Why Should You Trust Data?

You should trust data because data-driven insights are less biased than conventional gut-feeling-driven insights.

While the latter can be a valuable strategy as well, gut feelings are based strictly on your own unique set of previous experiences, and, in the vast majority of cases, they are not be reflective of the experiences of everyone.

Big Data analytics is highly effective because the larger the amount of data used, the greater the likelihood that the data are reflective of the overall population, and the lesser the risk that the conclusions reached from those data are biased.

Empower Your People With Data

Recognizing the power of data analytics is one thing; actually implementing it in an effective way is another. The primary disconnect at the managerial level is not in deciding to pursue data analytics, but in implementing the necessary processes.

So what are the steps to the successful implementation of a robust data strategy?

1. Clearly define your goals.

Simply stating, “we want to be more data-oriented” or “we want to start leveraging AI/Machine Learning” is not a clearly-defined goal. Instead, ask yourself, what is/are the specific question(s) you want to answer with your data?

Great example goals include:

• “I want to reduce my inventory cost”
• “I want to reallocate marketing dollars to the highest grossing mediums”

Being able to articulate a clear answer to this already puts you ahead of many organizations when it comes to data analytics.

2. Think about data input and output.

How does your organization collect data, and in what format does it come? What kind of data-based insights would your organization like to output? How do you want data to flow from the way you collect it to the way you output it? And which of these three steps ―ideally, all three― would you want automated?

3. Administer the proper tools.

As the field of data analytics has grown, so has the array of data tools available.

While Excel has for a long time been used as a catch-all data tool, its lack of specialization to the data input or output phase means it struggles with automation and scalability. Excel can still be a useful tool for the right purposes, depending on the situation. However, most companies who make the best use of their data don’t use Excel for every phase of their data processes.

For data collection, a state-of-the art data strategy includes: