June 2023 – 🔴 Red Button Data

Data Management LIGHTSONDATA

Jun 30 2023

Understanding the Data Lakehouse: An Easy To Understand Overview

The concept of the data lakehouse has gained significant attention in recent years as a new approach to managing and analyzing data. In this article, we will delve into what a data lakehouse is, its key components, and its benefits for organizations.What is a Data Lakehouse?A data lakehouse is a hybrid data architecture that combines […]

The post Understanding the Data Lakehouse: An Easy To Understand Overview appeared first on LightsOnData.

Architecting Real-Time Analytics for Speed and Scale

In today’s fast-paced world, the concept of patience as a virtue seems to be fading away, as people no longer want to wait for anything. If Netflix takes too long to load or the nearest Lyft is too far, users are quick to switch to alternative options. The demand for instant results is not limited […]

The post Architecting Real-Time Analytics for Speed and Scale appeared first on DATAVERSITY.

Cyber Detection: A Must-Have in Primary Storage

Enterprise storage is a critical component of a comprehensive corporate cybersecurity strategy. If an enterprise does not include cyber storage resilience in their measures to secure their enterprise IT infrastructure, it’s the equivalent of going on vacation and leaving the back door and back windows of your house open, so you have made it easier for criminals […]

The post Cyber Detection: A Must-Have in Primary Storage appeared first on DATAVERSITY.

5 Common Factors that Reduce Data Quality—and How to Fix Them

As any successful company knows, data is the lifeblood of the business. But there’s a stipulation. The data must be complete, accurate, current, trusted, and easily accessible to everyone who needs it. That means the data must be integrated, managed, and governed by a user-friendly platform. Sound easy? Not necessarily.

One problem that organizations continue to face is poor data quality, which can negatively impact business processes ranging from analytics to automation to compliance. According to Gartner, every year, poor data quality costs organizations an average of $12.9 million. Gartner notes that poor data quality also increases the complexity of data ecosystems and leads to poor decision-making.

The right approach to enterprise data management helps ensure data quality. Likewise, recognizing and addressing the factors that reduce data quality mitigates problems while enabling benefits across data-driven processes.

Organizations experiencing any of these five issues have poor data quality. Here’s how to identify and fix the problems:

1. Data is siloed for a specific user group

When individual employees or departments make copies of data for their own use or collect data that’s only available to a small user group—and is isolated from the rest of the company—data silos occur. The data is often incomplete or focused on a single department, like marketing. This common problem restricts data sharing and collaboration, offers limited insights based on partial data rather than holistic views into the business, increases costs by maintaining multiple versions of the same data, and several other problems. The solution is to break down silos for a single version of the truth and make integrated data available to all users.

2. A single customer has multiple records

Data duplication is when more than one record exists for a single customer. Duplicated data can end up in different formats, get stored in various systems, and lead to inaccurate reporting. This problem occurs when data about the same customer or entity is stored multiple times, or when existing customers provide different versions of their information, such as Bob and Robert for a name or a new address. In these cases, additional records are created instead of a single record being updated. This can negatively impact customer experiences by bombarding individuals with the same offers multiple times, or marketing being unable to create a full 360-degree profile for targeted offers. Performing data cleansing with the right tools and integrating records can remove duplicate data and potentially create more robust customer profiles.

3. Lacking a current, comprehensive data management strategy

Organizations need a strategy that manages how data is collected, organized, stored, and governed for business use. The strategy establishes the right level of data quality for specific use cases, such as executive-level decision-making, and if executed correctly, prevents data silos and other data quality problems. The right strategy can help with everything from data governance to data security to data quality. Strategically managing and governing data becomes increasingly important as data volumes grow, new sources are added, and more users and processes rely on the data.

4. Data is incomplete

For data to be optimized and trusted, it must be complete. Missing information adds a barrier to generating accurate insights and creating comprehensive business or customer views. By contrast, complete data has all the information the business needs for analytics or other uses, without gaps or missing details that can lead to errors, inaccurate conclusions, and other problems. Organizations can take steps to make sure data is complete by determining which information or fields are needed to reach objectives, then making those fields mandatory when customers fill out information, using data profiling techniques to help with data quality assurance, and integrating data sets.

5. Shadow IT introduces ungoverned data

The practice of using one-off IT systems, devices, apps, or other resources rather than leveraging centralized IT department processes and systems can compromise data quality. That’s because the data may not be governed, cleansed, or secured. These IT workarounds can spread into or across the cloud, leading to data silos, with little to no oversight and resulting in data that does not follow the organization’s compliance requirements. Offering staff easy and instant access to quality data on a single platform that meets their needs discourages the practice of Shadow IT.

Ensuring Data Quality Drives Enterprise-Wide Benefits

Having enterprise data management systems in place to ensure data quality can be a competitive advantage, helping with everything from better data analytics to accelerated innovation. Users throughout the organization also have more confidence in their results when they trust the data quality—and are more likely to follow established protocols for using it.

Achieving and maintaining data quality requires the right technology. Legacy platforms that can’t scale to meet growing data volumes will not support data quality strategies. Likewise, platforms that require ongoing IT intervention to ingest, integrate, and access data are deterrents to data quality because they encourage silos or IT workarounds.

Data quality issues are not limited to on-premises environments. Organizations may find that out the hard way when they migrate their data warehouses to the cloud—any data quality issues on-premises also migrate to the cloud.

One way to avoid data quality issues is to use a modern platform. For example, the Avalanche Cloud Data Platform simplifies how people connect, manage, and analyze their data. The easy-to-use platform provides a unified experience for ingesting, transforming, analyzing, and storing data while enabling best practices for data quality.

Related resources you may find useful:

Introducing Data Quality and DataConnect v12

What is Data Quality Management?

What is Data Management Maturity?

The post 5 Common Factors that Reduce Data Quality—and How to Fix Them appeared first on Actian.

13 Churn Prevention Strategies to Improve CX

Happy customers can be advocates for your brand, make repeat purchases, and influence others to buy from your business. This type of success is the result of using data to holistically understand each customer and developing a customer-centric business strategy that engages and rewards each individual when they interact with your company.

Elevating the customer experience (CX) is a proven way to connect with customers and prevent churn. It also helps with profitability, as it’s much more expensive to acquire new customers than to keep existing ones.

How to Retain Customers and Enhance CX

1. Simplify customer onboarding.

Ensuring a fast and painless onboarding experience is essential since it’s often your first opportunity to make an impression on the customer—and first impressions shape CX. An intuitive and positive first experience sets the tone for the customer journey. Whether onboarding entails filling out an online form, activating a new product, or hands-on training on a product, delivering an engaging experience gives customers the confidence that they’re making a good decision by working with your business.

2. Deliver timely, truly meaningful CX.

One of the best ways to prevent churn is to continually provide relevant and authentic customer experiences. These experiences nurture customers by delivering the next best action at the most opportune time. With the right cloud data platform and analytics, you can accurately predict what customers want and when they want it, then delight them with the right offer at the right time and at the right price point. This is where a comprehensive CX strategy that optimizes customer data delivers ongoing value.

3. Personalize all interactions.

Personalized CX is now table stakes for companies. According to McKinsey & Company, 71% of consumers expect organizations to deliver personalized interactions, and 76% are frustrated when this doesn’t happen. Personalization improves customer outcomes—and drives more revenue. Product and service offers must be customized, too. Once you’ve built 360-degree profiles, you can segment customers for special offers. You can even personalize offers to a single customer for a truly customized experience.

4. Engage customers at all touchpoints.

CX is an ongoing journey that requires support and nurturing at every step. Customers typically have several interactions with a company before making a purchase. Understanding each touchpoint and ensuring a positive experience is essential—or the customer could abruptly end the journey. These touchpoints, such as website visits, downloading an app, or social media views, shape the way customers view your brand, company, and offerings. This is why each touchpoint is an opportunity to impress customers and guide their journey.

5. Respond promptly to complaints or concerns.

Customer journeys are not always smooth or linear. Shipping delays, product glitches, and user errors all impact CX. Unhappy customers have a higher likelihood of churn, which brings the challenges of identifying these customers and addressing their concerns. This is especially important when it’s a high-value customer. Sometimes feedback is direct, such as a call or email to a customer service desk or sales rep. Other times, you need to identify negative sentiment indirectly, like through social media. And sometimes customers won’t proactively share at all, which is where surveys and post-sales follow-up provide value. Simply connecting with a customer is sometimes enough to make a difference and make them feel valued.

6. Reward loyalty.

Loyalty programs are a great way to know and recognize your best customers. You can use the programs to gather information about customers, then reward loyalty with special offers, like free merchandise, a discount, or a chance to buy a product before it goes on sale to the public. While these programs improve CX, they also encourage customers to engage with the brand more often to accumulate points. Another benefit is loyalty programs, which can turn customers into authentic advocates for your brand. In addition, studies have shown that consumers are more likely to spend—and spend more—with companies offering a loyalty program. Gartner predicts that one in three businesses without a loyalty program today will establish one by 2027.

7. Build excitement.

When you can build customer excitement, then you know your CX strategy is excelling. This excitement can organically inspire conversations, posts, and comments on social media about your brand. Effective ways to build this excitement include giving loyal customers a sneak peek at upcoming product releases, offering “behind the scenes” content, and creating customer contests on social media that award prizes.

8. Foster trust.

People want to do business with companies they trust and that share their values. Meeting or exceeding customer expectations and resolving problems before they occur builds trust. So does making an emotional connection with customers through your content. Other ways to foster trust include demonstrating that you protect their data, showing concern for the environment through sustainable business practices, and delivering products and services when and how the customer expects.

9. Listen to customers.

Your customers have a lot to say, even if they don’t tell you directly. They might be sharing their thoughts on social media or through their browsing history. Integrating customer data from all relevant sources allows you to understand each customer. You can then listen based on their behaviors and feedback before, during, and after a sale. This can help you determine which features and price points are most effective. Also, addressing any changes in behaviors and responding to complaints quickly can help mitigate churn.

10. Find out why customers are leaving.

Understanding why customers are ending subscriptions, switching to a competitor, or no longer purchasing from your company allows you to identify churn patterns. This can help you evaluate if you’re experiencing a level of churn the business is comfortable with—some amount of churn is to be expected—or if there’s a sudden spike or ongoing problem. Churn analysis offers insights into why customers are leaving, such as products that don’t meet expectations, prices that are higher than competitors, poor customer service, or other reasons.

11. Be proactive.

It’s important to identify customers at risk of churning, then engage them before they leave. Measuring customer sentiment helps to determine areas needing improvement and creates a consistent channel for feedback. Proactively addressing customers’ concerns before they spiral into full-blown problems can encourage them to stay. Being proactive requires a robust customer retention strategy and the ability to perform granular customer analytics for insights into the early stages of churn.

12. Know what your competitors are doing.

Knowing your business and your customers is not enough. You must also know what your competitors are doing. This allows you to better understand the competitive landscape and have insights into potential market changes—or major market disruptions. A competitive analysis can help you understand key differences between your products and competitors’ offerings. This can help you update your product design and marketing strategy, and even be an opportunity to poach customers.

13. Stay relevant.

Growing the business and staying relevant are ongoing challenges. It requires continually delivering innovative products and services, regularly connecting with customers, staying ahead of changing customer preferences, and updating the brand as needed. You also need to evaluate if you have gaps in your product or service offerings, and if so, plan how to address them. As customer wants and needs change, your brand also needs to change in ways that are relevant to your customers.

Let’s Get Started

Your ability to tackle customer attrition while enhancing customer experiences starts with data. You need the right data, and you need the ability to integrate it using a single, scalable platform for analytics. The Avalanche Cloud Data Platform can help you transform your churn and CX strategies by bringing together all of the customer data you need on an easy-to-use platform. Our advanced capabilities for data integration, data management, and analytics give you the insights and confidence needed to retain and engage customers.

Related Resources:

The post 13 Churn Prevention Strategies to Improve CX appeared first on Actian.

Building A Best-In-Class Data Stewardship Program

In today’s data-driven world, organizations are increasingly recognizing the importance of effective data stewardship. A well-implemented data stewardship program ensures that data is managed responsibly, enabling organizations to leverage their data assets for better decision-making and business outcomes. In this article, we will explore the key elements and strategies for building a best-in-class data stewardship […]

The post Building A Best-In-Class Data Stewardship Program appeared first on LightsOnData.

Using Customer Analytics to Create Lifetime Value

Moving clients from being one-time customers to doing business with them for life is the ultimate goal for organizations. It’s what ensures ongoing success and profitability. Your products, services, and business priorities can change over time, but one constant is the need for loyal customers.

Customer lifetime value (CLV) is a measure of how valuable a customer is to your business over the course of your entire relationship. CLV provides an expectation of what a customer is predicted to spend on your brand if you deliver the right experiences.

The process of successfully building lifetime customers starts with analyzing data at every step along the customer journey. You’re probably already using a data-driven approach to engage, reward, and retain customers. Here are ways to build on what you’re already doing to support a customer-for-life strategy:

Know Everything About Your Customer

All customer strategies—reducing churn, using targeted selling, creating customers for life—require you to know everything you can about the customer. This entails creating a single view of your customer by integrating all relevant customer data from all available sources for a complete 360-degree profile. From there, you can uncover deep insights into customer behaviors and buying patterns, then predict what customers want next so you can meet their emerging needs.

The ability to accurately identify customer wants and needs is essential to creating customers for life. It represents a significant shift in traditional customer-centric strategic visions. That’s because it takes a forward-looking view to understand what customers want before they tell you, instead of a rearview mirror approach that explains what has already happened. While past behaviors are important and help predict future actions, performing analytics across all relevant customer data is needed to forecast how customer preferences are changing.

Staying ahead of customer wants, needs, and challenges will inspire customers to trust your brand. But don’t expect to “find” customers for life. It’s up to you to nurture and reward current customers, then cultivate successful relationships that ensure loyalty. In other words, you have to “create” customers for life.

Engage and Delight Customers at Every Touchpoint

Creating customers for life is an ongoing process that requires consistently gathering and analyzing data to ensure current insights. Customer behaviors and needs can change incredibly fast and with little warning, which makes real-time data essential.

Your organization must have the ability to integrate, manage, and analyze all required data, including data from new and emerging sources. This helps you spot early indicators of changing trends or behaviors, allowing you to shift your customer experience strategy, serve up timely offers that meet customers’ current needs, and build long-lasting customer relationships.

Once someone makes a purchase from your company, you have an opportunity to entice that customer with the next best action—whether it’s a limited-time discount, exclusive access to a product or content, or another special offer—to drive a second sale. A repeat purchase puts them on the path to being a customer for life.

Have Full Confidence in Your Customer Data

Data needs to be trustworthy and easy to use to deliver the insights needed to understand your customers and guide their purchasing decisions. This includes customer data such as transactional details of when, where, and what products a customer has already purchased from your business. You also need the ability to integrate other relevant data, such as demographic information to help with segmentation, and behavior data, which offers insights into how customers responded to previous marketing campaigns and their past buying behaviors.

Analyzing the data can reveal which customers have the highest potential lifetime value so you can focus on ensuring they remain customers—you do not want to let these customers switch to a competitor. The analytics process must start by bringing together data for a single, current, and accurate view of each customer, including their purchase history across all channels—in-person, online, and via third-party resellers—to understand their habits and preferences. These insights are key to providing a personalized, nurturing experience with targeted offerings that lead to life-long customers.

The Actian Data Platform can offer the data and analytics capabilities needed to create customers for life. It integrates seamlessly, performs reliably, and delivers at industry-leading speeds to drive your customer strategy and maximize customers’ lifetime value.

Related resources you may find useful:

Connecting Data to Make Customer Experience (CX) Easier

Prioritizing a Customer Experience (CX) Strategy to Drive Business Growth

Boost Your Customer Data Analytics

The post Using Customer Analytics to Create Lifetime Value appeared first on Actian.

Effective Use of Generative-AI

While Generative-AI has undoubtably demonstrated powerful capabilities in generating images, music, and text, it is essential to approach the technology’s potential with a balanced and realistic outlook. The notion that Generative-AI will serve as a universal panacea for all industries is overly optimistic. The true value lies not in the technology itself, but in how […]

Who Needs A Data Model Anyway?

These days, there is much conversation about the necessity of the data model. The data model has been around for several decades now and can be classified as an artifact of an earlier day and age. But is the data model really out of date? And exactly why do we need a data model, anyway? […]

NIDG Perspective: A Trusted Trainer for Data

This column is a stand-in for a new column that will be announced for the 3rd month each quarter starting next cycle. I am pleased to fill in essays like this from time-to-time that provide additional perspective to the Non-Invasive Data Governance approach. In the discipline of data management, adopting the non-invasive approach to data […]

Auditing Database Access and Change

The increasing burden of complying with government and industry regulations imposes significant, time-consuming requirements on IT projects and applications. And nowhere is the pressure to comply with regulations greater than on data stored in corporate databases. Organizations must be hyper-vigilant as they implement controls to protect and monitor their data. One of the more useful […]

3 Data Strategy Pitfalls

Welcome to the Lights On Data Show, where today we delve into the topic of data strategy pitfalls. Joining us is Dora Boussias, an esteemed expert in the field of data strategy. Dora has an impressive background in IT and a deep understanding of the critical role data plays in organizations. Let’s explore the three […]

The post 3 Data Strategy Pitfalls appeared first on LightsOnData.

How to Build a Growth-Focused Data Analytics Tech Stack in 2023

In 2023, building a growth-focused data analytics tech stack is all about cloud deployment flexibility and cloud-native support. According to Gartner, more than 85% of organizations will embrace a cloud-first principle by 2025, but they will not be able to fully execute their digital strategies unless they use cloud-native architectures and technologies. Cloud-native technologies empower organizations to build and run scalable data analytics in modern, dynamic environments such as public, private, and hybrid clouds.

Cloud Deployment Models

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

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

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

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

Cloud-Native Technologies

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

The Cloud Native Computing Foundation defines cloud native as:

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

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

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

Scalability: On-demand elastic scaling offers near-limitless scaling of computing, storage, and other resources.
Resiliency: A cloud-native approach makes it possible for the cloud-native database to survive a system failure without losing data.
Accessibility: Cloud-native uses distributed database technology to make the database easily accessible.
Avoid Vendor Lock-In: Standards-based cloud-native services support portability across clouds.
Business agility: Small-footprint cloud-native applications are easier to develop, deploy, and iterate.
Automation: Cloud-native databases support DevOps processes to enable automation and collaboration.
Reduced cost. A cloud native database allows you to pay-as-you-go and pay for only resources that you need.

Get Started with the Actian Data Platform

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

Start your free trial of the Actian Data Platform today!

The post How to Build a Growth-Focused Data Analytics Tech Stack in 2023 appeared first on Actian.

Data-Driven Analytics Use Cases Powered by the Avalanche Cloud Data Platform

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

Customer 360

With the Actian (also known as Avalanche) platform powering Customer 360, organizations can rapidly personalize the customer experience through micro-segmentation, next-best action, and market basket analysis while improving customer acquisition and retention through campaign optimization, and churn analysis to increase customer loyalty.

Healthcare Analytics

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

IoT-Powered Edge-to-Cloud Analytics

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

ITOps Health and Security Analytics

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

Supply Chain Analytics

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

Machine Learning and Data Science

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

Why Actian?

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

Read the eBook to learn more.

The post Data-Driven Analytics Use Cases Powered by the Avalanche Cloud Data Platform appeared first on Actian.

Leveraging AI and Automation to Streamline Clinical Trial Data Management

Clinical trials are critical in developing and approving new medical treatments and technologies. These trials generate massive data that needs to be managed efficiently and accurately to ensure patient safety and successful research outcomes. The good news is that advances in AI and automation technology, such as AI-based data extraction, virtual clinical trials, and predictive analytics, […]

The post Leveraging AI and Automation to Streamline Clinical Trial Data Management appeared first on DATAVERSITY.

6 Steps to Leveraging Supply Chain Data to Inform Predictive Analytics

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

Six Steps to Inform Predictive Analytics

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

Collect Data

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

Clean and Pre-Process Data

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

Select a Predictive Analytics Technique

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

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

Train the Model

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

Validate the Model

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

Use the Model to Forecast the Future

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

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

Try our Actian Data Platform Free Trial to see for yourself how it can help you simplify predictive analytics deployment.

The post 6 Steps to Leveraging Supply Chain Data to Inform Predictive Analytics appeared first on Actian.

Adopting an Edge-to-Cloud Approach

Back in 2009, there was an enterprise technology with a lot of promise. But adoption, marred by questions about security and reliability, lagged. That technology – cloud computing – is now a $600 billion market. Edge computing technology has followed a similar trajectory to the cloud. It, too, was met with early cynicism and slow […]

The post Adopting an Edge-to-Cloud Approach appeared first on DATAVERSITY.

AI and Privacy: Navigating the Intersection of Technology and Personal Data

The rapid adoption of generative AI in the business world is transforming the way we work and improving our ability to engage with customers, streamline internal processes, and drive cost savings while introducing unprecedented challenges to the protection of individual privacy. As AI technologies continue to evolve and permeate various aspects of our lives, concerns about data […]

The post AI and Privacy: Navigating the Intersection of Technology and Personal Data appeared first on DATAVERSITY.

OLTP Database Solutions for Today’s Transactions

Online transaction processing (OLTP) enables rapid, accurate data processing for most of today’s business transactions, such as through ATMs, online banking, e-commerce, and other types of daily services. With OLTP, the common, defining characteristic of any transaction is its atomicity, or indivisibility. A transaction either succeeds as a whole, fails, or is canceled. It cannot […]

The post OLTP Database Solutions for Today’s Transactions appeared first on DATAVERSITY.

Myth-Busting Cloud Repatriation: The Misunderstood Trend in Cloud Computing

The term “cloud repatriation” is appearing more often as organizations redefine their strategic approach to where they locate their apps and workloads. The storage location depends on each organization’s specific cloud goals, needs, and requirements. This “trend,” however, is nothing new, and can have profound business impacts for organizations. Cloud repatriation refers to migrating workloads […]

The post Myth-Busting Cloud Repatriation: The Misunderstood Trend in Cloud Computing appeared first on DATAVERSITY.

Why Embedded Innovation Is Key to Digital Transformation

“Digital transformation” is the hottest phrase of 2023, encapsulating everything from supply chain disruptions to the AI gold rush. It’s the challenge companies are undertaking to make sure they keep pace with today’s rapidly evolving technology and interconnected digital world. For business leaders, the biggest focus is on making sure their internal processes are intertwined […]

The post Why Embedded Innovation Is Key to Digital Transformation appeared first on DATAVERSITY.

7 Ways to Stop Data Quality Issues in Their Tracks

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

#1. Automation of Data Entry

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

#2. Data Standardization

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

#3. Data Verification

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

#4. Use Data Integration Tools

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

#5. Encourage Self-Service Data Quality

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

#6. Implement Data Profiling

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

#7. Integrate Data Quality into your Pipelines

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

Benefits of Improving Data Quality

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

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

Get Started!

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

The post 7 Ways to Stop Data Quality Issues in Their Tracks appeared first on Actian.

Mitigate Real Risks: Create Ethical, Responsible AI

AI is everywhere, and it is growing. In a 2022 edition of an annual global AI survey, a leading consulting firm found that adoption among enterprises had more than doubled in five years, with about 50% of respondents using it in at least one business unit or function. Thirty-two percent of enterprises reported cost savings […]

The post Mitigate Real Risks: Create Ethical, Responsible AI appeared first on DATAVERSITY.

3 Ways to Up-Level First-Party Data Enrichment Efforts

Today’s consumers expect the companies they buy from to know their needs and preferences. However, as the advertising and privacy landscape evolves, marketers are facing impediments that limit their understanding of their customers beyond their brand walls. Marketers know that their first-party data has never been more important. But they also know it’s not enough on […]

The post 3 Ways to Up-Level First-Party Data Enrichment Efforts appeared first on DATAVERSITY.

A Holistic Approach to Govern Data

The concept of being holistic embraces a comprehensive and interconnected view of various aspects of life, considering them as a whole rather than isolated parts. The concept of being holistic emphasizes the understanding that everything in our existence is connected and interdependent, influencing and being influenced by one another. Holistic approaches look to address the […]

🔴 Red Button Data