October 25, 2016
Information Insight

Project Foresight for Project Insight

I had the honor of presenting on Kenway’s behalf at the Technology Executives Club of Chicago last fall. The topic was Information Insight, Kenway’s framework that integrates Data Governance, Data Management and Business Intelligence in order to deliver information, which enables business leaders at every level of the organization to make true data driven decisions.

I learned a lot while preparing and delivering that presentation, and I’ve continued to think about how critical these concepts are at a time when companies have so much data at their disposal. Why do companies struggle so much to make sense of their data? Why do projects so often run into problems, having failed to recognize early risks related to data? More importantly, why do some of these projects end up making it even harder to make sense of data?

Take for instance, a risk I encountered on a recent project: as the team was getting ready to deploy a new account installation system, we realized that we did not have a full list of product and plan relationships. In order to complete the installation or implementation of a new customer account, the solution had to interface with legacy systems which depended on having the correct relationship data for products and plans. Without it, the installation would fail, thus requiring a costly manual intervention. We also determined that there was no owner or process to maintain this data as new products and/or plans were added, making it very difficult for the client team to ensure the new solution had the most current list of relationships at all times. This issue has since been escalated to the newly established Data Governance team to address the data stewardship of these product/plan relationship lists to ensure legacy and new systems are in sync at all times.

I would argue that as much training as we provide our Project Management personnel in project planning and execution, time line creation, issues and risk management and the like, we are missing a critical opportunity to train these same people in basic Data Governance and Data Management principles and practices.

No matter the project, the data involved in the solution will generate the greatest value to the business when it’s well integrated and connected across the enterprise. Investments in business systems projects inevitably deal with business data, whether it’s capturing transactional data, transforming data for business consumption, or processing data for further downstream consumption or for reporting purposes. Projects with personnel who are familiar with Data Governance and Data Management principles are far more likely to deliver on the full business value of their projects.

So what are some of the basic tenets of Data Governance and Data Management with which all project teams should be familiar?

For starters, what is Data Governance and Data Management?

Kenway defines Data Governance and Data Management as follows:

Data Governance is the organizing framework for establishing strategy, objectives and policy for effectively managing corporate data. Data Governance consists of the processes, policies, organization and technologies required to manage and ensure the availability, usability, integrity, consistency, auditability, and security of a company’s data.

Data Management is comprised of the processes and technology used to achieve:

  1. The ability to link data together (e.g. provides the ability to know that an “A” customer is also a “B” customer as well as a “C” customer)
  2. Consistent data definitions throughout the enterprise
  3. An environment that offers controlled access to data
  4. People, processes and technologies centered around receiving, controlling and provisioning data

It’s important to note that while Data Governance provides high level oversight, Data Management provides the day to day execution and enforcements of the objectives and policies defined by the Data Governance framework. It is also important to note that companies have varying degrees of maturity in either of these practices, with the most mature having both domains thoroughly institutionalized throughout the enterprise.

When performing Project Management, one should understand whether the company has a Data Governance and/or Data Management framework and how to invoke it/them when related issues and risks are identified. In doing so, the Data Governance contact group will likely be able to help articulate the risk or issue as well as help determine and execute mitigation strategies.  Indeed, Data Governance standards should be considered from the earliest stages of a project to ensure that they will be met throughout the project lifecycle from definition and business case through and including deployment and operations. As such, it is not only Project Management that needs to be aware of the Data Governance and Data Management standards and frameworks, but also those involved in the very earliest stages of a project definition and business case development including Product Management, Team Leadership, Business and Technology leadership, etc.

In the absence of a Data Governance or Data Management framework, Project Management will be responsible for figuring out a) whether the risk or issue is properly articulated, b) the mitigation strategy and c) the appropriate business or technology groups to execute said strategy.

Either way, having some basic understanding of Data Governance precepts will help Project Management, and anyone involved in the early phases of a project, better manage and engage the right groups when data related issues or risks crop up.

Some of the data related challenges and risks that are common to technology implementation projects and can cause long term problems if not handled properly are as follows:

By providing Project Management some grounding in Data Governance and Data Management concepts and principles, they will be in a position to take more of an enterprise-wide view of data and ensure their project delivers on the company’s data objectives and strategy. This is important to ensure that data retains integrity across the systems landscape and can be leveraged to drive data based decisions and becomes an asset to the organization. Do not make the mistake of treating enterprise data as local and transforming/cleansing locally without a view to the organization – that is a sure fire way to let the enterprise data get out of sync and hamper the company’s ability to really harness the power of their data assets.

If you need help assessing the impact of your upcoming technology initiatives on your organization’s data environment, please contact us at

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