It is amazing how many businesses are reaping extreme benefits from people analytics and others lose a lot of money in the process.

If you are also losing your money on people analytics, it is likely that there is a fault in your approach. To help you identify the issue and rectify the fault, we have prepared a list of approaches that are used for people analytics. Analyze these methods and understand where you are going wrong.

The Difference

People analytics can become beneficial for the organization when used rightly. Let’s see how you can modify your approach for these benefits:

1. Infrastructure Intensive

We all know these people who spend most of their time on management, infrastructure, and handling that they forget to actually get anything done. Infrastructure intensive approach is the same. These organizations spend most of their time on infrastructure and no time on people analytics.

Here are the things these organizations execute:

• Setting up the infrastructure for projects that don’t require it or won’t be used in future, ever.
• Meeting with several stakeholders who can’t bring value to the business.
• Enhancing and increasing employees on data privacy when no value is being extracted from the data that is stored.
• Spending a huge chunk of time on integrating data from thousands of global sources, cleaning it, and not using even half of this data to extract value.
• Spending a fortune on getting the best technology and using it for minimum analytics that can be achieved manually as well.

Infrastructure intensive people have the money and resources to extract benefits of people analytics which can be used to enhance the HR sector of the business. But, due to wrong decision making, poor handling, and obsessiveness, it is hard for them to get past the infrastructure setting.

It is best to dedicate a designated time on the actual analytics and less time on management. For instance, if you have a tool, use it to analyze data you already have rather than gathering more data.

2. Reactive Data Analytics

Reactive data analytics is similar to infrastructure intensive organizations as these organizations don’t react when until they need information. When someone comes in asking for analysis, they react and offer valuable information with the available data.

Although reactive data analytics does something productive, it is not enough to extract the full potential of people analytics.

You can improve the significance of your analytics by asking, why you need this information.

Based on the responses from your employees, you can judge whether there is a business issue that you need to address or there is a regular need for analytics. Knowing the reason behind the analytics helps in proactively moving towards enhanced HR system where analytics are ingrained in the culture.

3. Data Mining

Data mining uses technology to analyze huge amounts of data and big data to find patterns along the lines. These organizations systematically crawl through the data to observe relationships. The tools used are mostly RapidMiner, SAS, or Tableau. Using these tools, organizations create beautiful representations, graphics, and visualizations.

However, these visualizations seldom help any organizations. These visualizations can’t be used for any real decision making because the relationships found in huge chunks of data are often not relevant.

The only business that can extract benefits from data mining are the ones that revolve around advertising such as Facebook and Google.

To rectify this issue, you can focus on analyzing the actual problems that the company needs to solve. Before diving in the pool of data, know the problems, issues, and answers you need from data.

4. Proactive Analytics

Proactive analytics is the one feature we are looking forward to. These organizations are small in number but often end up extracting the most value from data. This is because they go straight to improvements and enhancements of organizational features, even before gathering data.

Here are the activities carried out proactive businesses:

• They automate low-level or repetitive tasks that are unnecessarily engulfing employee’s time and decreasing their performance. These businesses also conduct training to execute this automation.
• They improve spends on the customer by employing people who are dedicated towards the customer. After which, training, encouraging, and rewarding these employees for enhanced delivery of services becomes easy.
• They identify the need for innovation in various aspects of the company such as culture and environment.

Conclusion

In conclusion, we would only say that many companies are actually making money from people analytics. However, this number is small because most of the other organizations are focusing on the wrong aspects of analytics. Hence, refer the above information, know where you are going wrong, and rectify the issue to extract valuable information from your data.