Data Maturity: Why is it important?

 

Imagine having an ample amount of data in your organization. You can run analytics to help in your decision-making process in order to develop your product and boost your business. However, you have so much data that you cannot find it when it is most needed. Or, imagine having two different metrics on the revenue generated from the sale of your product because you have two different sets of data. Which one do you use? These are not mere speculations but problems that are plaguing various businesses. Data Maturity helps solve these problems.

Solid data maturity is one of the four components required for developing data culture within an organization. It deals with the raw material, i.e. data, and its management. An organization with good data maturity has high standard data quality and there are checks in place to maintain it.

Components of Data Maturity

Having a solid data maturity requires various things in order. To begin with, an organization needs to put in place a Single Source of Truth – a practice of accumulating data from different sectors within an organization to a single location. This ensures that the data is standard across all teams, allowing them to make decisions from the same set of data without any discrepancy.

The organization also needs to implement Data Governance, which ensures that high-quality data is available across the organization. A proper data governance strategy results in better analytics.

The organizations need to record Data Lineage, which helps in understanding what happened to it since its origin. Other factors that affect data maturity are usability, ease of access, and scalable and agile infrastructure.


Also read: Lack of Data Maturity is the biggest barrier to a Data Culture for the events and hospitality industry


Besides these, there different types of Data Maturity models that deal with managing data.

Types of Models:

  • Descriptive – The Descriptive Analytics phase asks the question “what happened” by performing operational reporting, data exploration, and benchmarking.
  • Diagnostic – Diagnostic Analytics asks the question “why did it happen” and “why is it happening”. It analyzes past data to produce insights about the present.
  • Predictive – Predictive Analytics tries to answer the question “what will happen” by utilizing statistical analysis, predictive models, forecasting, and scenario planning. 
  • Prescriptive – This model focuses on “what should we do about it?” It improves the accuracy of our predictions.
  • Cognitive – Cognitive Analytics involves machine learning and natural language processing. It answers the question “What don’t I know?”