Data is a core enterprise asset for every organization irrespective of its type and size. There is no doubt that organizations today have more data than ever at their disposal. Many organizations are grappling to manage their existing data to uncover meaningful insights for making quick business decisions. However, having such sheer volume of data does not necessarily make an organization data-driven or mature enough.
Data maturity of an organization is indicated by understanding the following parameters such as:
- How data is handled within an organization?
- How an enterprise is making use of this massive data to shape its business decisions?
- Are the stakeholders ready to use the enterprise data to get predictions and run sophisticated analysis?
The fact of the matter is data maturity in an organization can become meaningless unless the data is being transformed into actionable intelligence. To thrive in a competitive business environment, it is imperative for an organization to leverage data for advanced analytics and/ or predictive analytics.
The analytics maturity of an organization enables them to make wise business decisions, drive increased efficiencies, as well as, streamline business operations and processes to achieve organizational goals.
So let’s first understand what is data maturity and analytics maturity?
Data Maturity is the critical level at which an enterprise strategically manages all its data from various internal and external sources that include structured, semi-structured and unstructured data. Achieving data maturity enables an organization to transform data into actionable insights and allows them to respond to situations proactively. It also helps organizations to address data gaps, align information/ data with the business strategy and ensure data governance so that the business decisions are not based on the junk data.
Reaching a level of data maturity is a strenuous journey for any organization. When an enterprise advances along the data maturity spectrum, it becomes easier for them to provide superior customer experiences. Companies at a higher maturity level recognize the effectiveness (and efficiency) of data, and have greater potential to build sustainable relationships.
The analytics maturity is a benchmark based on the organization’s ability to manage and analyze data to gain meaningful insights and to drive business growth. Analytics maturity has become a strategic priority for most organizations to improve efficiency, increase revenues and to make better decisions faster.
Reaching the highest level in the analytics maturity curve is a step by step process. Once an enterprise reaches the peak of analytics maturity curve, it clearly exhibits its maturity to handle analytics initiatives within the organization in comparison to its competitors. At this stage, an organization develops the ability to produce actionable insights, make wise business decisions, improve processes and increase ROI and profitability. It also enables its stakeholders to take the right business decisions.
Outlined below are three parameters to define the fundamental differences between data maturity and analytics maturity in an organization:
Business Objective: Success or failure is determined by understanding how precisely the business objectives of an organization are defined. Organization at the bottom of the data and analytics maturity spectrum will only rely on basic reports or excel sheets. However, matured companies will have clear goals defined, that can be measured using Key Performance Indicators (KPIs).
Governance: An organization that’s at an early stage of data or analytics maturity, often gain insights for a business challenge based on the information stored in silos or with specific team or department. On the contrary, a matured organization will have clear methodologies, roles and responsibilities to collect, manage, analyze and use data or tools in synergy to achieve common business goals.
Team and Expertise: Organizations invest heavily in tools, but it’s the people who have to use them. But in a highly matured organization, the teams are skilled in using various tools based on various functions and roles. Even the leadership team/ stakeholders in such organizations use data-driven decision-making methodologies.
Today, organizations can achieve success by making steady progress on both the data and analytics maturity spectrum. The winning formula requires an organization to re-engineer its process, culture and strategy while staying focused with the business realities.
To be successful at any level in the analytics journey, organizations must have proficiency in data management. At the same time, clearly defined goals and the right tools, along with the necessary skill sets can help businesses drive exceptional value from their analytics efforts.