AI by offering unique itineraries based on traveler preferences.

Creation of New Products/Services

By harnessing the creativity of generative models, AI helps create new offerings while optimizing the resources and time invested. By accelerating time-to-market, generative AI can propose virtual prototypes, speeding up the development process. In product design, it elaborates innovative concepts based on market needs.

Preparing Sales Pitches

Once the products and services have been created, they need to be supported in their marketing by making the case for both their strengths and the promises they hold for their intended targets. Here again, generative AI can make a valuable contribution by facilitating the preparation of sales pitches. It does this by analyzing target market data and highlighting competitive advantages, illustrating usage scenarios, and reinforcing the credibility of the product or service.

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 Platform

The Data Challenges of the Business Technologist

Teresa Wingfield

September 15, 2023

According to a recent report from research firm Gartner, “Organizations that effectively support business technologists are 2.6 times more likely to accelerate digital transformation.” It makes sense: Because these professionals exist in the space between IT departments and non-tech teams, they can design and deploy solutions that deliver on both business and technology objectives.

However, achieving these goals comes with a challenge: effectively managing the exponentially increasing volume and variety of data created by business operations and customer interactions.

The Evolving Role of Data in Business Technology

The democratization and commercialization of technology brought IT into the mainstream. While tech professionals remain bastions of specialized and in-depth knowledge, the evolving impact of cloud data platforms, business intelligence tools, and mobile applications has changed how users perceive IT and how C-suites leverage technology to achieve business goals.

Business technologists help mediate and manage this new environment by leveraging data to develop new approaches and implement new strategies that benefit business operations. While rapidly growing data sets offer the insight technologists need to fulfill their role as business facilitators, however, this is only possible by addressing — and overcoming — emerging data challenges.

The Biggest Data Challenges for Business Technologists

Big data in the 21st century is regularly compared to the resource of oil 300 years ago—vast, largely untapped, and possessing massive potential for change. Much like 18th-century drilling pioneers, however, businesses often find no clear roadmap to making the best use of this resource to directly impact business decisions. While cloud-based data warehousing and analytics solutions offer the potential for positive outcomes, they’re only effective if companies know where to put digital shovels in the ground—and how deep they should dig.

As a result, five data challenges are now top-of-mind for business technologists.

1. Automating Data-Heavy Manual Tasks

Manual tasks remain a source of frustration for many businesses. Not only are these tasks inefficient, but the volume of data entry required significantly increases the chance of errors or omissions. As a result, C-suite members often tap business technologists to develop automated approaches capable of collecting and curating data at scale. In practice, this frequently takes the form of robotic process automation (RPA) implementation and rule-building to help streamline these tasks at scale.

2. Optimizing Real-Time Analytics Operations

Analytics empowers on-demand decision-making—but only if businesses have access to the right data at the right time. As cross-department facilitators, technologists are now tasked with optimizing these operations, which includes finding and deploying tools capable of handling multiple data sources, and designing processes that provide business departments with relevant, real-time data.

3. Integrating Customer Data Across Networks

Customer data provides insight into buyer behavior and makes it possible to improve business decision-making. Ideally, companies are looking for “customer 360,” or a 360-degree view of the customer experience from their first brand encounter to website and app engagement to purchase and any follow-up service interactions. Business technologists are uniquely equipped to help address this need. Still, they require both C-suite support and data warehouse solutions able to find and analyze data, no matter where it resides across business networks.

4. Bridging the Gap to “Small Data”

While big data remains a business priority, businesses are starting to see the value of “small data”, which leverages more focused data sets and machine learning (ML) algorithms to produce real-time insights that speak to current trends and inform short-term decision-making. For technologies to bridge the gap, however, it’s not enough to simply repurpose existing data tools. Instead, businesses need solutions capable of handling structured and unstructured data sources to produce a clear picture of current events.

5. Ensuring Data Integrity

Finally, there’s a growing need for technologists to help ensure data integrity across the enterprise. Given the multiple use cases for single data sets, businesses must prioritize the consistency of information across operations to create a single source of truth. Here, business technologists benefit from tools that deliver visibility across cloud and edge data sources anywhere, anytime.

Delivering on Data Potential

Data makes it possible for business technologists to facilitate IT operations and deliver actionable C-suite insight. Achieving these aims, however, requires a combination of cross-departmental communication skills and big data tools capable of increasing automation, optimizing analytics, integrating customer data, bridging the small data gap, and delivering on data integrity.

About Teresa Wingfield

Data Analytics

How to Use Financial Analytics to Detect Fraud

Teresa Wingfield

September 14, 2023

Fraud detection using financial analytics

According to the Association of Certified Fraud Examiners, organizations lose 5% of their revenue to fraud each year. It’s no wonder that financial analytics for fraud detection is in such high demand given this alarming statistic. Fortunately, financial analytics can play a crucial role in helping businesses detect and prevent fraud by analyzing various patterns, discrepancies, and anomalies in financial data and flagging suspicious activities.

The list of use cases for fraud detection leveraging financial analytics seems to be endless, but here’s a breakdown by industry (banking and finance, healthcare, insurance, retail, and telecommunications) of some of the most common examples.

Banking and Finance

Credit Card Fraud: Flags credit card transactions that fall outside the scope of normal activity such as multiple transactions to one card in a short period of time, multiple rush orders to the same address, or an unusually high charge card amount.
Money Laundering: Analyzes transactions and the flow of funds across different accounts to identify suspicious activities such as structuring transactions to avoid reporting thresholds, layering funds through multiple accounts, or using complex transaction networks to obfuscate the source of funds.
Insider Trading: Identifies abnormal trading volumes, unusual price movements, and correlations between trading activities and significant corporate events.
Identity Theft: Flags accounts with unusual behavior such as sudden changes in spending patterns or unexpected transactions in new locations that may indicate that someone is illegally using another person’s data or account.

Healthcare

Fraudulent Claims: Identifies claims with excessive or unnecessary procedures, and services that are inconsistent with a patient’s medical history.
Fraudulent Billing: Spots unusual coding patterns, phantom billing, upcoding, unbundling, and disproportionate billing compared to peers.
Collusion: Analyzes claims and payment data to detect a high number of patient visits to different providers or patients who may be helping providers charge for tests they do not need.

Insurance

Application Fraud: Spots false information, fictitious beneficiaries, and agents opening and canceling policies to make quotas and bonuses.
Fraudulent Claims: Detects frequent or excessive claims, inflated claims, staged accidents, duplicate claims, and inconsistent information across claims.

Retail

Credit Card Fraud: Flags credit card transactions that fall outside the scope of normal activity, such as changes in the frequency of orders, higher orders than the average use transaction, changes to a shipping address, bulk orders for the same item, and unmatched or suspicious IP addresses.
Refund Fraud: Analyzes data such as the frequency and timing of returns, products returned and their value, and return reasons to discover potential fraud.

Telecommunications

Revenue Reporting Fraud: Examines billing data, contract terms, and revenue streams to identify discrepancies, such as unbilled services, underbilling, or revenue leakage that are likely to be due to fraudulent activities.
Subscriber Fraud: Analyzes subscriber behavior patterns and financial transactions to detect unusual account activities, such as frequent Subscriber Identity Module (SIM) card changes, abnormal roaming behavior, or suspicious calling patterns.

How Actian Can Help

Fraud is increasing both in frequency and amount. With so much at stake, businesses need to either adopt or ramp up their financial analytics to control fraud. Actian can assist you with a new project or help scale your existing analytics deployment. We are a trusted advisor with over 50 years of experience helping customers manage the world’s most critical data.

Actian makes financial data easy. We deliver cloud and on-premises data solutions that simplify how people connect, manage, and analyze data. We transform business by enabling customers to make confident, data-driven decisions that accelerate their organization’s growth. Our data platform integrates seamlessly, performs reliably, and delivers at industry-leading speeds.

About Teresa Wingfield

Data Management

9 Aspects of Enterprise Data Management Your IT Team Must Have

Teresa Wingfield

September 12, 2023

We live in a data-driven world. The amount of data/information generated, gathered, copied, and consumed is forecast to reach 180 zettabytes by 2025. With this rapid expansion comes tremendous opportunities for organizations to gain actionable insights to improve business outcomes. However, to realize the full potential of data, comes the need for effective enterprise data management.

Data management is the management of all architectures, policies and procedures that serve the full data lifecycle needs of an organization. It is an IT practice that aims to make sure that data is accessible, reliable, and useful for individuals and the organization. The term can also refer to broader IT and business practices that enable the use of data in the most strategic way possible.

Data Management Skills Every IT Team Should Have

Data analytics involves a broad range of data management skills to effectively handle data collection, storage, deployment, processing, security, governance, analysis, and communication. Here are some of the key data management requirements your IT team should have.

Data Integration

The ability to combine data from diverse sources and systems involves data modeling, extraction, transformation, loading (ETL), data mapping, and data integration tools. Depending on the data integration tool and integration requirements, SQL proficiency may be needed to query and manipulate data.

Data Quality Management

Understanding how to ensure that data is accurate, complete, current, trusted, and easily accessible to everyone who needs it. Techniques include data auditing, data profiling, data cleansing, and data validation.

Data Storage and Processing

Choosing appropriate storage and database technologies for analytics while taking into account your current and future requirements for data volume, velocity, and variety and other uses.

Cloud Deployment

As fast-growing data volumes and advanced analytics are driving deployment in a cloud or hybrid environment, data management and enterprise data management professionals need to build their cloud skills.

Database Design and Development

Knowledge of designing the database to handle large volumes of data and to support complex analytical queries. This includes indexing strategies, partitioning techniques, and query optimization to enhance performance.

Data Analysis

Proficiency in how to empower users to extract meaningful insights, identify trends, and make informed decisions based on available information. This involves not only traditional reporting, business intelligence, and data visualization tools, but also includes advanced analytics such as machine learning, to uncover hidden trends and patterns and to forecast future outcomes. In addition, users are seeking real-time data analytics to empower “next best actions” at the moment.

Data Security

Protecting data is a data management must. This includes strong security safeguards and countermeasures to prevent, detect, counteract, or minimize risks, including user authentication, access control, role separation, and encryption.

Data Governance

This involves determining the appropriate storage, use, handling, and availability of data. You’ll need to know how to protect privacy, comply with regulations, and ensure ethical use, while still allowing visibility into your stored data.

Communication and Collaboration

Collaboration skills are crucial for working in cross-functional teams and aligning data management efforts with organizational goals. To be successful, you’ll need to understand your users’ needs and how they measure the success and challenges users face in getting the insights they need.

Data management skills for analytics involves a mix of technical, business, and managerial competencies and vary greatly by role and objectives. Also, keep in mind that technology is always advancing, so you’ll have to stay on top of the latest trends and tools and develop new skills as the need arises.

Need Help with Your Data Management?

Actian is a trusted data management advisor, with over 50 years of experience helping customers manage the world’s most critical data. Contact us to learn how we make managing a data platform for analytics easy.

Additional Resources:

About Teresa Wingfield

Data Intelligence

What is Data Normalization?

Actian Corporation

September 12, 2023

Automation Software To Archiving And Efficiently Manage And Information Files. Document Management System (dms).internet Technology Concept.

Are you concerned about data quality? If so, you should be concerned about data normalization. Data normalization consists of transforming data without distorting it, so that it corresponds to a predefined and constrained set of values to improve its efficiency.

Discover the importance of this technique, which has become indispensable for data-driven companies.

As with any company that turns to data to improve its productivity and efficiency, or the relevance of its offer or its approach to its market, data representativeness is crucial. Your challenge is to maximize the intelligence derived from your data. To achieve this, you need to do everything in your power to limit the distortion of information. This is the vocation of data normalization.

Data normalization is commonly used in statistics, data science, and machine learning to scale the values of different variables within the same interval. The main company objectives in normalization are to make data comparable with each other and to make them more easily interpretable by analysis and modeling algorithms.

Why is Data Normalization Important for Companies?

In many cases, data can have very different scales, i.e., some variables may have much larger or smaller values than others. This can pose problems for certain statistical techniques or machine learning algorithms, as they can be sensitive to the scale of the data. Normalization solves this problem by adjusting variable values to lie within a specified interval, often between 0 and 1, or around the mean with a given standard deviation.

What are the Benefits Associated With Data Normalization?

Data normalization improves the quality, performance, and interpretability of statistical analyses and machine learning models by eliminating problems associated with variable scaling and enabling fairer comparisons between different data characteristics. In practice, this translates into concrete benefits:

Maximum comparability: Normalized data are scaled to the same level, enabling easier comparison and interpretation between different variables.
Optimized machine learning: Normalization facilitates faster convergence of machine learning algorithms by reducing the scale of variables, helping to achieve more reliable and consolidated results more quickly.
Enhanced model stability: Normalization reduces the impact of extreme values (outliers), making models more stable and resistant to data variations.
Improved interpretability: Data normalization facilitates the interpretation of coefficients, making analysis more comprehensible.

What Methods are Used to Normalize Data?

There are several methods of data normalization, but two stand out from the crowd starting with the Min-Max Scaling method. It is based on the principle of scaling the values of a variable so that they fall within a specified interval, usually between 0 and 1. This technique is particularly useful when you want to retain the linear relationship between the original values.

Another method, called Z-Score normalization, is a more standardization-oriented technique. It transforms the values of a variable so that they have a mean of 0 and a standard deviation of 1. Unlike Min-Max normalization, standardization does not impose a specific upper or lower limit on the transformed values. This technique is recommended when variables have very different scales, as it allows data to be centered around zero and scaled with respect to standard deviation.

Other methods may also be considered for data normalization, but these are more marginal. Decimal Scaling and Unit Vector Scaling are two examples.

Decimal normalization involves dividing each value of a variable by a power of 10, depending on the number of significant digits. This moves the decimal point to the left, placing the most significant digit to the left of the decimal. This technique adjusts the values to lie within a smaller interval, thus simplifying calculations.

Unit vector normalization is used in machine learning. It consists in dividing each value of a data vector by the Euclidean norm of the vector, thus transforming the vector into a unit vector (of length 1). This technique is often used in algorithms that calculate distances or similarities between vectors.

What’s the Difference Between Data Normalization and Data Standardization?

Data normalization and data standardization address the same issue of data representativeness but from different perspectives. Although they are both data scaling techniques, they differ in the way they transform variable values.

Data standardization transforms the values of a variable so that they have a mean of 0 and a standard deviation of 1. Unlike normalization, standardization does not set a specific range for the transformed values. Standardization is useful when variables have very different scales and allows data to be centered around zero and scaled with respect to standard deviation, which can facilitate the interpretation of coefficients in some models. Depending on the nature of your data and the lessons you wish to learn from it, you may need to use either data normalization or data standardization.

About Actian Corporation

Data Analytics

10 Ways Your Financial Firm Can Use Data Analytics to Level Up

Actian Corporation

September 5, 2023

A comprehensive data analytics strategy gives financial firms a competitive edge, helping them inform decision-making, drive overall financial performance, and improve business outcomes. The fact is, all types of financial firms, from banks to investment companies, are finding new uses for analytics while optimizing proven use cases. You’re probably leveraging analytics for some use cases, but there’s more you can do. Embedding analytics processes across your organization can deliver more value—and deliver value faster. Here are 10 ways to benefit from data analytics at your financial organization:

1. Deliver Personalized Financial Services

Tailored offerings are mandatory for success in financial services. Connecting customer and financial data for analytics gives you a better understanding of each customer’s financial goals, risk profile, and financial status. You can then deliver personalized offerings to meet customers’ unique needs. Offerings can include cash back on credit cards, or personal or business loans at a favorable interest rate. Meeting each individual’s financial needs improves customer experiences while enabling cross-selling opportunities that improve revenues.

2. Gain Real-Time Insights

Real-time insights position your firm to seize opportunities or enable you to take action if you spot a potential problem. For example, you can deliver special terms on a loan or make a limited-time debit card offer while someone is browsing your site, or take immediate action if you suspect fraud on an account. For example, credit card companies use real-time analytics to approve transactions exceptionally fast and also analyze purchases for fraud. Likewise, in stock trading, every millisecond can make a difference when buying or selling at market prices, making real-time insights invaluable.

3. Improve Operational Efficiency

Analytics let you automate processes to improve operations. Manual and repetitive tasks can be automated to minimize human intervention and errors while speeding up processes. For instance, onboarding customers, approving loans, and processing transactions are common tasks ripe for automation. Data analytics can also play a key role in digital transformations, enabling digital processes and workflows that make operations more efficient. For example, Academy Bank transformed operations in a hybrid environment, saving more than four hours of manual data entry per day and developing new online services to improve the customer experience.

4. Manage Risk Across the Enterprise

The financial industry is constantly exposed to risk—market risk, credit risk, operational risk, and more. Data analytics offers early insights into risk, giving you time to proactively mitigate issues before they become full-blown problems. Applying analytics lets you better understand and manage risk to protect your organization against potential losses while supporting financial stability. For example, analyzing customer data, historical data, credit scores, and other information predicts the likelihood of a person defaulting on a loan.

5. Inform Financial Investment Decisions

In an industry as complex as financial services, you need the ability to analyze vast amounts of data quickly to understand trends, market changes, and performance goals to guide investment strategies. Sophisticated data models and analytic techniques offer granular insights and answers to what/if scenarios to inform investments. In addition, financial analytics can help you strategically build a diversified investment portfolio based on your risk tolerance and objectives.

6. Ensure Accurate Regulatory Reporting

In your heavily regulated industry, timely, trustworthy reporting is critical. Compliance with myriad rules that are constantly changing requires analytics for visibility into adherence and to create accurate compliance and regulatory reports. Data analytics also helps monitor compliance to identify potential issues, helping you avoid penalties by ensuring operations follow legal protocols. Plus, analytics processes offer an audit trail in reporting, giving your stakeholders and auditors visibility into how the reports were created.

7. Enhance Fraud Detection and Prevention Capabilities

Fraud is ever-present in the financial sector—and fraudulent tactics are becoming increasingly sophisticated and harder to detect. Your business must be able to identify fraud before financial losses occur. Analytics, including advanced fraud detection models that use machine learning capabilities, help identify patterns and anomalies that could indicate fraud. Analytics must also prevent false positives. For example, analysis must be able to distinguish between a customer’s legitimate purchases and fraud to avoid suspending a valid customer’s account.

8. Create Accurate Financial Forecasts

Forecasts directly impact profitability, so they must be trustworthy. Data analytics can deliver accurate forecasts to help with budgeting and investments. The forecasts predict revenue, expenses, and organization-wide financial performance. Having a detailed understanding of finances enables you to make informed decisions that increase profitability. Data-driven predictions also inform scenario analysis, which lets you evaluate potential business outcomes and risks based on assumptions you make about the future.

9. Determine Customers’ Credit Scores

Credit scoring is essential in finance, allowing banks and other lenders to evaluate a customer’s creditworthiness based on their credit history, income, and other factors. Analytics can determine if the person is a good credit risk, meaning the customer will repay the loan on time and manage their credit responsibly. Analytics can be used for any sort of financing, from offering a loan to raising credit card limits.

10. Understand Customer Sentiment

Like other industries, financial services firms want to understand the perception customers and the public have about their business. That’s where sentiment analysis helps. It interprets the emotions, attitudes, and opinions behind social media posts, reviews, survey responses, and other customer feedback. This lets you better understand customer feelings about your brand and services. You can determine if your customer and business strategies are working, and make improvements accordingly. Customer sentiment also serves as an economic indicator, giving you insights into how optimistic customers are about their personal finances and the overall economy.

Unify Data for Financial Services Use Cases

Data analytics has become an essential part of decision making, automated processes, and forecasting for financial services. The insights help firms like yours stay competitive and proactively adjust to changing market conditions and customer needs. New analytics use cases are constantly emerging. One way to capitalize on these use cases is to have all data unified on a single, easy-to-use cloud data platform that makes data readily available for analysts and anyone else who needs it. The Actian Data Platform does this and more. It connects all your data so you can drive financial services use cases with confidence and enable enterprise data management for financial services.

Additional Resources:

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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

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:

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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.

How to Optimize Customer Analytics to Improve Customer Experience

Over-Reliance on Customer Segmentation

Insufficient Personalization

Predictive Analytics Can’t Always Forecast Churn

Let’s Make CX Easy Together

About Teresa Wingfield

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Revolutionize Customer Experience With Customer Analytics

Customer Journey Analytics: Seeing the Bigger Picture

What is a Customer Journey Map?

Stages of the Journey

Awareness

Consideration

Decision

Retention

Advocacy

Touch Points

Customer Actions and Experiences

What Insight Does a Customer Journey Map Provide?

Know Your Customers Better

Assess Touchpoint Effectiveness

Prevent Customer Churn

Anticipate Future Needs

Getting Started on Your Customer Analytics Journey

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Do I Need an Object-Oriented Database Management System?

What is an Object-Oriented Database Management System?

What is an OODBMS Used For?

Complex Data Structures and Relationships

Performance

Persistence

Highly Extensible

Capable of Handling Diverse Data Types

Schema Evolution Support

What Are Some Common Applications Built on Top of Object-Oriented Databases?

Computer-Aided Design (CAD)/Computer-Aided Manufacturing (CAM)

Content Management/Digital Asset Management

Financial Applications

E-Commerce Applications

Healthcare Applications

Gaming Applications

Why NoSQL?

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What is Generative AI?

How Generative AI Works

How Can You Benefit From Generative AI?

What are the Risks of Using Generative AI?

What are the Most Inspiring Use Cases for Generative AI?

Improving Customer Experience

Personalizing Customer Paths

Creation of New Products/Services

Preparing Sales Pitches

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The Data Challenges of the Business Technologist

The Evolving Role of Data in Business Technology

The Biggest Data Challenges for Business Technologists

1. Automating Data-Heavy Manual Tasks

2. Optimizing Real-Time Analytics Operations

3. Integrating Customer Data Across Networks

4. Bridging the Gap to “Small Data”

5. Ensuring Data Integrity

Delivering on Data Potential

About Teresa Wingfield