Data maturity refers to an organization’s ability to derive relevant insights from its data and use them to drive decision-making.
A data maturity assessment (DMA) is a framework for assessing an organization’s data maturity. Although there are various models for doing a DMA, most of them will define multiple phases of data maturity to represent an organization’s data capabilities and the effectiveness with which those capabilities are deployed.
What is Data Maturity?
A data management audit (DMA) is essentially an audit of an organization’s data resources, data governance practices, and data management processes. The goal is to determine the organization’s data maturity and make plans to improve that data maturity.
But what exactly do we mean when we say “data”? In a business setting, data refers to all of the information gathered by the company. It’s data about your customers, including who they are, what they buy, what services they use, and what marketing materials they interact with. It’s financial data such as business costs, wages, income, and profits, among other things. It’s employee data and supply chain data in your company applications, such as an ERP, which collects and centralizes your accounting, operations, HR, inventory, product development, and planning data in a single business management system.
The volume of data generated from the numerous touch points available to internal and external stakeholders often overwhelms data and IT leaders. This is exacerbated by their inability to keep up with the rate of business transformation.
As a result, many executives must change and modernize their data capabilities to start moving the needle with current data management techniques and advanced analytics use cases that support company success.
However, this will not be possible without a solid data foundation developed by an enterprise data strategy. Due to the complexity and scope of their difficulties, most businesses lack a unified plan or roadmap for moving forward.
Why is data maturity important for enterprises?
With Data Maturity Assessment, you’ll see how effective your existing data management strategy is compared to industry and best practice benchmarks. The Data Maturity Assessment produces a collection of specific advice as primary takeaways on what processes to employ to achieve changes and knowledge of how your firm is already doing. Based on these results, your organization can develop or improve a data strategy to maximize the value of your data and the potential of your business.
Let’s take a closer look at the three main reasons for conducting a Data Maturity Assessment:
Determining development priorities and identifying deficiencies
Data Maturity Assessment assesses your data governance procedures, including cataloguing, stewardship, profiling, quality monitoring, and data repair. Many customers, in our experience, either cannot identify fundamental deficiencies that jeopardize their business or focus their resources on correcting a subset of them.
Better resource allocation in the development of data governance
At the same time, Data Maturity Assessment allows you to assess priorities inside your organization. There’s a decent chance you’re running processes that are good in general but aren’t the highest priority for your company.
Data governance is aligned with the company’s objectives.
Another aspect to consider is if your data governance processes are aligned with your company’s goal. Ascertain that your overall business strategy and resources are in sync with your data management or, better yet, that your data is working for you. With DMA, you can verify that the data governance methods you use to support your business objectives and that the insights you gain from your data help you reach your goals.
Why data maturity should be the first step in data strategy?
Because every company is unique, their data tactics will be as well. A strong corporate data strategy, on the other hand, will include many facets of the company’s business processes, as well as data governance and analytics. The following are a few of these aspects:
- Planning how the company will carry out the data-related tasks required to achieve its objectives
- Explicitly demonstrating the changes that must be made for the company’s data operational efficiency to be maximized.
- Outlining the financial foundation for planned data operations, how they benefit the firm, and how to use insights to commercialize data and increase productivity.
- Creating policies that regulate how these assets are used
- New sorts of information assets are being classified.
- Managing sensitive information assets
- Defining the milestones and priorities for the planned activities
- Across divisions or geographies, integrating with other systems
- Planning for the worst-case scenario
A data strategy should be a philosophy that evolves with the company’s aims and objectives. An organizational data strategy should be clear and actionable, but it should also be flexible enough to adapt to changing conditions. As a result of our experience, we’ve developed four pillars that make up a data strategy.
Defense and Offence
According to our findings, companies with the most advanced data strategies began at one place and progressively relocated to a new, stable position. As their data defence matured or competition heated up, they may have altered their focus from defence and data control to offense. It’s conceivable to go in the opposite direction, from attack to defence and flexible to regulated, but it’s usually more difficult.
Consider how CIBC’s data strategy has evolved. The bank created the job of chief data officer a few years ago and kept it on the defensive for the first 18 months, focused on governance, data standardization, and developing new data-storage capabilities. When Jose Ribau became CDO in 2015, he decided that CIBC’s defence was strong enough that he could focus on attack, which included more advanced data modeling and data science work. Ribau believes that the new focus on offensive will result in higher ROI from data goods and services and the development of analytical talent in the future. CIBC’s data strategy is currently split 50/50.
Conclusion
Emerging technologies may enable a new generation of data-management skills, making defensive and offensive plans easier to deploy. In several of the companies we looked at, machine learning is already assisting in constructing a single source of truth.
