Today, the goals of better storage and organisation have been replaced with that of deploying data science - the process of applying advanced analytics techniques and scientific principles to datasets. The purpose of this? So that valuable information can be extracted for better business decision-making, strategic planning, and effective, overall change management.
Successful deployment of data science will take us to a place where predictive models are built that can add powerful layers of intelligence to core business functions. Although we aren’t quite there yet, there are questions that can be asked today, the answers to which could expedite a more capable tomorrow.
Do I need to be using digital engagement tools?
Even in today’s world of biometrics, tablets, apps, and QR codes, many organisations wanting to build a picture of employee satisfaction still persist with antiquated opinion surveys. Though such surveys can provide useful snapshots, they age quickly and by the time they are acted on, are often no longer representative of the workforce.
Instead, some organisations are experimenting with a new generation of real-time employee opinion tools that reveal much more than a set of annual views. With feedback received in real-time, managers can immediately react to the efficacy of existing engagement tactics and begin making interventions in days rather than the weeks and months typical with legacy surveys.
“Though such surveys can provide useful snapshots, they age quickly”
These enriched datasets can then be input into predictive models that accurately reveal the actions needed to hasten adoption of new processes, practices, and behaviours among targeted employee groups.
Implementing digital engagement tools in this way could ensure that future data-driven change initiatives can enjoy maximum success.
Can I use data differently when matching candidates to roles?
Data has always been used when assessing candidates but it’s only ever really been able to tell us how they’ve performed in previous roles, leaving everything else pretty much to guesswork.
A new approach is to invite candidates to participate in psychometric testing and evaluation before they undertake a given task or project. Data derived from these tests and evaluations can then be overlaid onto project or task outcomes to reveal the human characteristics, idiosyncrasies, and skillsets most likely to generate superior results.
There’s also scope for this to be extended to intelligent team-building. Tools are in use today that can integrate data sources and produce visuals that senior leaders use to place individuals together who are most likely to collaborate successfully. This can go as far as helping newly formed teams anticipate possible wrangles within team dynamics so they can be pre-emptively addressed.
The questions will never stop coming
As data today is so abundant and so heavily relied upon, as organisations accumulate more of it and build increasingly accurate models, questions surrounding its application will constantly manifest. Though this may feel demoralising at times, it’s important to remember that every answered question is a step towards a more efficient, intelligent, dynamic, and prosperous operation.
Getting there won’t happen overnight nor will it be achieved using out-of-the-box solutions; it will take years of capturing data, building models, and perfecting dashboards. Nevertheless, organisations could get to a place where they are reliably predicting how actions and initiatives in change programs might alter outcomes and build pathways that avoid failure.
It seems hard to fathom now, but by changing our relationship with data today, we could be ridding the future of failed cultural transformation programs.