Data Intelligence

Everything You Need to Know About Data Warehouses

Actian Corporation

February 12, 2023

Futuristic Technology Retail Warehouse: Worker Doing Inventory Walks When Digitalization Process Analyzes Goods, Cardboard Boxes, Products With Delivery Infographics In Logistics, Distribution Center

No one doubts anymore that data has become one of the most strategic assets for a company. Competitiveness, productivity, and adaptation to the market – data and analytics have become essential to meet the challenge of performance. Business teams base their thinking and strategies on reports, dashboards, and analytic tools. Their challenge: extract information from their data, monitor business performance, and support decision-making.

The essential mission of data warehouses is to feed these reports, dashboards, and tools. How? By efficiently storing data and providing relevant results to queries in just a few minutes. As an operational and strategic tool, the data warehouse is now essential.

Want to go further? In this article, learn everything you need to know about data warehouses.

The Architecture of a Data Warehouse Broken Down

The architecture of a data warehouse is most often built around three layers: the Bottom Tier, the Middle Tier, and the Top Tier.

The Bottom Tier

The bottom tier, which is also called the storage layer, is dedicated to the storage of data. It most often gathers relational databases or distributed file management systems (DFS) that are intended to store raw data. It also includes indexes to improve query performance.

The Middle Tier

The middle tier (or transformation layer) is used for the data cleansing, transformation, and consolidation phases. To do this, the middle layer relies on ETL (Extraction, Transformation, Loading) tools. It can thus extract data from different sources, clean it, and transform it before sending it to the data warehouse.

The Top Tier

The top layer, also known as the front-end layer, is the layer that provides access to information for end users. This layer includes reporting, visualization, and BI (business intelligence) tools to allow users to create reports, dashboards, and visualizations from the data in the data warehouse.

The Benefits of a Data Warehouse

Relying on a data warehouse is a major advantage for a company that wants to leverage its data assets. Among the main benefits associated with the data warehouse, we will note in particular:

The ability to centralize all available data in a single location in order to benefit from an optimized analytical capability, facilitating faster and more informed decision-making.
The ability to store and leverage historical data and older data to identify long-term trends.
Integration of data from different sources to provide a 360-degree view.
Performance optimization using data shaping techniques.
The opportunity to provide access to data to different users (and profiles) or to specific businesses within your organization.

Data Warehouse vs. Database: What are the Differences?

Too often, there is confusion between the data warehouse and the database. Yet, they are indeed two different components that fulfill specific missions and functions.

Thus, you can consider that a database is a data management system that allows for the storing, organizing, and accessing of data.
Databases store real-time information for common applications, such as CRM or supply chain management systems.

Data warehouses, on the other hand, are systems dedicated to data analysis that store historical data from different sources. Data warehouses are used for long-term analysis, forecasting, and strategic decisions.

Also, to enable fine-grained data exploitation, data warehouses are often designed to be used by less data-savvy profiles (such as business managers and data analysts), while databases are more dryly accessible, and often reserved for more experienced users.

In conclusion, if databases are used for short-term data management, data warehouses are rather reserved for long-term data analysis and for more strategic trade-offs.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.

Data Management

7 Customer Analytics Techniques to Get to Know Your Customer Better

Teresa Wingfield

February 9, 2023

Would you like to know more about your customers to improve engagement? Are you able to make them loyal and increase their lifetime value? These are some of the questions that customer analytics can help answer. Customer analytics, also called customer data analytics, is the systematic examination of a company’s customer information and behavior to identify, attract and retain customers.

Here are seven of the top customer analytics techniques (in no particular order) that give you the insights you need to know your customer better. Real-time customer profile analytics can tell you about your:

1. Customer Profiling

A customer profile is a detailed description of your customers that identifies their multi-channel purchasing behaviors, pain points, psychographic data, and demographic data. A customer profile provides a complete view of a customer for further use in customer analytics and marketing efforts.

For example, personalized marketing uses in-depth knowledge of a customer to tell you the targeted offers that will resonate, the best time and place to connect, and the best ways to personalize the customer experience to win more business and drive up brand loyalty. Personalized marketing leveraging customer profiles can greatly influence customer purchasing behavior. According to Infosys, 78% of customers say they are more likely to purchase from a company that provides them with more targeted offers.

2. Micro-Segmentation

Segmentation divides broad customer or business markets, into sub-groups based on some type of shared characteristic. Micro-segmentation is a more advanced form of segmentation that groups small numbers of customers into extremely precise segments. Micro-segments allow for highly personalized predictive analysis and marketing action optimization. This customer data analytics technique can uncover relationships among customers and their key purchase drivers while identifying new segments that provide a competitive advantage.

3. Customer Churn Analysis

Customer churn analysis predicts which current customers are likely to defect based on similarity to prior defectors. Companies often use customer churn analysis because the cost of retaining an existing customer is typically far less than the cost of acquiring a new one.

Churn risk scores help you understand the likelihood of customer churn. Classifying customers likely to churn by behavioral traits enables targeted, personalized, and proactive retention efforts. This prevents churn, while also classifying customers by value, to help you understand which customers you can’t afford to lose.

4. Next Best Action

Next best action considers the different actions that can be taken for a specific customer and decides on the best one. This technique determines optimal action by analyzing the customer’s past behavior, recent actions, interests, and needs in the context of the business’s sales and marketing goals.

Marketers often use next best action in real time to increase purchases, conversions, and sign-ups, by delivering relevant messaging, content, or product or service offers. These depend on the customer’s needs during the interaction and the benefit to the company.

5. Market Basket Analysis

Market basket analysis, also referred to as affinity analysis, analyzes large data sets, such as purchase history, to reveal products and product groups that customers are likely to purchase together. Identifying relationships between items that people buy provides tremendous opportunity to know your customers better which, in turn, helps you improve customer experience.

Some of the top areas where market basket analysis can have a significant impact include cross-selling, recommendation engines, product placement, affinity promotion, customer behavior targeting, inventory management, store, website traffic, and more.

6. Customer Lifetime Value

A customer’s lifetime value is the total amount of money a customer is expected to spend with your business during the lifetime of the relationship. Knowing customer lifetime value helps marketers develop strategies to acquire new customers and retain existing ones, while maintaining acceptable profit margins.

Knowing where each customer stands provides many opportunities to elevate marketing. Targeted marketing strategies can turn low-and mid-level customers into higher value ones and incent higher value ones with the right offers and rewards. Customer lifetime value can help you make better choices on the right spend and targets for customer acquisition. And customer lifetime value helps improve demand forecasting to make more intelligent decisions around inventory, staffing, production, and other activities.

7. Customer Sentiment Analysis

Customer sentiment analysis is the automated process of discovering emotions in text or conversations to find out how customers feel about your product, brand, or service. This technique is used to analyze emails, social media posts, surveys, phone calls and more.

The applications of customer sentiment analysis to help businesses gain insights and respond effectively to their customers are numerous. Businesses can improve customer service by better understanding sources of satisfaction and frustration. Insights from customer sentiment analysis can enhance products and services by discovering new features customers want or defects or issues that are causing dissatisfaction. Customer sentiment analysis can also help businesses monitor their brand reputation and optimize marketing strategies by keeping on top of customer opinions about industry trends and new product introductions.

The Foundation to Power Customer Data Analytics

Actian empowers the data-driven enterprise with a cloud data platform to make it easy to meet the needs of the most demanding customer analytics techniques. Thousands of forward-thinking organizations around the globe trust Actian to help them solve their toughest customer challenges and to transform how they power their marketing efforts with data.

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

Bringing Your Database into the Modern Era

Teresa Wingfield

February 9, 2023

Blue Digital cyberspace and digital data network connections concept. Transfer digital data hi-speed internet, Future technology digital abstract background concept

The world of database management can be a complex one. Applications continue to be built on top of special-purpose databases that make it hard to merge data. New innovations such as Internet of Things (IoT) sensors are generating never-before-seen quantities of data that must be integrated as well. Here we’ll look at how businesses can simplify some of the challenges associated with database management, along with insights on building an agile, effective management strategy.

The Challenges of the Modern-Day Database

Businesses are generating and processing data at an unprecedented volume. While this surge has great benefits for businesses, it can provide a challenge when it comes to the way the databases are maintained. For starters, one of the biggest challenges is when an organization has multiple special-purpose databases storing diverse types of data at various locations throughout the business. There are many examples: in-memory databases for high-speed application needs, document-oriented databases for text searches, time-series databases for time-based events, data warehouses for analytics, and more.

Even with traditional Online Transaction Processing (OLTP) systems, applications such as Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) are often built on top of their own dedicated database.

In addition to infrastructure complexity, data quality can be an issue when using multiple databases. Incompatible, duplicate, and missing data makes it challenging to pull information together and analyze it coherently. Also, multiple project efforts for managing the same data in separate databases drive up time and costs.

Generally, the more underlying database architectures a business has, the larger the disparity between file formats and system management tactics. This can make it incredibly hard for data analysts to look holistically at all the information. If your business offers multiple different services based on a piecemeal database, it becomes more difficult to effectively put that data to use. This can also have a trickle-down effect on customer service, especially if one or more databases suffer a loss in quality.

As data and technology grow in complexity, businesses need to get a handle on their database management strategies so they can be better positioned from an operational standpoint moving forward.

Meeting & Tackling the Challenges Where They Are

It’s crucial for businesses to have a clear picture of what their objectives are before considering an upgrade to their database management strategy. With businesses generating so many datasets every day, it can be hard to find a core narrative amongst the noise. IT and business leaders need to align on what their databases need to be used for, how they need to be configured, and if these databases can be easily accessed.

There isn’t a “one size fits all” database approach; you’ll need to select the type of database to meet your use case needs. It’s important to develop a data management strategy to better plan for this complexity. A data management strategy is an overarching framework that guides the complete data lifecycle, how your business sources, stores, integrates, and uses data to make decisions.

The Actian Data Platform can help you meet the goals of your data management strategy. It integrates, transforms, orchestrates, and stores your data in a single, easy-to-use platform. Using the platform, you can:

Bring together data with different formats and infrastructures.
Resolve duplicate, incomplete, and incompatible data.
Break down data silos that prevent creating a single-pane view for insights and analysis.
Make it easy for users to access data.

Check out how the Actian Data Platform can help bring your database into the modern era.

About Teresa Wingfield

Data Intelligence

Data-Driven Data Access – BARC Data Culture Survey 23

Actian Corporation

February 8, 2023

Laptop Data Storage. Filing Cabinet On A Laptop Screen. 3d Illustration

In last year’s BARC Data Culture Survey 22, “data access” was selected as the most relevant aspect of BARC’s ‘Data Culture Framework’. Therefore, this year, BARC examined the current status, experiences, and plans of companies concerning their efforts to create a positive data culture with a special emphasis on ‘data access’.

The study was based on the findings of a worldwide online survey conducted in July and August 2022. The survey was promoted within the BARC panel, as well as via websites and newsletter distribution lists. A total of 384 people took part, representing a variety of different roles, industries, and company sizes.

In this article, discover the findings regarding enterprise data access of BARC’s Data Culture Survey 23.

“Right-to-Know” vs. “Need-to-Know” Principles

53% of best-in-class* companies rely on the right-to-know principle. But only 24% of laggards concur.

In their study, BARC describes two principles that can be observed regarding data access: Need-to-know refers to a more restrictive approach, where users must ask for authorization to access data. In contrast, the right-to-know model refers to the propagation of a data democracy, where data access is free for all employees, limited only by intentionally restricted data (e.g., secret, personal, or similar data).

The need-to-know approach has always been the predominant model for data access, with 63 percent of participants confirming that this approach prevails in their organization. However, significantly more than half of the sample consider the Right-to-know to be the most beneficial model.

For many respondents, however, there is still a significant gap between their wishes and reality. Right-to-know is practiced mainly by small companies. This is not surprising due to their simple and flat organizational structures and straightforward communication channels. In fact, BARC found that as the size of a company increases, so does its organizational complexity and the demands on data governance. The need-to-know principle tends to prevail in this case.

Companies that predominantly practice the right-to-know principle believe that they generate greater benefits from data than companies adopting need-to-know. For example, they report a much higher rate of achievement when it comes to gaining a competitive advantage, preserving market position, and growing revenue.

The Technologies and Tools Associated With Data Access

It is no secret: data access requires technical support. According to BARC, around two-thirds of the companies surveyed use traditional data warehousing and BI technologies. And 69 percent use Excel and 51 percent use self-service analytics tools. These figures aren’t surprising If the objective is to solve these challenges with existing enterprise tools.

It is worth mentioning that 32 percent use code to manage data access, which corresponds to BARC’s general market perception that languages such as Python are gaining a stronger foothold in the enterprise data landscape.

In turn, the need for transparency to be able to find data, features, and algorithms in an uncomplicated manner and to integrate them securely is also increasing. Thus providing the breeding ground for software providers to offer new solutions that help to manage and monitor code in order to have a controlled and monitored process.

The survey shows that there is a great deal of catching up to do in terms of technologies for data access. Fewer than 25 percent of the companies surveyed use data intelligence platforms or data catalogs. However, it is precisely these solutions that help to compile knowledge about data outside of the BI context, across systems, and make it analyzable, thus addressing the main challenges to data access.

The importance of data knowledge has been recognized above all by best-in-class* companies. 58 percent use data intelligence platforms, compared with only 19 percent of laggards*.

The Lack of Competence in New Technologies

Of course, technology is only half the solution to data access problems. As mentioned in a previous article, many challenges have their origin in a lack of strategy or organization.

The added value of technologies for increasing data access is limited. Only just over half succeed in improving data access through BI and data warehouse technologies, and only one in three companies manage it with self-service analytics tools.

Data virtualization tools, data intelligence platforms, and data catalogs play a remarkable role in the technical support of data access. These tools can clearly add value, but BARC states that there is probably a lack of knowledge and training to be able to use them extensively.

Indeed, 39 percent of respondents complain about a lack of skills as the second most common obstacle to data access.

*The sample was divided into ‘best-in-class’ and ‘laggards’ in order to identify differences in terms of the current data culture within organizations. This division was made based on the question “How would you rate your company’s data culture compared to your main competitors?”. Companies that have a much better data culture than their competitors are referred to as ‘best-in-class’, while those who have a slightly or much worse data culture than their competitors are classed as ‘laggards’.

About Actian Corporation

Data Analytics

6 Predictive Analytics Steps to Reduce Customer Churn

Teresa Wingfield

February 7, 2023

depiction of reducing customer churn using predictive analytics

Keeping customer churn in check is dire, as it typically costs more to acquire a customer than to retain customers. To help businesses retain their customers, data scientists and IT analysts should consider customer churn analysis to better predict customer behavior. Here’s a quick overview of the tools necessary to predict customer churn.

1. Calculate Your Customer Churn Rate

A good way to get started is to know your organization’s customer churn rate. This is a key performance indicator (KPI) used to measure customer attrition. To calculate this KPI, use the following formula:

(Lost Customers ÷ Total Customers at Start of Chosen Time Period) x 100

When calculating your churn rate, it’s important to be accurate. This often depends on the sales cycle for the product or service. This is also true for defining the target variable in your churn prediction model discussed below. While churn rates vary widely across industries and businesses, a churn rate greater than 7% is generally a sign of high customer dissatisfaction.

2. Integrate Data

Predictive analytics analyzes data to make predictions about future or otherwise unknown events. However, its accuracy requires lots of historical data to train a prediction model, both qualitative and quantitative. You’ll need to combine traditional transactional and account datasets with call center text logs, website logs, marketing campaign response data, competitive offers, social media, and many other customer data sources to develop a truly holistic understanding of past churn behavior.

3. Build a Churn Prediction Model

A churn prediction model typically uses supervised machine learning to segment customers into two groups—the ones likely to churn and those likely to stay. Supervised means that the churn prediction model needs to learn from historical training data using target variables and features. The target variable is the dependent variable that you’re trying to forecast (the customer left or stayed). Features are input variables that are important to identify customers who churn, things such as customer account information, demographics, socio-economic data, products, and services owned, customer service interactions, and more. It’s important not to use too many features which can increase the chance of false predictions.

In the training phase, machine learning algorithms will uncover shared behavior patterns of those customers who churned. Then, once trained, algorithms can check the behavior of future customers against these patterns – and point out potential churners.

4. Assess Your Churn Risk Score

A churn prediction model using a machine learning algorithm with a churn risk score helps understand the likelihood of customer churn. The model assigns each customer a churn risk score with some ranging usually from 1-100; the higher the score, the higher the likelihood the customer will churn.

There are three churn risk groups:

High Churn Risk: 76-100.
Medium Churn Risk: 51-75.
Low Churn Risk: 0-50.

5. Segment Your Customers

You can use customer segmentation to group customers based on shared characteristics to aid sales, marketing, and service efforts to prevent churn. Churn risk groups are an effortless way to target customer segments who are likely to churn. However, you may want to use machine learning algorithms for creating finely-tuned segmentations that produce better results.

For example, you can use behavioral segmentation to group customers likely to churn according to behavioral traits, such as low product usage or poor customer service interactions. This knowledge enables targeted, personalized, and proactive retention efforts to prevent churn.

Combining risk scores with value-based segmentation is also especially useful for understanding which customers to retain. Just as not all customers are equal, neither are all customer segments. Some groups of customers are high value, purchasing your products and services repeatedly, ordering large quantities, and generating large profit margins. Other customer segments are low-value, with larger customer acquisition costs, low-order volumes, few repeat purchases, and low profitability. due to price competition and discount demands.

6. Use a Cloud Data Platform

A cloud data platform offers the best foundation to execute predictive analytics for customer churn. The Actian Data Platform enables data scientists and IT to collaborate across the full data lifecycle with immediate access to data pipelines, scalable compute resources, and preferred tools. In addition, the Actian Data Platform streamlines the process of getting analytic workloads into production and intelligently managing machine learning use cases, such as predictive analytics to reduce churn.

With the Actian Data Platform’s built-in data integration and data preparation, aggregation of model data has never been easier. Combined with direct support for model training tools, and the ability to execute models directly within the data platform alongside the data, it’s easier to capitalize on dynamic cloud scaling of analytics compute and storage resources.

About Teresa Wingfield

Data Management

5 Important Customer Experience Scoring Methods

Teresa Wingfield

February 7, 2023

hand showing customer experience scoring methods

After reading “Forrester 2023 Customer Experience Predictions”, it was surprising to see one of its projections – 20% of customer experience (CX) programs will disappear this year. Forrester explains that companies for which great CX is not part of their brand identity will demand proof that CX spending is necessary. Unfortunately, 54% of CX professionals said their teams can’t prove the return on investment (ROI) of their projects. said their teams can’t prove the return on investment (ROI) of their projects.

Forrester’s disconcerting forecast motivated me to write about how to measure CX to show the value of a company’s customer experience strategy. Although businesses can use a lot of key performance indicators (KPIs) to track their CX, including customer lifetime value, customer churn, marketing campaign performance, average resolution time, cart abandonment rates, customer reviews, and much more. This blog focuses solely on five notable CX scoring methods used to improve customer experience.

If you’re not familiar with CX scoring methods, here’s an opportunity to explore them and to begin experimenting in your organization. Your effort doesn’t need to be perfect or foolproof. The idea is that your team learns more about customers to provide the best experience and to demonstrate a positive ROI for CX initiatives.

1. Net Promoter Score (NPS)

First developed in 2003 by Bain and Company, millions of businesses use Net Promoter Scores to measure and track customer loyalty and satisfaction. It gauges customer perception based on a single question:

On a scale of 0-10, how likely would it be for you to recommend [company name] to a friend or colleague?

Respondents give a rating between 0 (not at all likely) and 10 (extremely likely) which places them into one of three categories:

Promoters with a score of 9 or 10 and are loyal and enthusiastic customers.
Passives with a score of 7 or 8 are satisfied customers but are not happy enough to be promoters.
Detractors with a score of 0 to 6 are unhappy customers who are unlikely to buy from you again and may even discourage others from buying from you.

To calculate NPS, subtract the percentage of detractors from the percentage of promoters.

Note: Employee Net Promoter Score (eNPS) measures employee engagement and loyalty towards a business. Since employees are so intricately linked to positive customer engagement, businesses are increasingly using this measure when evaluating CX.

2. Customer Satisfaction Score (CSAT)

CSAT score, is a metric to improve customer experience that measures happiness with a business, product, or interaction through a customer satisfaction survey that asks some variation of this question:

How satisfied were you with your experience?

Respondents answer using the following scale:

Very unsatisfied
Unsatisfied
Neutral
Satisfied
Very satisfied

To calculate CSAT, use this formula to arrive at a percentage score: (number of satisfied customers (4 and 5) / number of survey responses) x 100 = % of satisfied customers

3. Customer Effort Score (CES)

CES measures how much effort your customer needs to use to complete a transaction, resolve a support issue, or interact with your company/product in general – whether online or in person. CES asks respondents to select how much they agree or disagree with statements such as:

The company made it easy for me to [customer interaction].

The respondent can choose a number between 1 -7 where 1 = Strongly Disagree and 7 = Strongly Agree

CES is calculated by finding the average of all responses: (total sum of responses) ÷ (number of responses)

4. Customer Health Score (CHS)

CHS is a customer retention metric that helps you determine whether a customer is planning to stay with or leave your brand. There’s no straightforward way to determine what to measure or how to calculate CHS – this depends on what’s important to your business and your customers. Here’s an example of an activity that might be useful to assess CHS for a software application:

Depth of product usage: how many key features customers are using?
Breadth of product usage: how many users are using the product?
Frequency of product usage: how often are users coming back to your product?Number and status of support tickets: how many issues have customers logged and submitted and have these been resolved?
Results of in-app surveys: what is causing customer satisfaction/dissatisfaction?
Upgrades and renewals: are customers expanding usage?

You calculate CHS by choosing what activity you want to measure, assigning an impact value based on relative importance using a scale of 1 (lowest impact) to 10 (highest impact), and recording frequency for a specified time. Here’s an example:

Activity	Impact (1 to 10)	Frequency (last 30 days)	Total Value
Subscription Renewal	10	10	100
Unresolved Support Tickets	5	4	20
Active Users	9	500	4500

To calculate your CHS, just add the total value for each activity.

5. Customer Service Satisfaction (CSS)

CSS helps businesses understand whether their customers are happy with services anytime they interact with you, especially during post-purchase service.

You can measure CSS through surveys anywhere after the service is completed, by e-mail, phone, online, or on social media. Questions typically ask the customer to rate factors related to their overall customer service experience and the customer support agents’ performance, CSS is calculated as the sum of all ratings/total number of respondents.

Next Steps

You may be wondering which CX metric you should use. One metric isn’t necessarily better than the other, as long as you’re choosing the one that best fits your organization’s strategic priorities. If you discover that your scores are low, the Actian Data Platform, can provide insights that help you improve your customer experience delivery.

About Teresa Wingfield

Data Intelligence

Gartner’s 10 Technology Trends That You Can’t Miss in 2023

Actian Corporation

February 5, 2023

Businessman Draws Increase Arrow Graph Corporate Future Growth Year 2022 To 2023. Planning,opportunity, Challenge And Business Strategy. New Goals, Plans And Visions For Next Year 2023.

Each year, Gartner predicts the major technology trends for the upcoming year. These 10 technology trends draw up an uncompromising assessment of the most successful and promising innovations.

The 2023 edition highlights the technologies that address four key priorities: Optimizing resilience, scaling productivity, pioneering customer engagement, and becoming more sustainable.

The COVID-19 outbreak definitely accelerated the need for digital transformation. While 2022 should have been the year of a return to normality, new challenges emerged. Workplace changes and the challenge of attracting and retaining talent, have called into question the place of technology in many companies. The war in Ukraine, which has led to an explosion in energy costs and historic inflation in raw materials, has also pushed companies to make complex choices.

To overcome these difficulties, some companies are seeking to reduce their costs, while others are pursuing existing expansion plans. The latter are radically changing the direction of their business strategy. In all cases, the 10 technology trends identified by Gartner are likely to help organizations adapt.

Trend 1 – Digital Immune System: On the Road to Digital Immunity

Guarantee the availability and trustworthiness of your enterprise’s tools and systems – this is the concept of digital immunity. In fact, it’s about creating the conditions for flawless resilience by scrupulously ensuring that you deploy all the means to make your technological environment reliable.

Trend 2 – Applied Observability at the Service of Data Intelligence

Gartner analysts consider observable data to be one of the most valuable assets of the enterprise. Applied observability is the foundation for developing a data intelligence strategy. Data intelligence that makes sense by getting as close as possible to business uses. In short, applied observability is first and foremost about anchoring data in field uses.

Trend 3 – AI TriSM: Between Transparency and Control

As the world becomes more digital and one crisis follows another, there is a natural distrust that is developing in all parts of society – regarding digital technologies in general and data in particular. One of the key trends identified by Gartner is the need for transparency in the use of data and the use of artificial intelligence. The challenge: building a foundation of trust to reinforce the acceptability of AI and data.

Trend 4 – Industry Cloud Platforms: Toward Cloud Verticalization

Combine IaaS, PaaS, and SaaS by tailoring them to compose a set of functionalities and features for a given business. In other words, the Industry Cloud Platform is a trend that is already well established and offers more operational cloud solutions that are more rooted in business uses.

Trend 5 – The Era of Platforms or Platform Engineering

What if the answer to your quest for productivity was to use self-service tools to automate a large number of tasks? The idea: build seamless platforms that allow the sharing of reusable tools and functionality. With Platform Engineering, your teams can have free and transparent access to a range of tools and features that are essential to accelerate workflows.

Trend 6 – Wireless-Value Realization: Generate More Value

IoT, 5G, WiFi 7, or even satellite connectivity…, Gartner has stated: wireless connectivity carries with it the ability to develop new services while delivering on the promise of reducing operational costs for businesses.

Trend 7 – Superapps: So Much More Than Apps

An app that would concentrate the functional potential of several apps to offer a service and an overall experience. For Gartner, Superapps carry the seeds of tremendous potential by bringing together an app, a platform, and an ecosystem all at once.

Trend 8 – Adaptive AI: Ever More Agile

Forget about fixed algorithmic models for good! For Gartner, artificial intelligence will now be adaptive. In other words, to enhance the quality of AI, it will have to be more flexible and the models rethought and adapted continuously and in real-time for more relevance.

Trend 9 – The Metaverse: Can (Still) do Better

Despite the setbacks of Meta and the lack of operational answers, Gartner still considers the metaverse as a technology with great potential, even though they stress that metaverses should be considered “It is a combinatorial innovation made up of multiple technology themes and capabilities”. So a technology still in the making!

Trend #10 – Sustainable Technology: A Groundswell

After a record-breaking year in 2022 in terms of weather and climate, it’s no surprise that Gartner ranks sustainable technology as one of the key areas of focus for 2023. Sustainability ranks technologies in a more cross-functional dimension within companies and must contribute to reducing the carbon footprint of IT.

About Actian Corporation

Data Management

How to Protect Your Cloud Data Platform

Teresa Wingfield

February 2, 2023

badge representing a lock and protection from a cloud data platform

The Cost of a Data Breach Report 2022, conducted by Ponemon Institute, found that data security breaches now cost companies $4.34 million per incident on average – the highest cost in the 18-year history of the report. Forty-five percent of all breaches were cloud-based. This percentage is likely to increase, given that 43% of organizations had not started, or were in the early stages of applying practices to secure their cloud environments.

Cloud data platforms are attractive targets for threat actors, given that they can be a treasure trove of customer, sales, and financial data. Protecting this data must be a priority and involves multiple approaches. Although necessary and important, think of data security as table stakes for data platform protection. Data governance, compliance, and security automation features also play significant roles in data platform protection, as I will explain.

Data Security

Strong data security is the foundation for protecting cloud data platforms. It includes safeguards and countermeasures to prevent, detect, counteract, or minimize security risks. Here’s a previous blog that covers many of the data security controls you’ll need to keep your data platform safe, including user authentication, discretionary access control, role separation, and encryption (at rest and in motion). Here’s another blog on protecting cloud services with isolation, a single-tenant architecture, a key management service, federated identity/single sign-on (SSO), and end-to-end data encryption.

Data Governance

Data governance determines the appropriate storage, use, handling, and availability of data. Your cloud data platform will need to provide fine-grained techniques to prevent inappropriate access to personally identifiable information (PII), sensitive personal information, and commercially sensitive data, while still allowing visibility to pertinent data attributes. For example, data masking is a technique frequently used to hide information. Because sensitive data de-identification is a mandatory requirement for achieving PCI DSS, GDPR, and HIPAA compliance, the ability to mask or redact sensitive fields is necessary for cloud data platform security in industries governed by these regulations.

In addition, a role-based security model provides a way for administrators to control user and group access. According to the role the user or group is expected to perform within the organization, role-based security policies will help you reduce the time and effort to comply with data and privacy regulations, without compromising the usefulness of data to intended consumers.

Compliance

Regulatory compliance can be broadly defined as the adherence to laws, regulations, and guidelines created by government legislations and regulatory bodies applicable to an organization, based on the industry and jurisdiction in which it operates.

Audit logs form a critical part of data protection and compliance because they record all or specified classes of security events for the entire cloud data platform installation. Selected classes of events, such as use of database procedures or access to tables, can be recorded in the security audit log file for later analysis. Security alarms enable you to specify events to be recorded in the security audit log for individual tables and databases. Using security alarms, you can place triggers on important databases and tables. If any user attempts to perform an access operation that is not normally expected, the security alarm will raise an alert.

Automation

Security automation is the automation of security tasks, so they require less human assistance. This provides numerous benefits to an organization by enabling security teams to scale to handle growing data, workloads, and security threats. Automation is useful to find threats faster and ensure preventative measures are implemented in a timely manner. Patches are a perfect candidate for automation in a cloud data platform. If these aren’t deployed when they become available, cyber attackers will have a window of opportunity to exploit a vulnerability.

Check Out the Actian Data Platform

If you’re evaluating cloud data platforms, be sure to include the Actian Data Platform. It offers comprehensive data security, data governance, compliance features and security automation for on-premises and cloud deployments.

About Teresa Wingfield

Data Analytics

Ventana Research Analyst Perspective, Actian Manages Avalanches of Data

Teresa Wingfield

January 31, 2023

Matt Aslett, VP & Research Director at Ventana Research, just wrote an Analyst Perspective called Actian Manages Avalanches of Data. He gives the Actian Data Platform a positive review and concludes by saying,

“I recommend that organizations migrating to the cloud and looking for a cloud data platform with integration and management capabilities should evaluate Actian.”

One of the things Matt likes most about the Actian platform is that it’s one solution for data integration, data management, and data analytics. He sees customers struggle with data integration. For example, he cites Ventana’s Analytics and Data Benchmark Research that found that difficulty in integration and in accessing data sources top the list of complaints about analytics and business intelligence (BI): 35%, find it hard to integrate with their business processes and 31% find it hard to access their data sources.

As a single solution for data integration, data management and data analytics shown in the diagram below, the Actian Data Platform (formerly Avalanche) lowers risk, cost, and complexity, while allowing easier sharing and reuse across projects than cobbling together point solutions. Let’s take a look at some of its best features and benefits.

Data Integration

The Actian Data Platform has easy to use drag-and-drop integration and pre-built connectors to pull data from any source – SaaS applications, structured data from enterprise databases or applications, unstructured data lakes, and edge devices or the web. Using the platform, you can easily build automation and use templates to build data pipelines. Its data profiling allows you to continuously improve data quality.

Data Management

Data management includes things such as monitoring, maintenance, security, backups, and patching. These capabilities are all offered as a managed or co-managed service. Data management capabilities scale quickly and easily with separation of storage and compute. You can control costs with auto start and auto stop and you can scale your cloud data platform without shutting down using dynamic scaling.

Data Analytics

After data is moved into the Actian Data Platform, consumers can build applications, gain insights, and explore data. The platform has the most performant analytics engine around, outperforming alternatives by 7.9x in a recent Enterprise Strategy Group Economic Validation.

The Actian Data Platform provides many additional unique advantages. Its REAL real-time analytics empowers users to decide on the best courses of action in the moment. The platform offers the flexibility to connect to third-party tools for BI, advanced analytics, and application development. While most alternatives focus on security, the Actian platform offers security and data privacy capabilities. The Actian Data Platform supports encryption, data masking and column-level de-identification to make it easy to democratize data while protecting privacy, complying with regulations, and ensuring ethical use.

Learn More

Download the Ventana Research Analyst Perspective, Actian Manages Avalanches of Data, to learn why you should include the Actian Data Platform in your cloud data platform evaluation.

Check out the Actian Data Platform to learn how it makes it easy to connect, manage and analyze data.

And, you can read about how the Actian platform provides a unified experience for managing integration and other data services.

About Teresa Wingfield

Data Intelligence

Everything You Need to Know About Databases

Actian Corporation

January 31, 2023

Multiple Database Is Placed On Relational Database Tables With Server Room And Datacenter Background. Concept Of Database Server, Sql, Data Storage, Database Diagram Design. 3d Illustration.

If we often talk about databases as a unique and monolithic set of information, they are, in fact, quite the opposite. Is your company on an ambitious data project journey? In that case, it is essential to know the different types of databases and their specificities. Here is an overview:

Databases have become a necessity for companies. For their customers, stocks, products, and internal organization, databases are vital for a successful business. But behind the concept of a “database”, we find different types of information, uses, and projects that require distinct types of databases.

The Types of Databases

Asking about the different types of databases is a bit like opening Pandora’s box. Indeed, there are approximately ten types of databases that are likely to coexist and interact within your information system.

Centralized Database

In a traditional database system, data is stored in different files. Each client’s data can for instance be stored in a separate file. In the case of a centralized database, all data is stored in a single file. The advantage? Data management is easier and data retrieval is simplified.

Cloud Database

A cloud database is a database created and accessible via a cloud platform. One of the major benefits of a cloud database is the ability to host databases without having to invest in dedicated hardware for storage purposes. The ease of access to a cloud database from any location is another major advantage. Finally, cloud databases are easily scalable and therefore very flexible.

Relational Database

A relational database stores data points that are related to each other. In a relational database, each row in the table is a record with a unique identifier, called a key. The columns of the table collect the attributes of the data stored in it, and each record has a value for each attribute. This way, it is easier to establish relationships between every data entry.

Distributed Database

A distributed database is spread across multiple sites that do not necessarily share physical components. There are two modes of storing data in a distributed database. The first mode relies on data replication, where the systems keep copies of the data. The second mode is called fragmentation, where relationships are fragmented and each fragment is stored in different sites where they are required. Fragmentation has the advantage that no copies of data are created.

Graph Database

Also known as Graph Oriented Database, this type of database stores nodes and relationships instead of tables or documents. Graph-oriented databases provide a conceptual view of data that allows you to visualize the relationships between data. They do not replace relational databases but rather effectively complement them.

NoSQL Database

NoSQL databases store data in documents rather than relational tables. They are designed to store and process large amounts of data at scale. NoSQL databases simplify application development, especially for real-time interactive web applications. They are also characterized by high flexibility to handle data that has not been normalized.

Object-Oriented Database

Object-oriented databases combine object-oriented programming concepts with relational database principles. A specific operation that leads them to treat data directly as complete objects. They have the advantage of being very comfortable with complex and particularly heterogeneous data.

Open-Source Database

An open-source database can be viewed, downloaded, modified, distributed, and reused for free. Open-source licenses give developers the freedom to create new applications using existing database technologies.

Operational Database

An operational database is a software designed to allow users to easily define, modify, retrieve and manage data in real-time. They can be either SQL or NoSQL based.

Personal Database

This local database model allows a single user to store and manage data and information on their own personal system. In this scenario, only one computer is involved to store and manage the database. Thus, the data can be processed faster and more reliably, limiting the risk of being misrepresented, altered, or exposed to compromise.

About Actian Corporation

Data Intelligence

5 Essential Features for a Five-Star Data Stewardship Program

Actian Corporation

January 27, 2023

You have data – and lots of it. However, it is messy, incomplete, and scattered across several different platforms, databases, and even spreadsheets. On top of this, some of your information is inaccessible or, worse, accessible to the wrong people. And as the go-to data experts of the company, Data Stewards must be able to identify the who, what, when, where, and why of their data to build a reliable stewardship program.

Unfortunately, Data Stewards face a major roadblock to success – the lack of tools to support their role. When dealing with large volumes of data, maintaining data documentation, managing enterprise metadata, and tackling quality & governance issues can be quite challenging.

This is where the Actian Data Intelligence Platform steps in. Our data intelligence platform – and its smart and automated metadata management features – facilitates the lives of Data Stewards. Discover 5 of them in this article.

Feature 1: Universal Connectivity

Automatically extract and inventory metadata from your data sources.

As mentioned above, a lot of enterprise data is spread across many different information sources, making it difficult, even impossible, for Data Stewards to manage and control their data landscape. Actian Data Intelligence Platform provides a next-generation data cataloging solution that centralizes and unifies all enterprise metadata into a single source of truth. Our platform’s wide range of native connectors automatically retrieves and collects metadata through our APIs and scanners.

Feature 2: A Flexible & Adaptable Metamodel

Automate data documentation.

Documenting information can be extremely time-consuming, with sometimes thousands of properties, fields, and other important metadata that need to be implemented for business teams to fully understand and have the necessary context on the data they are consulting. Actian Data Intelligence Platform provides a flexible and adaptable way to build metamodel templates for pre-configured (datasets, fields, data processes, etc) and an unlimited amount of custom objects (procedures, rules, KPIs, regulations, etc).

Import or create your documentation templates by simply dragging & dropping your existing properties along with your tags, and other custom metadata into your templates. Made a mistake in your template? No problem! Add, remove, or modify your properties and sections as you please – your items are automatically updated after you’ve finished editing them.

Feature 3: Automatic Data Lineage

Trace your data transformations.

In order for Data Stewards to build accurate and trustworthy compliance reports, data lineage capabilities are essential. Many software developers offer lineage capabilities, but rare are those who understand it. Via a visual and easy-to-interpret lineage graph, the Actian Data Intelligence Platform offers your users the possibility to navigate through the lifecycle of their data. Click on any item to get an overview of its documentation, relations to other assets, as well as its metadata to obtain a 360° view of your catalog items.

Feature 4: Smart Suggestions

Quickly identify personal data.

With the GDPR, California Consumer Privacy Act, and other regulations regarding the security and privacy of the information of individuals, it can be a hassle to go through each existing set of information to ensure you’ve correctly indicated the data as personal. To always ensure your information is correctly labeled, the Actian Data Intelligence Platform analyzes similarities between existing personal data by identifying and giving suggestions on which fields to tag as “personal data”. Data Stewards can accept, ignore, or delete suggestions directly from their dashboard.

Feature 5: An Effective Permission Sets Model

Ensure the right people are accessing the right data.

For organizations with various types of users accessing their data landscape, it doesn’t make sense to give everyone full access to modify anything and everything. Especially when dealing with sensitive or personal information. For this reason, the Actian Data Intelligence Platform designed an effective permission sets model to allow Data Stewards to increase efficiency for your organization and reduce the risk of errors. Assign read-only, edition, and admin rights in all or different parts of the catalog to not only ensure a secure catalog but also save time when data consumers need to find an asset’s referent.

Ready to Start Your Data Stewardship Program?

If you’re interested in the Actian Data Intelligence Platform’s features for your data documentation & stewardship needs, contact us for a 30-minute personalized demo with one of our data experts.

About Actian Corporation

Data Management

Three Steps to Better Data Management in 2023

Traci Curran

January 26, 2023

line graphs that exemplify someone developing better data management

Data management is a vital part of every modern organization. The best businesses today are using their data to inform business strategy, improve customer interactions and ultimately benefit the bottom line. So how can you improve your company’s data management system to reap maximum benefits in the new year? Let’s look at three tips to set you on the right course to start 2023 off strong.

Implement Data Quality Management

High-quality datasets are crucial to inform companies as they map customers, track their customer lifecycle stage, and prepare next steps for future engagements. However, simply being able to access high-quality data is not enough. To maintain a competitive edge, companies must use strong data quality management (DQM) strategies to make the best use of their datasets. DQM involves the tools, knowledge, and processes associated with data to inform business decisions in an agile way.

Organizations can use DQM to improve data flow efficiency and help realize the maximum potential of their datasets. DQM allows businesses to standardize how they handle data, better communicate across silos, and strengthen data governance models. This process is becoming increasingly collaborative as teams emphasize better data integration across the organization and aim to solve problems quickly while reaching toward shared goals and priorities.

Inform Customer Interactions With Data

To make smart business decisions, companies must be up to date on their customer preferences. Customer data can change frequently, so it is crucial to rely on fresh, clean, and accurate data. A Customer Relationship Management (CRM) system allows you to manage your relationship and touchpoints with all customers, current, and future. It relies on a CRM database to support the lifecycle management of customer data throughout the entirety of a customer’s relationship with the company.

Using a CRM database allows organizations to better understand customers and improve their experiences with products and services. Gaining a sense of understanding of your customers’ satisfaction levels, as well as potential underlying causes for any dissatisfaction, can transform the way you conduct your business and service clients.

Insight into customer growth, decline and retention rates, as well as the underlying reasons for any shifts, informs sales and marketing teams on areas that customers are responding well. In turn, this allows them to identify areas for potential improvement and advance relationships with customers. Companies can better access and analyze customer data using their CRM system, allowing them to make customer-focused decisions and improve their bottom line.

Improve Data Protection

Data security is of the utmost importance, but protecting that data seems to be increasingly challenging. Over the past decade, the number of data breaches in the U.S. has risen from a mere 662 in 2010 to over 1,000 in 2021 (Statista). It’s crucial that organizations protect their data for the safety of company information, as well as customer privacy.

Companies in all industries must adopt the latest technologies and security practices to ensure they are keeping their data safe. Establish a team of well-trained, highly qualified security professionals who can take on the job of keeping the organization up to date on best practices. Security must be managed quickly and efficiently when a possible threat becomes known.

Keeping customer data secure also means protecting customer information against external threats, as well as the risk of potential rogue employees. Set your team up for success by investing in regular training, both for employees and customer partners, and regularly reminding them about security threats. Stay on alert for common cyber threats, such as social engineering attacks, and teach employees to recognize the signs of a potential attack.

Actian DataConnect Helps Organizations Improve Data Management

Modern enterprises are making the most of their data by using it to fuel company decision making. In 2023, outdated data sets and rundown security processes will not cut it. Actian DataConnect can help you put your best foot forward in the new year, helping you use only high-quality data, create better customer interactions with that data, and protect company and customer privacy. Visit the Actian DataConnect page to find out how your organization can improve data management processes in the coming year.

About Traci Curran

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

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About Teresa Wingfield

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About Teresa Wingfield

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6 Predictive Analytics Steps to Reduce Customer Churn

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2. Integrate Data

3. Build a Churn Prediction Model

4. Assess Your Churn Risk Score

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5 Important Customer Experience Scoring Methods

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Gartner’s 10 Technology Trends That You Can’t Miss in 2023

Trend 1 – Digital Immune System: On the Road to Digital Immunity

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How to Protect Your Cloud Data Platform

Data Security