Aug 10, 2015
Mar 23, 2015
In continuation to my last post Part 1, today, I would like to bring few more observations from the industry that revolve around whether data scientists are supposed to run algorithms (or) they are meant towards solving business solutions?
q Firstly, hardly any checks exist for “does whatever data scientists applied for mining the data (let it be small or big) is helpful in providing solutions that can be combined with the sphere of real world understanding and their needs? (Reasoning or Strategic Reasoning)”.
q Secondly, ignoring client requirements deeply and presenting the technologies they are comfortable in, which cannot be heart and soul of data sciences.
q Finally, its became a habit of searching a better algorithm once and using the same for years ignoring new data dimensions which are adding day-by-day, wherein, its failure later provides blame on complete data sciences.
Author undertook several projects and programs towards data sciences, views expressed here are from his industry experience. He can be reached at mavuluri.pradeep@gmail or firstname.lastname@example.org for more details.
Feb 24, 2015
This is what happening in the industry from the observation over a period of time; fresher’s/juniors’ forced to become more proficient with either a programming language or Statistics/ML theory, which is leading to these guys “trying all possible models with the hope that at least one will fit the data well”. This generally can also lead to misuse of the model or algorithm, wherein, here, domain knowledge which is critical and helps them in (as listed below) are ignored completed while imparting the data sciences:
q Rightly define or appropriately refine the business problem e.g. economic reasoning between price and sales help in accurately determine the model or model tasks for analysis.
q Can guide and provide right direction as an ad hoc model, may be difficult to justify both analysis and potentiality of the results obtained.
q More importantly, can provide past knowledge and reduces some trial and error involved in choosing the "right" model for empirical analysis that provide actionable analytical insights.
Author undertook several programs towards data sciences talent development, views expressed here are from his industry experience and personal observations. He can be reached at mavuluri.pradeep@gmail or email@example.com for more details.
Nov 18, 2014
In continuation to the last post which was published last week BigDataHR_Part1, today I would like to highlight further insights on what happened when the organization went with penalizing mood for the average performers and rewarding higher performers. However, this was for one of their average earning revenue division over a period of time, herein also, organization has expected that growth will be exponential. But, it was for the shorter period (it took some momentum and pushed the division growth to good number) what happened later, whether growth momentum continued, again herein, I tried to summarize through below graph.
If actual growth momentum that was observed initially would have continued, then, organization’s business growth (cycle) should have taken the green line of business growth curve, since, now no more exists that heavy tyre as explained in the last post BigDataHR_Part1, that can push its pace down. However, it took course of red line, that resembling initial pick up in growth followed by flat line there after; one of the main reasons for this was that after certain period, high performer’s couldn’t alone drive growth with out the support of average performers was very clear in observation.
Nov 14, 2014
Below graph, explains the summary of analytical insights obtained from a large organization’s performance data with respect to growth over a period of time. Presented results are obtained from one of their high revenue division, wherein, first identification of high performers and average performers happened. Said organization had rewarded their high performers promptly with larger benefits expecting that growth will be exponential. But, to surprise, next year they didn’t observed expected growth in the division. However, ignoring it, the same has been continued for the coming year, yet, not seen expected growth. Continuing the same policy, organization thought of giving a data-driven approach about what was happening?
When observed such large division performance and growth data over a period of time, following insights came out, which I tried to summarize through above graph. Organization was expecting a exponential form of growth curve year after year which is represented by green line of business growth (cycle) driven by rewarding high performer’s timely. However, organization had a large number of average performers, though they are not rewarded as good as compared to high performers, organization’s resource utilization towards them got out weighted such that high performers alone were unable to drive the growth cycle and it tilted down entire growth cycle to take slow paced curvy linear growth curve represented by red line.
Watch out for other Big Data HR Analytical Insights in coming posts.
Author has worked extensively in the HR Analytics and can be reached at mavuluri. pradeep@gmail for related discussions/projects.