A recent Wikibon report found that enterprises are struggling to derive maximum value from Big Data. While they expect a return of around $3.50 on the dollar, their return to-date is just $0.55. So what stops the business from deriving value from analytics? Based on my 25 years of experience in the Australian analytics industry, I believe that the single biggest show-stopper is the skepticism of analytics consumers regarding the value of analytics and the consequent lack of endorsement for embedding analytics insights in their decision process.
Let me illustrate with two different analytics projects that I have been involved in, one for a telecommunication company and the other for a wholesale clothing manufacturer.
Both projects were decision support systems generating upsell leads for consultants selling into a declining corporate market. Each telco consultant looked after a number of corporates within their region, while each wholesale consultant was responsible for selling more products to a single large retailer. In both cases, the consultants’ bonuses were tied to sales and hence there was a compelling business case to use analytics to uplift sales.
Both projects had access to historical data needed for analysis and could process it successfully. The telco provider had the historical quarterly revenue summary for each customer, while the wholesaler had the weekly sales and stock for each stock item (SKU) at each store (Weekly Point of Sales data).
Both projects successfully created predictive analytics solutions that provided demonstrable uplift over the existing methods despite requiring significant innovations to overcome hurdles(See KDD 2005 Proceedings). Hence, both got management support with funding for deployment for regular use.
It is the endorsement from the business users that gets sustained business value from an analytics investment.
Both deployments had an analytic back-end that generated predictive models based on the most recent data, and a front-end that presented an opportunity list sorted by the size of the opportunity. For telco, the back-end needed a manual start each quarter and the presentation was via Excel spreadsheets. The wholesale solution was fully automated with the back-end developing models from weekly data feeds and the self-serve front-end was directly accessible by the sales team. Both systems also provided explanation of how the opportunities were derived, e.g., Figure 1 shows the sell rate for two similar products P1 and P2, and the fact that despite similar demand P2 is under performing.
The Telco application has been in use for more than 10 years despite the lack of full automation and continues to provide leads for the sales consultants to follow up each quarter. On the other hand, the use of the automated wholesale application has been patchy. The few consultants who use the application attribute more than 10% of their sales to the opportunities identified by analytics yet others continue to ignore these opportunities.
The main difference is how the system has been embraced by the consumers of the analytics insights. The telco sales consultants believe the predictions, find them useful and continue to train new staff so the use of analytics is embedded in their sales process. In the wholesale business, there is skepticism about analytics among a large group of the users. Hence, despite the analytics system still generating great opportunities from the automated data feeds, it continues to be ignored by them. Only early adopters use the system and get the sales uplift.
These two analytics projects are not isolated examples, and operational analytics insights, whether deployed manually or automatically, generate substantial business value only when their consumers believe in the analytics insights and adopt its use. And while C-suite support may be necessary to get analytics off the ground, it is the endorsement from the business users that gets sustained business value from an analytics investment.
Bhavani Raskutti is the Domain Lead for Advanced Analytics Teradata ANZ . She is responsible for identifying and developing analytics opportunities using Teradata Aster and Teradata’s analytics partner solutions. She is internationally recognised as a data mining thought leader and is regularly invited to present at international conferences on Mining Big Data. She is passionate about transforming businesses to make better decisions using their data capital.
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