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The Why, What And How Of Composable Data And Analytics

Composable Data And Analytics – The Why, What and How
Analytical teams are constantly coming up with ways to make sense of the millions of bits of
information that flow into an organization, sometimes on an hourly basis. Before the Covid-19 pandemic, data analytics took a more rigid approach with a monolithic data architecture, which can be described as a “generalist, all-in-one” solution. That method, however, lacked the expertise to manage all elements of the data infrastructure effectively.

As a counter to that rigid system, a new, trending way today is the deployment of composable data analytics. This is a process by which organizations combine and consume analytics capabilities from various data sources across the enterprise for more effective, intelligent, and above all, faster decision-making.

As the Gartner team pointed out in the Gartner Analytics Summit Americas in 2021, data and
analytics leaders can generate new business value by using such a modular approach.

The aim of composable data and analytics is to use various data, analytics, and artificial
intelligence (AI) solutions to link data insights with business actions faster. Through the
introduction of low-code and no-code capabilities, organizations can develop tailored analytics experiences with analytics capabilities that are modular rather than monolithic applications.

The Express Analytics’ customer data platform Oyster has a similar modular approach. It is a
single-point platform that can integrate all business data across advertising, marketing, sales,
commerce, and service.

So by now, it would have become clear to you that composable data analytics is not a single tool but a combination of a set of tools that provide the solution to a problem. It can provide greater agility than traditional approaches, and feature reusable, “swappable” modules that can be deployed anywhere, including containers. In fact, composable data analytics help enable organizations re-imagine how they acquire, clean, store, and analyze the type of terabytes of data in which they traditionally reside. All of which helps reduce their costs and improve performance in support of new business needs.

What Is Composable Analytics?

Composable analytics allows you to assemble and re-assemble data processing pipelines, as well as data products, in a modular way. That way, you can configure pipelines of a certain
complexity and feature set, and then compose them into larger workflows. For example, when
you want to process a data set, you can start with a pre-configured analysis, and then use
composition features to build more advanced pipelines. You could, for example, use composition to build an analysis pipeline out of several pre-configured analytics.

The development of applications has become modular. Large monolithic applications should be broken up into smaller, individual services and accessed through Web services and APIs. But what about data analytics and its huge data sets? Some people say the time is ripe in the post- Covid world for comprehensible analytics as well.

How Composable Data and Analytics Benefits Businesses?

Why Composable Data And Analytics?

As we’ve said before, the need for composable data analytics is being felt acutely in the post
Covid-19 pandemic. Suddenly, even as they were battling the after-effects of the virus on
business and economy, it has dawned on enterprises that they have no choice but to become agile very fast in order to be able to keep their businesses going. In the post-pandemic world, digital-first is the new mantra, but the monolithic data analytics architecture was a challenge.

So while everyone agreed that data analytics was changing everything in business – from how
businesses compete to how they use their assets and resources – the reaction time was still pretty slow. One reason for that was analytics had never kept pace with the growth in IT. Businesses are today drowning in data even as the world marches on the path of digitization. More digitization, more data.

The performance of traditional ERP systems and applications is also dramatically changing with the advent of next-generation applications. The demands on these legacy systems are enormous. Existing systems are unable to meet the agile requirements of an organization.

Also, disparate systems have grown organically, and are not fully integrated, leaving companies unable to obtain complete visibility of their data sources. In addition, the failure to create and maintain unified processes throughout the enterprise has led to repeated outbreaks of errors, creating cascading effects throughout the organization. The need for systems that meet the needs of data-centric organizations is critical. They must be able to provide a complete end-to-end understanding of an organization&’s business, enabling business leaders to make intelligent decisions about their enterprise.

The need of the hour was thus to have a re-look at the data analytics architecture.

Composable Data And Analytics Architecture: How Are Things Different?

Composable data architecture is one that is built from the ground up first. It has individual
components, and this way, it is the best method to create a data infrastructure that ideally suits the needs of any company. Each module of this “composed” architecture comes with its own purpose in itself – data quality, data storage, or data analytics. The overall result is an
infrastructure that is optimized from start to end and can provide feedback to each other
elements.

According to Gartner, the building blocks of composable architecture include business
architectures, technologies, and thinking. Composable architectures are scalable in terms of
storage, networks, databases, and compute functionality. Building a composable architecture
allows you to use APIs to manage your ecosystem and enable you to scale your IT footprint
faster. This will help you prepare your IT infrastructure for the digital age.

The initial steps comprise finding out the requirement for composable architecture and
identifying the task at hand. Questions like “Why does my enterprise require a faster time to
market?” need to be asked. Here, we will also use our components like customer journeys, data fabric, etc, to prioritize where to step up efficiency and speed up the entry time to market.

To put it in very simple terms, composable data and analytics is like when you buy a basic laptop and then start adding graphics cards or extra RAM in order to suit your need for better display or perhaps faster processing.

Composable Analytics at Work

From supply chains, transportation, health to telecommunications, more and more companies are now switching to using composable data and analytics. It is a natural fit for all companies in the digital economy that have huge amounts of data and require high-performing machine learning algorithms to make sense of it.

With the help of composable analytics, computers can learn from data at a much faster pace than before, as they can use different types of machine learning models for different tasks. This speeds up the time needed to create a predictive model, for example, allowing companies to innovate more quickly, scale more efficiently and reduce costs.

In the past, machines were programmed to learn from data and then fine-tuned. Now, the power of machines is unleashed by providing them with necessary data at different points in time for optimal performance. Thus, making sense of your growing data demands is today easier than ever. This change is particularly evident in the airline industry, where data is especially critical to business operations.

In conclusion: With almost all sectors adopting a digital-first mode post the Covid-19 pandemic, the age of composable data and analytics has begun. The objective is to utilize components from various data, analytics, and artificial intelligence solutions for a flexible and user-friendly experience that will encourage leaders to link data insights to business actions.

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