09 Feb Keboola Data Monetisation Series – Part 2
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. In this post, 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 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 task. What about our social channels, IBM Silverpop, Adobe Omniture, Google Ads, LinkedIn Ads, Facebook Ads, SEO as well as various spreadsheets and manually created reports supplied by agencies?
We are noticing that in more and more instances, marketing teams have to manage and continuously optimise multiple channels. This number can easily shoot into the dozens.
“Measuring marketing efforts in a complex way becomes truly challenging as no two companies in the world come with the same data sources, data sets and objectives. This leads to a strong belief that custom-built analytics systems are a must for those willing to succeed.”
- 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 dedicate?
- Will this project be managed by IT, our marketing analytics team, or both, and can they work together?
- If IT is leading the project, the tools stack we will be using will require a much different skill set than if our marketing analytics team is spearheading the effort.
- Are we going to enjoy external vendor for the project build? And if we are, are we choosing collaborative approach friendly stack of technology?
These questions probably deserve (and will get) their own blog post. In a nutshell, we want to make sure we have the right people with the right tools and the right objectives to maximise 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, there are more questions to answer.
- 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? Demystifying the #realtime buzzword will soon get its own blog spot.
- How are we going to 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 visualise the data and analytics for end user consumption?
- Will the business users be able to run their own ad-hoc reports or will this be managed through report requests to an analyst/IT?
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 for 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 answer more difficult business questions, 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!