An Introduction to Composable Data and Analytics

First, there was operational data analytics. Then there was AI-powered analytics with Big Data. Meet the next iteration of data analytics: Composable analytics.

We’ve come a long way in data analytics’ business adoption in the past 10 years. It’s no longer about using data to fuel business decisions – it’s about making the data better, more available, and more relevant. It’s also about moving analysis tasks away from data teams and into the hands of business data consumers. (Or, at any rate, getting them as close as possible.) One solution for what we might call the inter-departmental, organization-wide democratization of data analytics is Composable data analytics.

What is Composable Analytics?

Composable analytics is based on two things: flexibility and reusability. Essentially, this paradigm is all about using data, analytics, AI, and delivery components that work together to form a solution – and these components are connected using low- and no-code tools.

In other words, think of the various parts of the data analytics system as being components that users (usually analysts) can select and connect to form their own customized tool. Because these components can be connected in different ways and reused multiple times, they allow a new flexibility into the data analytics process.

It’s interesting that composable analytics are also sometimes called “modular” analytics – they can be reused and reassembled piece by piece, unlike traditional analytics.

Is There a Need for Composable Analytics?

As businesses struggle to scale analytics, they run up against many of the same production bottlenecks. But composable analytics can do things that embedded data analytics can’t.

For example, suppose your company has a centralized data solution that anyone in the organization can query to get answers to their business questions. Well and good. However, each team is likely to have different questions and require their own particular subset of the data. This could keep teams of data professionals busy and result in long turnaround times. But with composable data analytics, each team could use a no-code app builder (for example) to connect to the database and run their own queries – without specialized data or programming knowledge.

The various models and analyses are among the reusable components in composable analytics; this adds to the flexibility of composable analytics. This also allows businesses to increase the reach of their analytics while still providing agility and customized solutions.

However, you can’t simply snap your fingers and expect composable analytics to integrate with your existing infrastructure. Like anything else, it requires advance planning.

The Foundations of Composable Analytics

It’s not surprising that composable analytics requires a correspondingly flexible and scalable foundation. Briefly, these foundations include:

1. Data Fabric 

   Like composable data analytics, data fabric is not a single tool or piece of equipment. Rather, it’s a way to “separate” the management and storage of data from its availability. You can read more about data fabric elsewhere in our blog; suffice it to say that data fabric makes it possible to use data regardless of where it lives in the organization.

2. Automation

   Making data analytics truly self-service would be impossible without the automation of data processing and delivery, amongst other things.

3. Packaged Business Capabilities 

   PBCs make up the composable “parts” that users can connect to make their own analytics tools. Gartner defines PBCs as “software components representing a well-defined business capability, functionally recognizable as such by a business user. Technically, a PBC is a bounded collection of a data schema and a set of services, APIs, and event channels.” These blocks have to be designed in such a way that they are both self-contained (i.e., fully able to perform their function) and connectable through low- or no-code tools.

4. Architecture

   Composable analytics needs composable architectures: something that can support interconnected and reusable elements. This means that storage, networks, data, and computing all have to be flexible and scalable.

The Evolution of Data Analytics Is Composable

Often, business evolution is driven by technological evolution. Composable analytics lets us take a top-down approach to solving business problems: we start out with what we need to know and then connect it to a composable analytics architecture to make it happen. This has so much potential in how we provide and consume analytics; it will be very interesting to watch how it drives even greater business transformation.

Authored by:  Neehal Lobo, Director – AI, ML, DL, Big Data, Analytics, IOT at Infogain and Priscilla Sam, Assistant Manager, Intelligent Enterprise at Infogain

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