you don’t have to be a rocket scientist to be a data scientist

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Page 2: You Don’t Have to be a Rocket Scientist to be a Data Scientist

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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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Page 3: You Don’t Have to be a Rocket Scientist to be a Data Scientist

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

Page 4: You Don’t Have to be a Rocket Scientist to be a Data Scientist

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 friendsIdentifying 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 upAccording to Gartner, the BI and analytics market is evolving toward a

business-led, self-service analytics paradigm. Partly this means that there is a

Page 5: You Don’t Have to be a Rocket Scientist to be a Data Scientist

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 [email protected], we’d love toemail us at [email protected], we’d love to

chat!chat!

By: Tony Capobianco, Business AnalystBy: Tony Capobianco, Business Analyst

TOPICS: BUSINESS INTELLIGENCE, BLOG

Page 6: You Don’t Have to be a Rocket Scientist to be a Data Scientist

Written by Tony CapobiancoTony Capobianco

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