What is Analytics Maturity and Why is it Important ?

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What is Analytics Maturity ?

Analytics Maturity is a model for assessing an organization’s ability to effectively practice data exploration and decision making. Therefore it uses different levels or stages.

Four components of Analytics Maturity support any organization’s journey towards Data Literacy. These components are data-driven leadership, data Literacy across the organization, decision-making process, and data Maturity.

Four Components :

Data-driven leadership: The leaders of the organization understand the power of data. And they use analytics. Therefore they have a strong motivation to lead by numbers.

Data Literacy across the organization: Both the analytics and the organization’s non-analytic side have an appropriate Data Literacy level.

Decision-making process: There is a mechanism to make decisions in the organization. This is aligned with the business’s critical drivers so that everyone feels motivated to work on their project. It is so because the decision-makers understand how their work moves the key metrics and thereby adds value to the company.

Data Maturity: There is a single source of truth for data. There are different types of analytics maturity models.

Types of Model :

1.Descriptive – The Descriptive Analytics phase asks the question “What Happened” by performing operational reporting (which is often done manually and laboriously Excel driven), data exploration, and benchmarking.

2.Diagnostic – Diagnostic Analytics asks the question, “why did it happen, why is it happening.” It analyzes past data to produce insights about the present.

3.Predictive – Predictive Analytics tries to answer the question,” what will happen” by utilizing statistical analysis, predictive models, forecasting, and scenario planning. 

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4.Prescriptive – “What should we do about it ?” Prescriptive Analytics tries to answer this. It improves the accuracy of our predictions. And continually processing and automating new data, in turn fully optimizing decision making.

5.Cognitive – Cognitive Analytics involves machine learning and natural language processing. It answers the question, “What don’t I know?”

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