Why Data & Insight are key drivers of Marketing Operations success (post 1 of 5)

July 8, 2022 Kristin Connell

Data is the fuel that powers modern marketing, enabling better understanding of customer needs that leads to better engagement, better content, accurate measurement, and better marketing ROI. Data by itself is useless, however, without a mature data management infrastructure of people, processes, and technology to turn raw data into actionable insight that drives engagement and revenues. Our “Data & Insight" series focuses on building that mature data infrastructure.


Data & Insight have become more important than ever, especially given today’s rapidly changing market and customer behaviors – as well as today’s more complex digital environments – including the crumbling of third-party cookies. 

The essential role and “fit” of Data & Insights into the Marketing Operations triangle of people, processes, and technology is anchored in use cases, which are prioritized based on business needs. Data & Insights are meaningless unless they support a specific, measurable, and revenue-connected goal. Yes, strategy comes into play at a higher level where you define where your organization wants to go and how you want to get there. 

Your use cases inform what Data & Insights you need and how you’ll leverage them. Without use cases, you’d be operating in chaos and/or at status quo, only able to react instead of proactively driving measurable results with data and insights. 

Data use cases: Driving revenues and continuous improvement

The ultimate goal of leveraging data and insights is being able to predict and grow pipeline and revenue, and continuously improve your marketing effectiveness. One of the dependencies of continuous improvement is the ability to measure and benchmark exactly where you are at any given time. Another key expectation is gaining a unified view of contacts that reflects all interactions across all channels. 

For many Marketing organizations, getting from here to there with their data infrastructure requires a significant investment in time and resources. It’s a heavily cross-functional approach that can require a massive paradigm shift in how Marketing organizations operate within themselves and within the larger business organization. There could be countless use cases under the umbrella of these larger, strategic goals, depending on where you are in your data maturity.

Status quo as the biggest obstacle

The biggest challenge to improving and maturing your data and insight capabilities is the status quo, because an organization’s desire to change must be greater than its desire to stay the same. The legacy mindsets that say, “that’s the way we’ve always done things” represent huge hurdles to overcome, especially when compounded with other challenges such as a low value associated with data in general, little to no action on insights, silo’ed functions, and beyond.

Even if you somehow move past the “inertia” challenge, without a strong culture of collaboration and teamwork in place, you’ll still be faced with finger-pointing, lack of cross-functional communication and/or lack of communicating marketing’s value to the larger business organization in terms they can understand (i.e., pipeline and revenue). 

4 steps to an improved data infrastructure

Change is never easy and rarely linear, but you need to begin somewhere. Here’s how:

1. Business cases will be needed as the context for any defined data and insights initiative. Each business case should identify and ideally quantify the pain points and costs created by the data and insight challenges you currently face – and when I say “quantify,” I mean that the numbers must show value that’s in alignment with business impacts such as time savings, cost savings, lead creation, pipeline growth, and/or revenue impact. After you’ve brainstormed those business cases for your organization, they should be prioritized. 

2. In a best-case scenario, change to the status quo would need to be driven by an executive-level Champion who brings a cross-functional mindset to driving measurable impact and who defines business case priorities, including the clearing of obstacles as needed. Since data is cross-functional in how it’s collected and leveraged, initiatives involving data must be cross-functional and have top-down support.

3. Once a business case has been presented, and received approval, a formal project plan should be built, resources assigned (having been addressed in the business case), and timelines agreed to. 

4. I’d strongly suggest reaching out to us for a complimentary data review with our Sojourn team in order to help you identify opportunities to create the most value from your data and systems, and get on the right starting path to improvement.

An example of Data & Insight transformation

We worked with a healthcare client a few years ago that engaged us for a digital transformation project. One of the executives felt that a CDP would be the best way to solve their many challenges around data and insights. 

Once we came into the project and studied their goals and resources, we realized that the organization was lacking a full integration of their core systems (MAP, CRM, ERP) causing: (1) lots of unneeded manual work such as data exports for targeting, analysis, reporting, etc. and (2) leaving many silos across the client’s functions and teams, as well as their martech and their data.

Our recommendation was a plan to optimize their core systems integration, providing them with a nearly 360 degree view of their customers. In the end, we saved them an annual cost of roughly $750K in new technology, resources, and projects that the business wasn’t in a position to correctly implement and support anyway. By integrating their core systems and teams, they also developed more cross-finctional capability in how they leveraged data and insights to engage their customers and generate pipeline/revenues. 

Optimizing your Data & Insight capabilities

You must understand where you are now in order to get where you want to be. I’d recommend starting with some sort of Assessment (directional), Discovery (deeper directional), or Health Check (more diagnostic) in order to:

(1) baseline/benchmark your current state of play regarding data and insight capabilities, and 

(2) build consensus and adoption to move forward aligned to an agreed-upon maturity roadmap for data and insight.

For more insight and help in improving your data quality and data management maturity, reach out to us today.

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