THE DATA ANALYST CHALLENGE
Story
Because data is becoming more widely available , it appears that analytics should be able to deliver on its promise of value creation . Data , on the other hand , grows on its own terms , and this expansion is frequently fueled by IT investments rather than by well-defined marketing objectives . As a result , data libraries resemble the classic cluttered closet , where it ' s difficult to tell what ' s important from what ' s not .
The CMO Survey also found that only 1.9 % of marketing leaders reported that their companies have the right talent to leverage marketing analytics .
By Siddhi Wahal
The discrepancy between analytics ' promise and reality indicates a problem that must be addressed . To achieve the promise that analytics can bring to marketing managers , companies must better link their data strategy with data analyst personnel . Even tremendous data can go unused in the absence of personnel , preventing a company from realizing its full potential . What are some of the qualities that businesses should seek in data scientists ? They ought to :
Clearly define the business problem
Managers who rely on data scientists to know what might be possible to do with the data often find great value in simply having that person help define the problem . For example , a marketer coming to a data analyst asking questions about driving conversions might not realize that there are also data at the top of the purchase funnel that might be even more germane to driving long-term sales . Rather than taking requests as they are stated , data analysts should take requests as they should be asked , integrating advice tightly with the needs of the company . For example , a request to assess how marketing promotions affect sales should also account for the effect of promotions on brand equity
Understand how algorithms and data map
to business problems - Companies will see more effective data analytics if teams are clear on firm objectives , informed of the strategy , sensitive to organizational structure , and exposed to customers . To enable this understanding , data analysts should spend physical time outside of data analytics , perhaps visiting customers to give them an understanding of market requirements , attending market planning meetings to better appreciate the company ’ s goals , and helping to ensure data ( IT ), data analytics , and marketing are all aligned
Understand the company ’ s goals
Data analytics is beset by multiple requests , like a waiter serving too many customers . Clear recognition of a firm ’ s goals enables data analysts to prioritize projects and allocate time to those that are the most important ( those that have the highest marginal value to a firm ). Requests should be centralized , and then prioritized by a ) whether the findings have the potential to change the way things are done and b ) the economic consequences of such changes . Several companies develop standardized forms to ensure requests are assessed on an equal footing . An attendant benefit of this process is that it mitigates the potential for opportunistic research clients to approach analysts asking them to conduct a study to support a preconceived strategy for political reasons , instead of deciding between strategies that are in the best interests of the firm ...