Degrees of data standardization - Part 3

As part of this series, in Part 1 and Part 2, I shared with you foundational activities necessary to prepare for your master data management program.

At this point we have a better understanding of our core data elements and their current state of quality - primarily completeness and accuracy. If you are working with a consulting partner, it is a good idea to ask them to share with you most common core elements they have seen at other companies within and across industries. Most of the consulting companies have reference data models supporting each industry. This gives your data organization an opportunity to synthesize and harmonize the data elements from across the organization and include any missing data elements that is required to complete master data. Slowly you will see the emergence of data groups and elements towards your new data model.

This is also a good time to begin thinking about ways to source the new data elements. I will address the sourcing of data from both internal and external data providers in a future blog. Note, how we are switching back and forth between managing structure of data and its content.

With profiling and interpretation of current state of your data, you have by now identified data elements that are candidates for standardization. Though it is tempting to standardize as many data elements as possible for consistency across the organization, I suggest you exercise caution. This also applies to managing hierarchies across business units, divisional and regional boundaries.

Data standardization challenge is not about technologies or processes. It is about people!

Consumers and managers of data have a strong proximity to data they currently use. It is a relationship that has evolved over years, if not decades. They have grown with this data. They have turned a blind eye to some of its problems. They have found ways to work around it by interpreting it differently or by making "minor" changes in presenting this data to others. Ask them to adopt a different set of data and all of a sudden you have a battle on your hands.

This is a perfect opportunity to bring the passionate owners of data together. I have been part of several global consensus workshops to drive common data hierarchies (sales hierarchies, product hierarchies to name a few). These workshops become forums for vociferous turf protection, they are extremely political, even "Me Vs You" exchanges and have all the ingredients of a battle that is not for the faint of heart.

You must use this opportunity to make data consensus a cornerstone of your master data future.

When sparks fly during data standardization exercises you cannot but help admire the positive side of the issue. Any opinions you had until now that data is not respected or there is nobody who cares about data and all other ramblings about lack of organizational focus on data ... begins to evaporate. You are convinced that data is not just a byproduct of your applications anymore. All your organization needed until now was a forum to bring people together to decide on what is best for the company and information evangelist(s) to guide them through this process.

Data standardization just gave you that forum.

What's in it for the data stewards?

With data waves getting complex with time, data stewards have enormous responsibility to enable, sustain and improve "trustability" of data. Positive improvements in data trustability hinges on solid data stewardship.

Three things I have seen work well when assigning stewardship responsibilities.

First, prior credibility with business leaders particularly the process owners. Second, a "what is possible" tenacious attitude with attention to detail instead of "this is how it is done here" mindset. Third, a positive personality with strong interpersonal skills. If you are a multinational or transnational organization, then an understanding of cultural specificity would go a long way in completing the profile.

Let us say your data stewards are doing a great job. What's in it for them?

Addressing the questions below can help you formulate or refine policy to reward and motivate your data steward(s):

1. Do you have a formal buy-in from Human Resources to include stewardship metrics for annual performance evaluation?

2. Is data stewardship their primary responsibility, a part-time activity or a tertiary chore?

3. Do some of the stewards receive bigger rewards arising out of higher value contribution to your organization from a particular data domain (e.g. Customer)?

4. Is there a monetary reward for significant enhancements in the trustability of your data or are you continuing with traditions of giving out certificates or acknowledgments at team meetings?

5. Are you giving them opportunities to gain visibility with company leaders and creating avenues to share their expertise and educate others?

Data stewards in your organization are unsung heroes managing your strategic asset - your data. By failing to reward them you are taking a giant first step in discounting the value of your asset. If stewards of your other assets, such as financial holdings, get rewarded for improvements in value of assets shouldn't your data stewards too?

Degrees of data standardization - Part 2

Thanks for your emails on Part 1. Is data profiling art or science? One of the emails asked me.

Historically data has been treated as a byproduct of applications and has not received the attention it deserves. As the late Rodney Dangerfield would say "It gets no respect". In the pursuit of the next killer app, "cool" technologies and the forever re-inventing three letter acronyms, data has not gone to the All Star game.

Until now.

Smart executives are realizing the powerful drivers of decision rights and information flow is critical for successful execution of any strategy. Non-standard data is one of the major barriers of free information flow within and across organizations. Profiling of current data is thus essential to begin the discovery towards data standardization.

Key challenges of profiling is to identify which data and how much of it to profile. I have heard war cries "Let us profile everything", "We have tools that can do 10 levels of profiling", "Our technologies can profile millions of rows in seconds". I suggest taking some time to establish data sources to profile and categorize data elements (attributes) targeted for profiling into primary and secondary sets. This will throw some light on your data ownership and stewardship dimension as well.

Data sources you are profiling have one or more data domains of interest e.g. Customer, Product. Each domain has an optimal number of primary and secondary attributes that provide insight into your data, depending on your industry. Contact me for this matrix with helpful guidelines on volumes of data you must profile to get a realistic picture. This can save your project(s) a great deal of time and effort.

It is interesting to note that parsing of current data sources will be a fun challenge. If you do not leverage parsing algorithms from major vendors or your disparate data sources prompt you to custom build your own parsing framework, you have a challenge on your hands and also an incredible opportunity to learn about the building blocks of your data segments.

Profiling should be approached in two phases, surface profiling for leading indicators and deep profiling for additional insight, driving/confirming data relationships and identifying opportunities for enrichment. There are about 40-50 profiling dimensions, which can be found in any trade books. I can help you choose 5-7 dimensions depending on your industry and domain of interest; again, saving you money!

Interpreting profiled results needs deep expertise. Most of the tools can tell you 86% of your data has 3 areas of density for 58,000 rows. OK. So? How will you translate this to your business users in ways they relate to the observation, understand the data situation AND most importantly get encouraged to fixing/alleviating the problem. This is one opportunity you do not want to lose to align IT with business on current state of data. As a leader, the onus is on you to pin point the quality of data, explain business impact and promote the way for its enrichment.

Stay tuned for Part 3 of this topic on data standardization.

I hope this helps you answer the question; Is data profiling art or science?

Degrees of data standardization - Part 1

One of the challenges of a master data management program is the decision on standardization. The opportunity to establish common definitions, format and domain ranges (allowed values) also presents a risk to introduce delays to your program. If you are a diversified business or a global organization, the delays are compounded.

How do you address the standardization challenge?

First step is to bring data awareness within your organization. This needs good amount of groundwork by IT leaders. Profiling of data, confirming data ownerships (or lack there of), reviewing information visibility requirements et al.

Second, identify data elements (attributes) that are potential candidates for standardization. Categorize your data using a tiered approach. Your industry may drive the amount of attributes to be standardized.

Third, bring awareness about your data and the need for standardization beginning with your immediate information consumers. Translate your findings from step 1 and 2 into simple narratives that business users can relate to and understand. Let this awareness percolate across the organization prompting attention of business leaders.

This will lay the foundation for your next (and generally more challenging) steps for data standardization. Stay tuned for Part 2.

If you are in the midst of a MDM program, what were your challenges in executing Steps 1 to 3 ?

Master Data and DNA - Drawing a parallel

Wikipedia definition:

Deoxyribonucleic acid (DNA) is a nucleic acid that contains the genetic instructions used in the development and functioning of all known living organisms and some viruses. The main role of DNA molecules is the long-term storage of information. DNA is often compared to a set of blueprints or a recipe, or a code, since it contains the instructions needed to construct other components of cells, such as proteins and RNA molecules. The DNA segments that carry this genetic information are called genes, but other DNA sequences have structural purposes, or are involved in regulating the use of this genetic information.

Master data contains key business entities and their definitions used in the development and functioning of all business events. The main role of master data is the accurate long-term institutional reference of information. Master data is often compared to a set of blueprints or a recipe, or a code, since it contains information needed for product/service leadership, closer customer interactions and operational efficiencies.

How familiar are you with your organizational DNA ?