Why should you measure Data Maturity in your organisation?
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In the fundamental sense, data maturity is the extent of an organisation's data usage capabilities and the extent to which the organisation is using these capabilities. Data maturity assessments reveal where an organisation stands on maturity levels, providing a quantifiable metric to evaluate existing data strategies. A data maturity model also allows the organisation to establish the foundations of strategy and actions for reaching a desired data maturity level.
A high level of maturity is reached when data has been integrated and used heavily within the organisation's strategic decision-making processes. It is where data has weaved itself deep into an organisation's fabric and when data has been integrated into every decision an organisation makes. Once the highest level of maturity is achieved, data becomes a bedrock for each company's business strategies and processes, creating significant competitive advantages.
While innovative companies are becoming more mature, there are still plenty of organisations just starting out on the data journey. Organisations that reach a higher level of maturity are also looking for new ways to use their data to create business opportunities. At this level of maturity, it is only natural for companies to begin having conversations around data democratisation as a way of expanding the reach of existing insights.
As the amount of available data continues to increase and becomes more readily accessible, how organisations use the data--and get more data-savvy--will continue to evolve. In fact, once organisations make a significant shift away from viewing data purely as a source of information and, over time, start realising its true potential to act as a formative factor or even as a disrupt or of decision-making, the appetite for organisations to be data mature is likely (and immediate) to grow tenfold. A mature organisation will be keenly aware of the importance of data as a core business resource and manage and govern it accordingly.
In a mature organisation, the processes for managing, accessing, and leveraging data assets for innovation are in place. At first, assets produced from such analytics work are owned, stored, and organised by dedicated resources. Still, as the uptake of data science grows, the only way to scale is through systematically managing all assets consumed and produced (data, functions, models) by different teams throughout an organisation.
Organisations need to measure and govern the scope of data used. A data-matured organisation measures the economic impacts of use cases and reports these consistently across the enterprise, so there is a clear picture of the value generated from data investments. Data-mature organisations consistently track and report the economic impact of use cases to ensure the value generated from the data investments is clearly understood across the company.
As a result, organisations must understand and determine the maturity or sophistication of their data analytics operations. An organisation needs to understand and document the current data analytics and governance capabilities as well as data strategy requirements (future states). Organisations should develop a data strategy, define their key data sources, and the overall infrastructure and data architecture, evaluate the basic skillsets and required competencies, identify underlying process infrastructure supporting data analysis, and measure the technologies and applications supporting an organisation's data needs.
Data maturity assessments (DMAs) help evaluate existing data governance processes and capabilities, identifying how well they address mission needs, and suggesting opportunities for improvement. Articulating a data strategy will also help determine the organisation's level of data maturity, as well as people, processes, and technological capabilities that will support both emerging and ongoing initiatives in the areas of data & analytics. There are various models to conduct DMA, but most will define multiple stages of data maturity, representing the organisation's data capabilities and how effectively an organisation is deploying these capabilities. The five stages of data maturity also serve as a road map for organisations seeking to create longer-term plans for developing their data teams to become stronger assets. As organisations grow in the usage of data and in the uptake of advanced analytics, they will develop the characteristics that we identified as the five stages of data maturity.
Data Governance Maturity refers to the point the organisation has reached when implementing and adopting data governance initiatives. The effectiveness of any organisation is strongly related to its data maturity, so it is essential to be aware of where one is in the process. When the organisation has reached a higher level of data governance maturity, they see tangible results.
With the complete picture from assessing data maturity the organisations data analytics maturity at either lagging or leading maturity levels, AI and advanced analytics readiness can be determined. An organisation maturing in data analytics capabilities builds flexible infrastructure that is capable of effectively integrating new in-house or external data sources.
As organisations ramp up efforts to improve time-to-market, using data as an asset to enhance enterprise flexibility and readiness for the market is critical. Assessing data matruity will highlight that investment in core capabilities--such as building data talent, standardising data governance throughout the company, improving documentation, creating effective metadata infrastructure, and standardising technology lifecycle processes--specific to advanced analytics instrumentation is critical. It is essential for organisations to focus on an evaluation of their existing data platforms, strategies, and architectures in light of their defined domain.
Mature organisations have an overall data strategy in which they lay out their plans and goals across these dimensions. Mature organisations maintain analytics models through the entire lifecycle, and leverage external data to improve the value of these models. The easiest way to think about analytics maturity is as a measurement of how effectively an organisation uses their data.
The Data Governance Maturity Model provides a way for an enterprise to explore ways of managing data efficiently, providing user access, ensuring that the data is high-quality, and making sure everyone within an organisation is benefiting from those advances. Companies around the world are in various stages of data maturity and realise the kinds of decisions that can be made through deeper analytics. At a business-wide level, data will be used for innovation and collaboration, as well as for better business decisions, and those same organisations will avoid huge penalties if data protection regulations are not followed.
One of the biggest drivers for defining your Data strategy is integrating your existing data from your source systems. This is essential as it gives you a complete view of your customers so that you can personalise and customise your products and services to delight your customers.
Enriching your data with additional external data could further enable you to discover new opportunities. A great example is how one could combine unstructured data from a data lake with enterprise data warehouse data to create new insights and opportunities.
When you integrate your data and know all things to be known about your data, you can optimise your processes and simplify your customer service. This will not only generate more revenue because of efficiency, speed of delivery and refinement of your supply chains, but it will save human and machine costs.
Through a data governance strategy, you ensure that your customers and your organisation's data are safe from data breaches. You can then control the quality of the data, which will infuse trust in your data internally and with your customers.
Sources
[0]: https://medium.com/ai-1st/how-do-i-measure-my-companys-data-maturity-269354c53c17
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