Much of the promise of big data analytics and data analysis has been around unearthing myriad actionable insights that could lead to new customers who bring with them new revenue streams.
But strange as it may seem, the secret sauce to big data doesn’t lie in huge data warehouses or number-crunching supercomputers.
Too many companies focus time and attention on the technology to solve big data questions without taking the time to ask the important business questions first, notes Matt Ariker in a Forbes blog post.
“Tremendous insights do exist in big data,” according to Ariker, the COO of McKinsey’s Consumer Marketing Analytics Center. “Companies that use it well are leaping ahead of their competitors. One of the big reasons for that, however, is that they have a very clear sense of what they want to do with all that data before they start.”
Here’s how companies can use pencils to bolster their data analysis and big data efforts:
1. Define business impact. This goes beyond highlighting broad goals like “increasing wallet share,” Ariker notes.
“You want to lay out what business questions or problems you expect to be able to solve when you have finished the analysis,” he says. “The very act of writing at this level of specificity will help you clarify what you’re looking to do, and how you will define and declare success before you start.”
2. Set challenging and measurable goals. Many goals like revenue growth and increased profitability are worthy, but companies should go beyond these things.
“Write out how the improvement you’re shooting for will impact the P&L,” Ariker says. “For example, if you want customers to stay longer, are you expecting them to increase their product usage too? If you’re looking to reduce customer churn, how much of a reduction, for how long, and how much stronger will profit be because of it?”
3. Lay out milestones. While installing the necessary technology is part of a big data initiative, it’s not the best milestone to write down. Instead, create a milestone like “convert first high-end target into a customer” or “deliver insights report to CEO,” Ariker notes
“Just as importantly, take that pencil and put people’s names against the various milestones so that everyone is clear who needs to deliver what and when throughout the entire big data gestation lifecycle,” he says. “Make sure your team is clear and accountable about these milestones (again, kill that ambiguity).”
All the steps above would be best initiated by the lines of business and later supported by IT. Thomas Redman, a data consultant and author of “Data Driven: Profiting from your Most Important Business Asset,” suggests that responsibility for data indeed should lie with the business and not the IT department.
“The two most important moments in a piece of data’s lifetime are the moment it is created and the moment it is used,” Redman suggests in this Harvard Business Review post. “Most of these moments don’t occur in IT. They occur in the trenches, when a salesperson signs up a new customer; in middle management, as a group struggles to understand and improve market share; in the analytics group, when a data scientist is seeking a new discovery in big data; and in an executive’s office.”
In addition, management responsibility should lie with the department that has the most to gain or lose with data initiatives, Redman suggests. When a line of business gains new, valuable insight from data, it can post hefty gains; however, IT will see little of that gain. And it’s not IT, but the business, that will be hurt when data is wrong.
“You don’t manage people assets the same way you manage capital assets,” Redman adds. “Nor should you manage data assets in the same way you manage technology assets. This may be the most fundamental reason for moving responsibility for data out of IT.”