Data Analytics

REAL Real-Time Data Analytics: When Seconds Matter

Teresa Wingfield

April 7, 2023

Real-time data analytics cloud

According to Gartner, real-time analytics is the discipline that applies logic and mathematics to data to provide insights for making better decisions quickly. For some use cases, real-time means analytics is completed within a few seconds after the arrival of new data. Actian calls this REAL real-time data analytics.  

Analytics solutions vary greatly in their real-time capabilities, with many having only “near” real-time analytics. REAL real-time analytics means that you can immediately deliver real-time data and consistently execute ultra-fast queries to inform decisions in the moment. Here’s a quick overview of how the Actian Data Platform achieves these two requirements.   

Real-Time Data

Real-time data is information that is delivered immediately after collection. This requires real-time, event and embedded processing options so that you can ingest your data quickly. You will also need an integration that includes orchestration, scheduling, and data pipeline management functionality to help ensure that there is no delay in the timeliness of information.  

The Actian Data Platform is noted for its fast delivery of real-time data using the above data integration features. In a recent Enterprise Strategy Group economic validation, customers reported that the Actian data platform reduced data load times up to 99% and reduced integration and conversion time up to 95%. 

Real-Time Queries

A columnar database with vectorized data processing has become the de facto standard to accelerate analytical queries. While row-oriented storage and execution are designed to optimize performance for online transaction processing queries, they provide sub-optimal performance for analytical queries.  

A columnar database stores data in columns instead of rows. The purpose of a columnar database is to efficiently write and read data to and from hard disk storage to speed up the time it takes to return query results. 

Vectorization enables highly optimized query processing of columnar data. Vectorization is the process of converting an algorithm from operating on a single value at a time to operating on a set of values (vector) at one time. Modern CPUs support this with Single instruction, multiple data (SIMD) parallel processing.  

Additional optimizations such as multi-core parallelism, query execution in CPU cores/cache, and more contribute to making the Actian Data Platform the world’s fastest analytics platform. The Actian Data Platform is up to 7.9 x faster than alternatives, according to the Enterprise Strategy Group.  

The Actian Data Platform also has patented technology that allows you to continuously keep your analytics dataset up-to-date, without affecting downstream query performance. This is ideal for delivering faster analytic outcomes. 

When Seconds Matter

So why does speed matter? Real-time data analytics allows businesses to act without delay so that they can seize opportunities or prevent problems before they happen. Here is a brief example of each type of benefit.  

Online Insurance Quotes

Insurance comparison websites in the UK give top billing to insurers who respond fastest to online requests for quotes. Insurance uses the Actian Data Platform for real-time analytics to deliver a risk-balanced, competitive insurance quote with sub-second speed. 

Proactive Equipment Maintenance

As manufacturers incorporate more IoT devices on their plant floors, they have opportunities to analyze data from them in real-time to identify and resolve potential problems with production-line equipment, before they happen, and to spot bottlenecks and quality assurance issues faster.  

The Actian Data Platform is a single solution for data integration, data management, and real-time data analytics. Check out how the platform lets you integrate anytime.  

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About Teresa Wingfield

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

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Data Analytics

Leveraging Segment Analysis and Predictive Analytics for CX

Actian Corporation

April 4, 2023

leveraging segment analysis and personalized customer experiences

Today’s customers expect a timely, relevant, and personalized experience across every interaction. They have high expectations for when and how companies engage with them—meaning customers want communications on their terms, through their preferred channels, and with personalized, relevant offers. With the right data and analytics capabilities, organizations can deliver an engaging and tailored customer experience (CX) along each point on the customer journey to meet, if not exceed, expectations.

Those capabilities include segment analysis, which analyzes groups of customers who have common characteristics, and predictive analytics, which utilizes data to predict future events, like what action a customer is likely to take. Organizations can improve CX using segment analysis and predictive analytics with the following steps.

Elevating Customer Experiences Starts With Seven Key Steps

Use a Scalable Data Platform

Bringing together the large volumes of data needed to create customer 360-degree profiles and truly understand customers requires a modern and scalable data platform. The platform should easily unify, transform, and orchestrate data pipelines to ensure the organization has all the data needed for accurate and comprehensive analytics—and make the data readily available to the teams that need it. In addition, the platform must be able to perform advanced analytics to deliver the insights necessary to identify and meet customer needs, leading to improved CX.

Integrate the Required Data

Unifying customer data across purchasing history, social media, demographic information, website visits, and other interactions enables the granular analytic insights needed to nurture and influence customer journeys. The insights give businesses and marketers an accurate, real-time view of customers to understand their shopping preferences, purchasing behaviors, product usage, and more to know the customer better. Unified data is essential for a complete and consistent customer experience. A customer data management solution can acquire, store, organize, and analyze customer data for CX and other uses.

Segment Customers into Groups

Customer segmentation allows organizations to optimize market strategies by delivering tailored offers to groups of customers that have specific criteria in common. Criteria can include similar demographics, number of purchases, buying behaviors, product preferences, or other commonalities. For example, a telco can make a custom offer to a customer segment based on the group’s mobile usage habits. Organizations identify the criteria for segmentation, assign customers into groups, give each group a persona, then leverage segment analysis to better understand each group. The analysis helps determine which products and services best match each persona’s needs, which then informs the most appropriate offers and messaging. A modern platform can create personalized offers to a customer segment of just one single person—or any other number of customers.

Predict What Each Segment Wants

Elevating CX requires the ability to understand what customers want or need. With predictive analytics, organizations can oftentimes know what a customer wants before the customer does. As a McKinsey article noted, “Designing great customer experiences is getting easier with the rise of predictive analytics.” Companies that know their customers in granular detail can nurture their journeys by predicting their actions, and then proactively delivering timely and relevant next best offers. Predictive analytics can entail artificial intelligence and machine learning to forecast the customer journey and predict a customer’s lifetime value. This helps better understand customer pain points, prioritize high-value customer needs, and identify the interactions that are most rewarding for customers. These details can be leveraged to enhance CX.

Craft the Right Offer

One goal of segment analysis and predictive analytics is to determine the right offer at the right time through the right channel to the right customers. The offer can be recommending a product customers want, a limited time discount on an item they’re likely to buy, giving an exclusive deal on a new product, or providing incentives to sign up for loyalty programs. It’s important to understand each customer’s appetite for offers. Too much and it’s a turn off. Too little and it may result in missed opportunities. Data analytics can help determine the optimal timing and content of offers.

Perform Customer Analytics at Scale

Once customers are segmented into groups and organizations are optimizing data and analytics to create personalized experiences, the next step is to scale analytics across the entire marketing organization. Expanding analytics can lead to hyper-personalization, which uses real-time data and advanced analytics to serve relevant offers to small groups of customers—or even individual customers. Analytics at scale can lead to tailored messaging and offers that improve CX. The analytics also helps organizations identify early indicators of customers at risk of churn so the business can take proactive actions to reengage them.

Continue Analysis for Ongoing CX Improvements

Customer needs, behaviors, and preferences can change over time, which is why continual analysis is needed. Ongoing analysis can identify customer likes and dislikes, uncover drivers of customer satisfaction, and nurture customers along their journeys. Organizations can use data analytics to continually improve CX while strengthening customer loyalty.

Make Data Easily Accessible

To improve CX with data and analytics, organizations need a platform that makes data easy to use and access for everyone. For example, the Actian Data Platform offers enterprise-proven data integration, data management, and analytics in a trusted, flexible, and easy-to-use solution.

The platform unifies all relevant data to create a single, accurate, real-time view of customers. It makes the customer data available to everyone across marketing and the business who needs it to engage customers and improve each customer experience.

Additional Resources:

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About Actian Corporation

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

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Data Intelligence

Metadata Management vs. Master Data Management

Actian Corporation

April 3, 2023

Business Information And Infographics Concept.

In order to stand out from the competition, offer an adapted customer experience, reinforce innovation, and improve internal processes or production flows, companies are strongly relying on data. Many organizations are looking for ways to better leverage this massive resource and ensure rigorous data governance. In this article, discover the differences as well as the similarities between two concepts that are essential to a data-driven approach: metadata management and master data management.

According to a study entitled “The Strategic Role of Data Governance and its Evolution“, conducted by the Enterprise Strategy Group (ESG), at the end of 2022, companies saw their data volumes double every two years. On average, the organizations that were surveyed reported managing 3 petabytes of data, about two-thirds of which is unstructured data. The survey data also shows an average annual increase of 40%. And 32% of respondents even report a yearly increase of more than 50%.

In the context of exponentially increasing data volumes, companies must face a major challenge: ensure optimal data and metadata management, at the risk of exposing themselves to an explosion of costs related to errors. According to recent Gartner estimates, poor data quality costs companies in all industries nearly $13 billion annually. Metadata management and master data management (MDM) provide organizations with critical processes to gain the knowledge they need to meet their market challenges while limiting their exposure to the risk of excess costs.

The Definitions of Metadata Management and Master Data Management

First, let’s define each term clearly. Metadata management corresponds to the set of practices and tools that enable the management of metadata of an information system in an efficient and consistent way. As such, metadata management aims to guarantee the quality, relevance, and accessibility of metadata, as well as their compliance with data norms and standards.

Master data management (MDM) brings together all the techniques and processes that enable reference data to be managed in a centralized, consistent, and reliable manner. This reference data, also known as “master data”, is critical information, absolutely essential to the company’s activity. It can be information about customers, suppliers, products, operating and production sites, or data about employees. The purpose of master data management is to build a single repository for this reference data. This repository is then used by the different applications and systems of the company and guarantees access to reliable and consistent data.

What are the Differences Between Metadata Management and Master Data Management?

Although both concepts are related to data management, metadata management and master data management (MDM) serve different company objectives and take distinct approaches.

While metadata management is primarily concerned with the management of information that describes data, its context, and its use, MDM focuses on the management of business-critical master data. These two different scopes make metadata management and master data management two complementary disciplines for your data strategy. Where metadata management focuses on the description and use of data, MDM focuses on the management and harmonization of business-critical master data.

What do Master Data Management and Metadata Management Have in Common?

The first thing master data management and metadata management have in common is that they both contribute to the efficiency and success of your data-driven projects. Both aim to guarantee the quality, relevance, and consistency of data. MDM and metadata management also both require dedicated processes and tools. Finally, both disciplines integrate and contribute to a broader data governance approach.

Combined, they allow you to be more agile, more efficient, and more responsible at the same time.

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About Actian Corporation

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

What is a Data Lakehouse?

Actian Corporation

April 3, 2023

Server Room Center Exchanging Cyber Datas And Connections 3d Rendering

For organizations seeking to go further in their data collection, storage, and use, a data lakehouse is a perfect solution. While data lakes and data warehouses are commonly used architectures for storing and analyzing data, a data lakehouse is a third way of unifying the two architectures and revealing their full potential.

In this article, we’ll explain all you need to know about data lakehouses.

A data lakehouse is the best of both worlds. The best of information storage and the best of data exploitation. The main promise of a data lakehouse is to store large amounts of data from different sources in a single source of truth. However, a data lakehouse does not limit itself to the storage of information. It also provides a wide variety of advanced functionalities to ensure different data exploitation tasks, such as the transformation, analysis, and modeling of this data.

Indeed, a data lakehouse is defined as a data architecture that combines the advantages of a data lake and a data warehouse in a single platform. As such, it can be illustrated schematically as an extension of the data lake concept that is enriched with advanced data processing functions. In a data lakehouse, data is most often stored as raw or semi-structured. The transformation into structured data for analysis and business purposes takes place at a later stage.

What are the Functionalities of a Data Lakehouse?

The primary function of a data lakehouse is to store large amounts of data in a single platform. A centralizing approach that promotes easy and efficient access to information and data management. Unlike a data warehouse, a data lakehouse can store raw data and semi-structured data without distinction. This means that your data teams can easily extract information from unaltered data.

A data lakehouse can also facilitate real-time data processing. This means that decisions can be made more quickly and accurately because they are based on real-time data analysis. Among the advanced functionalities available in a data lakehouse, there are also query functionalities that allow your teams to extract value-added information from your data.

Finally, the data lakehouse can be easily integrated with data analysis tools, such as data visualization and machine learning tools, to go even further in the analysis, exploitation, and valorization of your data.

What are the Benefits of a Data Lakehouse?

There are many advantages of a data lakehouse, but the main advantage is that of scalability. Indeed, the size of a data lakehouse can easily be adjusted to store large amounts of data. Like many companies, you are probably faced with the explosion of the volumes of data you generate and exploit. With a data lakehouse, you’ll never be left behind!

Because they leverage open-source technologies and cloud services, data lakehouses are also extremely competitive in terms of deployment and operating costs.

Last but not least, in terms of security and compliance, the data stored in a data lakehouse is natively secure and complies with current security standards. Therefore, using a data lakehouse is a guarantee that your data is protected against cyber threats and data breaches.

Data Lakehouse vs. Data Lakes vs. Data Warehouse

A data lake is used to store raw or semi-structured data in its unaltered format. As for the data warehouse, it stores structured data in a predefined format. The data lakehouse opens a third way by allowing at the same time to store raw, semi-structured, and structured data in their raw or preprocessed format.

The data lakehouse also distinguishes itself from the data lake and the data warehouse by allowing the processing of data in real-time and the analysis of historical data – whereas data lakes are designed to process data in real-time, and data warehouses are limited to the analysis of historical data.

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About Actian Corporation

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

Deciphering the Data Story Behind Supply Chain Analytics

Teresa Wingfield

March 30, 2023

Increase efficiency, reduce costs, and grow revenue with real-time data

When it comes to supply chain data, there’s an intriguing story to be told. If businesses have access to accurate data in real-time about their supply chain operations, they have tremendous opportunities to increase efficiency, reduce costs, and grow revenue. Here’s a look at some of the types of supply chain data and the data story that supply chain analytics can reveal.

Procurement Data

This includes information about the type, quality, quantity, and cost of raw materials and components used in the production process. Analyzing spend can help businesses identify areas where they can reduce costs and make data-driven decisions about how to best allocate their budget. For example, real-time comparisons of supplier pricing can help sourcing teams negotiate more favorable prices.

Supplier Data

This includes data about suppliers, such as their performance history, delivery times, and product quality. Supplier data is key to reducing order fulfillment issues and identifying and proactively planning for supply chain disruption. Companies are increasingly leveraging supplier data in real-time to enhance their environmental, social, and governance efforts.

Production Data

This includes data about manufacturing processes, including production schedules, output levels, and equipment utilization and performance. Faster insights into production data can help optimize material availability, workforce, and processes needed to keep production lines running. Businesses can also more quickly spot quality control issues and equipment problems before they lead to costly downtime.

Inventory Data

This includes data about the quantity and location of inventory, inventory turnover and safety stock requirements. Demand forecasting using predictive analytics helps to determine the right level of inventory. Real-time visibility is essential to dynamically adjust production up or down as demand fluctuates and to offer promotions and sales for slow-moving inventory.

Transportation Data

This includes data about the movement of goods from one location to another such as shipment tracking, transit conditions and times, and transportation costs. Predictive analytics can estimate transit times to determine the best possible routes. What’s possible today was inconceivable a decade ago: using sensors to track things such as temperature and safe transportation at any point in time to protect goods and improve driving habits.

Customer Data

This includes customer data such as order history, purchase behavior, and preferences. Companies can meet customer expectations and increase sales when they understand and anticipate what their customers need – and when they are able to create personalized experiences and quickly adjust the supply change based on constantly changing customer behavior.

Sales Data

This includes sales data such as revenue, profit margins and customer satisfaction. Companies use demand forecasting based on past sales to help them adjust production, inventory levels, and improve sales and operations planning processes.

Create Your Data Story

What’s your supply chain data story going to be? It all depends on the data platform you choose to process your supply chain analytics. The platform will need to be highly scalable to accommodate what can be massive amounts of supply chain data and must support real-time insights into supply chain events as they happen so decision makers can form next-best actions in the moment.

The Actian Data Platform provides data integration, data management, and data analytics services in a single platform that offers customers the full scalability benefits of cloud- native technologies. The Actian platform provides REAL, real-time analytics by taking full advantage of the CPU, RAM, and disk to store, compress, and access data with unmatched performance.

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About Teresa Wingfield

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

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Data Analytics

Are You Building Your Data Strategy to Scale?

Teresa Wingfield

March 28, 2023

scale and build your data strategy with the right infrastructure

A data strategy is a long-term plan that defines the infrastructure, people, tools, organization, and processes to manage information assets. The goal of a data strategy is to help a business leverage its data to support decision-making. To make the plan a reality, the data strategy must scale. Here are a few pointers on how to achieve this:

Infrastructure

The right infrastructure is necessary to give an organization the foundation it needs to scale and manage data and analytics across the enterprise. A modern cloud data platform will make it easy to scale with data volumes, reuse data pipelines and ensure privacy and regulations are met while also making sure that data is accessible to analysts and business users. The platform should use cloud-native technologies that allow an organization to build and run scalable data analytics in public, private, and hybrid clouds.

People

The talent shortage for analysts and data scientists, particularly for advanced analytics requiring knowledge of artificial intelligence, is a big challenge. With the U.S. Bureau of Labor Statistics projecting a growth rate of nearly 28% in the number of jobs requiring data science skills by 2026, the shortage will continue to grow.

To cope with the shortage, businesses will need to invest more in training and education. The more teams know about advanced data analytics techniques and how to use and interpret data, the more value an organization can derive from its data. Also, with demand for analytics skills far exceeding supply, organizations will need to make of the talent pool they already have.

Tools

A cost-optimal solution should not only process data analytics workloads cost-effectively, but also include data integration, data quality, and data management that add more costs, and complexity when sourced from multiple vendors. However, there is no such thing as a one-size-fits-all tool when it comes to analytics. Increasingly, organizations are adding many types of advanced analytics such as machine learning to their analytics tool portfolio to identify patterns and trends in data that help optimize various aspects of the business.

Businesses will also need to devise strategies for users to easily access data on their own so that limited technical staff doesn’t bottleneck data analytics. Embedded analytics and self-service help support the needs of data democratization. Self-service gives users insights faster so businesses can realize the value of data faster. Analytics embedded within day-to-day tools and applications deliver data in the right context, allowing users to make better decisions faster

Organization

For a data strategy to scale, an organization needs to build a data driven culture. Transitioning to a data driven approach requires a corporate cultural change where leadership views data as valuable, creates greater awareness of what it means to be data driven and develops and communicates a well-defined strategy.

Processes

There are many processes involved in a scalable data strategy. Data governance is particularly critical to democratizing data while protecting privacy, complying with regulations, and ensuring ethical use. Data governance establishes and enforces policies and processes for collecting, storing, using, and sharing information. These include assigning responsibility for managing data, defining who has access to data and establishing rules for usage and protection.

Get Started With the Actian Data Platform

The Actian Data Platform provides data integration, data management, and data analytics services in a single platform that offers 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.

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About Teresa Wingfield

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

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Data Analytics

Discover the Top 5 Data Quality Issues – And How to Fix Them!

Traci Curran

March 23, 2023

two people combatting top enterprise data quality issues

‍Poor quality data can lead to inaccurate insights, wasted resources, and decreased customer satisfaction. It is essential to ensure that all of your data is accurate and up-to-date to make the best decisions. Still, common issues and mistakes cost organizations millions of dollars annually in lost revenue opportunities and resource productivity.

Thankfully, these pitfalls are well-known and easy to fix!

Duplicate Data

Duplicate data occurs when the same information is entered into the same system multiple times. This can lead to confusion and inaccurate insights. For example, if you have two records for the same customer in your CRM system, notes, support cases, and even purchase data can be captured on different records and leaving your organization with a fractured view of a single customer.

Missing Data

Perhaps worse than having duplicate data is having incomplete data. Missing data occurs when some of the necessary information is missing from the system and can lead to incomplete insights. Many systems allow application owners to determine required data fields to prevent missing data.

Outdated Data

While capturing and retaining historical data can be very beneficial, especially regarding customer data, it’s critical that data is kept current. It’s essential to have a regular process to ensure that your organization purges information that is no longer relevant or up-to-date.

Inconsistent Data

Date formats, salutations, spelling mistakes, number formats. If you work with data, you know that the struggle is real. It’s also probably one of the trickier problems to address. Data integration platforms like DataConnect can allow data teams to establish rules that ensure data is standardized. A simple pass/fail ensures that all your data follows the established formatting standards.

Data Timeliness

Imagine buying a house without having the most current interest rate information. It could mean the difference of hundreds of dollars on a mortgage. But many companies are making decisions using days, weeks, or months old data. This may be fine for specific scenarios, but as the pace of life continues to increase, it’s essential to ensure you’re getting accurate information to decision makers as fast as possible.

Tips for Improving Data Quality

Data quality is an ongoing practice that must become part of an organization’s data DNA. Here are a few tips to help improve the quality of your data:

  • Ensure data is entered correctly and consistently.
  • Automate data entry and validation processes.
  • Develop a data governance strategy to ensure accuracy.
  • Regularly review and audit data for accuracy.
  • Utilize data cleansing tools to remove outdated or incorrect information.

Data quality is an important factor for any organization. Poor quality data can lead to inaccurate insights, wasted resources, and decreased customer satisfaction. To make the best decisions, it is essential to ensure that all your data is accurate and timely.

Ready to take your data quality to the next level? Contact us today to learn more about how DataConnect can help you start addressing these common quality challenges.

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About Traci Curran

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

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Data Analytics

What Makes a Great Machine Learning Platform?

Teresa Wingfield

March 20, 2023

leveraging machine learning to enhance enterprise data analytics

Machine learning is a type of artificial intelligence that provides machines the ability to automatically learn from historical data to identify patterns and make predictions. Machine learning implementation can be complex and success hinges on using the right integration, management, and analytics foundation.

The Actian Data Platform is an excellent choice for deploying machine learning, enabling collaboration 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 from the edge to the cloud.

With built-in data integration and data preparation for streaming, edge, and enterprise data sources, 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, can capitalize on dynamic cloud scaling of analytics computing and storage resources.

The Actian Data Platform and Machine Learning

Let’s take a closer look at some of the Actian platform’s most impactful capabilities for making machine learning simpler, faster, more accurate, and accessible:

Breaking Down Silos

The Actian platform supports batch integration and real-time streaming data. Capturing and understanding real-time data streams is necessary for many of today’s machine learning use cases, such as fraud detection, high-frequency trading, e-commerce, delivering personalized customer experiences, and more. Over 200 connectors and templates make it easy to source data at scale. You can load structured and semi-structured data, including event-based messages and streaming data without coding.

Blazing Fast Database

Modeling big datasets can be time-consuming. The Actian platform supports rapid machine learning model training and retraining on fresh data. Its columnar database with vectorized data processing is combined with optimizations such as multi-core parallelism, making it one of the world’s fastest analytics platforms. The Actian platform is up to 9 x faster than alternatives, according to the Enterprise Strategy Group.

Granular Data

One of the main keys to machine learning success is model accuracy. Large amounts of detailed data help machine learning produce more accurate results. The Actian Data Platform scales to several hundred terabytes of data to analyze large data sets instead of just using data samples or subsets of data like some solutions.

High-Speed Execution

User Defined Functions (UDFs) support scoring data on your database at break-neck speed. Having the model and data in the same place reduces the time and effort that data movement would require. And with all operations running on the Actian platform’s database, machine learning models will run extremely fast.

Flexible Tool Support

Multiple machine learning tools and libraries are supported so that data scientists can choose the best tool(s) for their machine learning challenges, including DataFlow, KNIME, DataRobot, Jupyter, H2O.ai, TensorFlow, and others.

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About Teresa Wingfield

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

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Data Intelligence

What are Industry Cloud Platforms?

Actian Corporation

March 19, 2023

Cloud Computing Concept. Communication Network.

With 60% of enterprise data now stored in the Cloud, and companies around the world turning to Cloud solutions to manage their data, many are finding that general-purpose Cloud platforms are not always able to meet the specific needs of their industry. They must then turn to an Industry Cloud Platform.

In this article, find out everything you need to know about Industry Cloud Platforms.

All industries have specific requirements for managing and securing their data. Industry Cloud Platforms are Cloud platforms designed to meet the specific requirements of a given industry or sector.

Unlike general-purpose Cloud platforms, such as Amazon Web Services (AWS) or Microsoft Azure, Industry Cloud Platforms offer features and services tailored to industries such as healthcare, finance, logistics, retail, energy, agriculture, and many others. The popularity of these platforms continues to grow.

According to Gartner, nearly 40% of companies have already considered adopting an Industry Cloud Platform. 15% of them are already engaged in a pilot project. Even better, about 15% more are considering deployment by 2026. As a result, Gartner predicts that by 2027, companies will use Industry Cloud Platforms to accelerate more than 50% of their critical business initiatives, up from less than 10% in 2021.

How Does an Industry Cloud Platform Work?

Industry Cloud Platforms provide robust and scalable cloud infrastructure, along with features and services that are ideally suited to the specific needs of companies in each industry. This can include features such as data analytics tools, supply chain management platforms, industry-specific security solutions, and custom business applications.

Industry Cloud Platforms can help companies improve operational efficiency, reduce costs, and innovate faster by providing easy access to specialized cloud services for their industry. In addition, these platforms can help companies better manage risk and comply with industry-specific regulations.

What are the Advantages and Benefits of Industry Cloud Platforms?

Using a specialized Industry Cloud Platform for your sector provides you with data analysis tools and customized business applications. The primary benefit of being able to rely on tailored tools and services is that you gain operational efficiency and productivity.

But that’s not all. Industry Cloud Platforms help reduce the cost of purchasing, maintaining, and upgrading your IT infrastructure by taking an industry-specific approach. The “hyper-specialization” of these Cloud Platforms and the services they contain means that you only have the solutions you really need, and you don’t have to invest in expensive infrastructure that you rarely use to its full potential. This is a “best of need” rather than a “best of breed” perspective.

Moreover, since Industry Cloud Platforms are designed to be scalable and flexible, they will enable you to adapt quickly to the growth of your business. You can easily add or remove Cloud resources as needed to quickly adapt to market fluctuations.

Finally, the use of an Industry Cloud platform increases your capacity to innovate by giving you access to data analysis technologies, adapted to your activity.

Examples of Industry Cloud Platforms for Different Industries

There are many major players in the Industry Cloud Platforms market, each offering specific solutions and services for a particular industry or sector. Here are some examples of major players:

  • Salesforce is one of the leading Industry Cloud Platform players in the sales and marketing industries, with its Salesforce Customer 360 platform.
  • Microsoft offers a range of cloud solutions for different industries, such as Dynamics 365 for Finance and Operations for the finance and manufacturing sector, and Azure IoT Suite for the Internet of Things.
  • IBM is positioning itself in this market segment with a dedicated cloud platform for several industries, including healthcare, financial services, and supply chain, with its Watson Health, IBM Cloud for Financial Services, and IBM Sterling Supply Chain Suite solutions.
  • Amazon Web Services (AWS) offers a range of cloud services for different industries, including AWS Healthcare for healthcare and AWS Retail for retail. These offerings are distinct from Amazon Web Services’ general-purpose offerings.
  • SAP has developed a cloud platform for several industries, including manufacturing, retail, financial services, and healthcare, with its SAP S/4HANA, SAP Commerce Cloud, and SAP Health solutions.
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About Actian Corporation

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

How Banks Can Use Analytics to Stay Out of the Headlines

Traci Curran

March 17, 2023

skyline of big banks

Financial institutions are making headlines around the world. There’s no shortage of press coverage on the recent collapse of Silicon Valley Bank and Signature Bank in New York, and there seem to be mounting fears about the overall health of the banking industry. While it is too early to know how these failures will impact the broader economy, regional banks are certainly coming under the spotlight.   

In times of uncertainty, meeting the hunger for quantitative data analytics becomes increasingly important. Financial institutions face various challenges, including economic uncertainty, changing customer behavior, and regulatory pressures. These changing conditions require banks to have trusted data and make decisions in real-time – before changing conditions can cause existential harm. By using data analytics, banks of all sizes can gain better insights into their customers, markets, and operations – and, most importantly – respond to changing conditions and understand their risk. 

Data Analytics Provide Insights Into Fast-Changing Market Conditions

Economic conditions can change rapidly, and banks need to be able to adapt quickly to stay competitive. Analytics can help banks to better understand economic trends and to make more informed decisions about lending and risk management. 

For example, banks can use predictive analytics to identify borrowers who are at high risk of default and give banks the insights needed to adjust their lending practices to maintain a risk-balanced portfolio. Banks can identify patterns and develop more accurate risk models and lending rates by analyzing customer data, such as credit scores, payment histories, and employment histories. This type of insight can help reduce exposure to high-risk borrowers. 

Understanding Evolving Customer Behaviors

Another challenge that banks face in uncertain times is changing customer behavior and sentiment. Many factors can influence customer behavior, including economic conditions, technological advancements, and changing consumer preferences. Banks need to understand these changes, then adapt their products and services to meet the evolving needs of their customers.  

Analytics can help banks to gain insights into customer behavior by analyzing customer data, such as transaction histories, account balances, and demographic information. By identifying patterns in customer behavior, banks can develop more targeted marketing campaigns, offer personalized products and services, and improve customer retention rates. They can also identify when customers may be in trouble due to a change in finances, such as a job loss, that could impact their ability to repay their loans.

Banks can also use customer segmentation to group customers based on their behavior and preferences. This allows banks to offer targeted products and services to specific customer groups, such as retirees, small business owners, or millennials. By tailoring their products and services to the needs of specific customer segments, banks can improve customer satisfaction and loyalty. Retaining loyal and low-risk customers can help offset losses caused by unexpected economic and geo-political changes. 

Managing Risk Requires Analytic Insights

In the wake of the collapse of two mid-tier banks, there is a lot of discussion around new regulations that may be needed to prevent future failures. There is an expectation that banks, especially those with under $200 billion in assets, will face increased regulatory requirements. Any new regulations will likely increase complexity and costs for banks and their customers. Strengthening operation analytics can help banks to comply with regulatory requirements by providing insights into their operations and risk management practices. 

Using analytics to manage risk, understand customer behavior, and comply with regulatory requirements can help banks of any size get in front of unforeseen market conditions. Mid-tier banking institutions need to learn from the Silicon Valley Bank experience by implementing robust risk management frameworks and increasing loyalty with their best customers. Having a data-driven approach to things like creditworthiness, liquidity, market volatility, and operational risks will allow both banks, and our economy, to weather unpredictable conditions.   

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About Traci Curran

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

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Data Architecture

How to Use Data to Get More Visibility into Your Supply Chain

Jennifer Jackson

March 17, 2023

two people looking at and using data

Supply chains have undergone—and continue to experience—major changes and disruptions. Worker shortages, rapidly changing customer demands, logistics problems, transportation bottlenecks, and other factors have all contributed to challenges. Even sales patterns that used to be easy to predict, such as those based on holidays and seasonal buying, have become much harder to understand, amplifying the need for visibility across the entire supply chain. 

Business and consumer needs change faster than ever, which has a ripple effect across supply chains that are trying to keep up. On top of this, global supply chains have become increasingly complex, making them more susceptible to delays caused by everything from inclement weather to shipping problems to raw materials shortages.  

Keeping the supply chain moving without interruption places new demands for data and analytics to provide visibility and insights. A supply chain that’s driven by a modern approach to data and analytics enables new benefits, such as improved operations, enhanced demand forecasting, increased efficiencies, reduced costs, and better customer experiences.   

Building a Resilient Supply Chain With Data Analytics

Data that can provide visibility into supply chains is coming from traditional, new, and emerging sources. This includes enterprise resource planning and point-of-sale systems, a growing number of Internet of Things (IoT) devices, inventory and procurement solutions, and more.  

Customer-centric supply chains integrate additional data to better understand the products and services consumers want. This entails data across social media, purchasing histories, and customer journeys to have insights into customer behaviors and sentiments.  

Supply chain analytics and enterprise data management capabilities are needed for organizations to know where their products and materials are at any moment and identify ways to optimize processes. These capabilities, for example, allow companies to track and trace products—from parts to sub-assemblies to final builds—as they move from one location to another through the supply chain until they arrive at their final destination. That destination could be a retail store or a customer’s front doorstep.  

Supply chain visibility helps organizations minimize risk while identifying opportunities, such as improving planning to avoid higher-cost next-day shipping to meet tight timeframes. Better planning allows companies to use less expensive shipping options without causing unexpected downtimes in factories.  

Visibility is also essential for building resilience and agility into the supply chain, allowing the business to pivot quickly as customer needs change or new trends emerge. The enabler of visibility, and for insights delivered at every point across the end-to-end supply chain, is data. When all relevant data is brought together on a single platform and readily available to all stakeholders, businesses not only know where their parts, components, and products are, but they can proactively identify and address potential challenges before they cause delays or other problems.   

A Growing Need for Supply Chain Resilience

Although companies need a resilient supply chain, most are not achieving it. Improving resiliency requires the business to move from analysis on basic forecasting data to connecting and analyzing all data for real-time insights that produce more accurate and robust forecasts, uncover opportunities to improve sustainability, and meet other supply chain goals. The insights help organizations identify macro- and micro-level issues that could impact the supply chain—and predict issues with enough time for the business to proactively respond.  

Manual processes and outdated legacy systems that won’t scale to handle the data volumes needed for end-to-end insights will not give organizations the resiliency or visibility they need. By contrast, a modern cloud data platform breaks down silos to integrate all data and can quickly scale to solve data challenges.  

This type of platform can deliver the supply chain analytics and enterprise data management needed to reach supply chain priorities faster. For example, manufacturers can know where raw materials are in the supply chain, when they’re due to arrive at a facility, and how a change in transportation methods or routes can impact both operations and profitability. Retailers can know when items will be available in warehouses to meet customer demand, fill orders, and nurture customer journeys.  

Easily Connect, Manage, and Analyze Supply Chain Data

Organizations that have the ability to bring together data from all sources along the supply chain and perform analytics at scale can gain the visibility needed to inform decision-making and automate processes. With the right approach and technology, organizations can turn their supply chain into a competitive advantage. 

Actian Data Platform makes data easy. It simplifies how people connect, manage, and analyze their data to modernize and transform their supply chain. With Actian’s built-in data integration, businesses can quickly build pipelines to ingest data from any source. Anyone in the organization who needs the data can easily access it to make informed decisions, gain insights, expand automation, and optimize it for other supply chain needs.  

Additional Resources:

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About Jennifer Jackson

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

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Data Management

Data Analytics for Supply Chain Managers

Teresa Wingfield

March 17, 2023

streaks of blue light showing data analytics for supply chain managers

If you haven’t already seen Astrid Eira’s article in FinancesOnline, “14 Supply Chain Trends for 2022/2023: New Predictions To Watch Out For”, I highly recommend it for insights into current supply chain developments and challenges. Eira identifies analytics as the top technology priority in the supply chain industry, with 62% of organizations reporting limited visibility. Here are some of Eira’s trends related to supply chain analytics use cases and how the Actian Data Platform provides the modern foundation needed to make it easier to support complex supply chain analytics requirements.

Supply Chain Sustainability

According to Eira, companies are expected to make their supply chains more eco-friendly. This means that companies will need to leverage supplier data and transportation data, and more in real-time to enhance their environmental, social and governance (ESG) efforts. With better visibility into buildings, transportation, and production equipment, not only can businesses build a more sustainable chain, but they can also realize significant cost savings through greater efficiency.

With built-in integration, management and analytics, the Actian Data Platform helps companies easily aggregate and analyze massive amounts of supply chain data to gain data-driven insights for optimizing their ESG initiatives.

The Supply Chain Control Tower

Eira believes that the supply chain control tower will become more important as companies adopt Supply Chain as a Service (SCaaS) and outsource more supply chain functions. As a result, smaller in-house teams will need the assistance of a supply chain control tower to provide an end-to-end view of the supply chain. A control tower captures real-time operational data from across the supply chain to improve decision-making.

The Actian Data Platform helps deliver this end-to-end visibility. It can serve as a single source of truth from sourcing to delivery for all supply chain partners. Users can see and adapt to changing demand and supply scenarios across the world and resolve critical issues in real-time. In addition to fast information delivery using the cloud, the Actian Data Platform can embed analytics within day-to-day supply chain management tools and applications to deliver data in the right context, allowing the supply chain management team to make better decisions faster.

Edge-to-Cloud

Eira also points out the increasing use of Internet of Things (IoT) technology in the supply chain to track shipments and deliveries, provide visibility into production and maintenance, and spot equipment problems faster. These IoT trends indicate the need for edge to cloud where data is generated at the edge, stored, processed, and analyzed in the cloud.

The Actian Data Platform is uniquely capable of delivering comprehensive edge to cloud capabilities in a single solution. It includes Zen, an embedded database suited to applications that run on edge devices, with zero administration and small footprint requirements. The Actian Data Platform transforms, orchestrates, and stores Zen data for analysis.

Artificial Intelligence

Another trend Eira discusses is the growing use of Artificial Intelligence (AI) for supply chain automation. For example, companies use predictive analytics to forecast demand based on historical data. This helps them adjust production, inventory levels, and improve sales and operations planning processes.

The Actian Data Platform is ideally suited for AI with the following capabilities:

  1. Supports rapid machine learning model training and retraining on fresh data.
  2. Scales to several hundred terabytes of data to analyze large data sets instead of just using data samples or subsets of data.
  3. Allows a model and scoring data to be in the same database, reducing the time and effort that data movement would require.
  4. Gives data scientists a wide range of tools and libraries to solve their challenges.

This discussion of supply chain sustainability, the supply chain control tower, edge to cloud, and AI just scratch the surface of what’s possible with supply chain analytics. To learn more about how the Actian Data Platform, contact our data analytics experts.

Additional Resources:

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About Teresa Wingfield

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

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