Mining Data in Search of Impact

Organizations invest considerable amounts of money into creating data capturing and management systems. However, in order to maximize the value of the data they collect, they need to be able to draw out actionable insights in order to drive bottom-line results.

As data analysts, the object of the game is to find connections and relationships between the data that we can use to build narratives that describe causal relationships. These narratives can then be used to link the connections and relationships to business outcomes and objectives.

What We’re Looking For

In the search for actionable insights, we can point out three different traits of data or data relationships that lead to impactful decisions.

What We Can Control

Air Canada Special Cargo Flights in Wake of COVID-19 Pandemic

Beginning with the most straightforward, we must look for data from business actions that we can control or manipulate. The key is to narrow our view on our business’s different levers. For example, in the wake of the COVID-19 pandemic, the airline industry could react to declining passenger flights by repurposing their aircraft to fly special cargo flights in order to maintain revenue streams. However, the airline industry would have a harder time reacting to the impacts of natural disasters like blizzards or hurricanes. While the data may tell them that blizzards are bad for business, they simply cannot impact the weather itself.

Connections to Bottom-Line Business Results

Air Canda Post COVID-19 Operations

Actionable insights depend on their ability to impact a business’s bottom line. Ultimately, that is the only reason a company would build a data collection and analysis organization. To follow the example of the airline industry, shifting passenger aircraft into cargo service keeps the planes in the air, generating revenue for the airlines as they continue to meet consumer needs.

Degrees of Reasonable Change

Lastly, data analysts must keep in mind the degree of the feasibility of each actionable insight they uncover. Insights are essentially only as valuable as their ability to be implemented, so it is, therefore, necessary to always rank the difficulty of implementing. In the case of the airline industry, consumer research may determine that consumers would feel comfortable resuming air travel if the airlines could guarantee completely sterile flights. However, it would be all but impossible for an airline to actually be able to make such a guarantee, rendering the insight close to useless.

Wrapping Up

Air Canada Cargo

Data analysts must be able to extract actionable insights from their organization’s data in order to maximize the value of that data. By finding data narratives that we can control, can link to bottom-line business results, and can reasonably act upon, we can truly leverage data to drive business outcomes and objectives.

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