In the Field with our Experts

The Dreaded Stairs

By Stephen Putman, Senior Consultant

Recently, a friend of mine posted a link on Facebook that reinforced a philosophy that I have had for a long time that applies to all activities in life that are not duty-bound:

The Dreaded Stairs (part of The Fun Theory project)

I have long felt that humans do things for two reasons:

A) They're fun

B) They're lucrative

This applies to the field of Data Governance and Quality as it does everything else. One of the reasons data governance and quality initiatives are not more widely adopted and followed is that the work is not terribly fun - data owners must be identified, policies and processes must be adopted, and the entire process must be monitored and attended once it is in place. It's also not seen as lucrative in a direct sense - the act of cleansing the data in a transaction usually doesn't provide immediate financial reward, and while the implementation of governance and quality initiatives can affect the company's bottom line, the benefits are very difficult to quantify in a traditional sense.

Phil Simon has produced a terrific series for The Data Roundtable on incentive ideas for data quality programs, so I will not address these here - he says it much better than I can. I am concerned with "fun." The video above demonstrated an innovative idea to make a mundane but healthy activity (climbing stairs) into a joyful experience. What sort of innovative programs can be created to make managing high-quality data fun?

"Fun" is a difficult concept because it means something different to everyone. One way to find out what is "fun" to your employees is by conducting surveys or workshops to ask them directly. Another possibility could be to have a "company carnival" in your parking lot, and award employees who identify quality issues with raffle tickets or a "boss' dunk tank." The White House holds a yearly contest with government employees for the best quality improvement or cost-savings idea (this is more of an incentive, but some people also consider contests like this fun).

These are just a few ideas off the top of the head - do you have creative people who can come up with other ideas? If it is indeed true that fun makes unpleasant activities more palatable, this would be time well spent to reinforce data governance and quality in your organization.

photo by Robin Fensom via Flickr (Creative Commons license)

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Stephen Putman has over 20 years experience supporting client/server and internet-based operations from small offices to major corporations.  He has extensive experience in a variety of front-end development tools, as well as relational database design and administration, and is extremely effective in project management and leadership roles. He is the co-author of The Data Governance eBook, available at baseline-consulting.com/ebooks.

Linked Data Today!

By Stephen Putman, Senior Consultant

I begin today with an invitation to a headache...click this link: The Linking Open Data Cloud Diagram

Ouch! That is a really complicated diagram. I believe that the Semantic Web suffers from the same difficulty that many worthy technologies do - the relative impossibility to describe the concept in simple terms, using concepts familiar to the vast majority of the audience. When this happens, the technology gets buried under well-meaning but hopelessly complex diagrams like this one. If you take the time to understand it, the concept is very powerful, but all the circles and lines immediately turn off most people.

Fortunately, there are simple things that you can do in your organization today that will introduce the concept of linked data to your staff and begin to leverage the great power that the concept holds. It will take a little bit of transition, but once the idea takes hold you can take it in several more powerful directions.

Many companies treat their applications as islands unto themselves in their basic operations, regardless of any external feeds or reporting that occurs. One result of this is that basic, seldom-changing concepts such as Country, State, and Date/Time are replicated in each system throughout the company. A basic tenet of data management states that managing data in one place is preferable to managing it in several - every time something changes, it must be maintained in however many systems use it.

One of the basic concepts of linked data is that applications will use a common repository for data like State, for example, and publish Uniform Resource Identifiers (URIs), or standardized location values that act much like Web-based URLs, for each value in the repository. Applications will then link to the URI for the lookup value instead of proprietary codes in use today. There are efforts to make global shared repositories for this type of data, but it is not necessary to place your trust in these data stores right away - all of this can occur within your company's firewall.

The transition to linked data does not need to be sudden or comprehensive, but can be accomplished in an incremental fashion to mitigate disruption to existing systems. Here are actions that you can begin right now to start the transition:

• If you are coding an application that uses these common lookups, store the URI in the parent table instead of the proprietary code.
• If you are using "shrink wrap" applications, construct views that reconcile the URIs and the proprietary codes, and encourage their use by end users.
• Investigate usage of common repositories in all future development and packaged software acquisition.
• Begin investigation of linking company-specific common data concepts, such as department, location, etc.

 Once the transition to a common data store is under way, your organization will have lower administration costs and more consistent data throughout the company. You will also be leading your company into the future of linked data processing that is coming soon.

photo by steve_lodefink via Flickr (Creative Commons License)

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Stephen Putman has over 20 years experience supporting client/server and internet-based operations from small offices to major corporations.  He has extensive experience in a variety of front-end development tools, as well as relational database design and administration, and is extremely effective in project management and leadership roles. He is the co-author of The Data Governance eBook, available at information-management.com.

Keep It On Track

By Stephen Putman, Senior Consultant

In my recent blog posting, "Metadata is Key," I talked about one way of changing the mindset of managers and implementers in support of the coming "semantic wave" of linked data management. Today, I give you another way to prepare for the coming revolution, and also become more disciplined and effective in your project management whether you're going down the semantic road or not...

 rathole (n) - [from the English idiom “down a rathole” for a waste of money or time] A technical subject that is known to be able to absorb infinite amounts of discussion time without more than an infinitesimal probability of arrival at a conclusion or consensus.

 Anyone who has spent time implementing computer systems knows exactly what I'm talking about here. Meetings can sometimes devolve into lengthy discussions that have little to do with the subject at hand. Frequently, these meetings become quite emotional, which makes it difficult to refocus the discussion on the meeting's subject. The end result is frustration felt by the project team on "wasting time" on unrelated subjects, with the resulting lack of clarity and potential for schedule overruns.

One method for mitigating this issue is the presence of a "rathole monitor" in each meeting. I was introduced to this concept at a client several years ago, and I was impressed by the focus they had in meetings, much to the project’s benefit. A "rathole monitor" is a person who does not actively participate in the meeting, but understands the scope and breadth of the proposed solution very well and has enough standing in the organization that they are trusted. This person listens to the discussion in the meeting, and interrupts when he perceives that the conversion is veering off into an unrelated direction. It is important for this person to record this divergence and relay it to the project management team for later discussion - the discussion is usually useful to the project, and if these new ideas are not addressed later, people will keep these ideas to themselves, which could be detrimental to the project.

 This method will pay dividends in current project management, but how does it relate to semantics and linked data? Semantic technology is all about context and relationships of data objects - in fact, without these objects and relationships being well defined, semantic processing is impossible. Therefore, developing a mindset of scope and context is essential to the successful implementation of any semantically enabled application. Training your staff to think in these terms makes your organization perform in a more efficient and focused manner, which will surely lead to increased profitability and more effective operations.

photo by xJasonRogersx via Flickr (Creative Commons License)

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Stephen Putman has over 20 years experience supporting client/server and internet-based operations from small offices to major corporations.  He has extensive experience in a variety of front-end development tools, as well as relational database design and administration, and is extremely effective in project management and leadership roles. He is the co-author of The Data Governance eBook, available at information-management.com.

Metadata is Key

By Stephen Putman, Senior Consultant

One of the most promising developments in data management over the last ten years is the rise of semantic processing, commonly referred to as the "Semantic Web." Briefly described, semantic processing creates a "web of data" complimenting the "web of documents" of the World Wide Web. The benefits of such an array of linked data are many, but the main benefit could the ability for machines to mine for needed data to enhance searches, recommendations, and the like, where humans do this now.

Unfortunately, the growth of the semantic data industry has been slower than anticipated, mainly due to a "chicken and egg" problem - the systems needs descriptive metadata to be added to existing structures to function efficiently, but major data management companies are reluctant to invest a great deal into creating tools to do this until an appropriate return on investment is demonstrated. I feel that there is an even more basic issue with the adoption of semantics that has nothing to do with tools or investment - we need the implementers and managers of data systems to change their thinking about how they do their jobs; to make metadata production central to the systems they produce.

The interoperability and discoverability of data is becoming an increasingly important requirements for organizations of all types - the financial industry is keenly aware of the requirements of reporting systems that are XBRL-enabled, for example. If we leave external requirements to the side, the same requirements can benefit the internal reporting of the organization as well. Reporting systems go through extended periods of design and implementation, with their contents and design a seemingly well-guarded secret. Consequently, effort is required for departments not originally included in the system design to discover and use appropriate data for their operations.

The organization and publication of metadata about these reporting systems can mitigate the cost of this discovery and use by the entire organization. Here is a sample of the metadata produced by every database system, either formally or informally:

• System-schema-table-column
• Frequency of update
• Input source(s)
• Ownership-stewardship
• Security level

The collection and publication of such metadata in standard forms will prepare your organization for the coming “semantic wave," even if you do not have a specific application that can utilize this data at the present time. This will give your organization an advantage over those companies that wait for these requirements to be implemented and will need to play catch-up. You will also gain the advantage of your staff thinking in terms of metadata capture and dissemination, which will help your company become more efficient in its data management functions.

photo by ~Brenda-Starr~ via Flickr (Creative Commons License)

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Stephen Putman has over 20 years experience supporting client/server and internet-based operations from small offices to major corporations.  He has extensive experience in a variety of front-end development tools, as well as relational database design and administration, and is extremely effective in project management and leadership roles. He is the co-author of The Data Governance eBook, available at information-management.com.

Don Quixote and Change Management

By Carol Newcomb, Senior Consultant

What does “change management” mean to you?  Is it the same thing that other people think of?  Do you even know?  Do you have a definition?  I’ll bet you have more than one!

Some things I’ve heard in my recent travels include: 

• Project plan for implementing changes to data management processes
• Communications plan for alterations in the SDLC
• Cultural change required to digest or accept more formalized processes and standards
• High-level leadership directives enticing more cooperation among the ranks

If you get the feeling that “change management” is a mirage, one of those windmills in the distance that keeps fading in and out of focus, maybe you’d better get off that donkey and start asking some questions.  Like “where did this idea come from” and “whose idea is it?”  Let’s set some ground rules for change management.

I.  What’s the end goal? 

This is the vision, the purpose.  Is it enlightenment, or something more tangible, like faster time to delivery, more efficient team coordination, or a call for cultural change?  Change management needs a name and a specific goal.  Otherwise, my windmill looks different than your windmill.

II.  How will you measure it?

If you can’t measure it, it doesn’t exist.  Think about it—anything tangible has a weight, a height, a mass, or a velocity per unit.  Even black holes can be measured.  Change is a progression from one state to another, and not to put too tight a noose around it, but if you don’t know where you started, you’ll never know where you are in your journey.

III.  How will you know when you have achieved it?

Back to the vision-- or the goal.  Is there an objective to your journey, or are you just wandering and taking it all in?  How far away are you from reaching your destination?  Do you have a sense of how many more resources you need; what kind of resources, what roadmap will guide them to the destination?  When can you say to the team, “Enough already”!

IV.  How will you share it or communicate it?

Can other people learn from your experience in your journey, or will it seem like another distant mirage to them as they embark on their own travels?  What kind of institutional learning can you build to help other areas or individuals achieve similar goals?  What kinds of differences are relevant and notable?

Change management will be a guide and a journal for others in the future.  Don Quixote never found his windmills, but NASA put a man on the moon.  So, take a practical approach.  After all, once you’ve seen all those windmills, and you never reach them, how much more time and effort are you going to expend before ditching the whole journey?

photo by spotter_nl via Flickr (Creative Commons License)

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Carol Newcomb is a Senior Consultant with Baseline Consulting. She specializes in developing BI and data governance programs to drive competitive advantage and fact-based decision making. Carol has consulted for a variety of health care organizations, including Rush Health Associates, Kaiser Permanente, OSF Healthcare, the Blue Cross Blue Shield Association and more. While working at the Joint Commission and Northwestern Memorial Hospital, she designed and conducted scientific research projects and contributed to statistical analyses.

The Data Governance eBook: Maps, Mechanics and Morals

Recently, we launched our newest e-book, The Data Quality eBook. We ran an excerpt from that book last week. But did you know we have another e-book out there? Earlier this year, we published The Data Governance eBook to rousing success. If you haven't had a chance to download and read this book by Kimberly Nevala and Stephen Putman, don't worry—it's still available online.

Here's a description:

When the fictitious business team at the SpectraDynamo corporation launches a new data governance initiative, the trials and tribulations begin. The Data Governance eBook, authored by the experts at Baseline Consulting, is an entertaining business parable with a message aimed at helping you launch data governance the right way. This bold new look at data governance is more than just a cautionary tale. It's what Baseline partner Jill Dyché calls "a prescription for getting it right." The Data Governance eBook includes all the nuts and bolts necessary to transcend the requisite decision-by-committee and role confusion so endemic to companies’ information management efforts.

Here's a brief excerpt that showcases what you'll find in our first e-book:

The moral of the story: data governance can be tough. By definition, innovation is disruptive and antithetical to established paradigms. Applying rigor in the form of governance and oversight to information is no exception to this rule. Mix the laundry list of competing information needs and issues in the average enterprise with the inevitable confusion regarding ‘what does it mean’ and the data governance proposition quickly becomes overwhelming. The multitude of data governance definitions and methodologies parlayed by both industry analysts and vendors—is it: data quality? information policy? a dashboard? a council?—certainly doesn’t help.

The fact is: there is no standard playbook for data governance. The structure of your program is dependent on the specific problems governance needs to address and the organization’s incumbent organization and culture.  Some companies—like SpectroDynamo—find a bottom-up approach centered around core data management functions provides the greatest initial leverage. Organizations that are more hierarchical or consensus driven in nature may require formal enlistment of executive sponsorship and deliberate assignment of decision rights for governance to take root. And yes, you may start somewhere in the middle...

What is consistent? The fundamental roles and responsibilities found in functioning data governance programs. In this section, we define those core functions: executive sponsorship, data governance (policy making and decision rights), data management and data stewardship. These functions align with and support everything from strategy alignment and organizational buy-in to tactical enablement and operations.

When evaluating these capabilities, remember the single most important success factor for entrenching data governance as a core operating principle: continuous improvement. Rather than boiling the ocean, identify key business pain points and the core capabilities required to address them. Prioritize and pick a pilot project. Finally, prototype data governance-related processes and don’t be afraid to refine them as your capabilities and reach grow.

Interested in knowing more? Download The Data Governance eBook today!

Responsible [Data] Stewardship

By Caryn Maresic, Senior Consultant

Contribute to society and human well-being.  Avoid harm to others.  Be honest and trustworthy.  Be fair and take action not to discriminate.  Those are the first four items in the ACM Code of Ethics.  The ACM, for those who may not be familiar, is the Association for Computing Machinery, whose mission is to advance computing as a science and a profession.

In the course of a recent assignment with a major insurance carrier our team was asked to create various target lists for sales and marketing based on certain selection criteria.  While it is likely that all of the things they asked for were legal and ethical, we never questioned it.  As good Data Stewards, what should we have done in this case?  Should we be asking the business to justify their selection criteria?  Should we be checking to make sure there are no legal or ethical violations inherent in the rules?  A little research on the topic turned up this presentation 
which is very interesting and thought provoking.  That being said, it focuses more on the hot-topic issues like privacy and identity theft than it does the ethical dilemmas of sales and marketing.

This article tells the story of an “Agent Profile System” set up by an insurer in Texas to rate its agents.  Agents who didn’t score well were punished by not getting any new business.  The agents filed suit contending this was illegal as it compelled them to drop clients with low credit ratings, low income, and/or those who lived in undesirable locations in order to boost their own score.  Is the IT team that built the Agent Profile System responsible, at least in part, for discrimination?

When we are dealing with situations where lives are in danger the ethical answer is clear.  For example, no reasonable person would deny that engineers working on Space Shuttle software have a duty to report concerns regarding possible malfunction.  In the BI community our issues are not always so clear cut.  Sometimes discrimination is good for the business’ bottom line, yet still unethical and possibly illegal.  If we go back to the statements “Avoid harm to others” and “Be fair” and “take action not to discriminate” it appears that we should take serious our responsibility to be involved in how the business uses data.  In fact, I would argue that we should make ethical considerations part of our data governance program.

photo by Robert S. Donovan via Flickr (Creative Commons License)

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Caryn has over 20 years experience in providing high-quality data solutions to clients in the areas of Business Intelligence, Data Warehousing and System Integration.  Caryn has expertise in across industries with an emphasis in Pharmaceutical, Manufacturing, and Insurance.  Prior to joining to Baseline, she ran her own consulting company.

Data Architect – Who is right for the job?

by Caryn Maresic, Senior Consultant

The Data Architect is the core of any BI team.  It is important to choose a person with the right skill set.  As I tried to put together a list of skills I looked to IT Toolbox and Database Answers for help, but my mind wandered a bit.  System Construction. Data Architect. Data Warehouse. Software Factory.  We like to portray what we do in terms of construction and/or manufacturing.  A recent client bemoaned her departments inability to move from “building custom cars” to “an assembly line”.  Comparing ourselves to these burly industries might make us feel strong, but it does it accurately represent what we aspire to be?

What is a Data Architect?  What should they know how to do?   I borrowed the following description from this article. Before you click, read on and see if you can guess what this is really describing.  I think it is a great description for a Data Architect:

A Data Architect is qualified by education, experience, and imagination to enhance the function and quality of systems. The purpose of this pursuit is to improve the quality of life, increase productivity, and protect the health, security, and welfare of the business.

The best Data Architects are capable of analyzing a client's needs, goals, safety and business requirements and integrating this information into a design that is both pleasing to the eye and functional. They will work with the client closely to develop preliminary design concepts that meet their aesthetic, functional, and economic needs while maintaining adherence to standards.

In essence, the best Data Architects are part detective, part artist, and part psychologist and they use these skill sets to create systems that fit a client's tastes and needs with their budget in mind.

Doesn’t that sound like a great job?  Sign me up!  What this is actually describing is an interior designer.  While I doubt that HGTV has any plans to showcase the next dashboard you build, we are indeed closer to Designing Women than Rosie the Riveter!  Stay tuned for future posts on the talents of a good Data Design Star.

photo by Annahape Gallery via Flickr (Creative Commons License)

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Caryn has over 20 years experience in providing high-quality data solutions to clients in the areas of Business Intelligence, Data Warehousing and System Integration.  Caryn has expertise in across industries with an emphasis in Pharmaceutical, Manufacturing, and Insurance.  Prior to joining to Baseline, she ran her own consulting company.

I’m All Ears

By Caryn Maresic, Senior Consultant

Most Data Warehouse designs include constructs for Address, Phone, and/or Email for Customers.  Len Silverston came up with what he calls a Universal Data Model that does a very good job of abstracting address, email and phone number data.  I have seen clients use the Contact Point portion of his model as-is and with a few simplifications with great success.  That being said, in the area of Marketing and Sales, the manner in which we reach out to our customers and prospects gets more diverse every day.  Disneyland has just partnered with Verizon so that park guests can get real time information about the park and play Disney games on their phones….and, of course, Disney gets access to more information about its customers!

How does this new and ever changing world of communication change the way we think about and model contact points?  What would my “address” look like if I were near the Haunted Mansion looking for a lunch spot?  Would it be different than if I were at Downtown Disney looking for a cup of coffee?  On Main Street looking for Winnie the Pooh?  In all instances I would be using the same phone, possibly the same IP address, but I would be in different locations which would be important to the marketeers at Disney.

As time goes by (and cell phone GPS systems become more accurate) I suspect that the way we run marketing campaigns to smart phones will be similar to the way in which we use billboards today.  Where the customer is physically located at any given time will be as important as the phone number and/or IP address, thus creating a two dimensional contact point.

Have you come across this issue in your organization?  Have you changed your data model to include two dimensional contact points?  If not, has the use of smart phones changed your data model in other ways?

photo by wrayckage via Flickr (Creative Commons license)

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Caryn has over 20 years experience in providing high-quality data solutions to clients in the areas of Business Intelligence, Data Warehousing and System Integration.  Caryn has expertise in across industries with an emphasis in Pharmaceutical, Manufacturing, and Insurance.  Prior to joining to Baseline, she ran her own consulting company.

A Data Governance Primer (Part 3 of 3)

By Carol Newcomb, Senior Consultant

Diamond in the Rough: Data Quality

The third part of my summertime primer addresses Data Quality Analysis.  Don’t even start a data quality analysis until you have completed the first two steps of your Root Cause Analysis--investigate & prioritize any potential causative factors, and start your metadata assessment.  Otherwise, you may be misled by your findings.

Data quality is defined as complete and accurate data that is ready for business consumption.  Sources of poor data quality may include lack of data entry rules, unclear data element definitions, inconsistent metadata definitions for field type, format or intent, or breakdowns in data transformation processes as data flow between systems or applications.  Poor data quality results in bad business decisions; it contributes to major problems in using data effectively, and costs companies millions of dollars/year in terms of rework and inefficiency.  Data quality, in combination with robust metadata definitions, is part of the foundation of good data governance.

Data Quality Analysis

A Data Quality Management process should be designed to enable an area to start with a simple approach and over time to mature to one that is more proactive and comprehensive.  Initially, investigation may be focused on single data elements or events.  As patterns, data commonalities and other relationships appear, the data quality management process will grow to support complete business processes.   A mature data quality management process will not just resolve individual issues; it will also track relationships between data elements, ensure that business rules are consistent and generate statistical analyses to monitor previously addressed issues to ensure that data quality is stable and that an early warning system is in place as part of the data governance program.  The goal is to design a data quality management lifecycle, as shown in this diagram:

Initial Data Quality Analysis Process

I. Define data scope

• Determine data elements that are associated with or are direct results of the reported issue
• Check that all metadata definitions are present and current
• Enlist the involvement of the Data SME or Data Stewards
• Identify all source systems where the data originates, is  entered or derived

II. Extract and profile the data

• Extract the relevant data from all key source systems.
• Design the profile.  A profile will consist, at a minimum, of total record counts, min/max values, frequency of unique values, and frequency of invalid values (if defined) for each data element profiled. 
• Profile the data to determine key characteristics that are contributing to the issue, such as:
• 1. Wrong values
  2. Missing values
  3. Corrupt transformation processes
  4. Incorrect business rules
  5. Incorrect usage rules

III. Analyze Data Profile Results

• Summarize the key findings from the profile detail
• Determine what key drivers are contributing to the impact
• Determine accountability for the data quality issue
• Involve other Data Stewards in troubleshooting and designing the data quality solution

IV. Design the Corrective Action Plan

Two types of plans should be developed to address known data quality issues: a corrective action plan to fix the immediate source of the problem identified, and an ongoing monitoring plan, where thresholds have been determined and metrics are routinely collected and reported to data stakeholders.  This monitoring process should be scalable based on the number of data elements being tracked.

1. Corrective Action Plan
2. • Does scope of problem warrant change in metadata definitions, business practices or data entry rules?
   • Does scope of problem warrant a data governance standard?
   • Does the corrective action plan include details on how to fix the source of the problem as well as ways to correct historical data in the system?
3. Preventive Action Plan
4. • This plan will be designed to minimize the probability of data quality issues from recurring
   • Determine ‘early warning triggers’ based on designated thresholds.  These thresholds should reflect the business tolerance for inaccurate data (is 95% acceptable?)
   • If data latency is the source of a data quality issue, then latency thresholds should be included in the monitoring plan
   • Determine how frequently results of the monitoring plan will be reported to data stakeholders or governance oversight committees

So, now that summer is officially here, this wraps up my Data Governance Primer series.  Time for some iced tea and my favorite beach towel.  Come August, these little refreshers might be just the thing!

photo by Swamibu via Flickr (Creative Commons License)

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Carol Newcomb is a Senior Consultant with Baseline Consulting. She specializes in developing BI and data governance programs to drive competitive advantage and fact-based decision making. Carol has consulted for a variety of health care organizations, including Rush Health Associates, Kaiser Permanente, OSF Healthcare, the Blue Cross Blue Shield Association and more. While working at the Joint Commission and Northwestern Memorial Hospital, she designed and conducted scientific research projects and contributed to statistical analyses.