A broad overview of composable analytics

BI has been around for a long time, but the world is changing. The market is moving faster than ever before, and customers expect more from their business, especially when it comes to personalization and highly relevant services. As a result, the era of faceless clients is over.

For example, a consumer seeking new information about a potential housing mortgage should never be told by a local branch that it will take two weeks to process without first getting to know that consumer’s background. This information should have already been gathered to better understand the client’s needs and anticipate their requests.

That’s just not acceptable when you think about how quickly we can get information from our phones or computers now (and how much easier it is). This change in customer expectations alone means that businesses need to be better able to meet those needs faster and more efficiently than ever before—and composable analytics can help them do just that.

What are composable analytics?

Composable analytics is a new way of doing business intelligence. It takes advantage of new technologies like machine learning, intelligent data management, and microservices to transform how companies use data.

Often, this involves using no-code or low-code solutions in combination with embedded analytics. This way, you can literally “compose” new systems that leverage all the available tools to garner value-based, data-driven insights.

Composable analytics is the next level of business intelligence. It’s based on two principles: composability and agility. The more we can manipulate the data we have gathered, the better the quality of the insight and outcome for future development. Not only does this require flexibility, but durability and quality of data as well.

Why does this matter to businesses?

Businesses have been expected to be nimble for decades, but the pace of change has increased exponentially in recent years. It’s not just about reacting more quickly than your competitors through agile frameworks like Scrum; it’s also about being able to respond rapidly to customer needs, new opportunities, and changing regulations.

This means that analytics must be done at scale—in other words, analytics tools and processes need to be composable so they can be combined as needed with other systems and methods.

It comes down to competition. If your top three competitors are all using this advanced technology to better reach clients, you must adopt the same tools or risk losing significant market share.

Why composable analytics matter

Composable analytics is a new approach to business intelligence that has been gaining in popularity, especially in the last few years. It’s more agile, flexible, and scalable than traditional BI. There are many reasons why this is so critical in today’s market.

As just a single example, composable analytics can be applied to a variety of use cases, from real-time insights such as fraud detection or predictive maintenance (the process of using sensors on machinery to predict when repairs are needed) through to historical analysis for compliance purposes or planning future product releases.

Another example would be to help in hiring. One area in which analytics can be used to find flexibility is by identifying the right employees and candidates for a job. This is important because it allows you to establish a team that has the ability to adapt to changing conditions.

When big data changes

You may have heard a thing or two about “big data” over the past few years. But perhaps you’ve noticed that the term is becoming passé and being replaced with a new phrase: wide data.

The distinction between big and wide is in how we define it. What makes big data big? It’s large quantities of high-quality structured, unstructured, and semi-structured data all collected in one place—sort of like a bank vault where you store your savings account statements and tax returns (along with some current information).

In contrast to this idea of collecting everything into one place are separate repositories for different types of information—like your 401(k) retirement plan statement from work versus your checkbook register list at home. With wide data ,there is no single repository. Instead, each piece of information lives in its own siloed location across many different locations around the globe.

So how does this relate to composable analytics? Well, if your organization collects lots of small pieces of information, then it will be easier for them to compose new insights from these disparate sources without having to go back through all those individual repositories manually when needed—which would be time-consuming at best.

Gaining greater insight

Datasets are generated and stored in a variety of formats across a wide range of sources. The ability to combine those datasets with other related data, such as metadata, crawl logs for websites, or internal documents, can provide significant insight into the true nature and behavior of an organization’s data assets.

Composable analytics is about making sense of this data—not just analyzing it but also exploring its potential uses.

Composable analytics is not primarily concerned with how you get your hands on the data, or even what kind it is (for example, structured vs. unstructured), but instead focuses on enabling a greater capability in data insight and exploration by making it easier to utilize tools that specialize in certain types of analysis.