Why data stewardship must be a core value that underpins learning experience design

Data stewardship is a mindset that seeks to preserve data quality and identify problem solving opportunities. Data-driven employee training is a prerequisite for a successful business model. Data reveals the need to bridge skill gaps, improve performance, increase productivity and enhance employee job satisfaction, motivation and morale to to further business goals and objectives.

In short, a return on investment is dependent on data-driven learning design.

Below are five areas where I've found that a data-driven approach has enhanced my learning design for greater impact.

1. Needs Analysis

Data is a central component to solving problems and identifying opportunities. Creating a data-driven strategy eliminates the risk of "metric explosion” - the overload of poor, ineffective data. My process of capturing data at the outset of a project through a needs analysis process allows me to compare what is expected to what is actually achieved to locate the need for training and development. This data collection simultaneously provides a clear snapshot of the learner demographic (job experiences, motivation, aptitude, learning style, etc.).

Collecting data for a needs analysis may include surveys, walk throughs of current processes, reviews of previous training efforts, reviews of sales records and customer feedback forums, interviews, focus groups, workshop sessions, group brainstorming or observations.

2. Data story analysis

The data I collect tells a story. Reading data stories enables myself, the learning experience design team and key stakeholders to identify current practices and generate projections to inform next-step training decisions (also known as descriptive, diagnostic, predictive and prescriptive analytics).

3. Defining Learning Objectives

Once needs are identified, I next design learning objectives that respond to the needs analysis to meet the company KPI’s or predefined metric values. While you cannot pick your data, you can (and should) pick your metrics.

Over the years, my use of Blooms taxonomy to design clear outcomes using guiding behavioural verbs has enabled the creation of key performance metrics to be monitored in a process known as Kirkpatricks. Kirkpatricks assesses learner reactions (engagement, relevance, etc.), learning (knowledge, skills & application), behaviour (compliance & actions) and results (return on investment).

4. The selection of relevant tools and coding techniques

Access to the right tools such as an LMS (learning management system)/LRS (learning record store) that has the right reporting capabilities, or an LMS/LRS with the option of integrating a solution enables data to be collected.

My work with a number of LMS platforms, LRS platforms and SaaS (software as a service) training delivery platforms have shown me there is flexibility to tailor a learning management and reporting solution for a company’s needs and budget. My work with SCORM and my experience working with Javascript xAPI code to collect data on my learners’ experiences has been extremely useful for tracking a learner’s journey and recording any learning experience, wherever and however it happens.

5. Making Metrics Visible

I clarify what kind of dashboard is required by each stakeholder. Making data visible on real time dashboards and tailoring these for key stakeholders is essential. For example, it may be valuable to customise separate strategic (long-range), tactical (medium-term) and operational (short-term) dashboards for company officers and management teams.

Endnotes

1. Nikitinsky, N.S. (2018), "Improving Talent Management with Automated Competence Assessment: Research Summary," In: , et al. Proceedings of the Scientific-Practical Conference "Research and Development - 2016". Springer, Cham. https://doi.org/10.1007/978-3-319-62870-7_8
   

2. Garg, S., Sinha, S., Kar, A.K. and Mani, M. (2022), "A review of machine learning applications in human resource management," International Journal of Productivity and Performance Management, Vol. 71 No. 5, pp. 1590-1610. https://doi.org/10.1108/IJPPM-08-2020-0427
   

3. Shah, N., Michael, F., & Chalu, H. (2020), "The Influence of Electronic Human Resource Management Use and Organizational Success: A global conceptualization," Global Journal of Management and Business Studies, 10(1), 9-28. <https://www.ripublication.com/gjmbs18/gjmbsv10n1_02.pdf>
   

4. CHYTIRI, Alexandra-Paraskevi (2019), "Human Resource Managers’ Role in the Digital Era," SPOUDAI - Journal of Economics and Business, [S.l.], v. 69, n. 1-2, p. 62-72, ISSN 2241-424X <https://spoudai.unipi.gr/index.php/spo7udai/article/view/221>

Further Reading

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