Is your Data Team investment paying off?
During the last few years, we have seen a significant impact of pay-per-use and subscription-based offerings on companies’ growth and acceleration of time to value.
Cloud-based solutions have made it possible to stand up a server in minutes and pay only for the time it is up and running. The cost increase over time goes along with the company’s growth.
It enables small businesses or startups to stand up a similar or better Stack than big and long-time established competitors by just making good and smart choices.
Many software providers shifted from selling an expensive perpetual license to offering monthly or yearly subscriptions, enabling companies to significantly reduce their initial investment in the software required to run their business, paying only for the services as long as they keep using it.
Long term, clients may end up paying much more to the software provider than they would have paid if they had purchased a life-lasting license, but they get the benefit of a small initial investment. Another benefit of this approach is the chance to test the software for some time and decide to stop using it without losing money.
When it comes to teams and people, something similar happens. Deciding to hire someone or build a team to execute a specific function/process is an investment decision. You would invest in recruiting, training, and time until the person/team starts providing value to the business.
Every business has critical areas with a clear relationship between investment in people and income growth or cost reduction. In other business areas, the balance between the value provided and investment could be more precise. When there are massive layouts, those are usually the main areas affected.
Business Intelligence and Data Analytics teams are usually in that group where finding a direct relationship between investment in people, and value is hard. It doesn’t mean it doesn’t exist, but it is tough to measure.
Business Intelligence and Data teams usually serve other areas of the company. The outcome or value they provide can only be measured by what the different business areas accomplish with the data/report/insight received.
If you have a Data or BI team in your company, the best way to prove their value would be by clearly defining the business outcome associated with each deliverable that this team provides and measuring it correctly.
Let’s use an example to illustrate this idea:
Business Need: I need a Marketing Dashboard to understand how each marketing channel contributes to ROI
What is wrong with this request?
If the business outcome is “understanding,” it adds no value. You will not generate more revenue by looking at a dashboard to understand what is happening. If that is the intention, the Marketing team should be able to create its dashboards in a spreadsheet by making a one-time consolidation of all data. There isn’t a need to invest in a data team to create a Dashboard that helps Marketing “understand.”
For a BI team time investment to be made and be able to be measured against value, the business need should be defined like this:
I need a Marketing Dashboard that allows me to find optimization opportunities on my marketing campaigns to increase ROAS (Return On Ad Spend)
This request will enable two things:
- The BI team can measure its impact and prove its value to the business. If ROAS increased after developing the dashboard, real dollars could be associated with the BI team that helped to accomplish that.
- The data product “Marketing Dashboard” can be assigned a dollar value in terms of cost, directly associated with the time spent by the team that built it. That amount can be considered a one-time marketing investment that needs to pay off over time. If it doesn’t, then the Marketing team may decide next time to invest the Data team hours in a different type of project that has a more direct impact on revenue.
Whether or not your company has a BI or Data team or decides to hire BI services externally, this mindset should help make the right Data investment decisions and iterate on the most effective uses of Data teams’ time.