Building Compound Analytical Interest with Your Company Data
Head of Communications, Sigma
Do you remember the first time you learned about the concept of interest? It probably seemed crazy that a bank would give you more money for agreeing to let your cash sit in an account for a while.
But then you learned about compound interest and your mind was blown. Interest on interest?! The concept of reinvesting interest has intrigued generations of economists, investment bankers, currency speculators, and anyone else who understands the relationship between time and money.
Compound interest isn’t just for the finance pros. It has value to data nerds, too. Companies are sitting on troves of valuable data, but they’re not extracting the value or building on the intellectual wealth that this “principal” provides.
What’s the issue? Organizations, especially startups and small businesses, face many challenges related to technical proficiency and domain knowledge. But barriers can be broken down and any business can start to build compound analytical interest with its company data.
Opening Up the Walled Garden (Setting Up the Principal)
You’ve probably heard this 1,000 times by now, but the future of business is data-driven. Almost everyone needs access to data, regardless of their role or department. So, if everyone knows this, why is data adoption so low? And why have data initiatives stalled?
At some organizations, data is a bit like money in a jar on a high shelf. Everyone can see what’s inside, but most people can’t reach it to put the money to work.
Self-service BI tools are like that jar. In theory, they’re meant to make data more transparent and accessible to business users, but that has not been the reality. Poor execution by most vendors has created more problems, leading to a greater divide between data and business teams.
Intentionally or not, mainstream BI tools have built artificial walls between teams. Because they require extensive training and knowledge of SQL— or a proprietary programming language —data teams become the de facto data owners, and business users depend on them for analysis.
This is not a good system; it forces both camps to take on tasks outside of their scope of work:
- Business teams have to learn specific technical skills to be able to answer a question.
- Data teams have to develop an SME’s level of understanding about a business area to develop an appropriate query.
Most of the time, neither team has the correct level of technical knowledge and domain expertise to do both jobs, and they shouldn’t have to.
So why not provide business users with a way to answer their own questions? Everyone is on the same team, and a vendor’s insufficient product shouldn’t be a source of friction.
Give the Business Teams Endorsed Data Sources to Work With
If you have some trepidation in giving everyone access to the data, that’s understandable. You need data integrity, single sources of truth, and you need the data to be clean before analyses and manipulations begin. Fortunately, there are options between the extremes of a data warehouse free-for-all and only offering useless static dashboards.
Using a cloud-native analytics tool connected to a cloud data warehouse provides a third way. Data teams can prepare the data in the warehouse and then create an approved workspace for business users. There they can ask and answer questions themselves, without actually touching data in the warehouse. Both teams can maximize their expertise, and those closest to the data have a seat at the table.
Avoid Redundant Analysis Silos (Going from Simple to Compound Interest)
Let’s say your company does have accessible data, but no central place for users to show off their analyses. This is a problem.
Folks are likely using Excel and saving their workbooks locally, possibly only sharing if they’re presenting in a meeting. Inevitably you’ll discover that someone else has done the same or very similar analysis. Conducting analyses in a vacuum is not a good use of time. It can slow company growth and put your security risk (especially if someone is saving sensitive data locally).
Build Shared Workspaces to Conduct Analyses Collaboratively
A team thrives when members share and elevate each others’ work. This is how you build compound interest with data. You initially draw insights from the principal, but then you (and others) draw insights from those insights.
Building a complicated model takes time, and doing it alone could prevent you from finding its full value. When someone else can take on the torch, your ideas can go much farther.
These compound insights aren’t just limited to teams. With the right tools, there’s no limit to how much it can grow— organization-wide or even with external partners.
Building Wealth with the Right Tools
Most of the BI tools available aren’t made to help you get the most of your data. They’re on-prem only, have limited collaboration functionality, and still enforce the barrier between business and data teams through code-based queries.
Sigma is different. Sigma is the only analytics environment where business nerds and data nerds can work together. We help you glean insights and make better and faster decisions with or without code. Schedule a demo or check out this video to learn more.