Getting Advanced Analytics Right
Consumer goods (CG) companies get real benefit from their investments in advanced analytics when they get three things right: talent, technology and culture.
Talent. Though the scale will vary, almost all CG companies will need an elite core of data scientists: highly trained professionals who know how to use the advanced statistical algorithms and machine learning protocols necessary to handle large and varied amounts of data coming in at high velocity.
Unfortunately, there aren’t that many people today who are trained to handle and analyze data sets of this type and magnitude. Inevitably companies, that make a bet on advanced analytics must be prepared to fight a war for talent.
And acquiring data scientists is only part of the battle. Just as important is to cultivate a group of people that we call “bilinguals” — people who can speak the languages of both business and analytics, and who can therefore translate between the advanced data scientists and the non-technical decision makers responsible for day-to-day business operations. Bilinguals fulfill the essential function of helping craft the solutions that will ultimately be of value to the business person.
Technology. Having the right technology infrastructure is an essential component of analytics success. And here, companies have taken different paths. One approach is to make a very big investment in the technology stack required to handle big data. We sometimes call this the Field of Dreams approach: if you build it, they will come. Unfortunately, what has often happened is that companies have overshot the mark. They’ve invested more in the technology than the organization was prepared to absorb, and then they had to scramble around for use cases to justify the investment. Other companies have taken the opposite approach; they’ve built only the technology stack required for their immediate needs.
The most successful path is the one that lies in between these poles. It’s better to plan investments for the mid-term, to anticipate some of the new use cases the company might not be ready for yet, but can anticipate. If your company is like most, the number of use cases requested will grow as decision makers experience first-hand the benefits of more robust data and analytics.
Culture. Some firms have no culture issues around analytics because it is baked into their business model — think Google, Amazon, Netflix and LinkedIn. Others have traditionally valued intuition and experience, and have a harder time incorporating analytics into their decision processes. What those companies have to realize is that ignoring advanced analytics is not an option. Cultures that view analytics as a long term capability, and build in time for experimentation and learning, can embrace it as a powerful aid to human judgment.
Talent. Though the scale will vary, almost all CG companies will need an elite core of data scientists: highly trained professionals who know how to use the advanced statistical algorithms and machine learning protocols necessary to handle large and varied amounts of data coming in at high velocity.
Unfortunately, there aren’t that many people today who are trained to handle and analyze data sets of this type and magnitude. Inevitably companies, that make a bet on advanced analytics must be prepared to fight a war for talent.
And acquiring data scientists is only part of the battle. Just as important is to cultivate a group of people that we call “bilinguals” — people who can speak the languages of both business and analytics, and who can therefore translate between the advanced data scientists and the non-technical decision makers responsible for day-to-day business operations. Bilinguals fulfill the essential function of helping craft the solutions that will ultimately be of value to the business person.
Technology. Having the right technology infrastructure is an essential component of analytics success. And here, companies have taken different paths. One approach is to make a very big investment in the technology stack required to handle big data. We sometimes call this the Field of Dreams approach: if you build it, they will come. Unfortunately, what has often happened is that companies have overshot the mark. They’ve invested more in the technology than the organization was prepared to absorb, and then they had to scramble around for use cases to justify the investment. Other companies have taken the opposite approach; they’ve built only the technology stack required for their immediate needs.
The most successful path is the one that lies in between these poles. It’s better to plan investments for the mid-term, to anticipate some of the new use cases the company might not be ready for yet, but can anticipate. If your company is like most, the number of use cases requested will grow as decision makers experience first-hand the benefits of more robust data and analytics.
Culture. Some firms have no culture issues around analytics because it is baked into their business model — think Google, Amazon, Netflix and LinkedIn. Others have traditionally valued intuition and experience, and have a harder time incorporating analytics into their decision processes. What those companies have to realize is that ignoring advanced analytics is not an option. Cultures that view analytics as a long term capability, and build in time for experimentation and learning, can embrace it as a powerful aid to human judgment.