Articles · June 5, 2019
The human factor in analytics practice
Descriptive and dynamic analytics demand different technical and human skills. Implementing an analytics practice goes beyond buying software and hiring profiles.
In “The 2 phases of analytics” we described two analytical processes that are key to capturing the value of information. Both require different technical and human skills: in practice, the same person will not always be able to perform both types of analytics satisfactorily.
Profiles for descriptive and dynamic analytics
While descriptive (static) analytics demands a good dose of intuition, common sense, and command of mathematical and computational techniques, dynamic analytics relies more on order and discipline, business sense, an understanding of causality, and a focus on results.
Experience shows that the human profiles required for these activities are different: the data-scientist profile needed for descriptive analytics is not the most appropriate for dynamic analytics tasks, just as the functional collaborator — skilled at implementing operational solutions — is not the best fit for tasks demanding concentration on pattern search.
The importance of analyzing information properly
Many organizations recognize the importance of implementing their own analytics practice: they have the technology to handle large volumes of information and accept that proper analysis is necessary to generate benefits.
“Implementing a business process of this kind requires not just a name for the department, but the methodology and understanding of the analytical processes.”
Implementing this kind of business process goes beyond creating a new area, selecting and purchasing software, and hiring people. It requires a working methodology, well-defined objectives and goals, coordinated roles and responsibilities and — critically — an understanding of the two analytical processes that must operate in coordination.
Methodology and adoption
Aleph5 has developed the methodologies and processes to implement an effective, results-oriented organizational analytics practice. These methodologies, supported by the Sherlock platform, can be implemented in any type of organization, institutionalizing knowledge and reinforcing a culture of continuous improvement.
