Before Scaling – Building Analytical Team Processes


Summary: Before adding a person to your analytical team, it’s important to create templates for reporting, centralize data access, and automating reoccurring reports.

I believe a lot of software development practices can be applied to business and analysis. I was inspired by this post on how one programmer built up his position and a team. He did so by automating and continuing to learn. As analytical teams begin to develop across the country, I’d like to pause and consider some best practices for growing your team.

A common situation: an analyst has been with the company for several years decides to take a job elsewhere. When someone else takes on that position (temporarily or full-time), they find some issues and try to improve the work of their predecessor. Or they decide that the predecessors work is garbage and they start all over. How can you stop this from happening?

Set up Repeatable Processes

There’s a great book called work the system that shares the author’s experience fixing his business by putting systems in place. It all comes down to writing down what to do every time.

Creating Reporting Templates: There are branding guides for creative departments to follow so why not a reporting guide? Something as simple as an outline with the necessary headings (purpose, key takeaways, discussion, methodology). You should have a cache of high quality reports in a swipe file(link) so that you can share them with your new employee to build off of.

Communication Funnel with IT: Whether you’re an analyst or a marketer, you should have a clear path to IT resources. Before you hire someone, lay the groundwork and formalize the relationship with IT. You could go so far as writing a Service-Level-Agreement. In my opinion, it is a bad idea to depend on solely informal relationships to speed things along in IT.

Central Analytical Data Source: If you do spend time working an analysts job, you will notice that data is messy and scattered everywhere. Before you hire another analyst, consider creating views or starting on a data mart for your analytical needs. A good analyst won’t be frustrated with messy data but they will be more effective with well organized data.

Automate the Boring Stuff

automation

Reoccurring Reports: Let’s say the average analyst salary is around $70,000(http://www.bls.gov/oes/current/oes131161.htm). There are 252 business days in a year (minus two weeks vacation) and I would assume they work 45 hours a week. That’s $30 an hour.

If a set of reports take two hours to complete every week, that’s $1,500 a year! If you could condense that work into minutes, you could work on more valuable projects.

Tools like Python and VBA can SQL stored procedures can be set up to do some monotonous task again and again.

Test / Quality Control: Not every analyst pulls accurate data the first time. It’s important to have a cheat sheet that every analyst can refer to for a double check. It should contain top metrics along with commonly reported stats. For example…

  • Past 12 months of sales.
  • Sales by product category.
  • Web traffic for certain segments
  • Total marketing pieces

Bonus points if you use a tool like Python or VBA to automatically check totals. This should be something that is generated automatically daily so that it is a trusted and reused source. At my work, we strive to create a table stored on a database for every user to access some baseline numbers.

Good for the manager better for the analyst

Getting more out of your employees is obviously a benefit to managers but this level of automation is more attractive to top analysts. As an analyst, I would appreciate more automation for a few reasons.

  • Spend less time doing the same things over and over.
  • Spend more time on important analyses.
  • Have more time to learn new things.

These benefits are the same things you can tell potential candidates… Personally, I would love to work at an organization that emphasizes making me as effective as possible.

Bottom Line: Spend time on improving the lives of your analytical team has a multiplicative effect and should be seen as an investment.