Everyone wants know how to operate their business to make better decisions and gain that competitive edge. However, many organizations have found producing more analytical results doesn’t necessarily result in better decisions. With the new trend of collecting, analyzing and visualizing business information, companies need to find a balance between producing more data and consuming more analytics.
Bridging the analytics production and consumption gap would, ideally, require overlapping skills from both decision makers and data analysts. Unfortunately, it usually costs too much time and energy to train executives in data modeling and data scientists in business practices.
Luckily, there are two solutions to this problem:
- Broaden understanding
- Diversify perspectives
It would be advantageous for both the execs and data scientists to be more engaged in what the other does.
For example, executives can familiarize themselves with terms used by data scientists (such as descriptive, predictive, and prescriptive analytics) when communicating about projects. Data analysts can also become more familiar with the context in which their results will be interpreted by staying informed about organizational issues and strategies.
If decision makers understand what statistical clustering is, or how a regression analysis can be useful, they could recommend specific analytical work. If executives communicate business needs and expectations, business intelligence dashboards could be designed more efficiently and comprehensibly.
On the other hand, if analysts understand the difference between clustering and marketing segmentation, they would be able to communicate the appropriate application of their results more effectively. Speaking the same language, whether that’s data or operational terms, is highly encouraging and can help motivate work.
Another way to help bridge the analytics gap is to hire a diverse group of analysts.
MITTERA’s data team is comprised of people with different backgrounds, including those in engineering, mathematics, statistics, economics, marketing, and information systems. The thing everyone has in common is a genuine interest in data and marketing solutions.
The diversity on our team enhances creative ideas during brainstorming sessions and helps with interpreting results as well as building and presenting reports in multiple forms.
Without formal training, the combination of communication and experience may be enough to start balancing data analytics production and business intelligence consumption.
Another solution to improve the application of business analytics could be to focus on direct and immediate tangible results.
Decision makers are more comfortable when they have confidence in the facts required to make decisions. Executives are also less likely to make large, dramatic changes, but rather an ongoing series of smaller decisions.
Financial numbers and information related to time and energy on projects, or processes, often hide powerful insights for cost-saving strategies.
For the more creative and ambitious analytical ventures, there are more ways to collect customer data today than ever before. Commonly, trends and patterns collected through physical purchases or digital touch points can be extremely valuable. This data can then be used to construct statistical models to tell stories about the past, present and future.
Before investing in fancy software or technologies to produce more data, it would be favorable to build strategies around consuming simple and meaningful analytics that can be understood and leveraged for better business decisions.
An analytical imbalance could mean a significant number of untapped opportunities for an organization. Use it to your advantage and reap the benefits of the analytics game before your competitor does.
