Businesses scaling analytics will need to train some translators first
DATA ANALYTICS can help companies do exciting things, especially in a world where innovations in sensor and network technology make data more accessible every day.
However, organizations are increasingly finding that although the technology is incredibly useful, rolling out and scaling data analytics solutions isn’t very easy.
A recent McKinsey whitepaper, therefore, said that companies should train analytics translators who can “perform some of the most essential functions for integrating analytics capabilities in a company”.
“Translators can help businesses climb the analytics learning curve quickly and roll out more use cases than they might otherwise.”
According to the think tank, analytics translators can acquire some of the requisite knowledge for the job through coursework — which most solution vendors provide — but they tend to make the biggest impact when they use their knowledge of the business and industry to really leverage data analytics for better results.
McKinsey, based on their experience with dozens of clients, cautions against expecting all employees to understand and use analytics, even if some basic training is provided. That’s a recipe for disaster.
Instead, the organization provides three intelligent tips to develop a sufficient number of analytics translators to bridge the talent gap in the short term, till employees climb the (analytics) maturity curve, gain an understanding of analytics, and get familiar with the tools:
# 1 | Recruit translators from the right divisions
When businesses dive head-first into analytics solutions, they must create a strategy and plan to implement it across the organization.
Based on the plan, business leaders need to map out how many analytics translators are needed and the role they must perform in each part of the business.
Further, the plan also helps understand which part of the business the translator must come from — so they have an understanding of the challenges faced by that division/unit/segment and can deliver the most value once they have been trained on analytics.
# 2 | Build basic awareness of business analytics
Once analytics translators have been chosen, they must be provided with the requisite training.
“The first stage of a translator-training program should equip employees with fundamental analytics knowledge: a basic understanding of how analytical techniques can help solve typical business problems, as well as a general familiarity with the process of developing analytics use cases.”
According to McKinsey, basic training might take up to a week — however, based on experience, they recommend that translators be encouraged to hone their skills and gain some hands-on experience during or immediately after the basic classroom training.
# 3 | Develop the ability to deliver use cases
Acquiring skills is the easy part, especially when working with sophisticated tools. In order to truly enable analytics translators to deliver effective use cases, translators need to be coached “on the field”.
In other words, McKinsey recommends that translators either shadow an experienced translator or work with an experienced translator who supervises the delivery of a use case.
Overall, the think tank says that translators typically spend six to 12 months in training. However, once trained, analytics translators continue to learn on the job — as they discover new data challenges and seek creative ways to overcome those challenges to deliver effective insights and drive the business forward.
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