For finance leaders, ROI is always top-of-mind. Data-driven decisions should help cultivate long-term, sustainable return on investment.
Finance leaders face a daunting task: Save money by making the right decisions and make more money by spending in the right places. It's a balance that isn't easily maintained and often puts finance executives at odds with both front-line staff and other C-suite members. Thanks to the evolution of big data and cloud computing technologies, however, it's now possible for organizations to discover what really drives emerging trends and what avenues of investment offer the best ROI.
Here's a look at the emerging discipline of data-driven decisions.
Fuel for the Fire
Jim Haas, principal architect of the ADP cloud puts it simply: "Data is fuel for business opportunities." But what exactly does this mean for finance leaders day-to-day?
Think of it like this: Every transaction, every email, every customer service call generates data. Much of this data is "unstructured," which means that it doesn't neatly fit into a spreadsheet or statistical analysis, and at first glance may not be related to other pieces of data. The emergence of cloud-connected, cluster-based big data analysis, however, offers finance leaders the chance to see how individual pieces of data are related, how they interact and how they may form specific patterns or trends which can then be leveraged to make better decisions.
Consider the example of a legacy CRM system. Is it worth investing in a new solution when the existing one "still works"? Maybe not. But if big data analysis reports that usage by front-line staff is declining even as customer satisfaction goes down, this investment may yield significant ROI.
It's worth noting that data doesn't exist in a vacuum. As noted by HBR, the "explosion" of big data has prompted a shift in decision-making strategy since stakeholders at all corporate levels now have access to information, while Chief Executive notes that a long view of emerging technologies — think a decade rather than one or two years — is necessary to optimize outcomes.
For finance leaders, big data offers an ideal starting point: information that's continuously generated, potentially relevant and always changing to reflect current trends. But data-driven decisions aren't enough in isolation. According to Money Management, organizations must be wary of the inherent bias present in both data modeling and analysis algorithms — while the underlying data is necessarily objective, the weighting and relevance of that data is assigned value based on human-generated code. The result? Data strategies require diversification.
Without reliable data, decision-making is little more than guesswork informed by "gut" feelings and the results are equivalent to random chance. Increasing the odds requires a data-driven foundation combined with other key decision strategies, including:
- Don't decide in isolation — According to innovator Richard Branson, making decisions in isolation can impact other business opportunities. From a big data perspective, this means going beyond the analysis of data at hand to consider current capital investments, future projects and potential timelines.
- Protect the downside — No decision comes without risk, and even if data-driven analysis suggests that a specific course of action will improve corporate fortunes, there's still the possibility of market failure or human error. The result? Build in downside safeguards. If you're considering a capital investment in technology or machinery, what (if any) warranties or guarantees exist? If this is a people-driven decision such as recruiting or promotions, what infrastructure do you have in place to support current business objectives?
- Consider opposites — As noted by Forbes, any decision-making process must make room for opposition. And while it's tempting to cite data-driven decisions as beyond the reach of typical criticism, it's worth getting multiple perspectives. Even if this doesn't change the ultimate outcome, it can help modify decision parameters to limit overall risk.
According to Haas, technology has advanced to a point where big data analysis is "no longer based on estimations, but reality." With cluster-based solutions able to handle large volumes of data at speed, finance leaders can be confident in both the veracity and value of this resource. Turning big data into ROI-driven decisions, meanwhile, requires both the big picture of big data and the small-scale specifics of human insight.
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