Analytics translators perform some of the most essential functions for integrating analytics capabilities in a company. They define business problems that analytics can help solve, guide technical teams in the creation of analytics-driven solutions to these problems, and embed solutions into business operations.
It’s specialized work, calling for strong business acumen, some technical knowledge, and project management and delivery chops.
Deploying translators is especially important during a company’s early efforts to use
That gap should shrink in the long term, as analytics pervades business and analytics training becomes a standard part of employee development. But in the face of competitive pressure, companies cannot wait to work with analytics on a large scale. Translators can help businesses climb the analytics learning curve quickly and roll out more use cases than they might otherwise.
While translators can acquire some of the requisite knowledge for the job through coursework, they make the most impact once they have developed practical skills through on-the-job experience.
Yet it is all too common for executives to assume that employees can act as effective translators, capable of delivering analytics solutions, once they complete a class on the rudiments of modeling. In fact, employees who only receive classroom training are more like teenagers who sit through a
Translators can only master their trade by observing seasoned colleagues at work and then working on actual problems with expert guidance. This progressive, real-world learning approach prepares translators to manage diverse teams of specialists, create replicable workflows, and apply business judgment while assessing trade-offs.
None of these steps can be skipped if a company hopes to apply analytics widely and generate significant value.
Recruiting translators and positioning them for impact
Before launching a translator-training effort, executives should map out a company’s analytics strategy and priorities. Then they can determine how many translators are needed in each part of the business—and target recruiting and training programs accordingly.
Translators typically sit within business units, in proximity to day-to-day operations in stores, plants, mines, call centers, and other sites where employees make products or deal with customers. These vantage points let them spot uses for analytics and ensure that analytics solutions are embedded into the business for impact.
Ideally, translators will have spent time working in business operations before starting translator training. Existing business staff often make better translators than new hires because they have an important quality that is hard to teach: knowledge of a business domain where analytics will be applied. To put this another way, business operations are the typical translator’s “mother tongue.”
In addition to business acumen, other qualities companies should look for in internal translator candidates include comfort working with numbers, project management skill, and entrepreneurial spirit. Training curricula can then concentrate on the technical knowledge and practical methods that translators need.
Building basic analytics awareness
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 general familiarity with the process of developing analytics use cases.
This level of knowledge is readily attained from a week or so of classroom training covering:
- The potential to use analytics broadly within their industry and, more specifically, across the business’s value chain.
- General techniques for prioritizing analytics use cases and defining their scope.
- An overview, and ideally a simulation, of the lifecycle of an analytics use case: defining a business problem, selecting target variables, brainstorming features of a potential solution, and interpreting results.
- The roles that translators and other specialists (such as data scientists, data engineers, technical architects, and user-experience designers) play at each stage of an analytics use case.
- The major types of analytical approaches (descriptive, predictive, and prescriptive), with deep dives into a few common algorithms (such as decision trees, neural nets, and random forests) and how they apply to business problems.
- Methods for evaluating the performance of analytics models and understanding the trade-offs associated with particular models.
- Agile ways of working—testing and learning from short development cycles, or “sprints”—that help multi-functional teams to deliver effective solutions swiftly.
- Practices for embedding analytics solutions in the business and overcoming implementation difficulties, such as cultural barriers.
Translators also need the technical depth to hold their own when discussing problem-solving approaches with data scientists. Many take online tutorials to learn common programming languages, such as R or
Click here to continue reading Louise Herring, Helen Mayhew, Akanksha Midha, and Ankur Puri’s article.