Always late bothers online food delivery business

Views expressed here are from author’s industry experience. Author also trains on Machine (Deep) Learning applications; for further details, he will be available at info@tatvaai.com or mavuluri.pradeep@gmail.com for more details.

Find more about author at http://in.linkedin.com/in/pradeepmavuluri

Why Record Linkage needs a scalable computing power?

Though, “Record Linkage” is a popular word among statisticians, and epidemiologists - “the problem of matching/joining records from one data source to another which describe the same entity”; has a long historical attention from the time since data collection gained (1960s) and continues to gain attention as new methods of collection, formats and stacks of data being added to the existing. The other popular terms for the same are deduplication, data matching, entity/name resolution, record matching, etc. Please, refer to the following paper https://homes.cs.washington.edu/~pedrod/papers/icdm06.pdf, for one of the good works in this field. Also, one can look at the below google trends graph for the attention to this filed from 2014 to the present.

The purpose of this blog is to bring forth, why record linkage needs a scalable computing power, for which I present my observations with an simple example as show below:

 

Views expressed here are from his industry experience. He can be reached at mavuluri.pradeep@gmail or besteconometrician@gmail.com for more details.

Find more about author at http://in.linkedin.com/in/pradeepmavuluri

Hard-nosed Indian Data Scientist Gospel Series - Part 2 : Certificate (or Degree) Mania

This is second in series, first is here.

Again, whole past decade before & after recession seems to be & seeming to be revolving around a mania called certificate or degree’s around some topic / tool. Let it be subject / concept namely., Analytics or Machine Learning or Data Science etc. and tool / technology namely., SAS or SPSS or R or Python etc. (where price of such unequal to (s) ranged from 0,000’s to 000,000’s).

This always reminded and reminds me that most of marketers duped aspirants around data science by hiding its important characteristic namely., “multi-disciplinary one”, that led to ending up with partial learning or incomplete or incompetent learning which couldn’t cater industry needs.

Author undertook several projects, courses and programs in data sciences for more than a decade, views expressed here are from his industry experience. He can be reached at mavuluri.pradeep@gmail or besteconometrician@gmail.com for more details.

Find more about author at http://in.linkedin.com/in/pradeepmavuluri

Indian_IT_Cos_HR_Analytics_Hurdle

There has always been a question to me time-to-time (because of my earlier experience with developing HR platform for few big fortune clients), on “why Indian IT companies are not towards advanced HR analytics?”

Below is true for more than 90% of the Indian IT companies, 'since animal representing management cannot bypass an important layer, a big animal representing employees, which is a pseudo big (*), hence, jumping is almost impossible for implementing all those insights brought out or meant for employees'. Herein, one might guess a missing component which most of employees feel, of not much use in Indian IT companies context …………….?

Imparting Data Sciences - Industry Practices - Part 2

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 pmavuluri@analyticaltis.com for more details.

Imparting Data Sciences - Industry Practices - Part 1

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 pmavuluri@analyticaltis.com for more details.

Lynchpins for Analytical Skill Development

As business are adopting more and more data-driven strategies (analytics) in their day to day life, I keep on listening from leadership or concerned people that training provided towards it, are not having anticipated impact. Herein, pragmatic confession would be happy with thought that 'it is not a pure science' (or) let’s appreciate the concepts and different relationships involved for their success:

Author has developed and undertook several programs towards analytical talent development, views expressed here are from his industry experience that lead him to develop/design analytical training's as fun concepts with games having clues. He can be reached at mavuluri.pradeep@gmail for more details.

Adoption of in-memory computing, a better choice for SMEs analytical capabilities

Delivering analytical solutions using in-memory computing can be a better choice for small and medium data enterprises (SMEs) if followed few good practices:

Author has worked and implemented in-memory analytical solutions and views expressed here are from his industry experience, he can be reached at mavuluri.pradeep@gmail for more details.

Big Data Analytical Services Environment (Success Struggles)

 

Observations are author's personal views after observing big data space over a period of time, he can be reached at mavuluri.pradeep@gmail for further discussion on this topic.