In our previous article, we discussed the importance of correctly optimizing the data and how it impacts organizations positively. The next logical question that arises is how mature companies are in their data.
But how well are the companies using their data to drive valuable outcomes? Simply put, In simple terms, data maturity is the ability of an organization to create, store and use quality data. According to a study by Splunk, only 11% of the companies are data mature companies. Companies like Airbnb, Uber, Tesla, and Netflix optimize their data so efficiently that they are basically categorized as data companies than hospitality, transportation, or entertainment companies.
How to understand the maturity level of your organization?
Companies worldwide are at different stages of data maturity. There could be significant disparities in maturity between otherwise similar organizations.
Data maturity assessment acts like a roadmap that helps organizations estimate the effectiveness of the business’s data. Several data maturity assessment models are developed across time and can be used to assess the data maturity of each company based on the company’s goals.
With maturity assessment, there is never a “one model fits all” situation. Although individual models exist for different organizations and vendors, most follow the “Capability Maturity Model” method.
DATA MATURITY ASSESSMENT MODELS:
In 2007 IBM came up with the model that helps companies assess and measure progress within each of their 11 data governance domains. These domains range from the outcomes companies expect from their data to base their decisions. Furthermore, based on the analysis, the model divides the company’s maturity into five different levels.
Similar to IBM, Gartner came up with their model in 2008. This model looks at the information management system of a company as one single unit. The main goals of the model are the smooth flow of information across the organization and metadata management. This maturity model has six stages of maturity, and each stage has its attributes and action items.
Stanford University launched its data maturity model in 2011, adapted from IBM and CMM’s models. Interestingly, this model focuses on both foundation and project aspects of data governance. This data governance maturity model was designed with the institution’s goals, priorities, and competencies in mind. Still, it can also be customized to meet your organization’s needs. An initial assessment in the early stages of your data governance program is recommended and then remeasured annually.
Developed by DataFlux in 2007, it was based on their ten years of experience developing the core components of data governance technology. After the first presentation, it was revised and updated to include the business perspective that drives the need for managing data as an asset. The model has four levels of maturity for companies to evaluate.
Developed in 2006, Oracle’s focus on creating the data maturity model was that data “does not come together all at once,” and a new approach is needed. Its model comprises six levels or milestones.
Published in July 2008, the model has six levels, similar to Oracle’s data governance maturity model or the 2008 Gartner EIM maturity model. The model’s levels can be compared to the life stages of a human. Hence, TDWI refers to them as life-cycle stages.
Becoming data mature requires a heavy investment in technology and people. There’s a significant return on investment, but it is a journey. Several companies have already crossed this bridge to build a sustainable supply chain, just like La Redoute. Discover in our latest WhitePaper on data maturity >