In my last post (Becoming Data Driven), we began a discussion about the goal of becoming a data driven organization. We determined it’s not so much about the tools as it is about leadership, philosophy, and decision processes of a company that help to reach a data-driven state.

If you are data driven then your analytic tools and insights are helping you drive another dollar of revenue, reduce another dollar of expense, find ways to do more with less, and secure your future against disruption.

As part of the post, we introduced the Business Threat Assessment (BTA), a mechanism used by business leaders interested in being more data driven. The outcomes of this assessment are a list of three tactical threats, three strategic threats, and the three most persistent challenges to an operation’s efficiency. It’s a way to get organized by establishing a meaningful priority set that should be evergreen. The BTA is as much a way of thinking as it is a process, and it should be scalable up and down your team. Your managers should be able to weigh in with their interpretation as it relates to their areas of responsibility.

In this post, we will begin to leverage the outputs of your Business Threat Assessment by introducing how to leverage the first level of analytics – descriptive analytics – to better align your data to your fundamental business goals.

Descriptive analytics encompasses how your current and historical data sets are produced, manipulated, and displayed (as compared to predictive, or forward-looking tools). Here are the foundational elements a company should include, in order of complexity, to develop a successful descriptive analytics solution:

  1. The Data Assets – All the data generated and captured in your operation, your company’s ocean of data, so to speak. In my first RUMBLE blog post (The Data Map: The Road to Managing Data as an Asset), we talked about creating a data map that inventoried these assets and this is a necessary first step for any company to pursue.
  2. The Data Management Infrastructure – The various repositories where you are storing data today – it might be organized, partly organized or not organized at all.
  3. The Data Presentation Layer – Your existing reports. 
  4. A Data Model – An overlay that ties all your data elements together, defines their types and values, and illuminates the relationship and sequencing between them. This can be as-built, a reflection of what has grown over time, or optimized (detailed later).
  5. A Data Archive – An advancement over “run of the mill” data management and storage, a Data Archive constitutes a designed repository and database infrastructure that typically integrates and organizes your data elements into an efficient structure that is more easily accessed and manipulated for reporting and analytics.
  6. A Business Intelligence Tool – These tool kits (Tableau, Qlikview, Ateryx and Microsoft Power BI) optimize the visual display of data and reporting by integrating user configurable dashboards, reporting schedulers, and distribution and publication functionalities.
  7. An Analytic Data Set and Toolkit – A specialized data repository created by your analytics’ team that is populated by the critical data element subsets most relevant to your analytic requirements. This advanced approach has been statistically validated through exploratory data analysis as the most useful subset for query and investigation.

In short, descriptive analytics manipulates your current and historical data assets we’ve listed above (including your reports) to make more effective business decisions possible.

Descriptive Analytics Applied

The goal of increasing efficiency in a systematic matter is a commitment to the philosophy and process of continuous improvement. Measure, analyze, respond, act, measure, analyze, respond. Repeat.

Common initiatives that fall under the applied descriptive analytics proven to increase efficiency include:

  • Reports Audit and Data Model Optimization
  • KPI Review and Testing
  • Attribution Analysis

In this blog, we’ll start with a more in-depth analysis of the first example.

  1. The Reports Audit and Data Model Creation or Optimization

Real-life Example: A client had decided to implement a new Business Intelligent (BI) tool and requested our help migrating the reporting infrastructure of their investment accounting team. We discovered a reporting infrastructure of over 200 spreadsheet reports that had accrued over the previous decade. Each was hand-built, hand-operated and tied to critical processes of month-end and quarter-end close cycles.

There was no data model, and data feeds driving these reports came from over 50 discrete sources. We conducted a reporting audit and found that many of the report elements overlapped. We were able to create a data model that established the key sources and data elements, including their relationships and location, and which populated a data repository we built for the BI tool. The results were a 50% reduction in the number of reports with an equivalent hours saved of 1.5 FTE headcount. 

Most companies can benefit from a process like this. Understanding the type, frequency, and audience of all the reports you produce allows you to establish control and impose efficiency where it may not be currently be present. If you can’t point to a data model that classifies and organizes your data elements, you can’t control the evolution of report creep. A good place to start is to consciously review the time, dollars and staff you have supporting your current reporting structure. This investment in TIME should provide you an ROI urgency to ensure a data map is created and optimized.

In my next blog I’ll introduce two additional applied diagnostic analytics examples: KPI Review and Testing and Attribution Analysis. Both will include real-life examples of the how organizations benefited from their use.

(Article by Chris Schultz, a principal at Analytic Marketing Innovations (AMI) and a RUMBLE Strategic Partner. Their solutions delivery approach identifies executable steps and recommends both near-term and long-term courses of actions, helping your business leverage data insights for growth and transformation.)