Guiding project requirements for analytics

In a recent post, we started scoping our executive level dashboards and reporting project by mapping out who the primary consumers of the data will be, what their top priorities / challenges are, which data we need and what we are trying to measure.  It might seem like we are ready to start evaluating vendors and building it out the project, but we still have a few more requirements to gather.

What data can we exclude?

With our initial focus around sales analytics, the secondary data we would want to include (NetProspex, Marketo and ToutApp) all integrates fairly seamlessly with the Salesforce so it won't require as much effort on the data prep side.  If we pivot over to our marketing function however, things get a bit murkier.  On the low end this could mean a dozen or so data sources.  But what about our social channels, Google Ads, etc, as well as various spreadsheets.  In more and more instances, particularly for a team managing multiple brands or channels, the number of potential data sources can easily shoot into the dozens.

Although knowing what data we should include is important, what data can we exclude? Unlike the data lake philosophy (Forbes: Why Data Lakes Are Evil,) when we are creating operational level reporting, its important focus on creating value, not to overcomplicating our project with additional data sources that don't actually yield additional value.

Who's going to manage it?

Just as critical to the project as what and how; who’s going to be managing it? What skills do we have out our disposal and how many hours can we allocate for the initial setup as well as ongoing maintenance and change requests?  Will this project be managed by IT, our marketing analytics team, or both? Perhaps IT will manage data warehousing and data integration and the analyst will focus on capturing end user requirements and creating the dashboards and reports.  Depending on who's involved, the functionality of the tools and the languages used will vary. As mentioned in a recent CMS Wire post Buy and Build Your Way to a Modern Business Analytics Platform, its important to take an analytical inventory of what skills we have as well as what tools and resources we already have we may be able to take advantage of.


What functionality will we require?

Although we know who will be running the project, we need to refine our focus to make the tool evaluation process more straightforward.  How often does the data need to be refreshed (daily, hourly…) and how will we integrate all of the data sources. Certain types of data, like unstructured, will require different functionality than something that's designed to capture sensor data.  Based on what we’re measuring, we may want to have snapshots of the data at a certain interval, as well as the capability to track data lineage.  How will we create the dashboards and visualize the data for end user consumption?  Will the users be able to run their own ad-hoc reports or will this be managed through report requests to an analyst / IT?  Depending on how we’ve integrated and warehoused the data for the project, there are a lot of different routes to go for visualization.  

Should we partner / outsource?

Another question to address is if we are going to outsource some or most of this project to a vendor?  Do we have dedicated developers or can we select an analytics platform that can free up our resources for another project?  Particularly with things like sales forecast analytics or embedded analytics, there are vendor with specific expertise and best practices that can add additional value to the project we may not get if we go it alone. In a nutshell, we want to make sure we have the right people with the right tools to maximize value and make the best use of our resources; these questions probably deserve (and will get) their own post.  

Will it scale?

Up to this point, we’ve tried to break down the project into components and do some light discovery for things to keep in mind.  After playing in the weeds a while, it’s a good idea to take a step back and ask a question about the broader project.  How will this solution scale?  Considering the talent we have available and the project requirements, how will the tools we select allow us to scale to more users, additional data sets and larger data volumes as the project grows?  The data landscape, and users needs will change; if we aren’t planning for the flexibility and growth, we might as well sink the ship right now and save the budget.  


Thanks for checking out my post, if you enjoyed it you might also like: 5 data science algorithms that can help you better understand your customers.


Colin McGrew

I write about data, analytics and developing client relationships.