3 Challenges In Creating Value-Driven Data
We often say that good data should align with your business strategy and goals. Easy to say, but harder to achieve. A 2021 survey of major global brands found that 70% don’t have a well-articulated data strategy and 40% of the insights they generate aren’t actionable aka numbers for the sake of numbers. The challenges with ensuring data creates value are:
Business Goals First. Data Second.
“Let’s look into the data and see what we can find.”
Exploratory analysis can be very rewarding in certain use cases, especially at a later growth stage. However, when creating your initial data strategy, this mentality can lead to solutions that do not solve key business questions, creating data for data’s sake.
Instead, you should always start with the business model, unit economics and growth objectives. Armed with this understanding, you can then create a data dictionary with a set of core KPIs and decompose them into measurable contributing factors. It acts as a glossary for your business, where anyone can look up the definition of data and your single source of truth. This data dictionary can then act as a roadmap, guiding the implementation of your data models.
Stay focused on the big picture.
It can be difficult for companies to decide what is most important for them to start with when it comes to modelling and centralising data. Often the needs of senior stakeholders are different from the day-to-day requirements of their team. If you’re not careful you end up with a laundry list of different metrics that need to be modelled and implementation of data can go on for months or even years.
We always advise companies to start with the core data source (e.g. operational database) and ‘North Stars’ (i.e.These are the Key Performance Indicators which answer fundamental questions about their business). From there you can start to expand your efforts but it’s always good to ask yourself “What do I NEED to know?” and “Once I know, what will I do with the information?”
Democratise Your Data Solution.
Data is most powerful when it is in the hands of the right people at the right time. There are many elements in achieving this and here are the our top tips:
Establish trust in data. Mistrust in data usually stems from 1) different data definitions across teams, and 2) inconsistent numbers across data sources. A standardised data dictionary should solve the first problem. To achieve data quality and consistency, centralise your data sources and create models that automate the transformation of raw data into business concepts. Integrate rigorous data checks to eliminate data discrepancies once and for all.
Make data easy to use and understand. First, you need a reporting tool that is user-friendly, intuitive and easy to adopt. Then design interactive dashboard suites that facilitate data exploration, following a top-down approach (e.g. Master Dashboard for top level KPIs and drillable into vertical-specific reports). Make sure to provide good support to business users on how to use the tool and various data use cases to get the most value out of your data investment.
How Can 173tech Help?
Sometimes having an outside viewpoint can help you to establish business priorities and create a data roadmap that returns value quickly. Having worked with a broad range of businesses at different stage, we can help you identify low-hanging fruits, aka data projects that deliver high impact early. Get in touch for a no-obligation call today.