How to build a data-driven culture
Many business have data, but few are actually able to use their data to increase customer value. This post describes the technology and processes you can implement to build a data-driven culture that transcends individual teams and differing levels of technical understanding.
“Data-driven”—it’s a buzzword for a reason. In a world in which consumers expect experiences to be tailored to their individual needs, organizations that are adept at turning customer data into correct decisions reap huge advantages over their less data-driven competitors.
When every business has data, how do you use it to build the products and experiences that attract customers and keep them around?
The answer is to build a data-driven culture that transcends individual teams and differing levels of technical skill—to empower your team to use data in day-to-day decisions and to balance it with their own intuition.
To help, we’re sharing our three-step process for helping organizations build data culture.
Why is data culture important?
Having data isn’t a competitive advantage anymore. Today, it’s about how well companies harness that data and turn it into decisions that yield a better product and experience for its customers. Internally, leaders assess their teams based on results and outcomes, not how much raw data they have access to.
Companies that leverage data well reap tons of benefits. Once you establish a robust data culture, it opens up possibilities like:
- Self-service data access that reduces bottlenecks and demand on data teams
- Empowered non-technical teams who can make better decisions, every day
- Faster testing and iteration to build better products without waiting on the data
Enacting these workflows requires a holistic approach to data management. It isn’t as simple as one team—say, Product or Marketing—deciding to be data-driven. Because customer data flows through systems and tools that span every team within the company, building an effective data culture throughout the organization requires an org-wide effort to ensure data is reliable, accessible, and digestible for everyone.
Why companies struggle to build a data-driven culture
The value of building a data-driven culture within your company is both clear and compelling. That’s why it’s no surprise so many companies want to enable teams to turn customer data into better decisions, products, and customer experiences—it’s a sentiment we hear a lot here at mParticle.
The problem is, there are a lot of barriers that can prevent (or at least delay) companies from democratizing access to data:
- Collecting, integrating, and maintaining customer data
- Creating cross-team collaboration and alignment around data quality and understanding
- Enabling cross-team access (both technical and non-technical) to relevant data in a timely and productive fashion
- Applying that data to actual business decisions and taking action
Meeting these challenges requires two things:
- A mindset shift throughout the organization and
- The right technical stack to manage your data
How to create a data-driven culture
Here are three straightforward steps that will help you build the right infrastructure to support a data culture and make strides toward instilling a data-driven mindset across your team.
Step 1: Lay your data foundation
Before you do anything else, you need to get the right data stack in place—one that solves for the technical challenges mentioned above while ensuring data quality and accessibility across the organization.
Data sources will vary from company to company, along with the specific analytics tools you pipe data into. But between these two sets of systems lies the crux of your data infrastructure: the Customer Data Platform (CDP).
CDPs bring together data from across your sources, clean it, and resolve it to persistent customer profiles that can be forwarded into any number of downstream marketing, analytics, customer support, and data warehousing tools.
Successfully implementing a CDP will ensure that your team has access to high-quality, real-time data in all of your favorite tools. You’ll need a solution that offers (at a minimum):
- Data quality management, data governance, and identity resolution
- No-code access to high-quality data for non-technical stakeholders
- Easy data collection from a broad range of customer data sources
mParticle, for example, makes it easy to collect, manage, and integrate customer data from multiple data sources in an accessible UI. Features like Audiences and data forwarding mean you can get real-time data to the right systems (and into the right hands), where it can be applied to everyday decision-making.
Step 2: Build data literacy and confidence
With your core data stack in place, it’s time to shift gears. The next step is all about building the confidence, competency, and habits that facilitate data culture throughout the organization.
Technical teams are likely adept with data already, but teams like Marketing, Product, and Support may need to develop these skills and learn to feel confident flexing them.
That comes down to three things:
- Adding applied analytics tools that don’t require a ton of technical knowledge
- Investing in onboarding and training for end users
- Maintaining data accuracy, consistency, and completeness, so stakeholders learn to trust the data they have access to
The downstream analytics tools you use will vary between companies and individual teams. For example, Product teams may use a tool like Indicative to visualize and query omnichannel customer journeys while other teams use a Business Intelligence tool (like Tableau) to build reports.
Regardless of which tools you use, the quality of your data is the most crucial part. Data that’s accurate, consistent, and complete will help to:
- Foster greater understanding around what key metrics and data points mean
- Build confidence among data-consuming teams that what they see in their analytics tools is reliable
- Improve and reinforce the impact of data-driven decisions, creating a feedback loop
Step 3: Turn data into action
This is the crucial step in building a data-driven culture: turning the data into insights and action. It’s also the step where many efforts to instill data culture stall out—because it requires data users to change their behavior and develop new habits.
It’s important to start establishing the mindset shift from the top down. Create the expectation that data should drive key decisions and model a focus on analytics and reporting.
With the right data stack in place, everyone in the company should have access to activation capabilities that lend themselves to actionable decision-making, like:
- Easy audience segmentation
- Quick A/B testing
- Simple, digestible analytics and reporting
Metrics are another key piece of this puzzle, too. Your data stack should ensure each team has access to the metrics that matter for them—numbers that actually facilitate everyday decisions within that role.
Step 4: Monitor and refine your data culture
Fostering a data-driven culture isn’t a set-it-and-forget-it kind of thing. You’ll need to consistently monitor teams and data setups to ensure:
- Everyone continues to feel confident deploying data in everyday decisions (and balancing it with their own experience and intuition)
- New hires are brought up to speed on tools and expectations around data use
- Your stack continues to meet each team’s needs as they evolve—supporting, for example, the adoption of new tools or new practices, such as Artificial Intelligence (AI) and Machine Learning (ML) predictions
- Data-driven culture is the key to building better products and experiences
- While many companies struggle to create a data culture, the challenges most face can be overcome by setting the right foundation, building data literacy and confidence among your team, and continuously monitoring and refining your data infrastructure
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