You are here:  Home  >  Education Resources For Use & Management of Data  >  Data Daily | Data News  >  Current Article

Why Non-Tech Execs Should Understand Data Modeling

By   /  May 24, 2016  /  No Comments

dmby Angela Guess

Eran Levy recently wrote in Smart Data Collective, “For many non-technical individuals in the business world, data modeling can seem like a strange and somewhat terrifying realm. Even those who are data-savvy and regularly consult and analyze data in their day-to-day operations will often view modeling as perplexing under-the-hood stuff that is best left to data analysts or IT staff. To an extent, there’s some truth to this: advanced data modeling can quickly become a complicated affair (although the right business intelligence software makes it much, much simpler) and is often best left to the pros. However, even if you’re a non-techie who isn’t going to be the one actually creating the company-wide data models, having a rudimentary understanding of the basic concepts can help you, the data analyst and the business achieve the best results from the BI processes in place – and here’s why.”

Levy goes on, “(1) Data modeling is the basis of all analytical processes. A statistic that’s cited to the point of cliché states that analysts might spend up to 80% of their time preparing data for analysis. Within the data preparation process, in addition to cleaning and normalizing data, creating the data model or models typically takes up a large portion of that effort. More importantly for our purposes, it will also define the types of analyses that can be performed, and consequently the types of dashboards or reports the end users will be able to view.”

He continues, “(2) Improve communication and collaboration with the analytical teams. As mentioned, you might not actually be the one working with data models in your organization. However, as long as you’re analyzing data – even completely passively, as a recipient of weekly reports – the data model in place affects the type of data you’re seeing and the conclusions you can draw from it. Being able to effectively communicate business rules to data modelers, and to understand from them what they need in order to make the data comply with these business rules, can do wonders to improve the quality and relevance of data being used in the organization.”

Read more here.

Photo credit: Flickr/ Samuel Mann

You might also like...

The AI Advantage in ITSM: Features and Use Cases

Read More →