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You Don’t Have to be a Rocket Scientist to be a
Data Scientist
POSTED BY TONY CAPOBIANCOTONY 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.”
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3. 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 PrescriptivePredictive, 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 sakeFirst, let’s first define our key terms. For the sake
of time, we’re going to keep these really simple.of time, we’re going to keep these really simple.
Descriptive analytics: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: Predictive analytics extract information from data

4. 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: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 withNow let’s see what this section of our dashboard might look like with
all these pieces put together:all these pieces put together:
What would the feedback be to your Business Intelligence orWhat would the feedback be to your Business Intelligence or
Business Analyst team if your reports/dashboards look like this?Business Analyst team if your reports/dashboards look like this?
Get help from friendsGet 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 upWrap 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

5. 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 toemail us at info@datasourceconsulting.com, we’d love to
chat!chat!
By: Tony Capobianco, Business AnalystBy: Tony Capobianco, Business Analyst
TOPICS: BUSINESS INTELLIGENCE, BLOG

6. Written by Tony CapobiancoTony Capobianco
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