Skip to main content

Data Management 101

Over the last 10 to 15 years data and analytics tooling has progressed significantly, both in capability and availability, continuing a progressive trend of data democratisation. However, during that time one thing has remained constant. There has been a need for good data management foundations and practices. I have seen cases where the full benefits of transformation have not been realised, and people have been lured into the prospects of new tools solving all the issues. As we support clients with their SAP enabled transformations, helping to unlock value with self-serve intelligent analytics it is ever more important to get data management right.

I see that a few key foundations should be considered before and in parallel to any technology enabled transformation to ensure we get the best outcomes and deliver on improved analytics, these can be summarised by 8 key components:

1. Data Strategy: Aligned to business strategy, it sets the vision for data within the organisation. It should outline the key data value cases, analytics approach and be underpinned by the ways in which it will be achieved. Generally covering the intended outcomes, technologies, architecture, processes, capabilities and operating model all supported by a clear roadmap for the short term deliverables.

2. Data Policy: The policy will describe the overall intentions and direction of the organisation with respect to data covering its use and management. This is vital as it sets the tone for the rest of the organisation and should be endorsed at the highest level sitting alongside other foundational polices.

3. Data Principles: In some cases principles can be embedded with the Data Policy, they outline a set of overarching principles for data that support in creating the right conditions to deliver on the Data Strategy. They should be simple and uncomplicated such that they are easily understood across the organisation and make it clear how data will be treated as a strategic asset, managed through its entire lifecycle and have clear accountability over its ownership and use.

4. Data Standards: The standards should outline what the minimum requirements are to deliver on the principles set out. They will provide specifics in terms of how data will be managed through its lifecycle from creation to deletion. They call out specifics around the data architecture, master / reference data, data quality criteria, data definitions and catalogues. Again, they should be simple and easy to understand across the organisation. Within the standards is where you start to get into the detail around what data you have, or should have, and the expected levels of quality required.

5. Data Controls: These help to remove ambiguity from the standards providing simple ways to measure and check the minimum requirements have or are being met. The controls will be aligned to both internal and any legal requirements for data and form part of an organisations wider control framework.

6. Data Guidelines: These are not mandatory, however should contain leading practice to offer people within the organisation ways to implement the standards. They may also contain suggested approaches, templates, processes and/or tools that may be used to support implementation.

7. Data Work Instructions: At the lowest level these help to implement in a consistent way (particularly important in large organisations). They outline clear instructions of what is needed to adhere to and/or implement the standard, again linked to any legal requirements that are also mandatory.

8. Data Communications and Community: As with any change journey, making improvements in data is no different and a simple approach and plan to communicate and engage with stakeholders across the business to build the community is required to make data part of the DNA.

Bringing this back to SAP enabled transformation, we need to include some or all of the above components where gaps exist to support and enable the technology being delivered. In the absence of one or many of the above, technology alone will struggle to deliver the full value of the proposed transformation. To avoid falling into this trap undertake a data maturity assessment to see where there are opportunities and where technology can have the biggest impact. This will give a view on the maturity of the above areas, but also offer an opportunity to deep dive on the condition or health of the data itself.

Poor data quality is common and, in many cases, is a result of missing or poorly implemented data management foundations. The findings of a maturity assessment can be used to pull a data remediation roadmap together, highlight and quantity the data risks that exist and ultimately develop solutions that support the other initiatives inflight or planned that rely on data. When you combined this with a technology project you can ensure the right process and business change practices are also defined and built into the work which can ensure a successful transformation.

Sign up for the latest updates