Data Analytics

Market Basket Analysis Will Improve Product Positioning and Profitability

Actian Corporation

July 17, 2020

Discovering sales on the Actian online store due to improved product positioning

“Would you like fries with that?”

If you’ve ever ordered a sandwich at a fast food restaurant, then you’ve likely heard these words, but have you stopped to consider why fast food chains train their employees to ask this question? It’s because of what’s called “market basket analysis,” a technique based on the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.

Market basket analysis is the key to cross-selling. It works by looking for combinations of items that occur frequently in transactions and helps companies better anticipate the purchasing habits of their customers. In the fast food example, the employee is asking “Would you like fries with that?” because marketing data has shown customers purchasing sandwiches are likely to want fries as a side item to accompany their meal.

In Pursuit of Profits

While the fries story is interesting, it only touches the surface of what market basket analytics can do. The goal of any company is to maximize profit – this is achieved both by selling more products/services and focusing on those offers that generate the largest profit margin. Not only are fries a product that customers are likely to demand, but also are a cheap product to produce and, therefore, very profitable for a restaurant to sell.

You will notice the restaurant employee does not offer fries to every customer or for every order. If a customer is ordering just a drink, an a la carte side item or a meal that includes a side item, then he or she is not likely to want to add fries to the order – only when ordering just a sandwich. This is a clear example of market basket analytics. By suggesting very profitable complementary products/services based on market basket analytics, companies can increase both revenue and profits.

Market Basket Analysis in the Digital Age

During the past few years, there has been a strong shift in preferences from brick-and-mortar retail to online (web) and mobile (app) purchasing. eCommerce has made it easier for customers to search for products and services, compare prices, and complete purchases. eCommerce is also an ideal setting for companies to leverage market basket analytics to increase profitability. Watch this informative video demonstrating how market basket analysis can be leveraged in your organization.

As a customer shops in an app or online store, he or she adds products and services to the electronic shopping cart. Companies can then compare this data to the purchasing habits of other customers (and past transactions) to determine what additional products the customer is most likely to purchase. These suggestions can either be embedded into the search/sorting results as the customer continues shopping, or more directly offered. When you visit an eCommerce site and see “customers who purchased x also viewed y” that is market basket analysis at work.

The Need for Speed

Modern eCommerce systems have all the data needed (and more) to perform market basket analytics and provide cross-selling suggestions to customers. The challenge for these systems is processing market basket data in real-time. This is important because processing latency of eCommerce sites is one of the leading causes of shopping cart abandonment.

Actian can help. Actian is a highly efficient data analytics database service that can process large amounts of data in near real-time by separating it into small chunks that are processed in parallel. What this means is you can perform market basket analysis behind the scenes to offer the products and services your customers are most likely to buy, without slowing the Web or app-based user experience. To learn more, visit https://www.actian.com/data-platform.

actian avatar logo

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

Gartner’s Top Data & Analytics Trends in 2020

Actian Corporation

July 16, 2020

data-trends

The recent global pandemic has left many organizations uncertain and fragile. It is therefore a fundamental requirement for enterprises to keep pace with data and analytics trends to bounce back from the crisis and gain a competitive advantage.

From crisis to opportunity, the role of data and analytics is expanding and becoming more strategic and critical. Society in general is becoming increasingly more digital, complex, and global, with ever-growing competition and emancipated customers. Massive disruption, crisis, and the ensuing economic downturn are forcing companies to respond to previously unimaginable demands to resource optimize, reinvent processes, and rethink products, business models, and even their very purpose.

It is therefore obvious that data and analytics are central to enterprises navigating their way out of the devastating effects of these crises. However, the lack of trust and access to data has never been a greater challenge.

Success at scale for maximum business impact with data & analytics depends more than ever on building a foundation of trust, security, governance, and accountability.

Here are the current Data & Analytics trends to help your business thrive:

1 – The use of new AI techniques

By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.

Within the current context, AI techniques such as machine learning, optimization, and natural language processing are providing vital insights and predictions about the spread of the virus and the effectiveness and impact of countermeasures. With the more commercial use of AI, organizations are discovering new and smarter techniques, including reinforcement learning and distributed learning, interpretable systems, and efficient infrastructures that handle their own complex business situations.

2 – Less Dashboards

By 2025, data stories will be the most widespread way of consuming analytics, and 75% of stories will be automatically generated using augmented analytics techniques.

Today, business employees struggle to know what insights to act on because Business intelligence platforms are not contextualized, easily interpretable or actionable by the majority of users. Visual analytics and exploration will be replaced by more automated and customized experiences in the form of dynamic data stories. As a result to the shift to more dynamic, in-context data stories, the percentage of time spent on predefined dashboards will decline.

3 – Decision Intelligence

By 2023, more than 33% of large organizations will have analysts practicing decision intelligence including decision modeling.

A brief definition of decision intelligence is that it is a practical domain that frames a wide-range of decision-making techniques and integrates them to all critical parts of people, processes and technologies. It provides framework that brings traditional and advanced disciplines together to design, model, and execute and monitor decision models and processes in the context of business outcomes.

The use of intelligent decision-making will bring together decision management and techniques such as descriptive, diagnostic, predictive and prescriptive analytics.

4 – Augmented Data Management: Metadata is the new Black

By 2023, organizations utilizing active metadata, machine learning and data fabrics to dynamically connect, optimize and automate data management processes will reduce time to integrated data delivery by 30%.

The combination of colossal data volume, data trust issues and an ever increasing diversity of data formats is accelerating the demand for automated data management. In response, the potential to utilize metadata analytics poses a new solution to augmenting data management tasks. It is no secret that organizations need to easily know what data they have, what it means, how it delivers value, and whether it can be trusted. Metadata will emerge from a passive state to a highly active utilization state. Active utilization leverages cataloging, automatic data discovery by interpreting use cases and implies taxonomy and ontology that is crucial to data management.

Through augmented data catalog, users can improve data inventorying efforts by significantly augmenting the otherwise cumbersome tasks of finding, tagging, annotating and sharing metadata.

5 – Moving to the Cloud

By 2022, public cloud services will be essential for 90% of data and analytics innovation.

As Data Management accelerates its journey to the cloud, so will data & analytics disciplines. Cloud environments enable a more agile, fluid, diverse ecosystem that accelerates innovation in response to changing business needs that are not readily available in on-premises solutions. It also provides opportunities regarding cost optimization. It is expected to see offers such as “cloud first” capabilities, eventually become “cloud only” capabilities.

Gartner clients can read more in the report “Top 10 Trends in Data and Analytics, 2020.

actian avatar logo

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 Architecture

Operationalizing Your Hadoop Data Lake

Actian Corporation

July 15, 2020

Hadoop Data Lake

Have you ever tried to join your fact and dimension tables together to generate operational analytics? If you want to perform large-scale data analysis on things like customer churn, you’re probably going to need to do this. The problem is, that these tables are very large, and when you bring them together, the resulting materialized data table is enormous (as in exponentially bigger than the source tables) and likely to time out before it ever completes. If you can successfully pre-join the data together, the resulting data is probably out of date (sorry). This is because the source data is constantly changing, and the resulting data is so big that the queries you try to execute against it aren’t going to perform very well either. There has to be a better way!

Vector vs. Traditional Databases – Columnar Orientation

Vector for Hadoop is designed to help address this problem by enabling you to bypass the intermediary step of pre-joining data in a materialized data table, and instead perform high-performance “on-demand” joins. The Vector database starts by reorienting your data into vectors where SIMD (single instruction multiple data) operations can be performed. Essentially, Vector is reorienting your data to focus on the relationships between attributes (across multiple records) instead of focusing on the attributes associated with a single record.  This is important for a couple of reasons.

Most of the business questions that you are trying to solve with analytics relate to aggregate data (average transaction size, number of sales in a period, etc.). You are trying to understand a process holistically instead of tracing individual transactions. This business purpose is important because it is the basis for why you need to be running analytics against a data warehouse instead of your transactional systems. Transactional systems are optimized for the processing of individual transactions (hence the name). Data warehouses are optimized for analyzing batches of data. Both systems are capable of doing the other’s job, but if you are looking for operational analytics, you need to be using a data warehouse and ideally one that has a columnar orientation.

Performance Comes From Specialization

Databases and analytics systems are just performing a bunch of mathematical operations on your data – comparing strings of characters, integers, etc. Different types of data have different operations that can be performed on them. Different sets of operations can be performed on different types of data. For example, you can do add, subtract, greater than, less than, min and max operations on numbers, but for strings of characters you can only really do character matching (equal to, contains, starts with, etc.). Computers are designed to do mathematical operations on numbers, so these types of transactions perform faster.

The problem with traditional row-based databases is that the attributes associated with a record of data will likely have mixed data types. To analyze this data, the system essentially must revert to the “least common denominator,” which, in most cases, is string comparisons. By reoriented the data into columns, Actian Vector enables you to perform (faster) numerical operations when the data supports it, thus speeding up overall query performance.

No Need to Wait for Your Data to Update

The traditional approach to pre-joining data to enable analytics works fine if you are dealing with historical data that is essentially static. If it takes a few hours to join the data, that isn’t a big deal. It becomes problematic when the source data is constantly changing, and the analytics insights you are trying to develop are being used for real-time decision-making. Increasingly, more companies are looking to operationalize their Hadoop data lakes and use the data they contain to power operations dashboards and real-time process monitoring capabilities that have little tolerance for data latency.  A big benefit of the Actian Vector solution is that it bypasses the need to pre-aggregate and pre-process data into the materialized data tables and instead run on-demand joins of the source data tables.  This is precisely what operational analytics demands.

actian avatar logo

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 Integration

Adobe Flash EOL and What That Means for DataConnect V10 Users

Actian Corporation

July 13, 2020

Laptop computer displaying logo of Adobe Flash

Please be advised that Adobe Flash is reaching the end of life on December 31, 2020. In collaboration with this announcement, browser vendors will stop supporting the Adobe Flash plug-in either before or at the end of 2020. Adobe is actively encouraging users to uninstall Flash by the end of the year.

(See https://www.adobe.com/products/flashplayer/end-of-life.html.)

(Additionally https://www.zdnet.com/article/adobe-wants-users-to-uninstall-flash-player-by-the-end-of-the-year/ )

It is very important to note that the User Interface (UI) for DataConnect v10 was written using Adobe Flex. Flex relies on the Adobe Flash plug-in to render its UI within a web browser. After web browsers stop supporting Adobe Flash, the v10 DataConnect UI will no longer function and migration to v11 will not be an option. Users will not be able to access their projects or any of their metadata inside the v10 repository. You should disable automatic browser updates now for those who use DataConnect v10 within your organization.

Actian DataConnect v11 does not use Adobe Flash but uses the Eclipse Plugin Development Environment (PDE) to render the UI.

Important! You should upgrade to Actian DataConnect v11 within the next few months. You will be able to export your projects to zip files within v10 and import those files using the import wizard in v11. Your export must include all project dependencies and macrodef files. You need to start exporting your v10 projects Now.

You should upgrade to the latest version of Actian DataConnect v11.5. Versions before v11.5 are either no longer supported or will end Enterprise Support on December 31, 2020.  See the Product Support Lifecycle Policy for dates.

If you have any questions about your entitlement to Actian DataConnect v11, you should consult your Actian Account Executive or email us as soon as possible.

If you have any questions or need assistance exporting your v10 projects, please contact Actian Support.

actian avatar logo

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 Integration

Why Do You Need a Data Hub?

Traci Curran

July 13, 2020

Data Hub

Are you still doing point-to-point integrations between your IT applications? If you are, please stop. It makes life difficult for your IT support staff, increases your overhead costs, and makes it harder for you to be agile and evolve your integrations as business needs change. Instead, consider whether this is the right time to pivot your integration strategy towards adopting a “data hub.”

Enterprise data integration isn’t a solution; it’s a goal that you aspire to achieve wherein all of your company data is aggregated in one place, organized, cataloged, secured, and accessed consistently. Your company data and the individual IT systems that are used to manage your data will change as time passes, but if you are successful in managing your enterprise data and do it consistently, the disruption of system changes to your business can be reduced.

What is Enterprise Data Integration?

Enterprise data integration is the consolidation of business information and other data sets from a variety of sources into a single interface that can be used across your company. This doesn’t mean you have to copy all the data into one place (that is data warehousing). Enterprise data integration is all about exposing data in source systems to enable integrations to be effectively developed and managed.

The data sets that you integrated don’t have to fit together seamlessly into a common schema (enterprise metamodel) or even be in the same format. One of the biggest value points for enterprise data integration is the ability to combine data from different source formats – relational databases, columnar or vector databases, spreadsheets, data warehouses, OLTP systems, and data subscriptions are just a few examples. By consolidating the different datasets into a common catalog with a consistent set of APIs and subscription mechanisms, you provide both application developers and business analysts the ability to easily access the full breadth of your company’s data assets.

What is a Data Hub?

A data hub is a technical solution for enabling enterprise data integration. Most data hubs are built upon an IPaaS (Integration Platform as a Service) solution that enables great levels of flexibility on what data can be integrated and provides a robust set of data management capabilities. A data hub is a common service modeled after the “pub-sub architecture) that is used by applications across the enterprise to both publish available data that others can subscribe to as well as a facility to consume data from other sources. Instead of doing a bunch of direct point-to-point integrations, a data hub gives your organization a one-stop-shop for all its data needs. As new data sources are added (when you deploy a new platform, app, or adopt a new SaaS solution), the data from the new system is published in the data hub for others to consume.

Why You Need a Data Hub for Enterprise Data Integration

To achieve the goal of enterprise data integration, you will need a data hub to serve as a traffic director for data flowing between systems. At the most basic level, the data hub provides a set of publishing and subscription capabilities. But a well-implemented data hub will give you a whole lot more.

Security and Access Control

Most organizations, apply some sort of constraints via data access policies on who can access what types of company data. For example, you probably don’t want customers or some types of employees accessing Finance and HR data. You might also only want some parts of your sales and marketing team to see what new products and services are in the development pipeline. Using a data hub gives you the ability to classify your enterprise data and control who is authorized to access various data sets.

Cost Savings

Reduction in the technical debt created by point-to-point integrations. Without a data hub, it is easy for data integrations between IT systems to evolve into an uncontrolled mess that looks like a pile of spaghetti. Not only does this make understanding your integrations more difficult, but point-to-point integrations also take a lot of effort to maintain and keep running. Over time, the technical debt becomes an operational cost burden on the organization. Data hubs enable you to create more integrations without the administrative overhead costs.

Agility

A data hub rapidly increases the implementation and integration time for new systems. Without a data hub, a new application project will need to go searching for the data it needs, negotiate custom interfaces with upstream and downstream systems and the project team will then need to develop the point-to-point integrations. This process can often add weeks or months to a project timeline. With a data hub, all the enterprise data is available in a single place, with a consistent set of APIs, a defined set of access policies, and a centralized subscription process. You select the data you need and keep moving. This enables IT development teams to deploy new business apps and integrate SaaS solutions faster to enable business agility.

Actian DataConnect is the industry-leading solution for enabling enterprise data integration within your organization. A full-featured IPaaS solution, DataConnect provides all the features you need to implement a data hub for your organization – giving you the ability to connect anything, anytime, anywhere, and manage your integrations efficiently and securely.

To learn more, visit our website.

Traci Curran headshot

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.