As in other areas of data science, extracting value from data in people analytics also has a process with well-defined stages.
Kenneth Jensen. Wikipedia Commons. Creative Commons Attribution-Share Alike 3
The basic process of any solution is compiling and incorporating
an organization’s HR knowledge to learn from using computational learning algorithms, data science,
and reasoning. Finally, faced with a demand for information, you
have to be able to respond (prediction, classification, recommendation)
by justifying the results with objective metrics.
The CRISP-DM Methodology was born in the 1990s. CRISP-DM
(Cross Industry Standard Process for Data Mining) provides a
the standardized description of the life cycle of a standard data analysis
the project, similar to how it’s done in software engineering with
software development life cycle models.
In our people analytics methodology, we generally simplify this
process from six phases to four steps. But, essentially, we adhere to
the model consolidated in the 1990s.
Step 1: Define HR Needs.
This initial phase focuses on understanding project objectives. In
people analytics, we think about this business need by asking a
question.
Have a look at this quote by John Bersin25 that demonstrates why
you need to question ”what for?”:
Step 2: Study and Understand the Data.
The data comprehension phase begins with the initial data collection
and continues with the activities that allow you to familiarize
yourself with the data, identify quality problems and discover preliminary
knowledge about the data. And then you may find interesting subsets
to form hypotheses about the hidden information.
Step 3: Modeling
The analysis process consists of executing Data Analytics techniques
to identify patterns that represent the relationship between
the data, applying algorithms to the variables under analysis, such
as regressions, k-nearest neighbors, neural networks, decision trees,
Bayesian networks, etc.
Step 4: Evaluate and Deploy. Action
The last step is to transform the generated patterns into concrete
action plans that can help the people in human resources to achieve
their goals.
Ref: People Analytics - Data and Text Analytics for Human Resources - Eduardo Valencia. Data Analytics Manager - Analytics to Grow.
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