Use these five strategies to align key resources and ensure that insights guide your decision-making.
Building a data-driven culture is a difficult, gradual process, even for organizations that grasp the value of data and invest heavily in tools to leverage it. As a Senior Technical Success Manager at Fivetran, I frequently work with clients who struggle to build a data-driven culture long after committing significant resources to that effort.
To become truly data-driven — to get to the point where analytical insights inform all or most of your strategic business decisions — you need your people, processes and technologies to move in the right direction for an extended period of time. That requires quite a bit more than getting buy-in from the C-suite and investing in modern data tools. Those are essential first steps, but they won’t get you to the finish line by themselves.
Here are five strategies to bring your talent, tools and processes into alignment and ensure that insights from data drive your strategic decision-making.
I’ve worked with many clients at the beginning of their analytics journeys, and they tend to start by analyzing siloed data. That’s understandable — their goal is to generate actionable insights as fast as possible — but they soon realize the importance of centralizing data into one platform. The most valuable business metrics require combining data from multiple sources and analyzing it holistically. To create an effective marketing attribution model, for example, you need to combine transactional, behavioral and campaign data from sources like Braze, Google Ads and Square.
Data teams that don’t employ automated data integration spend much of their time trying to access siloed data. A recent global survey found that 34% of analysts' time is wasted trying to access data, and, relatedly, that 68% lack the time to implement profit-driving ideas. To resolve this issue and ensure that your analytics team can focus on deriving value from data, it is imperative to fully automate the data integration process.
Success means automating every step in the process of getting your data from individual silos to an analysis-ready state in a single destination. This will allow your team to focus on creating actionable insights that deliver value, rather than spending time finding, fixing and stabilizing data.
While a data-driven approach must be supported from the very top of your organisation, it needs to be pulled by the business rather than pushed by the analytics or leadership teams. The goal here is to partner with an influential stakeholder to identify a measurable proof of concept (POC) that can positively impact the business.
The POC should aim to either increase revenue or reduce costs for the business. Examples of this could be customer acquisition and churn analysis, next-best action prediction, or implementing operational efficiencies. The first successful project will act as the catalyst for the rest of the organization becoming interested in using data to help in their decision-making process.
As you are implementing your POC, make sure you work closely with an influential stakeholder so they can act as your data evangelist. The role of the data evangelist is to help you engage the business, and they will play a critical role in ensuring that the business pulls the data-driven approach organically, rather than the analytics team pushing it.
Integrating large amounts of data can be transformative, but only if you derive useful insights from it. To move beyond simple BI reporting and become truly data-driven, you need to ensure that insights are actionable by key decision-makers. That means ensuring that the person consuming the analysis can quickly grasp what action needs to be taken.
To achieve this, it’s important to bring the data to life. One powerful way to do that is by making data visual. Visualizations can reveal trends, patterns or connections that are almost impossible to find in any other way. An effective data visualization will be easy to understand and highlight essential insights while removing noise.
In other words, data visualization is a way of telling a story with data. As researcher and Made to Stick author Dan Heath puts it, “Data are just summaries of thousands of stories — tell a few of those stories to help make the data meaningful.”
A major hurdle to adopting a data-driven approach is the lack of a single source of truth for business users. Inconsistencies or inaccuracies in the data are often found when multiple tools are available to business users, or, even worse, when users end up with two different versions of the same calculation or KPI due to how different systems manage, calculate and aggregate the data.
With this in mind, the next step is to provide one user-friendly business intelligence tool that allows non-technical users to easily investigate and interrogate their data. It’s vitally important to use one tool rather than multiple tools, so that adoption follows the same path for all business users.
A key element at this stage is to ensure that data, dashboards and insights are easily shareable across the organization. Whichever BI tool you choose, it should enable effective collaboration between key stakeholders in your organization. Fivetran partners with many user-friendly BI tools, including Looker, Tableau and Power BI; here’s a quick overview of popular solutions.
The final step involves scaling a data-driven culture across your organization. As your data evangelist helps to spread the word about the benefits of using data, more and more departments will proactively reach out to your analytics team for help deriving insight from their data. At this stage, it may become untenable for your data team to handle each and every request, and you should focus on democratizing data by implementing data fluency training.
Data fluency training is really about ensuring that people can access and interrogate data themselves, so that they can move independently from data access to data-driven decision-making. The training covers the basics of data analysis, data interrogation and data visualization. Many of my clients have invested the time and effort to implement data fluency training, and it has had a huge impact on moving their organizations toward a data-driven approach. People who understand the basics of data interrogation are much more likely to look for data when decisions are being made. Again, this training needs to be pulled by the business when the time is right, rather than pushed by the analytics team.
As you continue to explore strategies for becoming data-driven, consider a demo of Fivetran automated data integration, and you might find these articles helpful as well: