Productivity Can Be Stymied By Using Too Much Data

In business, productivity is the goal. Unfortunately, there are so many interruptions in the workday, you absolutely have to capitalize on the moments where you’re in the groove. To find the groove more frequently, and to measure the ability of workers to find their groove, companies have started using trackable systems fueled mainly by their management software--typically a CRM, but sometimes a larger, more integrated solution--to pump out metrics designed to give managers an idea how their teams are performing, and give them an idea about how to best utilize them. 

Companies track all types of things, typically in the form of KPIs, or Key Performance Indicators. These metrics are useful, if you use them properly, but when an organization starts using metrics to measure employee performance (and business performance) that don’t have any causal relationship with productivity, they get into an area where all the time and resources spent creating and mulling over these reports can have the opposite effect. Let’s look at how productivity can be hindered by too many useless metrics.

Employee Engagement

The first question you are going to want to answer surrounds the way you look at your business’ productivity. If you have no problem reducing your employees to numbers it becomes pretty easy to ascertain how they perform and their numerical value to your business. Of course, your employees are the most important aspect of your business, just look at your budget. For your business to be what you’d like it to be, your employees need to be engaged. The problem often becomes that the people you depend on to make the magic happen are less engaged when they are reduced to line-items on a spreadsheet. 

Studies show that the people that have the highest contact with customers tend to be the most disgruntled. That means your salespeople and the people who fulfill services. These people are a big part of everything you do, and if they aren’t at their best, neither is your organization. As a result, the metrics you would see in the back end of your CRM may not be the whole story. The more time and resources are poured into finding THAT employees are performing under expectations, don’t answer WHY they are, which is a much bigger concern. Sure, sometimes these two factors work in concert, but ultimately, all data analysis should be geared at better understanding your business, and making it clear that people are more than just numbers goes a long way toward setting the stage for productivity.

Missteps of Analysis

Not Scaling Your Analysis

As businesses continue the shift toward being more data-driven, decision making is more centralized. As where managers had autonomy of sorts just a short time ago, they are more frequently being asked to make strategic decisions based on the data all organizational decision makers have at their disposal. Effective managers are forced to use analysis that may not be specifically built for their situation, leaving their teams less effective than they would have been before their organization started using its data to meddle with productivity.

This one-size-fits-all approach to data analysis can actually hurt an organization's ability to maximize productivity. In others it can actively help it. Analysts need to understand the goals of a particular department, the varying needs of that department’s end-users, and the context in which data is useful. Business doesn’t happen in a vacuum and if you are using data that is irrelevant or not useful, the decisions made with that data will be inherently flawed.

Managing Bias

Bias can be a big problem on both ends. On one hand, if a production team works inefficiently, and the analysis of the data suggests the same, managers need to be cognizant that--while they may not think that they are performing under the organization’s expectations--they are, and need to do what they can to keep productivity up.

On the other hand, if a manager has been keeping productivity levels consistently high and analysis suggests that he/she has to change something fundamental, forcing changes may end up being detrimental to the department, as bias toward the numbers can also produce significant inefficiency, losing time to implement changes and retrain workers. 

Data collection and analysis isn’t going away, but until decision makers realize that just looking at data doesn’t necessarily give them the best perspective to change their business processes, maximizing productivity will be difficult. If your organization is looking to start being smart with its data and would like some help setting up a business analytics or business intelligence system, or learn how it can help you boost your organizational efficiency, call Gravité today at 1300 008 123. If you would like more information about how to move your company into the information age, subscribe to our blog.