A comprehensive guide on Data Maturity, is your organization Data Mature?

Data maturity in simple terms refers to the organization’s journey to fully leverage the power of data and data science in making data-driven decisions.

The data science industry is growing rapidly as all organizations today understand the importance of data-driven decision-making in taking their businesses forward, gaining competitive advantage and providing unmatched customer experience. Organizations big or small, and across all industries are rapidly adopting data science. This can be inferred from the fact that the data science market is growing at a CAGR of 27.7% to reach a market value of $322.9 billion by 2026.

However, most companies do not implement a data-driven culture. Is it because they ignore the value, they might gain by becoming data-driven or they lack the proper infrastructure to become data-driven, including the right data infrastructure and skilled data science professionals like data engineer and data scientists? Or maybe they do not have access to the right process or the right tool. It can be a combination of all the factors as well.

Let us see how organizations can address these challenges to achieve data maturity and leverage data science for their maximum advantage.

What is Data Maturity?

Data maturity refers to an organization’s capability to effectively collect, manage, analyze, and utilize data to gain insights and propel their business forward. There are several key elements that define whether an organization is data matured or not such as:

• Strong data governance: it refers to having clear policies and procedures in place to ensure the data is of right quality, is secure, and meets the compliance
• Standardized data infrastructure: the data format should be consistent and there should be centralized storage to facilitate seamless access and analysis.
• Advanced analytics capabilities: having access to efficient data science tools and expertise for extraction of insights from variety of data sets.
• Data-driven decision-making: organizations having a culture of making decisions after properly analyzing data and having proper insights.

It is a proven fact that investing in data maturity offers significant benefits to organizations. Data-driven organizations are 23 times more likely to outperform their competitors in terms of profitability (McKinsey Global Institute).

Stages of Data Maturity

There are four stages of data maturity where each stage is defined by an organization’s data-driven strategies, operations, and culture.

Here is a brief explanation of these four data maturity stages:

• Stage-1: Data Exploring

  This is the first level in an organization’s data maturity journey and here the focus is on exploring the possibilities of data and data science practices. This is basically the beginner’s phase where the organization understands the importance of collection of data to drive business-decisions. But they do not actually leverage the power of data science.

  In this stage, organizations lack the standard data science practices and policies for data management like who should be dealing with kinds of data, who should formulate the policies, and so on.

  At this level of data maturity, organizations still rely on their guts and intuitions to make decisions and rarely use data to make any informed decision.

• Stage-2: Data Informed

  During this stage, organizations take initiatives to take data-driven decisions. Company's leaders start evaluating the value of investing in analytical tools, and various data science practices. They research, explore, and invest in data science tools and technologies.

  Leaders put more focus on data collection and management. They give due consideration to each campaign data. They add proper metrices and analyze them to get insights. Though it is simple analysis, their team gets a basic understanding of analysis and its benefits.

• Stage-3: Data-Driven

  This is the stage where organizations have access to high-quality data and data science practices are embedded in the company’s culture. Organizations have complete data they need for planning and execution. Businesses in this stage rely on insights from data for all their major decisions. Leaders focus on data-driven decision-making for all departments across their organization. During this phase, the teams start to understand how to achieve business KPIs with maximum efficiency with digital practices.

• Stage-4: Data-Transformed

  This is the final stage where data becomes an integral part of the organization. Now every team within the organization has access to high quality data, skilled data science professionals, the right data science tools and techniques to perform their task. Teams are also ready to share data and insights across the departments to maximize overall efficiency and productivity of their company.

  The only challenge at this phase is to maintain and even enhance the same level of data-driven culture within the teams and departments to ensure a high level of productivity in the future.

Conclusion

Data maturity, today, has become highly essential for organizations across all industries to become successful in today’s highly advanced digital world. But in the rush of implementing a data-driven culture, organizations must pay proper attention and avoid getting trapped in overly complex data structures and processes. They must focus on their business value and democratization of data access. This will encourage a culture of experimentation within various teams and help organizations with innovations and success they need in today’s competitive market.