Data is a critical part of running any successful business, whether you’re trying to understand past performance, choose a strategic direction, or make a prediction about the future. In terms of the impact of data analytics on businesses, I see it running along two parallel paths: data that can lead to better and smarter products, and data that can be used to run the business better. I work in the latter group at my company, where our focus has been to deliver enterprise data and analytics that boost Sales productivity and profitability.

Where data can drive impact
Using enterprise data to support Sales raises the promise of addressing many longstanding questions more effectively. For example:

  • What level of bookings (or how much annual recurring revenue, or ARR) is each salesperson supporting?
  • Which technologies are specific customers focused on buying right now? 
  • How does a salesperson maximize current resources, and how profitable can they be because of that? 
  • Is a sales team driving enough topline revenue based on the number of people it has and the resources it is expending? 
  • Is a specific salesperson or team focusing on the right customers? 
  • Are customers optimizing their use of flagship products, and can they benefit from add-ons or adjacent products? 

Many of these questions also apply to partners, who might be selling into certain segments of your market on your behalf. Like sales, they want profitability. You want to drive higher productivity as a data and analytics leader, but with partners you also want to manage risk. Questions here might include:

  • How do we ensure that the pricing we provide to partners is the same pricing that ends up in front of customers? 
  • How do we create a scoring system that ensures fairness and transparency with partners from the beginning of every deal, so we manage risk while accelerating deal velocity? 

Data has a role to play in answering all these questions, as well as in helping Sales evolve beyond a “booking equals success” mindset. With data that supports more longitudinal thinking, customers can be encouraged to purchase more, realize more value through higher product consumption, and in the process place your company higher up on the value chain, ideally moving from vendor to long-term partner.

Clearing the path for data insight
Especially in multibillion-dollar enterprises, driving lasting productivity with data is much easier said than done. As a data leader you are likely to run into any or all of these challenges:

Groups that “own” but don’t share data. The whole idea of data producers as exclusive “owners” of the silos they oversee is one that we as data leaders need to help our organizations move past. We still need data owners to steward their data, of course, but they need to expand the scope of users for these assets beyond their immediate teams. If you are looking to find underserved areas of the market, for example, it’s not productive for Jack in product group X to wall it off from Susan in sales group Y. Working with this data in the cloud is the first step to make it more free flowing and find key connections, and it’s up to us to present this opening up of data as an opportunity. 

Data myopia. Another challenge is convincing product and sales leaders that they may know their own network and customers extremely well, but that they are unlikely to see anything new if they just keep looking at the same set of data, and doing it in the same way. By adding more context and common definitions to data, you can start to show them things about their customers they weren’t aware of. Very few sales leaders will turn down this kind of invitation. 

The digital data divide. Even in technology companies, we are far from 100 percent digital. Workflows today take in emails, meetings, conversations, and whiteboard notes. Contract negotiation is a great example of this pain point. Without a fully digital workflow, it’s often impossible to say with confidence who has the next move on the contract – the legal team, the deal team, the sales team, the customer contact, their legal team – especially when you may be months into negotiations. How can you accelerate deal velocity if the contract status is a black box and you spend half your time waiting around for updates? 

Back to basics, starting with the customer
It is a sometimes painful conversation, but the first step to driving more productivity with data is by agreeing on fundamental terms and then codifying those definitions. Here you can’t get more fundamental than the two things companies run on: products and customers. Which in this M&A-driven age can mean lots of products and many different definitions of customers. 

Sales teams may glaze over at this foundational work, at least initially. For example: “We sell to Harvard University, that’s our customer.” Well, Harvard is a global organization. Are you selling to Harvard College or Harvard Business School or Harvard Medical School? And from which CRM system are you pulling this definition? Large enterprises may have half a dozen or more. By the same token, Product X may contain a multitude of options, modules, updates, and versions. Which do you mean when you say Product X? Even annual recurring revenue is defined differently across acquiring and acquired companies. As you start down the path of understanding how to use data analytics to grow your business, right away you will see the work required to look at data in a connected manner.

The second step is to get serious about customer journey analytics. As I see it, tracking and analyzing the interactions, digital markers, and Moments of Truth that customers have with your company, across channels and across time, is the best way to surface the disjoined, frustrating experience customers often have, and to get the undivided attention of leadership. It’s also an ideal focusing point to do an end run on the thousands of report requests most data analytics teams receive. Being able to ask, “How does this report you want relate to this customer?” introduces a very different prioritization for data work and its potential impact.  

Getting to customer journey analytics requires understanding the roles of your key user audiences. These might include:

  • Lead salesperson
  • Finance analyst
  • Customer success rep
  • Product management lead
  • Sales planning and operations manager
  • Executive manager

Understanding a day in the life of these roles requires sitting down with them to ask about their data goals, the reports and workflows they currently use, and how changes in key product or customer definitions that refresh from the cloud as they’re updated could make their lives easier. 

This takes some effort, but in addition to helping these colleagues become more productive with data, it changes your relationship with them from a supporter to an enabler. It also gives you the perspective to be able to say, “Well, that report you want isn’t aligned to what the go-to-market team wants to drive.”  

Data as enterprise product 
Many data analytics groups still operate as a shared service. In practice this means that anyone can open up a ticket and say, “Hey, can you pull a list of customers and run a report?” even if this report (and thousands like it) will only be viewed ten times. Productizing data analytics significantly cuts down the number of support tickets you’re likely to see, and reduces the need for costly, manually intensive customization.

Why should you redefine enterprise data and analytics as a product organization? 

Because when you look beyond data as a point solution or a part of a project in terms of its potential impact, you begin to see it as something more persistent and valuable, less disposable, and more lasting. With a product mindset you can start to think in terms of product management, a delivery or engineering group, and a program office that is supported by a change management office. Everyone in the product framework you create –  data and analytics product managers, BI developers, data engineering, master data engineering, and supporting functions  – should see themselves as developing and improving products, no different than the way your key product groups do.  

Data as product is also a great way to build an internal community of understanding and support for data analytics’ importance. My company’s Customer Journey Analytics Slack channel now has 7,000 members, all focused on the customer experience we create. Here and in account review and planning sessions, which might be weekly, monthly, or quarterly, we look at each other's work in a more meaningful way and collaborate more effectively on what the customer is feeling from us right now. It’s hard to imagine being approached for these conversations – much less having the bandwidth to entertain them – with a service organization mindset.

Moving up the maturity curve
In addition to helping the sales organization thrive, all these activities can drive the next evolution of your maturity in data analytics. Executed well, you should see this maturity reflected in several new cultural beliefs about data and analytics:

  • that data silos are culturally counterproductive and ultimately harm customer relationships
  • that services may be delivered, but data and analytics products are never done (especially in the SaaS world) and can continue to deliver ever-greater value
  • that we are moving toward a place where an employee can join a company and have all the data and analytics they need at their fingertips on Day One  
  • that data, properly collected, shared, and consumed, can help any company react faster, whether those changes come from the customer or external market forces

Pursuing strategies like these should help you move into a stronger win-win relationship with your colleagues, so that both you and the organizations you’re serving – Product, Sales, Services and Support, and Finance – can honestly say to each other, “I'm not successful until you are successful.”