Sophistication and Simplicity: Striking the Right Balance in Data Analytics at HPE
It takes a strong sense of balance to hit a home run. It begins with a solid foundation of skill, but you also don’t need to overcomplicate the matter to hit the ball out of the park. When sophistication and simplicity come together in perfect balance, there’s no limit to how far you can go.
Before I came to Hewlett Packard Enterprise, I was a consultant for 17 years. I'm now a senior manager in the global marketing organization. Our team is responsible for campaign and account intelligence.
In 2015, the HP organization split into two separate companies. HPE, now a distinct (solely B2B) entity, was still a global marketing organization that had almost 3,000 full-time employees or long-term contractors. So, we were still a very large, complex organization, but the split provided an opportunity for us to simplify.
Our team size has fluctuated, over the past four years, from a few as 4 people to as many as 8. The broader team owns the operational hub for the marketing organization, which includes marketing campaign and activity master data as well as the campaign and account intelligence platforms (developed using QlikView and Qlik Sense).
Four years ago, HPE wanted to apply end-to-end analytics to our marketing ecosystem. We had a few goals in mind:
- Optimize demand flow through our funnel
- Take our digital transformation to the next level
- Connect the dots on marketing spend and outcomes
- Transform our existing analytics system
In marketing, we love to talk about the marketing funnel. You do your planning and targeting at the top of the funnel, and win deals at the bottom. But it’s in between where the challenges lie—at least when trying to create end-to-end analytics.
A major challenge is that, inevitably, there are a lot of handoffs to other teams in the organization. There are handoffs between marketing and sales, or marketing and finance for approvals. You’re targeting customers of different sizes who may have various sales cycle lengths. All those factors can create complexities when you're trying to achieve end-to-end analytics. Throw in multiple terabytes of potentially unstructured and disconnected data, and it’s tough to connect the dots.
Additionally, there were multiple “versions of the truth.” We had over 600 dashboards being leveraged across the marketing teams. Even our executives weren’t immune. One executive would go to their favorite data person who had their favorite analytics platform, and another executive would go to their favorite data person who went to a source system, and when the two executives attended the same meeting, they had different answers.
It wasn’t because the answers were incorrect, but because they came from different platforms. These systems may have had differing filters or business rules applied at different times. All those variations led to our executives having differing results or conclusions, which injected churn into the decision-making process.
When we started this effort four years ago, there was an emphasis on connecting our marketing ecosystem. We wanted to connect digital form submissions to leads, leads to opportunities, and opportunities to won deals. These connection points were (at times) housed in different systems with different structures.
When you have a goal like company-wide data alignment, it’s natural to think big. It’s natural to initially go in looking for a home run, but sometimes it’s better to hit multiple singles. You need to break your goal into small, manageable (and achievable) pieces to ensure success. You're not going to get it right most of the time, but as long as you remain focused on the goals you set out to accomplish, with the balance of pragmatism, progress is inevitable.
Rounding the Bases: Connecting the Dots with Qlik
To connect ourselves—and our data—we decided to partner with Qlik. Our initial goal in rolling out Qlik was to make it a self-service tool where everyone from C-level to the individual contributor would have their own reports within the dashboard.
Another initiative was to measure potential customers on their digital footprint before they landed in our ecosystem. So, when they landed on an HPE webpage, where did they come from? Did they click through from a display ad, a social ad, or a search ad—or did they find us organically? These were the questions we wanted answered. We wanted to know what decisions we could make in those areas to increase traffic at the top of the funnel, demand at the middle of the funnel, and revenue at the bottom of the funnel.
As we continued to progress, we may have gotten a little too complex, so we decided to refine our process by simplifying our structures to make it more user-friendly while focusing on “the bare minimum” end users needed to do their job. This included leveraging Qlik Sense as our visualization tool to simplify a number of our use cases.
What We Needed to Hear: Applying Coaching and Training from Qlik Professional Services
Qlik was very instrumental to our success in the beginning and they still are to this day.
Having worked in the consulting industry, I have come across two types of consultants. There are ones that tell you what you want to hear, and then there are others that tell you what you need to hear. And I have to say that our relationship with Qlik Professional Services has been the latter.
Qlik has pushed us to think about things differently. They challenged us—and I’m sure we challenged them, and there was always growth. When you look at how far we’ve come with our analytics, the results of that challenge speak for themselves. Analysis that initially took days or hours now takes minutes or seconds.
The Qlik Professional Services team and our small team blended together to do the heavy lifting in the background. We’ve created scalable data structures that have allowed us to simplify without having to re-design the entire platform. This project has taken us four years to implement, but without their support, insights, and willingness to tell us what we needed to hear, this process could’ve gone on much longer.
Today, we have about 800 active users across the organization that are actively using our platforms in their day-to-day work, but we aren’t all the way there. We never fully realized self-service (which is OK, once you understand the personas you are tailoring information and outcomes for) and we’ve made significant strides in establishing a single source of truth (through relationships and platforms). It’s easy to use data democratization as a relevant buzz word, but it’s difficult to put into practice when trying to accommodate different personas. Data democratization is ineffective if you can’t put the information into a format that every individual can understand.
Knocking Our Goals Out of the Park
Our next steps are to continue building on our Qlik foundation. We have a solid data model design, but we need to continue to focus on our audience and understand the business questions they are trying to answer. We’re going to focus on the fundamentals because, in end-to-end analytics, the most critical factor is data quality and data integrity.
The better your data is, the easier it is to connect and the more accurate your results are going to be. We’ll continue to be cognizant that different people process and consume information in different ways. You can democratize your data, but if it’s not easily understandable by your target audience, it’s useless. Some people want to be provided the answer, while others want to “twist and turn” the data. Regardless, we will continue to focus on the business questions (and answers) when helping our users to make data-driven decisions.
Our journey isn’t over yet, because it never truly is. But we’re now closer than ever before to achieving the perfect balance of sophistication and simplicity. By mastering the fundamentals and training others on how to bring their A-game, I’m confident we’ll be able to knock our goals out of the park.