The big data maturity model consists of 5 different levels:

Level 1: No usable data

Level 2: Big data

Level 3: The right data

Level 4: Prediction

Level 5: Strategy

Each level builds and refines the data in the previous level, which guides us to the end result of a better strategy for the company.

Let's assume that you have successfully gone through the first two stages of the data maturity model and are proactively moving into the 3rd level. How can you take data, either real-time or historical, and become proactive? Technically, all the data has happened in the past or present. How can we tell the future with it?

Reading the story

Your data tells you a story; you just need to know how to read it, and what it means.

A long time ago, I was in a management role with a large staffing agency. Much of that job involved with management and monitoring of people, promotion of productivity, and success at placing people into our clients’ jobs. Having multiple offices and multiple people who reported to me, I needed to find a way to help each person be successful in a very efficient way, as my time was too short to sit with each one for long periods of time while sorting out how to help them.

One thing I would do before going into an office was look at the weekly metrics report, which basically gave me a running number of the last 30 days’ productivity. This report would tell me how many calls they had made, how many interviews they had given, how many candidates they had presented to clients, and how many people had been hired. I would always spend time before every meeting to review and analyze the numbers; this reported guided most of each conversation.

Understanding the Story

Let’s face it: numbers are just numbers; we need to understand what they are telling us in order to make the data actionable.

Here was my approach: in the staffing agency world, you abide in some part by the law of averages. That means that if you make a certain amount of calls, you will likely get a certain number of interviews; if you have a certain number of interviews, you will likely get get a certain amount of submits; and so on.

What I found out is that when someone’s averages deviated from the general averages, there was a problem that needed to be addressed. For example, let’s say Bob (a fictitious recruiter), hits his averages when it comes to calls and interviews, but drops significantly from the submits to clients and hires. This automatically tells me that there is something going on in his interview that is throwing off the averages. Knowing that information, I would be able to quickly get to what we needed to address and adjust proactively before it affected Bob in a negative way. My time could be more productive and in the end would give us the results we needed to be successful.

Understanding YOUR Story

Your job and industry are unique. The key to making your data actionable and to becoming proactive, you need to know what really matters and what is just white noise.

As a practitioner you are in the best position to know what data points affect your business. By focusing on the key data points and ignoring the “white noise,” you will be able to make the most informed decisions. In the end, that is the goal of big data: to reduce uncertainty around issues to make better decisions for your business and your people.

Caleb Fullhart is a Principle Consultant with Strio Consulting, where he leads Strio's Systems Business. His work encompasses integration, configuration, and implementation of HR systems for a variety of companies and HR Systems to increase companies’ levels of efficiency and effectiveness. Caleb has been in recruiting for over 14 years and has specialized in candidate generation, social media, advanced sourcing techniques and Big Data within Human Capital. Connect with him on Twitter @cjfullhart or on Linkedin.

25 August 2014

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