Question: What is one major mistake companies make with big data?
Not Making Data-Driven Decisions
"Data equals power. I believe that most companies don't realize how much you can pull out of your data. There are many tools out there that can help you make data-driven decisions, which in turn can give you more predictable results (our company uses RJMetrics and we are thrilled with it!)."
Not Having Data Scientists
"Collecting big data is easier then ever and implementing tools to work with big data has also become much more accessible. The problem is oftentimes companies do not have a qualified Data Scientist or someone who can interpret or map/reduce the proper dimensions of data. Instead they rely on non-qualified personnel to interpret data. The improper analysis of data can be very harmful to a company."
Answering Trivial Questions With It
"The biggest mistake that companies make with big data is using it to answer relatively trivial questions such as “what.” Big data isn’t about “what” questions; it’s about “why” questions. Big data is about joining data sets that have never been joined before and asking questions that have never been asked. It’s about knowing why customers and employees are doing the things that they do."
Neglecting to Track Time and Cost
"Many companies don’t invest enough in time tracking and cost accounting. Especially for a service-based business, having the proper time-tracking tools for true cost accounting is very important to be better able to tell true margins across all offerings. This information can be used to increase efficiency and profitability. "
Confusing Correlation and Causation
"When companies work with big data, a major and common mistake is to assume that correlation implies causation. While you can use data to understand correlation, equating it to "cause and effect" can lead to false results and fruitless decisions. Making the distinction between correlation and root cause is critical to utilizing data for best results."
Overcomplicating the Process
"Too often we see an initial investment in expensive, slick "big data" tools that require a team of analysts, without the team in place or the ability to implement learnings. Make sure you have a foundation to utilize learnings based on strategic questions and goals. Don't overcomplicate it. You can start simple with basic tools and grow from there."
Thinking Too Big
"Big data gets a lot of buzz and rightfully so, but companies should think small in leveraging their data. A data project can be very expensive and create high recurring costs. Start these projects by solving real problems and expand on those solutions as you build your system out. This way you will have real ROI on your investments and pave the way for future learnings."
Being Data-Driven vs Data-Informed
"One mistake companies make is that they give data too much decision-making power, letting their data drive major product decisions. When leaning on data alone, product teams tend to optimize around a local maxima, when more impactful opportunities may exist as entirely different features. Use your data as an influencing factor, but not as the final voice in your product roadmap."
Not Factoring in Real Roadblocks
"Often, clients do not factor reality into their analysis. We worked for a bank in Africa to review its growth strategy to invest in technology solutions for its retail locations. Although their numbers looked great, the client (and the country for that matter) had power and infrastructure issues. What good is technology if 80 percent of the time your systems are down?"
Not Customizing It to Clients
"One major mistake companies tend to make with big data is that they tend to simplify and overlook the varying, complex nature of individual client needs. Don't put clients in a box! Companies need to listen, understand and find custom solutions based on what the client wants."