5 Signs You’re Failing at Data Science
Most businesses understand that big data analytics is where it’s at. They view data science as the one new thing they need to truly improve their operations and become even more successful as an organization. The problem, though, is that too many companies are failing at data science. One report from Pricewaterhouse Coopers (PwC) and Iron Mountain shows that 43 percent of companies don’t see much benefit from the data they collect, and only 4 percent are actually set up to derive success from big data. It’s clear that many companies simply don’t know how to do data science, instead viewing it as a quick fix for current problems and issues. In reality, it’s a process that takes time, effort, and resources. Realizing you’ve failed at data science isn’t something you should discover at the end of the process. There are several signs you can look for to ensure you’re not in the same boat as other organizations.
1. Too Much Emphasis on Data
For many companies, all the focus is centered on collecting as much data as possible from as many sources as possible. While gathering data is important, the second half of the equation — the “science” part — is too often forgotten. You need to approach your big data efforts from a scientific perspective to gain the most benefit from them. If not, you’re at risk of basing your decisions off of bad models, poor data quality, and erroneous assumptions. Emphasis should be placed on making sure your measurements are accurate and your biases are eliminated. Incorporating the science with the data is a much wiser move to make.
2. Lack of Proper Oversight
Data science can become a complicated endeavor regardless of your type of business. If data teams are simply unleashed to find whatever insights they want, they’ll quickly end up wasting resources in a directionless effort. Part of what makes those 4 percent of companies successful at data science is they’ve established an oversight body for all data governance, ensuring each data team is getting the right data and that they’re working with it in a way that satisfies business objectives. They also make sure the entire organization’s data processes are running smoothly and progressing in just the right way.
3. No Goals in Mind
Many businesses simply hear that big data is great and you should be using it, so they jump on board the bandwagon without much thought. If you want to use big data without having concrete goals in mind, then you’ll soon find yourself in a mess. Collecting tons of data without an end goal established will only make for confusion and frustration down the road. Create goals that allow you to measure your progress along the way. You’ll also need to take into account what data you need, what existing data you have, and how it all applies to your business objectives.
4. Misguided Hiring Practices
In the minds of many business leaders, assembling a data team means hiring as many of the best and brightest data scientists as they can get. The end result, however, usually leads to talented data scientists wasting their skills on mundane, even unnecessary tasks. Hiring the right support for data scientists should instead be a priority. In most cases, you should have more data engineers on hand than data scientists, since they’re the ones who work at cleaning up the information that data scientists can then put to good use.
5. Acting Like Big Data is Temporary
Some organizations go so far as to think of big data as a fad that will soon fade away. This is simply not the case. While it’s true that a ton of hype surrounds data science, there’s good reason for it. The way businesses work with data is constantly changing, but it will always play a crucial role in how companies operate. Ignoring it now will likely lead to your business quickly falling behind. In other words, don’t pay lip service to data science; embrace it and get as much out of it as you can.
Even with these pitfalls in mind, it’s still easy to miss the mark of doing data science correctly. With that said, catching signs that you’re failing at it early on can help you tremendously in righting the ship and moving in a desired direction. Pay careful attention to how you engage in data science. Only then can you catch mistakes, correct them, and improve you business as a result.