When you’re building a house, you always start with the foundation. It is the most important part of your house, and deserves your highest amount of focus and attention to detail. Why? Because if you make a mistake, it’ll only get worse as you build up and out. It’s a concept called compounding defects, and it essentially means: mistakes grow. If you skimp now, you’ll pay later. This same theory also applies to your analytics. Start with the foundation.
Start with your foundation
However, that’s easier said than done, especially with the amount of advanced technologies available today. Many companies are growing frustrated that they’re not further along in their analytics journey than they had expected. It’s understandable. Being insight-driven has proved to be elusive for most, especially at an enterprise level.
Augmented Intelligence is the shiny new object that everyone wants to get their hands on. It allows companies to go beyond just figuring out what is happening in their business and start predicting what’s going to happen in the future. It’s tempting to want to jump right in, but you can’t be ready for AI if you don’t first have a trustworthy analytics foundation you can rely on for support.
Beware of these building risks
Your analytics foundation can be shaken by many common problems, so before you start building, you need to ask yourself these four questions:
- First, is any data missing? This might mean a data dump left some key information behind, or maybe your sources aren’t broad enough. Think of the airline that recently “re-accommodated” a passenger, only to see its stock plummet in the fallout. You can bet its models were missing social sentiment data.
- Second, is any information incorrect? With the patchwork of spreadsheets and data sources, are you sure no mistakes were made in keying it in and stitching it all together? Even a small mistake might erode shareholder value, especially if you’re forced to re-state earnings.
- Third, are the numbers misrepresented? Nobody wants to think your employees would deceive you, but there’s a big risk if you’re wrong. For example, a big media company couldn’t figure out why revenues kept declining. It couldn’t be a customer satisfaction problem because the net promoter score or NPS was holding steady. Months later, the leadership team discovered their NPS had actually dropped 40 points. That’s when they realized the problem. Somebody’s bonus depended on good numbers, and that person had been manipulating the reports they were reviewing. This happens more often in business than anyone wants to admit. How can you be certain it’s not happening to you?
- And fourth, is any of the information misleading? Even if the data and the visualization are both correct, the meaning might be wrong. Different people have different perspectives that can lead to different assumptions. That’s especially true when you rely on your team members to interpret the analytics, instead of trained data scientists. Do you know the unintended biases that affect how each report author forms their questions and answers?
Build confidence in your analytics
To build real confidence in your analytics, you have to start by fixing these data problems that can undermine your analytics. That way you’ll cut through the uncertainty of the what, while getting deeper insights into the why. Once you’ve strengthened that foundation, you have to make advanced analytics attainable for everyday decision makers.
And that’s why companies are turning to IBM.
With the full power of IBM Analytics, you’ll create the structure to help your business users perform like data scientists. You’ll have the solid analytics foundation and be able to build up to predict what’s next and formulate the most effective strategies for your future so your whole house doesn’t come tumbling down.
Start Building Today!
Hear how you can solve these problems you don’t know you have by clicking here to attend our webinar on Thursday, February 22, 11 AM ET, featuring guest speaker, Joel Shapiro, a Professor and Director of Data Analytics at Northwestern University and myself, Brad Molzen.
Want to learn more about our vision for bringing Business Analytics and Data Science together so you can experience maximum operational efficiency and business impact? See how to avoid the most common traps that keep companies from achieving their objectives in this demo.