As we examined in part 1 of our Data Monetization blog series, the first step to increasing revenue with data is identifying who the analytics will be surfaced to, what their top priorities are, what questions we need to ask and which data sources we need to include. For this blog, let’s take a look at what tools we will need to bring it all together.
With our initial example of a VP of Sales dashboard, fortunately the secondary data sources (NetProspex, Marketo and HubSpot Signals) all integrate fairly seamlessly with the Salesforce CRM. This should allow for some fairly straightforward analytics built on top of all the data we’ve aggregated. If we pivot over to our CMO dashboard, things get a bit murkier.
Although our Marketo instance easily integrates with Salesforce, the sheer volume of data sources that can provide insight to our marketing activity makes this project a much more daunting ask. What about our social channels, Adobe Omniture, Google Ads, LinkedIn Ads, Facebook Ads, SEO as well as various spreadsheets. In more and more instances, especially for a team managing multiple brands / channels, this number can easily shoot into the dozens.
Just as critical to the project as what and how; who’s managing it? What skills do we have out our dispense and how much time do we have dedicated? Will this project be managed by IT, our marketing analytics team, or both? If IT is managing the project the tools we will require a much different skill set than if our marketing analytics team is spearheading the effort. Are we going to outsource some or most of this project to a vendor? These questions probably deserve (and will get) their own blog post. Do we have dedicated developers or can we select tools that can free them up for another project? 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.
We can’t quite start picking tools yet. Although we know who will be running the project, what functionality do we require? Do we already have dedicated storage and data warehousing (and someone to manage it) or is this something that we need to account for when selecting the appropriate platform / vendor? How often does the data need to be refreshed (daily, hourly…) and how will we integrate all of the data sources. 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 and analytics 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.
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.
Stay tuned as our journey continues!