The 5 Devastating Data-Driven Mistakes You May Be Making Right Now

Let me ask you a question. How important is data and reporting to your company? It’s interesting when I ask people this question, which I do almost every day. It varies from “not using data that much” to “we’re data driven masters who implement data discovery into almost every decision we make.”

There’s no right or wrong answer, but unfortunately there are right and wrong data decisions that completely, and sadly, can affect the companies business in undesirable ways. Even great companies make mistakes. So no matter where you’re at, I want to help you avoid the 5 devastating data-driven mistakes I see today.

Mistake #1: Undervaluing the benefits of data

People look at data initiatives in a pro’s and con’s and dollars and sense fashion, e.g., how much time is it going to take to implement a data analyzation technology, and how much will it cost? However, people who haven’t dealt a lot with data, tend to miss out on many pro’s, and more specifically underestimate the actual ROI that can come from understanding the data. They feel the time and money outweighs the benefits they see.

Since all they know are costs associated with a technology platform they don’t correlate a direct ROI with the platform. That is, until they understand what the data is telling them and then can make improved and strategic decisions to increase their bottom line.

A great example of this is our friends over at DannetteMay.com. They were trying to better understand the lifetime value of their customers, and in June 2018, they decided to implement a data dashboard. With the new insight gathered and new strategies implemented they saw their average sales from 15 to 350 a day! You can see in detail how they achieved this here.

Not knowing what KPI’s to visualize in your business is a fundamental error, and very costly. On the flip side, knowing what KPI’s to visualize could help you see a 2,000% increase in sales like our friends saw.

Mistake #2: Thinking dashboard implementations are easy and fast

The sales process of other single source dashboards usually pitch you a “Plug&Play” and “Easy to implement’ technology. Yet, the easier it is the less customizable it is for your specific company and your specific use cases. We are all unique companies, and although a fast and easy solution can help in some cases, most of them can be a mistake. Combining the right technologies from Quickbooks, Infusionsoft, Google Analytics, etc., and implementation and buildout should be considered carefully to creating the best dashboard. Simple solutions don’t allow that.

We relate Business Intelligence (BI) to the the growth of Bamboo. Bamboo takes five years of root development and small shoot sprouts for the tree to ramp up to its greatest potential.  But after those five years, ONE SINGLE bamboo shoot can break ground and grow up to 90 feet in 5 weeks, but without that root system that had been carefully developed, those heights are not possible. The complex root system supports the fast-rate growth of the 90-foot shoot and it’s greatness is only possible with that foundation.

Same thing goes with your data. The data sources need to be combined and cleaned first which takes time and development that doesn’t look all that impressive, it’s “underground” like the root system, on the “backend” of the end goal visualizations.

It’s easy for companies to get discouraged in the first phases when that “90 foot bamboo tree” doesn’t immediately sprout, but they need to understand that the combined and cleaned datasets are your dashboard’s “root system” and they are what will support the growth of your dashboards in the future.  

One dataset may fuel 50 potential visualizations in the future.  In the beginning, it may take 10 hours to create one dataset that powers one visualization, but in the future, it could take 5 minutes to create a visualization off that same dataset from the beginning.  

Datasets are the most time consuming component of BI, and they are the first component of BI, while visualizations are the last component. So, it’s easy to see why there are frustrations when companies aren’t aware of the delayed gratification that comes with dashboards.

Mistake #3: Over/under estimating the cost of BI

Let’s imagine you didn’t understand the value/ROI of BI. If someone came to you and said, “It’ll cost you $1,000/mo ($12,000 annually) to give you some pretty charts and graphs, and it’ll take us 3 months to show them to you, and it’ll cost $10K-$50K for us to build it for you” – you would laugh!

But, if you have properly analyzed the potential ROI of a $17,000 in increased profit per day, that would make the $50K implementation / $12k platform fee look like a no brainer. It’s all relative, but when expectations don’t meet reality, it leads to doomed projects.

The 2 biggest components of BI implementation are time and money. If you over-estimate the costs, and under-estimate the value/ROI, it stops you from taking action or ever beginning a BI project. If you underestimate the costs, and don’t understand the long term benefits/ROI, you will have a negative outlook on BI and we typically see clients completely write-off ever doing another BI project because the last one cost too much or they stopped before they ever saw the value.

Mistake #4: Assuming everything is tracking accurately

Tracking is the very first and most important step to an accurate dashboard. Most companies think that everything is tracking correctly in all of the different systems.  What they don’t realize is that their team isn’t following SOP’s or certain fields aren’t set up to be tracked.

The most under-utilized and error-prone tacking element we see for online SMB’s is Google Analytics. Companies don’t set it up correctly to begin with, don’t update it after the initial set up and try to use it as a dashboard.

Also, most companies don’t have a data dictionary. Data dictionaries are an all in one place source of truth. They provide reference points and descriptions for system generated columns and field names. Help in consistent naming conventions across the whole company. When creating a dictionary make sure to update and document consistently and that it’s available to everyone.

Mistake #5: Choosing the wrong platform

Remember in mistake #2 when I mentioned the pitches of most single sourced dashboards? Let’s dive more into that. Not all BI platforms are created equally and many companies mistake Single Sources Dashboards for BI tools.

The biggest difference between BI and visualization dashboards is the ability to: Extract, Transform and Load (ETL). Most people think of BI as data acquisition being translated into data visualization, but it’s the ETL’s that are most timing and technical. Remember the Bamboo analogy? That’s what I’m talking about here.

What exactly do we mean by ETLs? Well, simply, it’s having specific platform expertise to extract the data, coding the data to transform it correctly to play well with all other technologies your gathering data from, and loading/visualizing the data the right way. Don’t get fooled by single sourced technologies. They may provide you with what you need, but make sure you choose a BI and visualization dashboard that will provide real growth and understanding of your business and clients.

So, now you’ve learned the mistakes not to make, but what’s the solution? What can you do to make all your data challenges easier and stay within budget? The solution: build a data roadmap with the metrics mapping process. That is a whole other post I will write about later. For now you can watch the video to learn more about our 7 step process we’ve created after implementing over 50 BI projects in 5 separate BI platforms, or connect with and shoot me a message on LinkedIn and I’ll be happy to chat and tell you more!

 

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