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Wayne Eckerson

Welcome to Wayne's World, my blog that illuminates the latest thinking about how to deliver insights from business data and celebrates out-of-the-box thinkers and doers in the business intelligence (BI), performance management and data warehousing (DW) fields. Tune in here if you want to keep abreast of the latest trends, techniques, and technologies in this dynamic industry.

About the author >

Wayne has been a thought leader in the business intelligence field since the early 1990s. He has conducted numerous research studies and is a noted speaker, blogger, and consultant. He is the author of two widely read books: Performance Dashboards: Measuring, Monitoring, and Managing Your Business (2005, 2010) and The Secrets of Analytical Leaders: Insights from Information Insiders (2012).

Wayne is founder and principal consultant at Eckerson Group,a research and consulting company focused on business intelligence, analytics and big data.

Recently in Data Governance Category

Most business intelligence (BI) professionals understand the need for the business to drive the BI program to achieve success. But, most don't have a clue how to make this happen.

To be fair, some organizations are lost causes. Their executives view BI as a reporting cost-center driven by IT. They don't understand the value of information to optimize performance and deliver a sustainable advantage. They haven't figured out that "data is the new oil" and that whoever masters the means of data production, wins.

In most other organizations, the business is well meaning, but too busy and preoccupied to commit the necessary time to ensure the success of a BI program. When push comes to shove, they still relegate the duties of delivering information-centric applications to the IT department, entrusting them to make key decisions about semantics, metrics, and targets. And most competent IT teams are happy to oblige, since they often know the business as well as or better than many in the business.

BOBI on Board

Two BI Teams. However, there are a few organizations in which executives both talk the talk and walk the walk. These executives make substantial investments in BI but do so in a unique way: they don't just fund the acquisition of technology and hire IT staff to manage it, they create and staff a business-oriented BI team to complement the IT-oriented BI staff. In other words, they support TWO BI teams, one focused on the business, the other on technology.

This business-oriented BI team doesn't yet have an official designation in the BI lexicon. It currently goes by many names: enterprise data solutions, information management, business information analysis, business insights and analytics, and even business intelligence. But since it's a business-oriented BI (BOBI) team, let's just cut to the chase and call it BOBI.

BOBI teams typically report to an executive on the business side. Ideally, it's the chief operations officer or chief technology officer not the head of a department, like finance or marketing who can limit BOBI's activities to a too narrow domain. BOBI should have an enterprise focus. Working jointly with IT, BOBI should build the proverbial data factory to ensure clean, consistent, and accurate data to power all BI solutions throughout the organization.

More than BI Governance

I'll admit, BOBI is a revelation to me. Most corporate BI teams I've seen are part of the IT department and consist mainly of technologists with a business bent. I've always advocated that the primary duty of such BI teams is to foster a BI governance structure comprised of two voluntary, ad hoc committees: a steering committee of executive level sponsors and a working committee of business analysts and subject matter experts.

The executive steering committee provides funding, prioritizes projects, and approves the high-level BI roadmap, while the BI working committee works with IT to create the roadmap, flesh out data warehouse subject areas, select tools, and prioritize enhancements. I've always said these business analysts and SMEs can be your best allies or worse enemies, so it's best to make them full partners in the BI journey.

Missing Link. However, what I missed is that these business analysts should not be part-time volunteers with other priorities and bosses; they should be allocated full-time to the BI program. In addition, they should be assigned to a dedicated BI team led by a business-savvy BI director who also has ample experience running BI and technology projects.

BI-Lingual Professionals

I thank Nick Triantos and Andre Synnett for steering me straight. Nick is currently director of enterprise BI and Data Programs at McAfee and former director of Quality Data Systems at Cisco, while Andre is vice president of the BI Competency Center and the soon-to-be chief data officer at Caisse de depot, a large pension fund investment firm in Quebec. Both come from the business but have substantial technology experience. They are the proverbial "purple people" needed to succeed with BI: neither blue from the business or red from IT, but a perfect blend (i.e., purple) of both.

Both Nick and Andre run business teams dedicated to BI that sit between the business and IT. Team members, like themselves, are bilingual ("BI-lingual"): they can speak both business and technology.

On the business side, team members gather requirements while simultaneously evangelizing the capabilities of their respective companies' BI infrastructure to address current business objectives. They also develop the BI roadmap, manage the BI budget, oversee BI and data governance programs, and create change management programs. They often establish standards for the BI user experience and oversee the BI tool selection process.

On the technical side, they translate business requirements into technical specifications, manage metadata, and document best practices for delivering BI solutions, They work with data architects to flesh out the BI roadmap, project managers to accommodate shifting user requirements, technical architects to select and deploy BI tools, and help desk staff to ensureeffective end-user support and training.


To succeed with BI, you need to convince executives to step up and fund BOBI--a
permanent business-oriented BI team. Without such an investment, the odds of achieving BI success are stacked against you.

Posted October 4, 2011 7:11 AM
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I recently read an interesting interview with Andy Haylor, founder of The Information Difference, discussing the results of an in-depth survey on data governance practices that he conducted with the Data Governance Institute.

Having just completed a 40-page report titled "Creating an Enterprise Data Strategy: Managing Data as a Corporate Asset," I thought his results make a good supplement to my findings. While I focused more broadly on data management and data quality issues, he honed in on critical success factors for managing data governance programs.

In particular, he correlated successful data governance programs with the following characteristics:

  • A data governance mission statement
  • A clear and documented process for resolving disputes
  • Good policies for controlling access to business data
  • An active risk register
  • Effective logical models for key business data domains
  • Either business processes defined at a high level or fully documented at several levels and available for data governance
  • Data quality assessments that were undertaken on a regular basis
  • A documented business case
  • A link between program objectives and team or personal objectives
  • A comprehensive training program
  • A Web site alongside a broader range of communication methods

This isn't rocket science for sure. But it does take a lot of work to implement all or even some of the tasks or practices listed above. And if the program truly manages cross-functional data, then the process is that much more challenging since departmental politics begins to encroach. For that reason, I think the most important success factor in Andy's list is a "clear and documented process for resolving disputes."

Documenting the business case is also important, but it's something few organizations do. Most are just glad to get permission (or tacit approval) to launch a data governance program, citing that as justification enough. Usually, executives endorse such programs because they've suffered a major problem due to lack of clean, consistent data and recognize that data governance is simply a cost of doing business. However, without a clear cost/benefit analysis, it's too easy for data governance programs to be swept aside by changing currents in the organization, such as a new executive, an acquisition, or a new strategy.

It takes time for data governance programs and processes to take root and become an immoveable part of the corporate culture. Until that happens, program managers must do everything in their power to nurture and shepherd their fledgling programs until they achieve the "this is the way we've always done it" status for managing data. This is probably why only 23% of respondents to Haylor's survey said they have a "highly" or "quite" successful data governance program.

Posted June 22, 2011 1:53 PM
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Data vortex.jpg

I just finished writing the first draft of my upcoming report titled, "Creating an Enterprise Data Strategy: Managing Data as a Corporate Asset." This is a broad topic these days, even broader than just business intelligence (BI) and data warehousing. It's really about how organizations can better manage an enterprise asset--data--that most business people don't value until it's too late.

After spending more than a week reviewing notes from many interviews and trying to formulate a concise, coherent, and pragmatic analysis without creating a book, I can distill my findings into a couple of bullet points. And since I am still collecting feedback from sponsors and others, I welcome your input as well!

  • Learn the Hard Way. Most business executives don't perceive data as a vital corporate asset until they've been badly burned by poor quality data. It could be that their well-publicized merger didn't deliver promised synergies due to a larger than anticipated overlap in customers or customer churn is increasing but they have no idea who is churning or why.
  • The Value of Data. Certainly, there are cost savings from consolidating legacy reporting systems and independent data marts and spreadmarts. But the only way to really calculate the value of data is to understand the risks poor quality data poses to strategic projects, goals, partnerships, and decisions. Since risk is virtually invisible until something bad happens, this is why selling a data strategy is so hard to do.
  • Project Alignment. Even with a catastrophic data-induced failure, the only way to cultivate data fastidiousness is one project at a time. Data governance for data governance's sake does not work. Business people must have tangible, self-evident reasons to spend time on infrastructure and service issues rather than immediate business outcomes on which they're being measured.
  • Business driven. This goes without saying: data strategy and governance is not an IT project or program. Any attempt by executives to put IT in charge of this asset is doomed to fail. The business must assign top executives, subject matter experts, and business stewards to define the rules, policies, and procedures required to maintain accuracy, completeness, and timeliness of critical data elements.
  • Sustainable Processes. The ultimate objective for managing any shared service is embed its care and tending into business processes that are part of the corporate culture. At this point, managing data becomes everyone's business and no one questions why it's done. If you try to change the process, people will say "This is the way we've always done it." This is a sustainable process.
  • Data Defaults. In the absence of strong data governance, data always defaults to the lowest common denominator, which is first and foremost, an analyst armed with a spreadsheet, and secondly, a department head with his own IT staff and data management systems. This is kind of like the law of entropy: it takes a lot of energy to maintain order and symmetry but very little for it to devolve into randomness.
  • Reconciling Extremes. The key to managing data (or any shared services or strategy) is to balance extremes by maintaining a free interplay between polar opposites. A company in which data is a free-for-all needs to impose standard processes to bring order to chaos. On the other hand, a company with a huge backlog of data projects needs to license certain people and groups to bend or break the rules for the benefit of the business.
  • A Touch of Chaos. Instead of trying to beat back data chaos, BI managers should embrace it. Spreadmarts are instantiating of business requirements so use them (and the people who create them) to flesh out the enterprise BI and DW environment. "I don't think it's healthy to think that your central BI solution can do it all. The ratio I'm going for is 80% corporate, 20% niche," says Mike Masciandaro, BI Director at Dow, talking about the newest incarnation of spreadmarts: in-memory visualization tools.
  • Safety Valves - Another approach to managing chaos is to coopt it. If users threaten to create independent data marts while they wait for the EDW to meet their needs, create a SWAT team to build a temporary application that meets their needs. If they complain about the fast and dirty solution (and you don't want to make it too appealing), they know there is a better solution in the offing.
  • Data Tools. There has been a lot more innovation in technology than processes. So, today, organizations should strive to arm their data management teams with the proper tool for every task. And with the volume and types of data accelerating, IT professionals need every tool they can get.

So what did I miss? If you send me some tantalizing insights, I just might have to quote you in the report!

Posted May 19, 2011 9:25 AM
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