Five Steps for Using Analytics to Transform Your Business

Five Steps for Using Analytics to Transform Your Business

Five Steps for Using Analytics to Transform Your Business

Five Steps for Using Analytics to Transform Your Business
 
With analytics, optimization isn’t the end game. Business transformation is. Creating a transformative analytics program requires support, focus, and action from the top.
By Derek Slater
With big data driving known disrupters, such as Netflix and Uber, more and more organizations are jumping into analytics.
The proof is in the research. Over the next 12 to 16 months, companies will prioritize business intelligence and analytics above almost all other technology-driven strategic initiatives. This is according to 633 IT decision makers recently surveyed by Enterprise Strategy Group (ESG).
So-called optimization projects, says ESG senior analyst Nik Rouda, comprise a critical first step for many businesses breaking into big data.

For example, freight specialists, such as UPS or FedEx, use optimization. “Route optimization—just using less gas, getting more deliveries per driver, per truck, per day,” he says, “can have an impact in the millions of dollars.”
That kind of increased operations efficiency, he says, “is probably the number one use case for analytics right now.”
But optimization isn’t the end game. Business transformation is. And creating a transformative analytics program requires support, focus, and action from the very top of the organization.
Here are five steps for bringing your analytics programs to the next level. According to Rouda, these steps take an organization from optimization to disruption. Then, ultimately, on to true transformation.
 
Build the habit of asking data-oriented questions:
Projects that transform companies and even industries often start with a simple question: “What do the numbers tell us?”
Some companies “make the mistake of leaving this up to the data scientists,” Rouda says.
Leadership teams that develop the habit of asking this question, set themselves up for success. From there, you want to spread that habit across the organization.
Rouda also notes that by asking more data-focused questions, you get a cycle of collecting and integrating more data. In turn, creating more opportunities for transformative insight down the road.
 
Make data highly accessible:
Once companies are habitually asking data-oriented questions, Rouda says, it’s imperative to make the answers very easy to find.
Rouda previously led the marketing at a technology company, where he says a lot of potentially valuable data wasn’t always easy to access or analyze.
“Once we started getting better tools in place and creating things like dashboards” for sales leads, he says, “we started to get value out of the data.

Read Also:
Big Data – a roadmap for smarter data

 



Sentiment Analysis Symposium

27
Jun
2017
Sentiment Analysis Symposium

15% off with code 7WDATA

Read Also:
Big: Data, model, quality and variety

Data Analytics and Behavioural Science Applied to Retail and Consumer Markets

28
Jun
2017
Data Analytics and Behavioural Science Applied to Retail and Consumer Markets

15% off with code 7WDATA

Read Also:
7 Ways To Leverage Your Small Business Data For Enhanced Revenues

AI, Machine Learning and Sentiment Analysis Applied to Finance

28
Jun
2017
AI, Machine Learning and Sentiment Analysis Applied to Finance

15% off with code 7WDATA

Read Also:
How auto giants are using big data: A conversation with Ford

Real Business Intelligence

11
Jul
2017
Real Business Intelligence

25% off with code RBIYM01

Read Also:
5 things businesses need to know about data science

Advanced Analytics Forum

20
Sep
2017
Advanced Analytics Forum

15% off with code Discount15

Read Also:
Hypothesis driven thinking in data science

Leave a Reply

Your email address will not be published. Required fields are marked *