From Analytics to Growth: How Modern Data Teams are Leading the Charge
Working in data is no longer just about analytics, dashboards, and data science. Modern data teams are capable of being a critical driver of growth thanks to scalable data warehouses and force multipliers like dbt, Census and Hightouch. The data stored in a warehouse traditionally used for dashboarding and analyses can now directly enable business growth strategies some of which include:
Customer Email/Lifecycle Personalization & Targeting
First Party Marketing Attribution & Conversion Valuation Feedback
Customer Blended Enrichment & Identity
Product Growth & Experimentation
Proprietary LLM/GPT Experiences
But these projects can only be achieved if a data team is willing to take some risks outside their zone of control to become the source of truth not just for analysis but also for operational data flows like customer profiles, behavioral/usage data and website interactions for the business. By upgrading their processes and successfully tackling high value data projects that drive growth, a data team can earn a voice at the leadership table and directly influence the company's strategy.
Having that influence is how data teams can work more closely with business systems to integrate definitions and produce higher quality data. Data teams are going to be asked to tie it all together whether it is integrated or not! So by taking on responsibilities historically not attributed to data teams they can become a core business partner to many teams/systems and influence how they are built and leveraged (if they’re the source of high quality inbound data they get to shape how it is stored and used!). Through this partnership (consistent) higher quality data is produced and collected from frontline systems to enable more data flywheels to be created and enable organizational learning. Starting early on this path may require more work from the data team initially, but it will ultimately lead to greater rewards as the company's central data hub shifts from a complicated network of data flows to the data warehouse
Some of data teams will think this isn’t their job but I disagree. The goal of working with data is to continually be improving the quality and depth of analysis and tooling. In order to continue growing quality analyses and data products, it is the job of the data team to support a data driven transformation across the company. That process starts with business leaders and systems that power the organization. Expecting these data sources to provide better data over time without exchanging something of value is highly unlikely in a business climate of high productivity & efficiency for all teams. To explain why we need to start with leadership, let’s take a look at my own spin on the data science hierarchy of needs.
When I first came across the data science hierarchy of needs framework I loved its simplicity. Unfortunately it was built with inward looking data team blinders on. Starting with data collection, instrumentation and logging is never the first conversation I’ve had with an organization beginning a data journey. Don’t get me wrong, I would love to start there!
However, the data science hierarchy of needs omits the important principle that data is collected from systems & processes that are designed by people to accomplish a goal. In my experience the goal has never accidentally included “building a data driven organization”. The data collected from an inconsistent, intermittently followed system is likely to do more harm than good. Inconsistent processes cannot be used for experimentation or machine learning and therefore prevent the data team from ever answering questions around why things happen, they will always be left in the realm of descriptive analytics around what is happening. So whether a successful data team wants to get involved with data feeding in and out of business systems is a foregone conclusion in my mind. If the data team has to be involved then I recommend being fully involved. This creates a trade where data practices are being followed by stakeholder systems in exchange for clean, enriched customer data flowing back to those systems from elsewhere in the business. Everyone benefits but someone has to be willing to take the first step and cut the Gordian Knot of systems and data teams have the best tooling today!
A data team’s ability to execute and deliver wins at an organization depends not only on their own internal capabilities but also on the quality of their data they receive from other teams. If leadership (company or department level) isn’t willing to partner on system improvements then it is the data team’s responsibility to drive that change by building a vision for increasing revenue or reducing costs through directly enabling the business with the data they have. There are numerous projects that can achieve these outcomes that we’ll dive into in future posts!
Extra Note: The maturity model likely won’t progress equally across the business. Each department must work their way up the steps in the model so the data team should look around and find the most data driven leaders to partner with on their goals first to pave the way. It’s entirely possible that one department may be fully leveraging prescriptive ML insights while another isn’t actively logging activities or events for descriptive reporting. If one or more departments are highly resistant to making data driven improvements then your time may be better spent focusing on those who are ready to work directly with data. Over time as the innovators bring better and better reports with metrics trending in the right direction to leadership meetings the laggards will get the memo 😉.