Analytics maturity model: An Introduction

162

The importance of data analysis in decision-making is growing in the time of international digital transformation. Despite the fact that the majority agrees on the significance of an analytical approach to decision-making, as per Deloitte research, insight-driven organisations are fewer in number today than those that do not. Furthermore, according to the MicroStrategy Global Analytics Study, data availability is very limited, with 60 percent of employees reporting that getting the information they require takes hours or even days.

In this article, we will skim through the steps, techniques and uses of analytics maturity model as well as how it can help solve problems.

Analytics maturity model

A data analytics maturity model is a series of steps or stages that show a company’s progress in managing multiple data sources and using that data to make business decisions. These models evaluate and characterise how well businesses use their capabilities to extract value from data. They also act as a road map for the analytics digital transformation. Several of these models have been proposed over the years. Among the most well-known are:

  • Gartner’s Data and Analytics Maturity Model,
  • Tom Davenport’s DELTA Plus,
  • DAMM – Data Analytics Maturity Model for Associations,
  • SAS Analytic Maturity Scorecard, and many others.

Stages of analytics maturity model

The path that businesses take in their analytical evolution to build in digital transformation can be divided into five stages:

However, it can be noted that companies with no data science technology or say analytical processes are referred to as “no analytics.”

Descriptive analytics

It gathers and visualises past data to help us understand what happened.

Diagnostic analytics

It uncovers patterns and dependencies in data to explain why something occurred.

Predictive analytics

It uses machine learning techniques to operate large data volumes to develop likely estimates of what will happen in the future.

Prescriptive analytics

It gives alternatives for optimization, decision support, and insights on how to achieve the intended outcome.

Each level is distinguished by a distinct approach to data analytics. Various digital transformation technologies and procedures are employed, as well as the participation of various specialists. But, of fact, the change is slow, and enterprises at different levels sometimes adopt the intrinsic characteristics of one level. Keep in mind that as the organisation progresses to higher levels, such as predictive analytics, it does not automatically abandon other diagnostic or descriptive methodologies.

Steps to improve analytics maturity in organizations

It is self-evident that data analytics plays an important part in decision-making and the overall development of a firm. Here are some concrete steps you can take to improve your company’s analytics maturity and make better use of data.

  • Analyze your current degree of analytics maturity. Find out what data is being used, where it comes from, what tools are being used, and who has access to it.
  • Look into what other data sources are accessible to you, both externally and internally. To monitor performance, create and track KPIs, solicit and gather customer feedback, and use web analytics tools, among other things. It’s also crucial to track the results of any actions and modifications made.
  • Consider the metrics you track and the questions they help you answer. Prioritize measurements that provide actionable insights into “why” and “how” rather to metrics that just provide information about “how many.” This can assist you in deciphering the causes for business operations and customer behaviour, making forecasts, and taking actions.
  • Ascertain that all essential data is available to all stakeholders. To break down silos and ease data exchange between departments, you might wish to employ certain agility principles. Consider providing employees data access. Teach them how to use it and urge them to come up with new concepts.
  • Consider the final of such data analytics when investing in technologies that can assist you in interpreting available data and extracting value from it. If it is non-technical employees, for example, it is important opting for data visualisation tools with a consumer design to make reports simple to comprehend.
  • Assemble new technology and capabilities into your existing processes, combining them with existing institutional knowledge.
  • When correctly studied and applied, data may give you an unassailable competitive advantage by allowing you to gain a greater understanding of your customers, react faster and more accurately to market changes, and discover new development prospects.