Skip to main content
If you are a Planview customer, sign in to enable additional content.
Planview Customer Success Center

What Comes First: The Data or the Tool?

A traditional approach to Enterprise Architecture might suggest spending a significant amount of time (in some cases this could last as long as a year) gathering data from different parts of the business in the hope of building a holistic view of how the business looks today. Once you are sitting on this mass of data, you may then consider EA tools to help you make sense of it all. To some people this seems a logical sequence– we’ll do the heavy data lifting on our end then call in the analytics guys to tell us what it all means. In reality this approach will often lead to wasted effort and set back the timetable for delivering value, as we discussed in a recent post, Bigger Doesn’t Always Mean Better when it comes to data. With the right EA tool in place, organizations better understand the data that is actually needed to yield business benefits, dramatically increasing success rates and significantly reducing time to value.  

right_tool_030414 4 blog 030514-3.jpgWhy the data first approach is flawed 

This approach typically involves time wasted on gathering data that is not necessary or will not deliver significant value. Once the data has been collected organizations often invest additional time and effort verifying data quality, but that can become a daunting task as well if the amount of data is too great and the quality of the necessary data is compromised. In addition, data needs to be governed; otherwise it goes stale rapidly. If organizations are working with more data than they need, they end up spending even more time and effort governing data that isn’t useful. 

Successful data maintenance is federated, involving busy people who need to understand why they should spend time on this vital task.  Modern EA solutions come with governance tools and processes built-in, and knowledgeable tools vendors typically bundle in best practice guidance to help get data in good shape.  The do-it-yourself approach, without adequate guidance and tool support is very often the long, slow, hard, and potentially unsuccessful route.

For these reasons, start with the end in mind, find a tool that helps guide you to the desired outcome, and let it define what data is necessary.

Architect a better business in a different way

Start by identifying the key questions that need to be answered to help the organization better operate, compete, and grow. You will be pleasantly surprised – it’s not as many as you might think. Organizations have discovered that as few as 150 questions may be enough to understand the enterprise sufficiently in order to figure out how to change it.  Mature EA vendors will have incorporated the most common questions into their tools, questions such as: What new business capabilities need to be created? Is the business investing in the right places? Do our strategies support our goals? The tool will provide decision-making insights in the form of visualizations.  Also, don’t underestimate the power of the visualization to drive the data quality process: a CIO of a major European Telco told us “I’m interested in your tool because it will point my guys in the direction of what data to collect, and I believe lighting up your reports will drive the data quality process quicker than any other method”.  Spot on, sir!

Everyone is looking for a better way to achieve their business objectives. But in this instance doing the heavy lifting that comes along with collecting massive amounts of data might not be worth the effort. In fact, it’s better to start small. Get the most out of your EA initiative by focusing on what matters most to business. This will determine what questions need to be answered.  The right tool can guide you through what data is needed and deliver results – quickly.