Where Analytics, Data Science, Machine Learning Were Applied: Trends and Analysis
Gregory Piatetsky-Shapiro
Part-time philosopher, Retired, Data Scientist, KDD and KDnuggets Founder, was LinkedIn Top Voice on Data Science & Analytics. Currently helping Ukrainian refugees in MA.
Latest KDnuggets Poll asked readers to select
Industries / Fields where you applied Analytics, Data Science, Machine Learning in 2017?
Top 3 most popular areas were the same, with almost the same share as last year. Health care moved to no. 4 position from no. 5, and Fraud Detection up to no. 5 position from no. 7
- CRM/Consumer analytics, 16.8% (was 16.3% in 2016)
- Finance, 15.2% (was 15.0%)
- Banking, 14.1% (was 13.4%)
- Health care, 13.2% (was 12.0%)
- Fraud Detection, 13.0% (was 11.1%)
The icon color is greener if sector share increased in 2017 vs 2016 (red if decreased), and shape corresponds to changes over 2 years: Up triangle if share went up 2 years in a row by more than 1%, down triangle if went down 2 years by more than 1%, and circle otherwise.
Comparing to 2016 Poll: where you applied Analytics, Data Mining, Data Science in 2016?, we find the biggest increases, computed as (pct2017 - pct2016) / pct2016, were for
- Junk email / Anti-spam, up 106%, to 2.2% in 2017 from 1.1% in 2016
- Manufacturing, up 60%, to 9.0% from 5.6%
- Social Good/Non-profit, up 35%, to 2.7% from 2.0%
- Education, up 33%, to 9.4% from 7.1%
- Medical/ Pharma, up 31%, to 8.5% from 6.5%
- Supply Chain, up 31%, to 8.5% from 6.5%
- HR/workforce analytics, up 30%, to 4.7% from 3.6%
The industries/areas with the biggest decline in share of applications were
- Mobile apps, down -59%, to 1.3% from 3.3%
- Investment / Stocks, down -45%, to 3.4% from 6.2%
- Security / Anti-terrorism, down -42%, to 1.6% from 2.7%
- Entertainment/ Music/ TV/ Movies, down -32%, to 2.7% from 4.0%
- Telecom / Cable, down -30%, to 5.8% from 8.3%
It is very interesting that the average number of industries/areas per voter was 2.7, the same as in 2016 and only slightly different than 2.65 in 2015. Perhaps there is a limit to effective application of Data Science across industries?
Read the rest on KDnuggets:
Where Analytics, Data Science, Machine Learning Were Applied: Trends and Analysis
https://www.kdnuggets.com/2018/04/poll-analytics-data-science-ml-applied-2017.html
Consultor de Estrategia y Desarrollo de Negocio Banca/Seguros
6yAt first glance, the data on the two percentage points of growth in fraud detection is relevant. However, I agree with the other participants that having more information regarding this industry, population and location study would be necesary in order to draw more accurate conclusions. In any case they are key data that help to know the trends in these technologies.
Data Scientist at Solventum
6yThis looks like a breakdown by percentage of data scientists (in the sample) working on a particular type of problem, which wouldn't directly correspond to the metric a casual reader may infer from this article, namely the degree to which ML has penetrated these fields.
Statistician & Research Consultant
6yLike Angeline & Udula, I'd like to know the characteristics of the sample so I could infer something about to which population these (interesting) results apply.
Sales Manager Tissue Middle East/Africa chez BTG Group / BTG Eclépens S.A
6yindustry......
Experienced legal, eDiscovery and Investigations professional. Certified Fraud Examiner (CFE) with the demonstrated ability to organize and manage teams working in government, large, unstructured relational databases
6yThis is a great post. Many thanks for reflecting the growth of Fraud Analytics. They are gaining in large part because they have a symbiotic relationship across multiple industries including Consumer Analytics, Finance, Banking and Healthcare. Fraud Analytics provide invaluable insights and are an effective tool in the prevention and detection of fraud in a system.