Cross-posted from the PatternBuilders blog with permission.
Welcome back to the third post in our series on how to get value from your data.
As we stated in a previous post:
“Data, without the proper use of analytics, is meaningless. If data is the new oil, think of analytics as the oil drills—you need both to be successful.”
Of course, getting to “success” is not easy as anyone involved in an analytics project will tell you. This series walks you through our methodology on what it takes—from inception to proof of concept to implementation and deployment—to navigate project pitfalls. However, if you’ve assembled a great team, you will be able to drill for all that oil. In our experience, great teams tend to develop, manage, and sustain successful analytics projects: It all comes down to having the right people with the right skill set.
Assembling Your Team
One of the other tenets of PM 101 is that your Product Management/Development (PD) Team must have the right people with the right skill set. Now you may wonder why we define the customer before we look at the team responsible for implementing the product—some may even argue that it should be the reverse. For those of you who believe the former, I will ask this simple question: How can you assemble a team before you understand who the customer is? Who you need on your team in terms of skill sets depends upon who you are designing the analytics for and what the analytics will do. Thus, it’s Customer first, People (team) second. Enough said.
Does Acme have the right people from all the right functions?
You need to have representatives from all the groups in your organization that know anything about the data, the analytics, or the customer you are going to sell them too. This may mean that you are involving people outside your traditional PM/PD processes. For example, folks from IT who understand the structure of the Data; from Accounting or Finance that share data and metrics with the potential customer segment; from Sales and Marketing who have had requests from potential customers; from Customer Service that field support calls from current customers; and especially from the Analytics or Data Science team who will vet the accuracy and appropriateness your analytics math. For the pilot, Acme will also need:
• A Sales or Account Manager that knows the merchants (the new potential customers) along 3rd Ave and understands the analytics they need.
• A representative from the City (a current customer) that can advise on analytics that will help the City.
Does everyone on Acme’s team have the perspective that Data is an Asset?
The product team must absolutely see analytics based on your data assets as a marketable product just like anything else you already sell. Offering analytics as an add-on feature or product is a new frontier for many. Some will get it, and others won’t. You need to make sure that your team has a real-world perspective and not a fairytale view of what an analytics project entails.
Oftentimes, data privacy and security are areas where companies make mistakes. When it comes to data, trust is key. One way to ensure trust is to be transparent about how you collect and use data and how you protect that data. Members of your team must be able to handle data privacy and security issues—if these are weaknesses in your organization, make sure that you outsource that expertise. In other words, never just assume that you will figure this out as you go because once you lose customer trust, it is very difficult to win back.
In Acme’s case, all team members must understand that the analytics they are developing provide analytics insight into how “real” people use their other products (i.e. parking meters) in a “real” way on a daily basis. Since Acme is calculating analytics based on fast data, they must consider how they can dynamically deliver this information so their customers are getting real insights in near real-time. In other words, they will need to explore the best delivery mechanism to use and it may not be traditional vehicles like a website or report.
More on this in our next post.
Is data built into all of Acme’s Product Development processes?
Lastly, it is critical that all the data that goes into your analytics is coming from scalable, sustainable processes. You need to fully understand how the data is created, saved, enhanced, etc., as well as have processes in place to ensure that you can successfully access and analyze it on an ongoing basis. For Acme, this means ensuring that the operations, development, marketing, and support personnel understand that the data they are creating on a daily basis is an integral part of Acme’s product offering. And just as the folks in your organization must value data, they must always be on the lookout for parts of the business – financial processes, other products, strategic decisions, etc. – that can change, discontinue, or impact the processes that are feeding your analytics. We’ll talk more about this in our next post.
As you can see, figuring out how to extract value from your data, specifically to develop analytics, isn’t rocket science. (Well, not unless you are setting out to create analytics from rocket science-like data!) We’ve been responsible for all kinds of analytics projects and one thing we know for sure: Sound product and project management processes combined with the “right” team will ensure successful pilots and rollouts. Yes, you will have specific data and technology challenges to address—and that’s what we’ll look at in our next post!