Turn Data into a Strategic Asset

Manny Bernabe2022-01-08

The kick-off meeting was scheduled and we were all very excited: we were going to transform the customer’s business with AI and machine learning. This was a Fortune 1000 company, and we knew it would make for a great account. There was just one problem: the company didn’t have any data. Well, that pretty much ended that engagement.

Data fuels your future data, AI and machine learning services. It is a key factor in your success. Start thinking proactively about how you can cultivate and manage this resource.

Many companies want to do AI and machine learning but find that they don’t have the necessary data to get started. Even when they do, it’s often missing important factors or lacking quality. One of the first exercises you should think about is a plan to convert data into a well-managed and useful asset.

1– Map Strategic Goals Identify one business unit or product of focus and think about its strategic goals and key metrics. Work with your key data and machine learning expert to help you generate ideas and narrow your data improvement efforts.

2 — Conduct Data Audit

Next, take an inventory of your current data sources for that unit or product. There are a number of sources you might pull from, including business process data, product/service data and customer data. As you gather data, make note of two aspects about it:

  • Quality: How reliable and useful is the data source?
  • Accessibility: How accessible is the data?

3 — Concept Generation Once you have a sense of direction and inventory of your current data sets, it’s time to think about enriching and improving it. Gather seven to nine key people related to the business unit. Include folks from marketing and sales. Use this team to ideate, rank and prioritize specific ways to enrich existing data resources (or create new ones) that will best help you meet your long-term strategic goals.

4 — Execution Plan Lastly, develop an execution plan for your highest-rated initiatives. This may involve testing the concept, mapping new infrastructure upgrades, or simply developing a dashboard of expected metrics.

Apple’s Data Mistake

To highlight the importance of data, consider one of Apple’s rare missteps. In 2012, Apple looked to replace the Google maps app as the default on its operating system. The company introduced its own maps app, featuring the beautiful design we have come to expect from the company. However, it lacked one thing that ensured disaster from the very start: quality of data. Reports of inconsistent navigation, poor results, bugs and inaccurate location details plagued the app’s launch from the very start. Apple had drastically overlooked the amount of effort that Google had put into data quality. As a result of this oversight, the launch was an absolute disaster for Apple. If the mighty Apple can’t overlook the importance of data, neither should you.

If you want to learn more about AI Transformation, check out our free guide here.


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