Decision-making: Three reasons why you may be making bad decisions with Excel.
By D. Wayne McIntyre, June 2018
We’ve entered the age of big data, IoT, deep learning and AI, with more and more organizations recognizing the value of data. They are investing in it to drive better outcomes across functions from marketing to investment planning to HR and beyond. And they are making big bets on technologies that will help them collect even more data.
But there is a problem.
Is all this data generating significantly better outcomes? Are senior executives better able to confidently make the right decisions?
I spoke with 10 business leaders from different industries over the last few weeks to try to find out. The answers across the board were either “Not enough” or “No”. While they saw value at the functional level of their organizations, such as operations and maintenance, they did not see a direct personal benefit to their decision-making processes.
For many, the issue was trust. They simply do not fully trust “black box” technologies, data and algorithms. In addition, most data scientists and companies are not able to communicate their value in a way that a typical business leader can understand. As a result, most executives are still using the tried-and-true decision-making approaches that helped them reach the senior leadership positions. Usually, this involves a combination of Excel and intuition.
A recent HBR article entitled “Big Companies Are Embracing Analytics, But Most Still Don’t Have a Data-Driven Culture” reported a survey of executives of large organizations that was consistent with my un-scientific survey. Virtually all respondents (97%) said their organizations are investing in data technology, but most (67%) have not succeeded in shifting to a data-driven culture.
One of the problems is that many of us (me included) are hooked on Excel. It’s a great tool in many situations, don’t get me wrong. Where it fails is at the organizational level, leading to sub-optimal decisions.
The 3 biggest problems with using Excel for organizational decision-making are:
1. The “last mile” problem. Companies have invested heavily in ERP, CRM, IoT, Analytics and AI, but when it comes to decision and financial modeling, they return to the (over) simplicity of Excel models. Just like the last mile of copper connecting many homes to more powerful fibre, this approach prevents senior leaders from realizing the full potential of their investments in technology.
2. Lack of horsepower. Excel simply does not have the calculation or simulation muscle required to make full use of the massive volume of data and analytics available today. Most Python and other coding approaches are also not capable of delivering the power required to be truly predictive. Enough said.
3. Barrier to Engagement & Co-Creation. Best practice decision-makers have an organizational approach to decision-making that make use of data, analytics and human expertise from across their organizations. More distributed and informed decision-making leads directly to more effective decision-making. The problem is that Excel is a notoriously poor platform for collaboration.
For those of you with modeling experience, how often have you faced the challenge of source-less assumptions, impenetrable formulas, untraceable models and version confusion? I love Excel for many things, but when Excel meets collaboration the result is usual a Frankenstein-like monster of a decision-model that is guaranteed to contain hidden errors and lack key information.
There are multiple solutions on the market that address these problems and can provide a platform to drive more effective decision-making. I encourage you to explore your options, and a quick Google search for “decision modeling” is a good way to start. My company, DIREXYON, offers a solution designed for capital and investment intensive industries that has proven effective in our market niche.
Feel free to reach out for more information about decision-modeling, our solution or other solutions on the market if you are interested in learning more at email@example.com.