Oil has reigned for centuries as one of society’s most valuable resources. Throughout history, those who have controlled oil, have controlled the economy. However, in today’s “data economy,” it can be argued that data, due to the insight and knowledge that can be extracted from it, is potentially more valuable. Like oil, raw data’s value comes from its potential to be refined into an essential commodity.
When refined into insights, data can drive good decision making and result in revenue opportunities, cost savings and more efficient operations for companies. Refined data enables critical changes to be made before costs are incurred, as opposed to in the rear-view mirror.
At Northridge, data analytics is fundamental to our business. We have been utilizing data to help our clients gain insights and solve problems for close to 20 years. Through this experience, we’ve developed a few Guiding Principles that have helped our clients turn data into insights:
- The more transparent the input is, the more credibility the output will have. When working with clients, Northridge ensures the transparency of client data sources by having the clients’ Subject Matter Experts review the data we have collected and verified that nothing is missing or incorrect prior to the refinement process. We then recommend that business leaders thoroughly examine the opportunities that the data reveals after it is optimally refined. With their knowledge of the organization, culture, and priorities, they can leverage the business value of data by balancing it against their current environment to make the best possible decisions for the organization.
- Accurate data refinement necessitates collaboration with Subject Matter Experts. There will always be a need for human expertise to ensure that raw data is not misinterpreted and that appropriate metrics are applied. Increasingly, organizations have their own data scientists. Ideally, they should be aligned with Subject Matter Experts so, together, they can more efficiently manipulate data to decipher problems and gain insights.
- Raw data must be questioned to ensure its accuracy. When data is taken down to the lowest possible level (geography, product, customer, employee, etc.), different views of what is happening to emerge. For example, if a national product is delivering lower than anticipated revenue, it is possible that one negative region is dragging down the national average. Perhaps the cost of raw materials is above average in this region. The solution may be to price geographically, based on local costs. If the decision makers look only at national averages, they might inadvertently put capital into a high-cost area where they won’t get the same return.
- The validity of the metrics must be substantiated. After data is taken down to the lowest possible level, it must be aggregated back up. Once aggregated, the data will yield opportunities on the upside for revenue and the downside for cost. For example, we had a client who believed his call center was meeting all of its objectives. This belief was based on reported metrics and validated by raw data. Yet, as we walked through the center, we noticed multiple employees who appeared to have downtime. It turned out that, while there was nothing wrong with the raw data, there was a problem with how the metrics were constructed. The metrics didn’t address why people weren’t fully utilized, but this insight didn’t emerge until the data was examined at the lowest level.
Insights gained from refining data allow companies to spend money where it will be best spent. Northridge is often able to help clients get more value from their company’s internal data with analytics. When data is refined and analyzed across different departments such as Finance, HR and Marketing, the inefficiencies in the business become apparent. When these inefficiencies are reviewed with projected financials for 3-5 years going forward, patterns begin to emerge. With these insights, sophisticated decisions can be made and prioritized based on what the return will be. Businesses can minimize the chances of ill-conceived capital spends, accelerate the sun-setting of a product or increase spending on a product that performs better than expected.
In its most refined state, data can be transformed into knowledge which guides decision making and provides opportunities for operational efficiencies, cost savings, and increased revenue. In this form, the business value of data is exceptionally high. Unlike oil, there is no shortage of raw data. However, the data’s value comes from the refinement process. To maximize business outcomes, it is incumbent upon companies to collect and refine their own internal data, metaphorically transforming their “crude oil” into “jet fuel.”
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