Demystifying Data

Blog

July 22, 2019

Demystifying Data

By Mark Nilles

Learning & Development Guru

This blog was originally published on Mark Nilles' personal blog, Workplace Learning and Other Musings.

To say that data is important for good decision making is not breaking news. In fact, Sherlock Holmes is ‘credited’ with saying: “It is a capital mistake to theorize before one has data.”

These days, the influence of data is all around us. Articles on or referencing data appear in my newsfeed daily. Software is available to help with data analysis, data visualization, data management, and a range of other data-related needs. Companies are hiring and relying on data analysts to deal with big data, small data, and all the data in between. But what specifically is the role of data, and how can we all benefit from data regardless of our job title or software packages? And why does data sometimes seem so complex?

At a recent session at Humentum’s OpEx conference, Shreeya Neupene Banjade and I provided frameworks to help non-data experts understand, approach, and benefit from data. We focused on the role of data and Learning & Development (L&D) work, but the frameworks we presented can be applied and adapted for all functional areas in your organization.

First, it’s helpful to discuss what we measure in Learning & Development. Although there are other things to measure, we focused on two important measurements for L&D: usage and value.

Especially for online learning, we can track usage click-by-click. In face-to-face workshops, we know who registered and who signed into the workshop. Whether they engaged with the material or even returned from the coffee break, however, is difficult to know or track. Online learning, however, has allowed us to track who logs in when, what they click on, how long they stay online, what they return to, and what they skip. Completion rates and other analytics are compiled and used to demonstrate success and identify opportunities for improvement.

Although you can track usage at a granular level, we feel using data to understand value is even more important. Did learners learn? Are they using new knowledge and skills on the job? Are they achieving results? Ultimately, is the organization benefitting from the learning experience?

However, answering these types of value questions requires establishing metrics aligned with broader goals before collecting data. If you don’t know how you’re going to use data, the entire data journey could be a dead end. However, if the goal is well-understood and aligned to the organizational strategy or programmatic outcomes, the data journey will be well planned, well executed and worthwhile.

This leads us to the other framework: a straightforward three-step data lifecycle. We feel this simple approach can help non-data professionals more effectively use and benefit from data.

Creating and collecting data typically entails creating data collection tools, developing and executing the data collection process, and gathering data in a central location.

Preparing and analyzing data requires cleaning data, analyzing and reviewing data against standards or targets, and preparing reporting for stakeholder group needs.

Sharing and using data leads to the purpose of collecting and can include reporting results, conducting discussions, and, of course, taking action based on data to achieve goals.

To start the process on the right foot, here are some suggested questions to help you and your colleagues define data goals and plan for the data journey:

  • What problems do you need to address or what do you need to make better? What business (or programmatic) challenge prompts the need for data?
    • How can you translate this business question into a data question?
    • What does success look like? 
    • What data do you need to be able to answer these questions?
    • What are the best sources of data—either existing or new?
  • What insights or information will help you understand which actions to take or how to make good decisions? (e.g., “If we knew more about X, we could do Y better.”)
    • What data points can help you gain those insights?
    • What do you need to know to confirm or disprove your hypotheses?
  • Which stakeholders do you need to engage through the process and what form of reporting will speak to them most clearly?

With these types of questions answered at the beginning of the process, collecting, preparing, and sharing the data can be straightforward and purposeful. It will still take planning and thoughtful execution, but data does not need to be so complex to be out of reach to anyone interested in harnessing the power of data for decision making.

 

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