Tools and Culture: Stumbling Blocks for Effective Data and Analytics

Holly Lyke-Ho-Gland's picture

I recently spoke with Kevin De Pree, vice-president of Rand Group, to discuss the common organizational challenges related to data and analytics and gaps in common analytics to support organizations’ data and analytics needs.   

Why do organizations continue to struggle with foundational elements of culture and effective application for data and analytics?

A lot of organizations focus too much on the ‘how’ rather than the ‘what.’ What I mean by that is they get bogged down in the technical details—where is the data, how are we going to move it, how are we going to roll this all up—without understanding the purpose of their analytics efforts in the first place. For example, if an organization is trying to grow a particular product line the important question is: What are the key measures that show that we are actually achieving that strategy?

Rather than focusing on the business strategy and the measurements of success, organizations focus the bulk of their efforts on the need to create an Excel spreadsheet to capture information. By their own admission, some organizations spend three to four hours a day putting information into spreadsheets. While the people trying to grow that product line are only able to look at the information they need once a week or once a month. Which limits their ability to ensure their work is flexible and reactive to needs. Ultimately organizations spend a lot of time and effort to do these things without achieving the ability to use data for proactive problem solving.

What advice do you have for addressing these challenges?

Initiatives will often fail because they go on for months before the organization starts realizing the benefits of the project. Part of the reason this is so common is that people try to look at the entire organization and put together a comprehensive analytics system. It becomes overwhelming.

The best advice I can give is to start small. Pick two or three measures that are important for executing a business strategy and build analytics to inform decisions around those measures. Once these pilots are in place, continue to enhance the program. Ultimately organizations need to look at their application of analytics as a journey rather than a destination. This is going to be something that continues to evolve over time by expanding efforts with additional measures along the way.

You mention that traditional analytics tools are ineffective due to the volume, variety, and velocity of data in today’s business. What are the traditional analytics tools you are referring to and how are they insufficient?

The most often used analytical tool is Microsoft® Excel. I would venture to say that probably 100 percent of companies today are using some form of spreadsheet to manage portions of their business. It’s easy to use and manipulate. Organizations can build formulas and then present that data visually in bar charts, pie charts, or columns.

There’s really nothing wrong with the tool itself. The challenge is making sure that the information that that tool is displaying is up-to-date. The problem with Excel spreadsheets is that as soon as you export the data, it’s static and out of date. There are ways to connect the spreadsheets directly to data sources. However, in most organizations, regardless of whether you’re the CEO, the COO, or a line manager, the information you need doesn’t only come from one source. A solution that aggregates the right data from a myriad of systems—is the number one challenge today.

Another gap for traditional analytics tools is the need for predictive modeling—taking past performance and applying a predictive model to determine where you are going to be tomorrow, at the end of the month, or at the end of the quarter and using those insights to determine what decisions you need to make. Predictive modeling is something that a lot of corporate performance management solutions are starting to embed. We’re starting to see AI that looks at the data and analyzes it quickly to figure out where the trends are going. Using that predictive modeling is key. You need to find a platform that is going to allow you to look at historical information and predict future results.

For more information on this topic and insights on how to address these limitations, check out the full interview, or  join us on Tuesday, August 28th at 12:00 p.m. CDT for “The Inefficiency of Excel Spreadsheets—Simple Solutions for Managing Multiple Sources of Data”, where Kevin will discuss a simple solution to addressing the inefficiencies of traditional analysis tools in a world characterized by increased volume, variety, and velocity of data. Participants will learn how to:

  • Identify reporting and forecasting weaknesses 
  • Consolidate reports across the business for better visibility
  • Transform business data into actionable intelligence using Microsoft PowerBI and Office 365

For more process and performance management research and insights follow me on twitter at @hlykehogland or connect with me on 


Be the first to comment!