The Data Alchemy Conference that I recently attended was well worth going to. In contrast with a lot of these types of conferences, it was an interesting view of how to use predictive and other technologies to improve business outcomes, i.e. not the more common type of technology or data scientist oriented conference here in Silicon Valley.

One of the factors that was attractive was the way that vendors used case studies and best practices to elucidate some of the advantages and complexities of big data and analytics. People from companies such as PayPal, IBM, HP, SAP, Silicon Valley Data Science and others were speakers. There were also lots of industry practitioners in the audience.

The emergence of predictive analytics as a core tool in planning and monitoring in organizations is a relatively recent phenomenon, being less than 10 years old. Now, companies like SAS have been around for a long time, but it is only when IBM acquired SPSS in 2009 and applied their significant marketing engine behind predictive analytics that this market started to take off. 

Of course, it had been used with regards to risk analytics in insurance, churn analysis in telecoms companies and credit worthiness analysis in FICO scores, etc.

Since then we’re seeing predictive analytics being incorporated in many different areas in enterprises based on the growing amount of data and the increasing need to make decisions based on data.

This comes partly from increasing complexity in the business world, greater binary behavior (1 major company in each market that is 10x larger than #2), speed of growth and decline of companies, and decreased cycle times.

One of the most interesting talks was by Jenny Dearborn, Chief Learning Officer at  SAP, who spoke of the way they’re using predictive analytics with regards to employee turnover and onboarding. By using big data analytics on structured and unstructured data, it is possible to understand employee sentiment, training needs and likelihood of staying at the company.

A major challenge to analytics is data quality, what in common vernacular is termed bad or dirty data. Theresa Kushner, VP Enterprise Information Management at VM Ware mentioned that 1/3 of her staff were focused on data quality and cleansing.

It seems as if data quality is an even more important issue than being able to apply advanced algorithms to the data, and that by just ensuring that data is clean we can make better decisions that reduces the need for advanced algorithms in many situations.

In short, it was interesting to see how analytics is being advanced within organizations and getting a practical view of what challenges are faced from a business perspective.

This entry was posted on Tuesday, May 26th, 2015 at 1:15 am and is filed under Business info, Culture. You can follow any responses to this entry through the RSS 2.0 feed.
Tags: analytics, big data, KG Charles-Harris, turnover
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