Archive for the ‘Data Quality’ Category

Year 2011 has been somewhat different for data management field in terms of manifestos and predictions about what the future holds. At least in the field of data management (DQ/DG etc…) there have been predictions about what features will not hold. In her blog “Jills anti-predictions of 2011”, Jill Dyche identifies what will not happen in 2011 as far as data management /data governance/MDM is concerned within organizations, Dylan Jones in his blog What is or (anti) Data Quality Manifesto? suggest creating anti-manifesto as a viral (anonymous) marketing to force the awareness around perception of the data with management ranks.  This trend has been purely based on many of our collective experience when it comes to management’s lack of commitment and sponsorship (over the years)  to support, plan and execute holistic data governance strategies for supporting high quality data throughout organization for decision-making and operations.

More I think about it, more I feel that (in many instances) ignorance about the state of the data (or data quality) is by design rather than being out of ignorance or lack of understanding. Businesses often hires smart people (most of the cases) in the executive management roles because of their capabilities and the smarts they bring to the table. Many of these executives probably have MBAs and have gone through course work which highlights importance of using accurate data in decision-making. Many of these executives have worked (probably) in some capacities with data throughout their careers and have learned the importance of fact based rather than gut feel based decision-making.

Unwillingness to commit to data management strategies might be stemming from factors which have to do with how success of the executives is evaluated and rewarded.

1.       Pressure to perform on quarterly basis (managing expenses). Many public companies provide their financial results on quarterly basis; every executives focus is to maximize sales profitability in these 90 days. Every attempt is made to curb any unwarranted (in executive’s minds) expenses.

2.       Short term contracts CEO’s (and other executives) have with the board are not conducive for finding long-term solutions. Executives have to prove their worth and short period of time. (In the paper titled “CEO EMPLOYMENT CONTRACT HORIZON AND MYOPIC BEHAVIOR” by Moqui Xu, author concludes that CEO’s with short term contract invest less than their peers. CEO’s with short term contract tend to sacrifice long-term investments for short term value maximization)

3.       Attitude “If I can get “my” numbers and (correct?) data without investing more, Why spend money and efforts on it?”. Little do they know about the manual efforts involved in getting this accurate data, day in and day out to them.

So how can we make a case for investments in Data Management initiatives?

It’s human nature to work hard to avoid pain and/or negative outcome. As human beings, we will do more to avoid pain and negative outcome than to ensure positive results. Executive management will pay more attention to your proposals and business cases for data management when they are faced with situations which are somewhat negative in nature to the performance of overall business. Situations like a restatement of financial results, fines by governing bodies, de-certification or refusal by auditors to sign on compliance, introduction of new legislation around compliance and regulations(it’s no coincidence that many of highly regulated industries like insurance, health care are farther ahead when it comes to data quality/data governance initiatives implementation and adoption) are some of the examples of major negative events (I call them compelling events)within organizational operations which can be effectively used to get executives to listen to the business case for data management. Be ready with your business case, proposal all the time. And when the time is right, present this business case to executive management for their approval and sponsorship. Highlight how initiatives you are proposing will either help avoid these negative situations or help lessen the impact of those negative situations and as a bonus help with the strategic goals of the organization.

For example, recently in their blog, Utopia, Inc. highlighted how inaccurate statement of revenues to their executive rekindled the focus on the data quality/governance initiative within their organization. This is not to say that they were not committed to the data governance or data management initiatives, in fact, they had some of that already in place. This incident provided executive sponsorship and visibility to the data issues and hence commitment from executives for data governance/management initiatives.

I’m not saying that this is the only way to get executive management sponsorship to data management initiatives. There are instances, and there are organizations which will proactively adopt the data management initiatives. Many CEOs will understand strategic inflection points (Only the paranoid survive: Andy Grove) in their industry and would realize importance of effective data management in navigating through changing business conditions. This almost always results in proactively investing and adopting data management business cases.

In ideal world, if businesses adopt best practices for data management ground up, it will help businesses in leveraging data as an asset. Effective data management would help organizations potentially avoid getting into unfavorable situation in first place. Sometimes, though to make a business case one has to choose appropriate timing even though it seems counter intuitive to do so. Sometimes it has to be that way…..

Read Full Post »

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

Read Full Post »

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.

Read Full Post »

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?

Read Full Post »

Yesterday as I was driving to work, we had fog everywhere in the area in which I live. We where fogged in so to speak. Typical commute from my house to the nearest freeway takes about 10 minutes on a given day, yesterday, it took 25 minutes. Visibility was poor; I could hardly see more than 100 yard ahead of me and about the same distance behind me. This meant I was driving very cautiously; not at all confident about what was ahead of me. While I was driving through this dense fog a thought came to my mind, isn’t it true that business decision makers go through similar predicament when they are faced with an lack of availability of reliable, high quality data for decision-making?

Poor data quality means lesser visibility into performance of the organization; it also implies impairment of decision-making based on actual data. As with a fog, poor data quality means business decisions are done slowly, over cautiously and many a times based on gut feel, rather than factual data. Slowness in decision-making could mean possible loss of the edge business has over its competition. I feel that there is a lot common between driving through a fog and trying to run the business with poor quality data.

As sun rises and temperature increases, fog will burn out. In the same way effective data quality and data governance initiatives will help burn away the fog created by a lackluster data quality. Burning off the fog is a slow and steady process; all the right conditions need to exist before fog disappears. It is the same with addressing data quality holistically within enterprise. Right conditions need to be created in terms of executive sponsorship, understanding of importance of good data quality, clear demonstration of value created by data assets etc. before true fruits of data quality initiatives can be harvested.

Superior data quality and timeliness of availability of high-quality data has significant impact on day to day business operations as well as strategic initiatives business undertakes.

Read Full Post »

This post was encouraged by similar writing about good data management post by fellow practitioner and blogger Henrik Liliendahl (Right the First time)

For data professionals like me, who have been working and preaching importance of the data quality/cleanliness and data management, it feels really good when you see good examples of enterprise information management policies and procedures at play in real life. It feels like the message of importance of data as an asset, as a “critical success enabler for the business” is being finally heard and accepted.

Recently I had a wonderful experience shopping for a laptop at http://www.dell.com. As I was shopping on their website, I configured my laptop (I’m sure all the options for my laptop were being populated from a Products MDM catalogJ). When I was ready to check out, before calculating shipping charges, website prompted me to enter my shipping address. When I entered my address, website came back to me with two corrected addresses which where enriched with additional information such as four digit extension of the zip code, expanded abbreviations etc. Website forced me to choose one of the corrected/enriched addresses before I proceeded with my order completion. This probably meant that they have implemented a solution which checks for validity and conformance of address information being entered before letting this information enter into their systems. Obviously, this investment from Dell has many benefits for Dell and hence they must have invested this effort in implementing these data quality/standardization solutions as a part of broader Enterprise Information Management framework. I was really happy for Dell. This process also meant that my order was delivered ahead of schedule without additional charge.

I am writing this because I believe in applauding and appreciating efforts done the right way. For transparency purpose, I am not related with dell.com in any professional way (employment, contract etc…), also nor did dell hire me to write this blog post. I am one of the thousands of customers they have. I just want to say good job Dell.com

I would like to appeal to all fellow bloggers and practitioners to cite examples of good information management, data management or data governance practices at work from public domain and write about them. Tweet about them under #goodeim tag. We have heard too many horror stories; there are many organizations which have been diligently at work implementing very successful information management practices, let us encourage and applaud those efforts openly.

Read Full Post »

Older Posts »