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Can Too Much Data Hinder Productivity?

Productivity is an important business goal. But, a lot of workplace interruptions make it challenging to achieve these goals. Hence, companies are increasingly making use of tracking software like CRM to measure productivity. However, we need a holistic solution to have a bird’s eye view of teams and their performance when too much data is involved.

Usually, companies use KPIs or Key Performance Indicators to track employee performance. But, when the metrics start measuring unnecessary data, it becomes a complete waste of time and resources. 

This blog explores how too many metrics can act as a roadblock to productivity.

How to Measure Employee Engagement?

Employee engagement is a crucial indicator of productivity. Happier employees tend to be more engaged compared with unhappy ones.

Recent studies show that the workers with maximum customer interaction tend to be more dissatisfied. They are usually the salespeople and customer support personnel. These people form the backbone of your organization, and their performance can make or break your business. Hence, rather than just seeing the metric numbers, you need to dig deep and understand the reasons behind their underperformance. People should not be viewed just as numbers. Their performance must be tracked over a period of time using techniques like employee engagement surveys, 1-on-1 discussions, etc.

Using a One Size Fits All Approach

As businesses shift toward a data-driven ecosystem, decision-making is getting more centralized. In the past, managers had complete autonomy over business decisions. But now, they have to make an objective decision based on the available data. Sometimes, data analysis can be faulty, but managers are forced to make decisions based on it. It may not help the overall productivity of the team in the long run.

Hence the one-size-fits-all approach of data analysis stands in the way of maximizing business profits. On the other hand, each department’s requirements have to be separately studied before understanding the usefulness of the data. Irrelevant data can lead to faulty decision-making, thereby harming businesses drastically.

Inability to Manage Bias

Bias is an essential factor while measuring productivity. If the data analysis suggests work inefficiency and it is corroborated as correct, managers must make decisions based on organizational expectations and keep productivity high.

In contrast, if a team manager gives consistent high productivity and the data analysis suggests the opposite, it is wise not to make drastic changes. A number bias can be harmful for the organization, thereby resulting in unwanted changes. Also, you waste a lot of time and effort retraining workers.

Data analytics should not be taken as the ultimate truth. Managers should apply personal intuition to the data to come out with pragmatic business decisions. Else, maximizing productivity will become very tough.

If your organization is planning to set up a business analytics system, or if you are interested to learn its benefits for organizational efficiency, call MyTek at 623-312-2440. 

If you want more information about making a digital transformation, subscribe to MyTek’s blog today.

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