While there’s been no lack of attention paid to the vast potential big data has for bolstering business operations, many of the challenges to exploiting big data sources lie in transforming the people and culture within companies.
You Need More Than Good Data
That’s the assertion of McKinsey & Co. in a pair of videos detailing how companies need to adjust their talent pools and decision-making processes to avoid ending up with an avalanche of data but no actionable insights.
“Analytics will define the difference between the losers and winners going forward,” notes Tim McGuire, a McKinsey director, in the video. “The ability to get incremental results out of marketing dollars, the ability to get incremental margin out of pricing and promotion decisions, [and] the ability to get incremental sales out of better supply chain and management decisions – all of those things are starting to define the gap between the winners and the losers.”
However, McGuire notes that there are three big challenges associated with closing the gap between companies that have tapped the power of big data for real business results and those that have not. First, companies have to identify what data they want to use. In addition to the reams of internal data – such as transactional and performance data – there are now a wide variety of external sources that businesses can tap.
Marrying External and Internal Data
The real value is bringing external data – such as weather data, traffic pattern data, and details of competitive pricing – together with internal data, McGuire notes. Next, companies must focus on acquiring employees with the skills to make insights generated from analytics pay off.
While many companies start by questioning if they need a centralized or decentralized approach to analytics, that is not the most effective way to secure the best talent for big data mining, notes Matt Ariker, chief operating officer, McKinsey’s Consumer Marketing Analytics Center, in a second video.
“The first question is how do you make sure that the organization responsible for the analytics looks at their job as a services bureau and makes sure that they are providing useful and used analytics to internal companies,” according to Ariker. “Are your analysts doing analytics . . . to help the user and the business, and does that business user feel like they are being helped?”
Ariker says that most companies need three types of roles for the organizational change needed to embrace data-driven decision-making:
- Data scientists who create the advanced models and ensure they are repeatable
- Business solution architects who create data repositories with the right data
- Campaign experts who turn the models into campaigns, which touch consumers and ensure the models achieve results
Finally, McGuire says that companies must change their decision-making paradigms to rely on data.
“Unless you are willing to change the way you make decisions . . . all of the insight out of the data won’t solve anything,” he says.