Turning Data Into Profits: What Does It Take?

The top five data-centric enterprises — Google, Amazon, Apple, Facebook and Microsoft— recorded more than $25 billion in net profits for the first quarter of 2017 alone. Stats like that prompted The Economist to proclaim: “The world’s most valuable resource is no longer oil, but data.”

The only way forward on the path to greater profitability for your company today is to understand how to mine and refine your data.

1. Capture all your data — storage is a lot cheaper today.

No matter what you intend to do down the road, it all starts with data collection. You may not have the resources for data analysis and pattern recognition, but it makes good business sense to capture and store as much data as you can now.

Vast improvements in technology and newer business models have lead to affordable world-class data storage. For example, the average cost of a hard drive has gone down from $0.10 to $0.01 per gigabyte over just the last decade.

2. Enrich your data — look for the missing pieces in your data story.

The next step is to identify what you don’t know about your consumers today and how this information will impact your business. Then you map this information back to the relevant data sets that can generate the sorts of insights you need. Make a list of desirable data sets, no matter what you currently capture. This will direct your efforts in refining those data sets and highlighting which ones you need to acquire as you move forward. Separate out the real-time data sources from the static ones to ensure that you have a good mix, which will eventually serve as a basis for more valuable insights.

3. Analyze your data — see what you can do internally vs. outsource.

The third step is to determine whether your company’s core competencies include building data analysis tools. This is partly a matter of honest self-evaluation and partly a matter of strategic commitment. Does your organization share the collective will to build a data analysis package from scratch? Can you be sure you have the funding, resource capacity, talent and time to excel at it? If not, you will be better off assigning a task force to find a data analysis solution to generate insights quickly, efficiently and with the highest level of confidence in the results.

4. Scale right to win — find the right balance that fits your strategy.

After you’ve got a successful working model, the next hurdle is scaling. Scaling too fast or too slow can be dangerous, but a robust data analysis tool can help you scale fairly steadily. Teams that build data tools are typically heavily invested in enhancing the product and incrementally improving it while you turn your attention back to growing your business. At this stage, look at overlaying your data sets to map and understand consumer journeys and construct the context that will help your organization better understand customer behavior.

5. Solve real problems — it’s where the rubber meets the road.

The fifth and final step is to integrate the data analysis tool across your software stack, test its control limits and investigate how to apply it to solving real problems. Nothing is really possible until it’s practical, so work on streamlining how internal teams use the tool and how they use performance metrics to make it better.

When the project is complete, it’s time for a retroactive analysis of what went well, what could be improved in the next iteration and the total impact your investment. Where will your data analysis initiatives go in the future, and how do they complement your market strategy?

Measuring the delta on this data will help you double down on where the intelligence can be most impactful — strategy, marketing, operations, etc. Measure the top line and bottom line of this intelligence, and then define key metrics to maximize profitability in the new world of peak data.

Privacy should be a key concern at every point as data is especially vulnerable during transfers such as exporting to another platform or working with third-party data sources. Make sure your team has processes in place to protect sensitive data relating to your company and your customers and to meet compliances.

Also published in Forbes