Teams are drowning in data. The ease of collecting data has led to popularizing ideas like big data, data warehouses, and machine learning. The problem is that companies can get stuck trying sort through their data.
In this post, I want to share 3 strategies for helping you solve this problem. The goal isnâ€™t to see how much data you could collect. The goal is to uncover insights about your customers, your products, and your business.
Does Your Team Know Exactly What Data You Have?
The MoMA or Museum of Modern Art has a peculiar problem. They have one of the most extensive Persian rug collections globally, but they arenâ€™t sure whatâ€™s in the collection. The curator has never seen most of the rugs that he has purchased.
This is like the adage of the tree falling in the forest. If no one is there to watch or hear the tree fall, did it really happen?
Companies find themselves in a similar situation with their data. They have a lot of data, but no one is quite sure what is available. They have never seen some of the data they collected and could be hypothetical for all they know.
Thereâ€™s no point in keeping data hidden. Data doesnâ€™t become valuable until it is converted into insights. Before that point, it is simply information or computer bytes if we are technical. Executives, managers, and employees want more insights out of their data, not more data.
The first strategy for sorting through your data is to know exactly what is there. This can be done through an audit of everything that is being collected and store. The process sounds intimidating, and it can be for larger companies. Youâ€™ll need to hunt down obscure documents, check unused products, and scramble to find logins for software tools that no one ever uses.
Once you know whatâ€™s in your data vault, you need to tell people about it. This is commonly called â€œdata literacy.â€� It simply means that everyone in your company understands what data is being collected and how they could use it.
If they wanted to check on the latest purchases, they know where to go for that data and how to visualize. Whether this happens through SQL, Tableau, Power BI, or even in Excel, thatâ€™s beside the point.
Like education, the more you have of it, the more useful it is. If you have poor data literacy, itâ€™s like being surrounded by books you canâ€™t read. They are merely random characters on a page.
Trust Issues Can Be Detrimental
Have you ever had someone say, â€œI donâ€™t trust this number?â€� I call this Funky Data.
When you look at a report or dashboard, you canâ€™t seem to trust the numbers in front of you. You may not be sure why these numbers donâ€™t make sense, but thereâ€™s something weird (or funky) about them.
Ensuring that your team has trust in your data is the next strategy in our repertoire. You may have the best data in the world, and your team knows this, but if they donâ€™t trust it, they wonâ€™t use it.
Lack of trust is one of the most pervasive issues that I help companies work through. It starts small, but it can grow to become a monster over time. At its worst, people cannot trust anything they see in terms of numbers, so they rely on opinions and anecdotes.
There are 3 Funky Data scenarios that you need to be aware of:
- Technical Issues: this is what people typically think when they see a number they canâ€™t trust. Thereâ€™s a technical issue in how the data was collected or visualized. You solve this by debugging the report and deconstructing how a number came to be.
- Misaligned Expectations: in this scenario, people expected what a number must be, but the actual data doesnâ€™t support this. This is common for teams who havenâ€™t had much data and were operating on opinions for most of the past. You solve this by working through the expectations and showing that the actual data is correct.
- Unexpected Calculations: this is when a calculation doesnâ€™t show you what you expect. You may be expecting to see 1000 purchases, but the report only shows 800. However, the calculation limits the data to only new customer purchases, but you donâ€™t know this. You can solve this by working through the calculations and showing the assumptions that are taken into consideration.
Scenarios 2 and 3 are mostly psychological. These make them the hardest problems to solve. They require empathy and patience.
You Canâ€™t Quench Your Thirst with a Broken Fire Hydrant.
Our third strategy will deal with overwhelm by having too much data available to you. This is what everyone feels when they open Google Analytics for the first time. Thereâ€™s so much data available to you on one screen that you arenâ€™t sure where to even begin.
This is like trying to quench your thirst from a broken fire hydrant. The force of the water would be too much, and you would be tired from even just attempting it.
After your team knows what data youâ€™re collecting and solve any trust issues, you need to make data easily digestible. Remember that the goal is insights, so we donâ€™t get brownie points for the data volume that we collect.
Here are a few ideas to reducing data overwhelm:
- Create a unique dashboard for every team and individual. This allows you to limit how much data is available at any given time.
- Be opinionated on how data should be visualized but offer options for people to filter and slice the data.
- Convert the data into multiple formats: dashboard, email digests, SMS, CSV exports, and others.
- Use machine learning to discover patterns within data sets.
Data can be a goldmine, but you need the right equipment and approach; otherwise, youâ€™ll just be digging through the mud. Start by helping your team understand exactly what data is available, tackle any pervasive trust issues, and implement different ways to reduce data overwhelm.
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