Customer relationship management and marketing remain the top fields for data miners, according to a recent survey conducted by Rexer Analytics, a Boston analytics and CRM consulting firm.
Forty-one percent of data mining professionals polled for Rexer's 4th Annual Data Miner Survey reported working in the CRM/marketing field. Rexer said 735 data miners from 60 countries participated in its 50-question survey, conducted in early 2010.
CRM/marketing has remained the top field of respondents since the polling began four years ago. Other sectors employing large numbers of data miners include financial, academic, insurance, and telecommunications.
Source: 2011 Data Miner Survey, Rexer Analytics
Other conclusions from the survey:
Most data miners use decision trees, regression, and cluster analysis, though in the latest survey, 22 percent of respondents reported using ensemble models, which made their first appearance in the Rexer survey.
Data miners tend to prefer models with fewer variables. A third of the respondents reported building final models with 10 or fewer variables, while only 28 percent built models with more than 45 variables.
The majority of data miners reported using open-source tools and working on laptops or desktops with data stored locally.
The biggest challenges for data miners remain dirty and difficult-to-access data, as well as the ability to explain their work to others effectively.
Data quality and analytics capabilities continue to prove problematic. Only 13 percent of data miners rated their company’s analytical capabilities as excellent, and only 8 percent had confidence in the strength of company data.
Despite the challenges, the survey clearly shows an increase in the demand for data mining, a cause for general optimism in the field. More than 75 percent of data miners anticipate more projects at their companies in the near future. These are over and above increases reported in the previous survey.
In addition, Rexer said it has measured the use of the term “data mining” in a variety of online job ads and found strong requirements.
Have you seen an increase in the number of data mining projects and a rise in skill requirements in your company or organization? What fields do you believe make the greatest use of data mining today?
@Seth: from my experience, dirty and difficult data is a problem because it is difficult to convince stakeholders of the importance of cleaning it up. So we either publish untrustworthy results quickly, or we anger those who pay our salaries by taking too long. Tools are good, but this is time consuming and expensive and not always seen as valuable.
On the other hand, explaining the work effectively includes the above conundrum but goes far beyond it. There is a tremendously deep and wide set of significantly complex science involved in data analytics, but explaining this to anyone at all is challenging. It is hard enough to find qualified people to do the work competently. If you want them to communicate effectively also, you're reducing your talent pool even further.
I have definitely seen an increase in the requirments for analytics in many job postings and like the chart says, in marketing. Like others, I am surprised that CRM/marketing is number one, but I guess that makes sense as it's more profitible to retain and market to a current customer.
I would like to know more about the reasons behind "The biggest challenges for data miners remain dirty and difficult-to-access data, as well as the ability to explain their work to others effectively." Is it the lack of tools or the lack of understanding?
Yes, especially insurance given the potential importance of data mining in risk assessment. But I have to honestly say that I was also surprised by the distance that medical and Internet are behind, two fields where it is obvious to see the benefits of data mining.
Cordell, Thanks for the clarification of standard practice on credit scores. I see your point that it is not the variable in itself that is the issue, it whether is has any significant meaning to the question(s) at hand.
@dataguy. Certainly terms get jumbled a lot. I recall talking to a lot of people about "analytics" which took to mean predictive analytics and they were typically talking about reporting or BI. Some really confusing conversations! Nevermind people passing off analytics that aren't. See our discussion on sentiment analysis that's just keyword search.
@louis. I can confirm that most credit scoring models don't have more than about 15 characteristics though they may start with hundreds. It's not the management of variables that's the problem it's that after about 15, new variables add little to the model. Of course credit scores have been around for a long time and over the years the strongest variables are fairly well known so there's a good starting point already.
I totally agree about communication. If findings can't be communicated accurately then what's the point! This is an ongoing skill set deficit. Tough to find analysts that are good communicators. It's a stereotype I know but it seem to be directionally correct at least.
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