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Silver BlogHow to build a Successful Advanced Analytics Department


This article presents our opinions and suggestions on how an Advanced Analytics department should operate. We hope this will be useful for those who want to implement analytics work in their company, as well as for existing departments.



The tasks are different because estimating the full project scope up-front is impossible. Research is based on iterative hypothesis validation and incremental steps forward. What an Advanced Analytics department could commit to is to validate certain hypothesis in a given time frame, but not to build a model on a given precision or accuracy in a fixed time period.

It’s worth reiterating that software development of any kind is not a completely predictable process. There is a wide array of methodologies that help lead engineering projects more effectively and efficiently. At Appsilon Data Science we are fans of the Scrum methodology. It has proven very effective in our past and ongoing projects and can be adapted to Research resulting in a consistent project management. Download our Scrum for Data Science Free E-book if you want to learn more.

Get the right skills onboard

If you have adequately met the three points listed, then the fourth will be much easier. You will not need advanced Deep Learning or complex statistics if your project is Descriptive or Diagnostic. What you will need is someone adept at data processing, modeling, and reporting. Projects that have a significant portion of software development can have an experienced programmer with data processing skills at its core. This is obvious but it’s important to hire talented individuals as they will in many ways: build your team, determine your technology stack and build your analytics culture. Talented individuals have the inherent nature of attracting other talented individuals who want learn from them.

How not to fail

Even if you get everything right, there is still the risk of failure. We don’t have a silver bullet, but we know that anything below drastically increases your odds of failure.

Wrong KPI's

“There is nothing so useless as doing efficiently that which should not be done at all.” Peter Drucker

The way you measure your Advanced Analytics department will have a significant impact on their method of work.

A team working on one model such as a recommendation engine can use precision and recall as appropriate metrics for such a model. An alternative metric could be validating a given hypothesis. Both of these will not only affect the nature of the work and team morale but also the way in which management will view the team.

Make sure you can use that data

It is unfortunate to see AA work put on hold as a result of not following data processing regulations and legislature. These situations are not uncommon; we expect their number to grow when GDPR comes into effect.

Wishful thinking goals

Going for revolution instead of evolution is not a good idea. Research quality will suffer if the team experiences communication difficulties or is given unrealistic expectations from stakeholders. An example includes setting required model performance rates up front. Such a rate can be known if there are adequate business arguments but such rates should be considered a goal to aspire to.
Another example of such behavior is when the team tries to create something that is completely beyond their capabilities or experience. For example, a company at a Descriptive level would like to jump to a Prescriptive level. Such wide jumps inherently include a much larger risk but also tend to be much more expensive.

Thinking that Excel is Advanced Analytics

The last issue is the most common. Thankfully, the communities awareness of advanced analytics is growing quickly but there are still cases when individuals are overly anxious about moving away from spreadsheets. The number of solutions is large, but it is worth investing some time up-front. The open source ecosystem is very large; one does not have to look very long to find a much better alternative.
The number of problems associated with spreadsheets is as long as the number of benefits. We understand that spreadsheets can often be useful, but we find it difficult to imagine that, for example, a responsible approach to reproducible research would include spreadsheet calculations.

Wrapping things up

I trust that the advice you’ve read will be useful for you. We are always open to discuss how this can help your organization. If you know of situations where other solutions have proven useful, then let us know in the comments.
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Sources:
1. Reference 1
2. Reference 2
3. Reference 3
4. Reference 4

Original. Reposted with permission.

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