Big data is a huge buzzword in the tech world. However, it’s not limited to it. Data analysis keeps spreading its influence through more and more industries. Suddenly, everyone realized that properly interpreted information is an extremely valuable business asset. That’s why advanced analytics projects began to boom recently. Hunting for data specialists becomes fiercer. Data market investments in 2018 were estimated to reach $9 billion. So, data science market is rapidly growing, presenting opportunities for start-ups to enter the niche.
Having doubts? What if we tell you that 90% of all existing data in the world was created in the last 2 years? Despite such an impressing number, only 12% of companies use their data analytics.
For someone new to the industry, it may seem like companies are spending days and nights doing some heavy data analytics. And, of course, are uncovering unbelievable superhuman solutions. However, despite all the hype around supersonic power of data, 85% of data analytics projects fail. Wow, right?
Here’s the untold truth about big data obsession: barely anyone knows how to build and use such projects in the right way. Often companies become fooled with giant analytics projects like Amazon and Netflix. However, succeeding in advanced analytics project is rather exclusive than average, and requires high-end efforts. The recent study by NewVantage Survey stated that 77% of companies report having serious difficulties with adopting data analytics and AI technology.
Surprisingly, big data projects rarely fail due to lack of budget. In fact, companies often get lost in details and technical aspects. They lose sight of the big picture, which turns out to be an indispensable part of data analytics projects.
Let’s take a look at the most common reasons why your data analytics project could fail: