Three tips for a successful data-driven business transformation

But first, what do we mean by data-driven business transformation?

Data-driven transformation can take on a few different meanings today. Here, we are not talking about transforming data itself, but about how data leads to transformation, specifically digital business and marketing maturity. This transformation can be achieved by effectively leveraging 1st party, 2nd party, or 3rd party data to drive innovation, increase efficiencies, and create impactful consumer experiences that lead to measurable results.

From the increased accessibility of generative AI tools to new ways of tracking the customer journey through 1P data, technology is transforming businesses at a breakneck speed – so much so that it can be difficult to keep track of every nuance. In our first podcast episode, we explored the many components that make up this evolving landscape. 

Data-driven transformation deals with an organization’s data collection, orchestration, and activation processes, and their respective technical challenges. This practice falls under the broader umbrella of digital transformation, which focuses on new technology adoption and data-driven decision-making.

A strong mix of digital and data-driven transformation expertise is crucial to harness the correct insights from your data, manage ever-changing touchpoints and multiple customer engagement models, and ensure access to the right data at the right time for real-time, accurately informed business decision-making. Still, anyone who has tried to 'transform' anything in a business context knows all too well that such initiatives often expand beyond a single department. So, how can you ensure success along the way, and against your business objectives? 

At fifty-five we’ve accompanied many businesses in their digital transformation journeys, and we’ve seen what works and what doesn’t when it comes to driving real impact. Here, we’ll highlight three of the essential tips for mastering data-driven business transformation that we first mentioned on the podcast. To listen to the episode and hear a few examples of how we’ve worked with brands on this topic at fifty-five, follow this link. 

1. Define success early and measure often  

Set precise goals early on and communicate progress frequently to your stakeholders

First, it’s essential in any data-driven transformation journey to set a North Star, or a core business objective on which to unite your teams and measure your success. Make it as straightforward as possible, and avoid any contradictory objectives that could lead to mixed results or confused stakeholders. By defining early on what success would look like at the business level, team level, and even campaign or use case level with clear KPIs, you can more easily know when to pivot and how to garner internal support for your project.

Once your North Star and measures of success are in place, you must keep the momentum going throughout the project – a common challenge for our clients as digital transformation is a years-long, often ongoing journey with key milestones throughout. This is why we often recommend establishing a cross-functional steering committee made up of stakeholders from across the organization, all invested in the transformation initiative. This committee can champion the initiative, provide regular updates to the rest of the organization on the success and progression of data and digital transformation initiatives, and help foster awareness and adoption of any new tools or processes that are rolled out.  

And not only is it critical to define your measures of success early, but also to report on progress (whether the results are positive or negative!) frequently to your group of cross-functional stakeholders. Using the foundational metrics you’ve defined early on, assess progress often, share results and learnings across the team in ways that resonate with them – e.g., internal email newsletters, recurring meetings, or other forums, and continue to evolve both the measures and the tactics as you go. 

2. Put people first 

A use-case driven approach requires listening to your teams

At fifty-five, we talk a lot about being “use-case driven” in our approach to setting up martech solutions and digital infrastructures for our clients. But how do you identify and prioritize the right use cases to focus on, and understand which use cases will have a positive impact on both your business and your customer? It all starts with listening to your internal stakeholders or users. Think about the marketers, the data scientists, the product owners, the C-suite – who will be hands-on, daily or regularly using the new tool or process you’re hoping to launch? Spend the necessary time upfront asking them about their everyday needs, the challenges they’re facing, what they wish they could do but can’t do today, what keeps them up at night. If you don’t take the time to listen early on – across all levels and disciplines in the organization, you risk making investments in tech and tools that will never be adopted by your team, and end up causing drag instead of momentum for your business. 

Conducting detailed discovery and listening sessions with your executive sponsors and a wide array of potential “power users” upfront can also help you expedite and succeed in the change management process when you’re rolling out new ways of working, new insights, and/or new tools in your organization. Listening widely requires investing more time upfront, but can reap dividends down the road. When you put people first in the data-driven transformation process, listening to and incorporating their feedback and sharing transparent updates on the status of your project or initiative, you have a much better chance of securing their buy-in, gaining cross-functional, cross-level alignment, and ultimately driving adoption – and thus, success – of any new solution. 

The early discovery process is also where strong documentation really shines. At fifty-five, we use a “RACI” (Responsible, Accountable, Consulted, Informed) matrix to map out key activities in the project against a list of the decision-makers, potential power users, and parties who should be consulted throughout the process, with one clear owner or accountable party per step to keep forward progress flowing. Regardless of the specific tool or template you use to map out roles for the project, the key is to have a well-defined structure for fostering transparency, accountability, and teams that are unified in their vision. 

3. Own and master your data & tech

Don’t let the tech master you – train, automate, and iterate as you go

Once your data-driven business transformation goals are defined and your power users identified and onboarded, use cases identified, and any new tech capabilities, requirements or data sources are roadmapped out for implementation in a sequential fashion according to the priority of your use cases, the real work begins. This part of the journey can and should look differently for every business depending on factors such as your goals, budgets, internal resources, existing tech stack / partners, and desired use cases. Whether your business is driving transformation through centralizing disparate data sources into a single cloud platform, visualizing data and insights in real-time, automating more marketing and media optimizations, enhancing your audience targeting capabilities, or any other data-driven initiative, it can be helpful to keep a few guiding principles in mind as you go through the setup and deployment processes for your use cases: 

1. Ensure any new technology, data source or partner you onboard is aligned to business goals by investing in scalable solutions that can adapt to evolving industries, datasets, consumers, and tech, as much as possible  
2. Train and equip power users on new tools as you go, onboarding your team with trainings early and often, to drive a more seamless transition, accelerate adoption across the organization and promote a collaborative peer support environment 
3. Establish a measurement framework and roadmap of your prioritized use cases with clear and frequent milestones for evaluating success, to avoid letting perfection be the enemy of progress
4. Set up robust data governance policies, structures, and teams internally to ensure data accuracy, consistency, and security
5. Seek out and incorporate feedback from stakeholders, power users, and/or consumers at recurring intervals to ensure the proposed solutions and pipeline of use cases remain in sync with the dynamic needs of the organization and industry trends

I’ve only scratched the surface of what’s possible through data and digital transformation. For a great example of this in practice, I encourage you to check out our recent case study with Bayer, a client who worked with us on transforming their creative analytics reporting at a global level across  key markets. The Bayer team not only aimed to create an exciting internal product – an advanced AI-driven reporting suite to showcase the impact of creative campaigns on highly targeted audiences – but also used several of the transformation tactics we’ve highlighted above to generate internal support and team adoption for wider global success.

For more content like this subscribe to our podcast, Data Break by fifty-five- Global, here. Every month, we invite key industry experts at fifty-five and beyond to take a break with us and discuss trending topics in marketing and data analytics. Next episode, fifty-fivers Rico Dittrich and Julian Litvak will be diving into how Google’s major privacy updates - the deprecation of third party cookies and sunset of Universal Analytics - will impact marketers in 2024. 

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