Data Governance Litmus Test: Cataloging of the Data Issues

This is a eighth blog entry in a blog series highlighting the criticality and the importance of executive sponsorship for data governance initiatives. So far I have explored step-by-step approach of how one can go about developing the case for data governance by connecting initiatives under data governance umbrella with business strategy and outcomes.

In last post we explored how assessing data quality, reliability and timeliness will help towards establishing a baseline around data issues within the organization.

As the data governance teams are exploring data, policies and procedures around handling data, it is important to catalog key findings in a way such that key stakeholders can clearly understand the impact and the issues on hand. Clearly documenting data quality issues, policy issues and any other systemic issues associated with the specific business process (which has the highest influence on strategy outcomes) is very important for ultimately gaining executive sponsorship for initiatives which strive to fix those issues.

Attached is a sample example which demonstrates how findings could be summarized. If you’re using repositories or tools to capture some of this metadata I would highly recommend that you take the effort to summarize those findings in easy to understand fashion. This will help in articulating how data management issues are impacting overall business and in specific some of the key goals which organization is trying to manage.

Key here is clarity, simplicity and relevance. Providing some of the data/metrics around how current are the data management issues will help establishes credibility of your findings (in many instances these metrics may not be readily available, work with your counterparts from the business side and capture these metrics as a part of your discovery process). Always remember that your findings are only as good as the understanding of those findings and its impact by stakeholders in your organization. That is why it is important to make sure that you are presenting the findings in a simple yet impactful form.

You are three fourth of the way in getting executive buy-in, you have done your homework; identified the data management related issues; and presented your findings and the impact of those findings on key performance indicators. You are yet to have a formal agreement/shake hand with stakeholders around common understanding about the impact and possible course of action. In next blog posts, I will discuss how to go about reaching this agreement, and what additional information it might take to get to that point.

In the meantime, feel free to share your presentations/ideas or thoughts on how you explained your findings to key stakeholders in support of ongoing data governance investments.

Previous Relevant Posts:

Litmus Test for Data Governance Initiatives: What do you need to do to garner executive sponsorship?

Data Governance Litmus Test: Know thy KPIs

Data Governance Litmus Test: Know goals behind KPIs

Data Governance Litmus Test: How and who is putting together metrics/KPIs for executives?

Data Governance Litmus Test: Do You Have Access to the Artifacts Used by Executives?

Data Governance Litmus Test: Systems, Processes and Data Behind the KPIs and Goals

Data Governance Litmus Test: Quality, Reliability and Timeliness of the Data

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Data Driven Organization: Key to Data and Process Transparency

In his recent blog titled Data and Process Transparency Jim Harris makes the case that “a more proactive approach to data quality begins with data and process transparency”. This is very true of any organization striving towards availability of highest quality data for its decision-making as well as efficiency of business processes.

In order to embed transparency in every situation across organization, organizations need to be data driven. After all, what does it mean to be data driven organization, you may ask? There is great deal of literature around this topic in print as well as on the web. I will try to simplify this discussion and say that, to me, when culture of decision making is purely based on the factual data (KPIs/Metrics etc…) within an organization (and not on gut feel, emotions and subjectivity of decision making individuals), organization can be said to have become a data driven organization. Of course, this is a very simplistic definition (just for the purpose of this blog).

Many a times, depending upon organizational maturity you may have organizations which are completely data driven versus organizations which are more mature in one area (vis-a -vis data driven decision-making) versus other areas of the organization. For example, in some cases finance side of the organization might be much more data driven than either marketing or sales site etc.

Using data to make decisions drives both data and process transparency across organization. It discourages use of anecdotal information (and gut feel) and forces people to think in terms of realistic data and evidence presented by data. Also using specific KPI’s/metrics allows organizations to clearly define issues associated with underlying data or business processes more readily.

For example, if the sales operation team is discussing order return rates, they cannot simply say that we have a very low order return rate because of poor addresses in a “data driven organization”. They will say that they have 1% order return rate for (on average) 125,000 orders they ship every month because of the poor shipping addresses. This way of expressing performance not only helps everyone involved in understanding the importance of good data quality but also helps organization with creating sensitivity around capturing good data to begin with. Also expressing performance this way helps with ready-made business case for supporting underlying data management initiatives.

Transforming organizations to a data driven organization is a gargantuan change management task. It requires significant cultural/thinking change up and down the organizational hierarchies. During such transformations, organizational operational DNA is completely changed. Obviously, the benefits and rewards of being the data driven organization are immense and worth the efforts of transformation.

On the other side, during the data driven organizational transformation if organizations find that data is not of reliable quality, this finding will force data management discussion across organization and help kick start initiatives to fix the data as more and more in the organization start using data for decision making.

In end, I would encourage everyone to be as data driven as possible in their decision making and influence areas within your organization to be data driven. As data professionals, this will allow us to be more proactive in addressing data management challenges for the organization.

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Data Governance Litmus Test: Do You Have Access to the Artifacts Used by Executives?

This is a fifth blog entry in a series of blog entries highlighting how to go about securing executive sponsorship for data governance initiatives? In my last post I highlighted the need for understanding the KPIs which are tracked by executives and the importance of clear and very specific knowledge of the goals behind those KPIs.

As you might have already noticed, these steps one goes through to answers litmus test questions, helps data governance organization with establishing a direct relationship between data governance initiatives and organizational priorities. Getting executive sponsorship is not a one shot deal. It is an ongoing process which needs to be initiated, maintained throughout the lifecycle of data governance initiatives.

It is important to get actual copies of the reports/presentations/summaries which executives use to review the progress of the key KPIs in executive management meetings. This will help data governance team in multiple ways.

1. You will have very clear understanding of how the information provided by KPIs is consumed by executive management? Who is looking at this information and what frequency?
2. The process of getting these copies will get you access to executives or people around executives who can give you access to executives. This is extremely important as data governance programs seek executive sponsorship.
3. Making executives and people around them aware that data governance team is a critical recipient of the artifacts which are being used by executives, so that in future should any KPIs, goals, expectations, change executives/ executive office will notify data governance team. This way allowing you to establish data governance team as part (or recipient) of the priority/goal change management process.
4. These artifacts will help you understand individual executives’ styles around data presentation, consumption. This will be of immense help to you, when you present the data governance ROI and case to the executives.
5. Periodic copies of these artifacts will help you in establishing baseline for the KPIs and use this baseline to report progress around data governance initiatives.

As I write about these 10 questions for the litmus test of data governance initiatives to evaluate level and extent of executive sponsorship to the data governance programs, my approach has been to use these questions to help create a journey for data governance team which ultimately will help the team in garnering executive/business sponsorship. As you can see, working on getting answer to these questions will create necessary awareness, visibility amongst executives and business stakeholders. So when the time comes secure executive sponsorship it is not a surprise to the key people who will be asked for their support.

Previous posts on this or related topics:

Litmus Test for Data Governance Initiatives: What do you need to do to garner executive sponsorship?

Data Governance Litmus Test: Know thy KPIs

Data Governance Litmus Test: Know goals behind KPIs

Data Governance Litmus Test: How and who is putting together metrics/KPIs for executives?

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