Data quality strategy template

The four-step Data Strategy creation process can be seen in Figure 1. He goes into detail regarding areas business must consider. Marr recommends developing three to five use cases, including those identified as major or which take up more time and quick wins. The quick wins help demonstrate the value of a Data Strategy.

Marr recommends reusing this template for each the separate use case and assumes multiple Data Use Cases. That way a manager can prioritize and decide what to tackle first. Andrew White, a member of the Gartner Blog Network, writes about the challenges in reviewing Data Strategy documentation. The majority do not refer to measurable results and confuse Data Strategy with plans, decisions, goal, or directions.

Furthermore, current terminology does not describe which strategies refer to operations versus analytics or a combination of the two. So White came up with a graphic, Figure 3. The triangulation is based on a concept Valerie Logan developed, noting that productions are now people, process, technology, AND data.

The Data Strategy Template is designed to focus on how data is used. Based on this template, businesses can get a sense of their data use ontology. The end goal is to get a sense of how business outcomes may work and change with the data. Data Strategy templates provide a methodology toward ensuring the data is aligned with business strategies. It reduces the tendency to create a document that no one will read — or to not to get started at all.

One Data Strategy template does not fit all. This article has covered only three Data templates; others may be available. Of the Data Strategy templates discussed, the BCPI approach simplifies how to align with business strategies and see the process unfold over time. The Data Use Case methodology is similar in using Agile sprints toward developing a larger software product, but instead of breaking each sprint into user stories, it chunks the larger Data Strategy.

The triangulation uses what the business does to develop the Data Strategy. All methods provide Data Strategy templates on hand, to get started. You must be logged in to post a comment. Leave a Reply Cancel reply You must be logged in to post a comment. We use technologies such as cookies to understand how you use our site and to provide a better user experience. This includes personalizing content, using analytics and improving site operations. We may share your information about your use of our site with third parties in accordance with our Privacy Policy.

You can change your cookie settings as described here at any time, but parts of our site may not function correctly without them. By continuing to use our site, you agree that we can save cookies on your device, unless you have disabled cookies. Therefore, this is where you identify your top three data priorities for the year ahead.

Identifying these common themes at this early stage will help you to find the most effective and efficient ways to overcome them. In very simple terms, your data requirements boil down to: what data are you going to need and how will you source that data? Therefore, this is your opportunity to identify the common themes, issues and so on that are related to the data itself. For example, a common theme across your data use cases might be data diversity.

In other words, how will you combine different data internal and external, structured and unstructured, etc. This is a broad area, encompassing data quality, ethics, privacy, ownership, access and security. As such, there are bound to be cross-cutting data governance issues that are the same across your different use cases. As an example, maybe you identify here that data quality is an issue across the whole organisation.

Here, you want to identify cross-cutting issues that relate to technology and infrastructure. Or, to put it another way, are there any software and hardware requirements that are common across your use cases? For this, it helps to consider the four layers of data and pinpoint what technology is required for each stage:.

Do you already have software that can capture, store and interrogate that data to gather insights? Or will you have to invest in new software? If your plan is to work with an external data provider, one cross-cutting issue may be the need to transfer knowledge from that external partner back into the company. Here you should identify any common issues or requirements that might prevent you from turning your plan into reality.

What challenges will you need to overcome in the implementation of your data strategy? Therefore, a cross-cutting requirement may be to invest time in educating managers and teams on the benefits of data, and the need to base decisions on data, not assumptions.

However, a one-pager is never going to be detailed enough to explore all of the issues, challenges and requirements for data sources, data governance, technology, skills and implementation. Anyone interested in how businesses can start making better use of the explosion of data that's available for capture, analysis, and insights, has probably heard about the data skills crisis.

Businesses that distinguish themselves in how they work with data are leading the field. Every business could come up with hundreds or thousands of potential data projects. How do you prioritize? During and , cloud computing exploded as work went virtual and businesses adapted to the global pandemic by focusing[