Driving Data Governance Past Cultural Roadblocks

Recently, I wrote about getting caught in such data governance traps as pursuing quality for the sake of quality and imposing change on an organization without considering the cultural impact. In this post, I’ll explore the issue of corporate culture and what it takes to overcome barriers to change. Read more

Does your data hurricane have a name?

I’m sitting here in Boston listening to all the weather prognosticators about Hurricane Earl which is currently bearing down on the Eastern seaboard. What’s fascinating is the wide variance of reports; I swear some of these reporters are being paid by Home Depot so people buy more “storm supplies.” While a hurricane is no laughing matter, it gets me thinking about the data “hurricane” sweeping across companies worldwide. Read more

How Mature is Data Governance?

Last Thursday I was interviewed by Philip Russom, a veteran data management industry analyst, during a great webinar on MDM and data governance hosted by TDWI. In a report published in 2008, Philip wrote that “software automation specifically designed for data governance is somewhat light.” He repeated the other day that there remains a dearth of purpose-built data governance solutions and asked me what will catalyze the market. My answer, in a word, is maturity. Read more

A Brief History of Data Governance

Data management has gone through significant changes in the 20 years that I’ve been in this business. Data emerged out of the lockboxes of disparate legacy transactional systems, and data management came to be a separate and sophisticated discipline enabled by advanced software and hardware. Gone are the days when most people needed to be convinced that data is a valuable asset. Through three recessions (1990, 2001, 2008), the data management industry marched forward nearly unscathed; spending continues to increase faster than overall IT spending. Read more

A Simple Data Governance Framework

It’s not surprising that to new comers, data governance seems very fuzzy and unwieldy. Terms like rules, policies, procedures, standards, process, data quality, security, decision rights, and accountability have been used commonly to describe various aspects of data governance. At first blush, they appear to be a collection of disconnected concepts. Read more

The future has arrived.

On Tuesday, we announced the Kalido Data Governance Director, a new product that will enable companies to operationalize their data governance programs. As is our heritage, we are taking a more strategic view, creating a “tops down” data policy driven system that will give companies the ability to manage data policies, operationalize data governance processes and measure and improve policy compliance. This new product does something no product in this market does; it considers data, systems and business processes in context so policies are comprehensive and meaningful. Read more

Can we make it real?

The single biggest challenge for any software company is deciding what should go into their products. As we thought about the data governance, we considered the options; do we build on top of our existing MDM product or start from scratch? Most of the vendors we compete with in the MDM market were planning on extending MDM, but we felt this was a flawed strategy. Why? Because MDM is repository-centric and focused on managing master data only. We believed that in order to build an effective data governance product, we needed to consider ALL data, as well as business process and organizational context. Read more

Send in the Yellow Jerseys: Organizing for Data Governance

Data governance  institutes a system of accountability when it comes to an organization’s most important data assets. Data providers have an obligation to meet the needs of data consumers when the benefit outweighs the cost. Because this obligation exists across the organization despite functional or geographic distinctions, we have to start with the right organizational structure. Read more

Business Value Should Reign

I read this morning Evan Levy’s excellent blog “Blind Vendor Allegiance Trumps Utility.” In it, he laments the number of companies he spoke to at the recent Gartner MDM Summit who identified their leading MDM vendor (based on being a [Microsoft / IBM / SAP / Oracle / SAS] shop) before even defining their requirements. Read more

Traditional Approach to Data Management Only Treats the Symptoms

In my last blog, I discussed that although we’ve thrown a huge amount of money to solve data problems, the result is unsatisfactory. For poor data quality, we identified the root cause: the lack of transparency and accountability between providers and consumers of data.

In most organizations, because the relationships and rules of engagement between data providers and consumers are not transparent, data consumers naturally assume that the wizardry of IT is responsible for data. When data problems arise, IT gets the blame: IT becomes the de facto data owner. But IT typically doesn’t have the authority to address the root cause by telling data providers to bear the cost of good data for the benefit of the entire organization. So IT has to solve the problem in some other way. Read more