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You Don’t Have to be a Rocket Scientist to be a Data Scientist

Posted by Tony Capobianco on Sep 8, 2016 10:20:55 AM

 This is part two in a two-part series, focusing on creating actionable KPIs and goals for compelling dashboards. If you’re interested in reading the first part of the series, check out “Report Burnout? 3 Steps to Actionable Dashboards People Can’t Wait to Open.”

Now that you have added KPIs and actionable goals to your business intelligence portfolio, the next step is to start adding Data Science and provide individuals with Predictive, Descriptive, and Prescriptive reporting.

As we know, the goal of a data warehouse and business intelligence is to get actionable and insightful reporting into the hands of those that can effect positive change in your business. For some organizations, true analytics competency has moved from reporting on how many (“X, Y and Z’s” over a certain period of time) to actually predicting, describing and prescribing specific solutions to a challenge or objective.

Now don’t let the words Data Science scare you. Not all science has to be hard! And, data science is as much ART as it is science. In fact, maybe we should just call those who do this important work data artists!


Let’s say our goal is to increase sales 15% for this fiscal year. How do we go about adding predictive, descriptive and prescriptive reporting (essentially, data science insight) to this goal? It’s not as hard as you think, but it does take some effort. You will likely need to speak with coworkers and Subject Matter Experts (SMEs) in the area of focus for your actionable goal. They are the ones who will have the business acumen and knowledge to help fill in the blanks.

First, let’s first define our key terms. For the sake of time, we’re going to keep these really simple.

  • Descriptive analytics: Answer the questions – “What happened?” and “Why did it happen?”    Descriptive analytics look at past performance and understand what drove it by mining historical data to identify the causes of past successes or failures.

        Example of Descriptive Analytics: “Last week’s ad buy drove an increase of 20% in same-store             sales.”

  • Predictive analytics: Predictive analytics extract information from data and use it to predict trends and behavior patterns to hypothesize the likelihood of outcomes. Though the unknown event of interest is often in the future, predictive analytics can be applied to any type of unknown scenario, including the past and present. For example, predictive analytics can be used to identify suspects after a crime has been committed or credit card fraud as it occurs.

        Example of Predictive (Forecasting) Analytics: “Based on the sales figures in Q2, you are                     predicted to reach 67% of sales quota by the year’s end.”

  • Prescriptive analytics: This area is often referred to as the "final frontier of analytic capabilities.” It involves prescribing necessary steps to meet an objective, based on the data. It’s just like when your doctor gives you a prescription to get over a sinus infection. The objective is to get better, and the prescription is a recommended method on how to improve your condition.

        Example of Prescriptive Analytics: In order to increase sales revenue 15% by the end of the year,         we will need to maintain / increase our ad buys in NYC, Chicago, San Diego, and San Francisco.

Now let’s see what this section of our dashboard might look like with all these pieces put together:

What would the feedback be to your Business Intelligence or Business Analyst team if your reports/dashboards look like this?

Get help from friends

Identifying which values to look for and which reports to pay attention to is one of the biggest challenges when it comes to effectively utilizing a BI solution. When done right, organizations can obtain real insight from analytics and avoid report burnout.

Wrap up

According to Gartner, the BI and analytics market is evolving toward a business-led, self-service analytics paradigm. Partly this means that there is a refined focus on analytics programs that stress accessibility, agility, and deeper analytical insight. You can join this reinvention and generate greater analytical insight from your BI program by adding proper context to your reports and data sets and fine tuning your KPIs. Your report users will thank you.


If you would like to talk about your BI solution or for ideas on how to optimize your KPIs, email us at info@datasourceconsulting.com, we’d love to chat!

By: Tony Capobianco, Business Analyst




Topics: Business Intelligence, Blog

Written by Tony Capobianco