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: Quality, Reliability and Timeliness of the Data

This is a seventh blog entry in a blog series highlighting the criticality and the importance of executive sponsorship for data governance initiatives. In last entry we identified the need for capturing basic contextual information around systems, business processes, which has been automated by those systems and data being used by those systems to automate business processes. This information will help you in base lining current level of policies, procedures and their implementation as it is relates with systems, business processes and data. Armed with this information, you should have clear visibility into who (from the teams prospective), which (from a systems perspective), and what (from data perspective) will be involved in the prescribed scope (by KPIs) for your data governance initiative

At this stage it becomes necessary to profile the data, analyze the data for the accuracy, cleanliness and completeness. Reliability of the data from the perspective of business process which uses this data as well as the KPIs in which this data is used must also be evaluated. Profiling and analyzing data will also help you in capturing any policies and procedures which have not been explicitly defined, documented in the system of record, but yet are being implemented at data level (for example, after profiling data you might realize that only two payment terms are present in the data, but there is no explicit mention of this as a part of the policy definition)

In one of my consulting engagements with high-tech companies, I was methodically going through this process. CFO had identified reduction in DSO as one of the key goals for the year. Following these steps, when we mapped the actual systems being used to capture and report DSO, we realized that instead of being one financial application, this organization had three financial systems one which was supposed to be a corporate standard and two which had not been fully migrated as a result of past acquisitions. This was a big eye opener not only for me but all the people who have been crunching numbers to report DSO to CFO. To make the matters even complicate further, data at the summary level was being transferred from other two systems to the organizational standard financial application once in every 15 days. This meant that CFO was not getting accurate data for DSO on a week to week basis. Also, when we did some data analysis/reconciliation between these three systems, we found many data discrepancies (mainly associated with dates on transactions etc.) because one system was capturing summary level data versus other systems had detailed data.

Doing this detailed analysis of various systems and processes involved helped us (i.e. data governance team) in establishing the quality, usability, and accuracy of the data coming from three systems. It also helped us quickly identify the need to migrate two non-standard financial systems still being used to the corporate standard. The benefits of system migrations were very huge for the organization (standardization and optimization of collection processes, standard payment term usage and enforcement, being able to use appropriate cleansed addresses for billing thus reducing any delays because of wrong billing addresses, being able to leverage the data enrichments done to customer accounts in the main system, Being able to generate more accurate DSO metrics for reporting purposes etc.)

Documentation of this information in the metadata repositories and modeling tools really help application team responsible for maintenance of the financial systems in estimating and kick starting initiatives around system migrations. Also knowledge of specific data issues and possible benefits of fixing those data issues, standardizing policies (credit, collection, billing, etc. ) across the three areas of the organization helped us in demonstrating the benefits which can be had in improving DSO KPI.

By far, I would say that critical understanding of data issues, interdependency between different systems, teams (people) and business processes, and a clear understanding of lack of (which) policies or consistencies between the policies is extremely vital for the process of identifying impact of data governance initiatives on organizational strategic direction.

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

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

This is a sixth blog entry in a blog series highlighting the critical nature and the importance of executive sponsorship for data governance initiatives. In last few entries, I explored need to understand the KPIs, goals behind those KPIs and necessity to get your hands on actual artifacts used by executives in reviewing these KPIs and their goals.

My approach has been very simple and straightforward: data governance initiatives need to absolutely be able to demonstrate impact on top and bottom lines by helping executives improve on the KPIs which are used as means to achieve higher profitability, lower costs and compliance. The process of garnering executive sponsorship is a continuous one. Visibility of data governance organization, its impact across the board; helps in establishing awareness and understanding of how data governance initiatives help organizations. This visibility and awareness makes it easy to maintain ongoing executive sponsorship.

Once you, as a data governance team, have clearly understood KPIs, goals behind those KPIs and have access to the artifacts used by executives, it is time to go back to the technical details. At this stage it is extremely important to map which systems, business processes automated by those systems and data is either directly or indirectly responsible for the outcome of those KPIs. This process of mapping dependency between KPIs, systems, business processes and data can be somewhat automated using metadata management repositories. It is important to capture this information using tools and technologies so that this information can be readily available and shared with other teams and systems.  Technology solution will also facilitate change management, impact analysis in future. The lineage and the metadata I am talking about here, go beyond technical metadata and gets into the realm of business (process and semantic) metadata as well.

This dependency information will come in very handy in establishing scope, definition of the efforts being planned towards specific data governance initiative/project. When collecting information about the systems, business processes automated by those systems and data, it is important to capture relevant information with long-term, repeatable perspective. Information such as:

1.     System name and information,,

2.     Landscape information (where is it being installed/managed/housed, which hardware/software are being used? touch points with other systems etc.)

3.     Ownership and responsibility information from both business and technology perspective. (Which technology teams are responsible for managing, changing and maintaining these systems? Who are the business stake holders who approve any changes to the behavior of these systems? etc.)

4.     Change management processes and procedures concerning the systems and data.

5.     End-users/consumer information (who uses it? How do they use it? When do they use it? For what do they use it? In).

6.     Any life cycle management processes and procedures (for data, systems) which might be into existence currently.

7.     Specific business processes and functions which are being automated by the systems?

Many a times, some of this information might already be present with the teams managing these systems. This exercise should identify presence of that information and make a note of that information. The point here is not to duplicate this information. If the information does not exist, this exercise will help capture such information which is relevant not only for the data governance initiatives, but is also usable by system owners and other stakeholders.

Goal of this step/answering this question is to baseline information about systems, business processes automated by the systems and data. This information is going to help in subsequent stages for establishing, change management processes, defining policies and possibly implementing and monitoring policies around data management/governance.

From this phase/question data governance initiative starts transitioning into nuts and bolts of the IT systems and landscape. In next few blog posts, I will be covering various aspects which data governance team should consider as they start making progress towards establishing official program and start working on it.

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?

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

This is a fourth blog entry in a series of blog entries highlighting how to go about securing executive sponsorship for data governance initiatives. In previous posts, I have highlighted the need for  understanding specific KPIs/metrics which executives track,  and tangible goals which are being set against those KPIs.

Almost always, there is either individual or group of individuals who work tirelessly on producing these necessary reports with KPIs/metrics for executives. Many a times these individuals have clear and precise understanding of how these metrics/KPIs are calculated, what data issues, if any, exists in underlying data which supports these metrics.

It is worthwhile to spend time with these groups of people to get a leg up on an understanding of metrics/KPI definitions, knowledge around data issues (data quality, consistency, system of record). The process of engaging these individuals will also help in winning confidence of the people who know the actual details around KPI/metrics, processes associated with calculating and reporting on these metrics. These individuals will likely to be part of your data governance team and are crucial players in winning the vote of confidence from executives as it relates to the value data governance initiatives create.

In one of my engagements with a B2 B customer, executive management had the goal of improving business with existing customers. Hence executive management wanted to track Net new versus Repeat business. Initially sales operations team had no way of reporting on this KPI, so in the early days they reported using statistical sampling. Ultimately, they created a field in their CRM system to capture new or repeat business on their opportunity data. This field was used for doing new versus repeat business KPI reporting. Unfortunately, this field was always entered manually by a sales rep while creating opportunity record. While sales operation team knew that this is not entirely accurate, they had no way of getting around it.

In my early discussions with sales operations team, when I came to know about this, I did a quick assessment for a quarter worth of data. After doing basic de-duping and some cleansing I compared my numbers versus their numbers and there was a significant difference between both of our numbers. This really helped me get sales operations team on board with data cleansing and ultimately data governance around opportunity, customers and prospects data. This discussion/interaction also helped us clearly define what business should be considered Net new and Repeat business.

Obviously, as one goes through this process of collecting information around metrics, underlying data and the process by which these numbers are crunched, it helps to have proper tools and technology in place to capture this knowledge. For example

a)     Capturing definition of metrics

b)     Capturing metadata around data sources

c)      Lineage, actual calculations behind metrics etc.

This process of capturing definitions, metadata, lineage etc. will help in getting high level visibility of the scope of things to come. Metadata and lineage can be used to identify business processes and systems which are impacting KPIs.

In summary, this process of finding people behind the operations of putting together KPIs helps in identifying subject matter experts who can give you clear and high-value pointers around “areas” which data governance initiatives need to focus early on in the process. This process will ultimately help you in recruiting people with right skill set and knowledge in your cross functional data governance team.

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

Suggested next posts:

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

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Data Governance Litmus Test: Know goals behind KPI’s

This is the third blog post in a series of blog posts geared towards addressing “Why, What and How?” of getting executive sponsorship for data governance initiatives. In my last post Data Governance Litmus Test: Know thy KPIs I explored importance of knowing KPIs to be able to build link between data governance initiatives outcomes and the organizational strategy. In this post I’m going to explore why it is important to know specific goals of the KPIs which are monitored on periodic basis by executives towards fulfilling organizational strategy.

Data governance initiatives typically will span multiple organizations, key business processes, heterogeneous systems/applications and several people from different lines of businesses. Any time when one is dealing with such a complex composition of players and stakeholders, it is extremely crucial to be articulate about business goals and the impact of the actions on hand on the goals. Once people understand the magnitude of impact, and how they will be responsible for such an impact, getting their co-operation, alignment becomes relatively easy.

Once you understand the KPIs which are important organizationally, you need to drill down one level below to understand what specific goals are important? The process of understanding specific goals will undoubtedly reveal many contributing factors to the fulfillment of the overall goals.

For example:

If one of the major KPIs which executives are tracking is overall spend. At this stage it is important for the data governance initiative team to understand specific goals around this KPI. For example the specifics goals around this KPI could be:

1.     Chief procurement officer has been asked to reduce spend by 2% within four quarters

2.     2% reduction across the board represents $80 million savings.

3.     This savings alone would allow organization to improve its profitability by almost a penny per share. This ultimately will reflect positively in share price improvement and will benefit all the employees of the organization.

Once such details are known, establishing a dialogue with chief procurement officer and his/her key advisers might further reveal that

1.     Their focus is going to be in three specific areas (specific products/raw materials)

2.     Not having singular view of suppliers is a key concern. Because of this issue they are not able to negotiate consistent pricing contracts with the suppliers. They believe that streamlining contracts based on overall spend with suppliers; their subsidiaries will help them achieve more than 70% of their goal.

3.     Supplier contracts are not being returned consistently resulting in higher costs in terms of minimum business guarantees and price point guarantees.

Equipped with this information, it will be much easier for data governance team to highlight and link their efforts to overall goal of reducing spend. For example, with some of this information gathered, one can already pinpoint that teams which are working with suppliers/supplier development, contract negotiations, pricing etc…. are going to be critical to get on board data governance with this initiative. Also, it is clear from these nuggets of information that the overall spend, number of suppliers, number of materials/products being procured will be some of the key metrics and interrelationship between those metrics will be critical to link any ROI from initiatives to clean supplier data, build supplier MDM etc…

With this information data governance team now can not only communicate to their team members but also the executives, that X percent of duplicate data in supplier master would potentially represent Y dollars off excessive spend. Data governance team will be able to explain not only how this can be fixed but what is required to maintain this hygiene on an ongoing basis because of the impact it will have on overall excess spend.

In summary, it is really important to understand the goals behind “what?” of the organizational strategy. Other indirect benefits of this kind of exercise are

1.     Establish communication and contacts with the business stakeholders.

2.     Understand areas where you can focus upfront for the highest impact.

3.     Understand and learn the language which you could use to effectively communicate ROI of data governance back to the executives.

In my next post, I will explore who is behind putting together these KPIs for executive in the current situation. These people are ‘the most critical’ players in the Data Governance team at both execution and implementation levels as the initiatives are kicked off.

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

Suggested next posts:

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?

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Data Governance Litmus Test: Know thy KPI’s

In last post about Data Governance Litmus Test, I outlined 10 questions which could be used as a litmus test to figure out, how you are doing in terms of garnering executive sponsorship for your data governance initiatives? In this post, I’m going to explore why it is important to know organizational KPI’s/initiatives before and during data governance initiatives.

KPI’s or metrics which are looked at by CXO’s are the clear-cut indicators of where the organizational focus is from the perspective of operational excellence. They also serve as an early indicator of overall organizational strategy. Knowing these KPI’s firsthand helps the teams involved in data governance initiatives in internalizing what is important for the organizational strategy? Also it helps with understanding where executive focus is within the organization?

Many times when I asked this question (which KPI’s are being tracked by executive management) to the teams working on data governance initiatives, I typically get standard answers. “Our executives are looking at sales, cost related KPI’s.” This is a clear indication that the team has not made significant effort in understanding the KPI’s, establishing communication channel with executive management and has not emphasized the need for understanding KPI’s by the data governance team.

While ultimately the goal of organization is to increase revenues, minimize the cost and maximize profitability, there are several steps and ways by which these goals are achieved. From marketing, procurement, finance to sales there are specific goals which are set as a part of achieving business plan and these goals are tracked by executive management team on a periodic basis. Many a times these goals will change from time to time to adjust for change in strategy as well as changes in the overall goals. Understanding the details of the KPI’s across different parts of the organization helps data governance teams to link their activities to specific KPI’s and results associated with those KPI’s.

The process of getting engaged with executive management and make a case to understand KPI’s in detail helps in multiple ways to the data governance initiative:

1.     It helps with establishing communication channel, credibility, relationship with executive management and their goals/mission.

2.     It gives the team visibility into very specific KPI’s which are important for organizational growth, growth of individual executives within the organization.

3.     It helps create the context to the data governance discussion, change management process across the entire organization. No one can dispute the need/requirement for the reporting and improving these KPI’s.

4.     Once you establish a communication channel/relationship with executives around these KPI’s, and if you are able to demonstrate the value you and the initiative which you are proposing(data governance) can add to the KPIs, executives will get in the habit of involving data governance team as and when either KPI’s change or there are issues with reporting KPI’s.

5.     The confidence and trust relationship which you can build through this exercise will make it easy to ask for executive sponsorship. Executives will be more than willing to support your initiatives as they see a clear line connecting data governance  initiatives with their KPI’s and progress.

The process of getting to know these KPI’s is important one. When understanding the KPI’s or collecting information about these KPI’s, it is important to collect significant details around KPI’s:

1.     Name of the KPIs

2.     How executives are defining these KPI’s, that is in executives mind how this KPI is measured and calculated

3.     Understand from executive perspective, which business processes impact/influence this KPI, which roles and possibly names of the people will have the most influence on the outcome of this KPI.

4.     Periodicity: how often is this KPI reported on?

5.     Establish clear linkage between this KPI and a specific organizational strategy ultimately rolling up into the vision leadership has created for the organization.

6.     It may be beneficial to understand how these KPI’s will help executives in achieving their personal goals

As always, devil is in details. If CFOs goal is to reduce DSO, then being able to understand from CFO’s perspective how DSO is impacted by collection processes, CRM processes is important. For all you know unclean addresses might be at the root of lack of ability to collect the payments (at least one of the reasons behind larger DSO number). If you followed recommendations above you will be able to tangibly demonstrate linkage between cleanliness issue and DSO and will be able to garner support from CFO on this issue on a ongoing basis.

At this stage I am not focusing on specific technology investments, but as you can see any technology solution which will allow you to capture strategy, KPI’s and link business processes to these artifacts will be a good solution to capture this information.

In my next post around the litmus test questions, I will explore the need for understanding the specific goals around these KPI’s.

Previous relevant posts:

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

Suggested reading next:

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?

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

It was a long day in Cincinnati, we had full day of conference, demos and discussions around data governance/data quality topics. Some of my colleagues, friends in the industry decided to retire in a bowling alley. Over the bowling game and few beers we obviously resorted to talking about the same topic we have been discussing all throughout the day. After about couple of hours of discussion lane #7, and#8 and #9 came to same conclusion: one of the toughest parts of data governance initiatives is ongoing executive sponsorship and the need for demonstrating tangible ROI.

On my flight way back home I jotted down some thoughts around this topic and thought of creating a basic list of questions which could be used as a litmus test to validate, if all the right steps are taken to ensure ongoing executive sponsorship and tangible ROI proof points for data governance initiative.

Everybody who is involved in some sort of data governance initiative knows the criticality and the importance of having executive sponsorship for the overall success and viability of the data governance programs.

It is really important to have the right level of understanding about organizational goals and drivers. With the specific knowledge of organizational initiatives it is much easier to link data governance initiative to specific organizational goals/drivers. Creating this link between goals and data governance will help in creating the necessary ROI case for data governance as well as garner the executive sponsorship.

So here is the list of the 10 simple yet relevant questions which I am proposing every data governance team should use as a litmus test from time to time to validate, if they are going on the right track to ensure ongoing executive sponsorship and capacity to demonstrate tangible ROI to the organization.

1.       Every Monday morning CEO and his direct reports meet to review organizational KPI’s. Do you precisely know which metrics are being looked at on a weekly basis?

2.       Do you know what the goals are for those KPIs?

3.       Do you know how each of those metrics/KPIs is put together and by whom?

4.       Do you know which KPIs are not meeting their desired goals?

5.       Do you have sample presentation or a report which all of the executives look at, in the Monday morning meeting?

6.       Once you have an idea about the key metrics, people who put together those metrics for executives, do you know which systems are responsible for generating and managing raw data which is required for those metrics?

7.       Do you have an understanding of the quality, reliability, timeliness of the data which is being used to put together those metrics?

8.       Have you found issues with data quality, reliability, timeliness of the data or how the data is managed on ongoing basis?

9.       Have you shared you are findings of the quality and reliability of the raw data which is being used to put together weekly KPIs with the executives which are responsible for those KPIs?

10.   Have you reached any common understanding regarding the need to address data quality, reliability, timeliness or issues around how the data is being managed with the key executives whose KPI’s are being impacted because of the underlying issues associated with the data? And benefits of such actions/initiatives?

If you answered yes to all of the questions above, you are well on your way to generate tangible ROI, Garner executive sponsorship for your data governance initiatives. And you have very high chance of being successful at achieving all of your goals of data governance initiative.

On the other hand, if you did not answer yes to one or many of the questions above, it is time to go back to the whiteboard and understand how truly you have been able to justify the ROI? How realistic is that case? And do you truly have executive sponsorship and support to your data governance initiatives?

I would love to hear your thoughts around this topic, and in specific if you would add any more questions? Take away any of the above questions? Or simplify any of the questions?

In my opinion, how and when you ask these questions and take appropriate actions will differ based on where in the life cycle of the project you are. In future posts, I will discuss relevancy of this litmus test and other factors influencing your actions based on this litmus test for two scenarios:

1.       For teams who are just in the beginning phases of proposing Data governance program, but yet not started.

2.       Teams who are already working through the Data governance programs.

Suggested reading next:

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?

Read Full Post »

Context For Data Quality

Score Card for Prioratizing Data Quality Issues

Couple of weeks back I was having conversation with a fellow CTO, he was demonstrating analytics product to me.  There were many instances in the demo (dashboards/reports) where there was lot of dimensional data were missing (for example industry verticals, Product category etc…). Obviously during the course of the demo, discussion around data quality broke out. Fellow CTO mentioned that they do not encourage customers to spend time and energy to fix data quality issues from the analytics prospective if numbers around data quality issues represent  less than 1% of overall $ numbers.  I kind of agreed with his argument and justification (again from directional analytics prospective) around not fixing these data quality issues because:

1) These issues do not interfere with analysis if analysis hinges upon directionality of the business.

2) ROI from fixing these issues is not significant as the data represented by these issues will have less than 1% impact on the directionality of the analysis (which is statistically insignificant).

This got me thinking that data quality is truly multi-dimensional problem (like the story about an elephant in the room and  blindfolded men describing the elephant, everyone concludes it as a different object even though everyone is feeling the elephant).  As data quality professionals, it is important for all of us to bring that prospective in any data quality initiative. Best way to doing this would be to build a data quality score card with the quality assessment and its impact on the context in which data will be used. This type of score card can and should be used in prioritizing fixing of data quality issues. This will also help in justifying ROI of the data quality issues.

 As indicated in chart, each context is analyzed from the prospective of data quality attributes. Each context is given Red, Green or Yellow indicator. Obviously any red indicators need to be addressed before data can be used in that context. In this example, it helps to demonstrate that compliance reporting requirements cannot be met, until data quality issues associated with credit ratings, address data quality are completely resolved. This helps with demonstrating the need, necessary ROI and helps in prioritization of which attributes to be addressed first.

I would love to hear from you as to how did you prioritize and justified quality imitative, what tools/techniques you used?

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