Organizations have the opportunity to derive more value from their data than ever before. There is a wealth of data right at our fingertips and it's not always utilized effectively so making the most of it is something every organization needs to prioritize.
Converting data into actionable insight is not without its challenges. Does your organization have enough relevant data to work with? This is a perception. In most cases, it's not that the data isn't available, it's that the data is not it complete, organized and stored in a way that's consumable.
Many organizations have data science teams divided into two mutually dependent teams - the data warehouse team and the data analysis team.
Let's start with the data warehouse team. This team is responsible for managing the foundational data structure, collecting the data and ensuring it is stored in accessible, well-defined and readable formats. From there, the data analysis team steps in to harvest that information and make sense of it, converting it into actionable insight for the product management team.
Since the data scientist operation is an independent function within the organization, which answers to the C-suite, marketing, project managers and customer officers, therefore the results need to be relayed to different audiences in familiar, digestible formats.
Because the available data is often incomplete, an important part of making sense of it is to identify what is missing. For example – Marketing Campaigns – if we had plenty of information about which marketing campaigns were working and which keywords customers were looking for before they signed up for an account we would want to then also begin tracking which buttons users were clicking and how long it took from the time they arrived on the page, to the time they actually clicked the button to sign up.
Combining that new information with data we already had available to make a complete picture by grouping users based on their activity and core use cases, we correlated that to buying behavior and produced visual charts that identified what activity users who converted undertook, versus those who didn't convert.
This process will do more than just tell us how many users converted, or didn't, but will provide more insightful data around why the users didn't sign up or take a certain action. Based on those "whys," we will be able to make adjustments and increase user conversion rate.
Determining an appropriate action following data analysis is probably the most challenging part. Often, it will simply come down to experimentation. Where the data comes into play is in assessing the impact of this experimentation. Hence the booming market with data scientist.
As data science becomes more integral within organizations the perception of data analytics beyond mere number crunching will evolve. Their function is much more strategic, constantly in alignment with business goals and working closely with product teams.
For those looking to grow their data science operation and get buy-in from other business units, my advice is to Think Big and Start Small
. Start with a single problem and clearly communicate what you're doing and why. Even if it's a relatively simple problem to fix, the most important step is demonstrating the real value
that data science can unlock such as maximizing an applications' functionality and improving customer experience.