Tag Archives: Enterprise Information Management
Organizational Change Management and Business Process Re-engineering was the rage in 1990’s. Much of that thinking still persists today but it is no longer sufficient for the kinds of transformations that organizations need to accomplish on an ongoing basis today. A modern business transformation is data driven, global in nature, crosses functional boundaries, and changes behavior at multiple levels of the organization. To address these needs organizations need to adopt a business-led enterprise-wide planning capability. (more…)
Do We Really Need Another Information Framework?
The EIM Consortium is a group of nine companies that formed this year with the mission to:
“Promote the adoption of Enterprise Information Management as a business function by establishing an open industry reference architecture in order to protect and optimize the business value derived from data assets.”
That sounds nice, but we do really need another framework for EIM or Data Governance? Yes we do, and here’s why. (more…)
A few days ago, I came across a post, 5 C’s of MDM (Case, Content, Connecting, Cleansing, and Controlling), by Peter Krensky, Sr. Research Associate, Aberdeen Group and this response by Alan Duncan with his 5 C’s (Communicate, Co-operate, Collaborate, Cajole and Coerce). I like Alan’s list much better. Even though I work for a product company specializing in information management technology, the secret to successful enterprise information management (EIM) is in tackling the business and organizational issues, not the technology challenges. Fundamentally, data management at the enterprise level is an agreement problem, not a technology problem.
So, here I go with my 5 C’s: (more…)
Adrian gathered experts and built workgroups to dig into the issue and do root cause analysis. The workgroups came back with some pretty surprising results.
- Most people expected that “incorrect data” (missing, out of date, incomplete, or wrong data) would be the main problem. What they found was that this was only #5 on the list of issues.
- The #1 issue was “Too much data.” People working with the data could not find the data they needed because there was too much data available, and it was hard to figure out which was the data they needed.
- The #2 issue was that people did not know the meaning of data. And because people had different interpretations of the data, the often produced analyses with conflicting results. For example, “claims paid date” might mean the date the claim was approved, the date the check was cut or the date the check cleared. These different interpretations resulted in significantly different numbers.
- In third place was the difficulty in accessing the data. Their environment was a forest of interfaces, access methods and security policies. Some were documented and some not.
In one of the workgroups, a senior manager put the problem in a larger business context;
“Not being able to leverage the data correctly allows competitors to break ground in new areas before we do. Our data in my opinion is the ‘MOST’ important element for our organization.”
What started as a relatively straightforward data quality project became a more comprehensive enterprise data management initiative that could literally change the entire organization. By the project’s end, Adrian found himself leading the data strategy of the organization.
This kind of story is happening with increasing frequency across all industries as all businesses become more digital, the quantity and complexity of data grows, and the opportunities to offer differentiated services based on data grow. We are entering an era of data-fueled organizations where the competitive advantage will go to those who use their data ecosystem better than their competitors.
Gartner is predicting that we are entering an era of increased technology disruption. Organizations that focus on data as their competitive edge will have the advantage. It has become clear that a strong enterprise data architecture is central to the strategy of any industry-leading organization.
For more future-thinking on the subject of enterprise data management and data architecure see Think ‘Data First” to Drive Business Value
There is no shortage of buzzwords that speak to the upside and downside of data. Big Data, Data as an Asset, the Internet of Things, Cloud Computing, One Version of the Truth, Data Breach, Black Hat Hacking, and so on. Clearly we are in the Information Age as described by Alvin Toffler in The Third Wave. But yet, most organizations are not effectively dealing with the risks of a data-driven economy nor are they getting the full benefits of all that data. They are stuck in a fire-fighting mode where each information management opportunity or problem is a one-time event that is man-handled with heroic efforts. There is no repeatability. The organization doesn’t learn from prior lessons and each business unit re-invents similar solutions. IT projects are typically late, over budget, and under delivered. There is a way to break out of this rut. (more…)
If you build an IT Architecture, it will be a constant up-hill battle to get business users and executives engaged and take ownership of data governance and data quality. In short you will struggle to maximize the information potential in your enterprise. But if you develop and Enterprise Architecture that starts with a business and operational view, the dynamics change dramatically. To make this point, let’s take a look at a case study from Cisco. (more…)
There are three reasons why we haven’t achieved 1-click data management in a corporate data marketplace. First, it wasn’t a problem until recently. The signs that we really needed to manage data as an asset across the enterprise only appeared about 20 years ago. Prior to that, data management occurred at the application system level and we didn’t need a separate focus on Information Asset Management (IAM) at the enterprise level. The past five years however have a seen a strong growing awareness of the challenges and need for IAM; to a large degree driven by big-data opportunities and data privacy and confidentiality concerns. (more…)