Becoming a data-driven organization unlocks a competitive edge by increasing the capacity to identify and respond to the needs of the customer, market, and business.

Having realized this, organizations are actively collecting volumes of data to drive great organizational outcomes. The power of analytics plays a critical role in enabling success. Without powerful analytics data, by itself, is nothing.

Strong Analytics strategies are foundational to a data-driven organization. Good analytics help organizations create the right roadmap that drives success through actionable insights. In this age of digital transformation, the role of data analysis in decision-making increases manifold. As such, it becomes important for organizations to evaluate their analytics maturity level to ensure that they are getting the maximum value from the data.

What is Analytics Maturity Level?

The analytics maturity level evaluates

• The capability of an organization to turn data into actionable insights,
• How it uses and manages internal and external data to make informed business decisions
• How it uses the data resources to get value from data

Those organizations with high maturity have data ingrained in their organization across departments and all decision-making processes.

The analytics maturity model provides insight into the different levels of analytics maturity of an organization.

Stage 1 – The Nascent Stage

This is the stage when the organization is just about starting to understand the value of data. The analytics maturity level is at stage zero here since the organization is only focused on collecting, cleaning, and storing data. The data usage is spreadsheet-driven and usually IT-independent. Data is used to create ad-hoc reports. A lot of subjective truth guides analytics here.

A slightly more mature stage is where organizations can collect large volumes of data and use that data to generate insights to drive action. The data, however, is not expansive but is enough to power self-service BI. Organizations at this stage enable business users to access and explore data sets to create or customize individual reports or analyses.

While self-service BI does enable data-driven decision-making, given the increasing volume, variety, and velocity of data, improving analytics capabilities can unleash deeper, clearer, and more impactful insights.

Stage 2 – Diagnostic Analytics

At this analytics maturity stage, the organization reactively uses analytics. Analytics provide more of an insight into what happened and why it happened to perform simple operational reporting, data exploration and benchmarking. Data exploration sometimes goes a step further and analyses historical data to derive insights about the present.

Diagnostic analytics gets to work helping organizations understand patterns and dependencies in the available data to help them understand why something happened. In this analytics stage users, tools and processes are more opportunistic or even ad-hoc in nature.

Understanding market segmentation, identifying customer issues, IT backlogs, etc. are some areas where diagnostic analysis comes into play.

Stage 3 – Predictive Analytics

Those organizations that have created the base for data analytics and are already employing diagnostic analytics then need to add more capabilities to the analytics mix.

In stage 3, organizations examine the data at their disposal to predict future trends. Organizations in this stage use the Predictive analytics capabilities to ask questions like “what would happen if we change X?”  and get the answers using predictive modeling, data mining, statistics, and advanced analytics.

Stage 4 – Prescriptive Analytics

The first three levels help organizations answer what, why and what will happen. The next step is the more mature and advanced stage where the organization has the tools that help them learn from the data to determine the best course of action and predict future outcomes.

Prescriptive analytics moves ahead of predictive analytics and further improves the accuracy of predictions by continuously processing and analyzing data for optimized decision-making. At this stage, the analytics maturity level of the organization is quite high and can help organizations make decisions on any time horizon, from the immediate to the long term.

Stage 5 – Cognitive Analytics

Cognitive analytics is the highest level of analytics maturity. It uses multiple analytical techniques to analyze large data sets and give structure to unstructured data. The analytics system here can sift through extensive volumes of data that exist in its knowledge base to answer posed questions. This form of analytics can be thought of as analytics with human-like intelligence. Cognitive analytics employs AI and machine learning algorithms and extends the analytics journey to those areas that were previously unreachable by earlier techniques of BI, statistics, operations research, etc.

In Conclusion

Given the overwhelming volume of available, the need for analytics maturity becomes stronger. Using the analytics maturity model, organizations can identify where they stand and the journey that they need to navigate to reach higher maturity levels. Moving to higher levels needs the right tools and process design, and a UX that allows non-technical business users to leverage these tools to generate reports and insights.

The new technologies and capabilities also need to be embedded in the current processes and seamlessly combined with the existing institutional knowledge. The road from assessment to action demands a clear, multi-dimensional roadmap to help organizations move to the highest maturity level seamlessly to become data-driven in the broadest sense of the term.

The right analytics infrastructure and insights, the right tools, necessary skillsets, and clearly defined goals are imperative for improving analytics maturity.

Connect with us to identify where identify your analytics maturity level identify how to take your analytics game to the next level.