You probably had some big ideas in mind when you first started thinking about adopting big data solutions for your business. There’s usually a tinge of excitement when it comes to big data, and business owners are eager to tap into all its potential. Hiring a qualified data science team is usually one of the first priorities, along with all the investment in equipment and technology needed to properly collect and analyze all the big data you’ll want. Over time though, that excitement might have worn off. Insights from big data analytics were likely coming in, but not at the pace you were hoping for. Is this a result of your data scientists simply not getting the job done well enough? Is it a case of laziness on their part? As easy as it is to think that big data insights should be reached one after the other in a short amount of time, more than likely the data scientists on your staff are doing everything they can. There are reasons for them not being more inventive, and it has nothing to do with their work ethic.
There’s a lot that goes into a data scientist’s job. Some of their time is spent exploring the vast amounts of data they have to work with. Some of it requires preparations of data visualizations. And still other times they’re working on extract, transform, and load (ETL). While these are all valuable tasks in their own right, chances are most of their time is taken up in something far less glamorous. It’s sometimes referred to as data cleaning, but other terms include data wrangling and data munging. Many data scientists jokingly refer to themselves as data janitors, with a lot of time spent getting rid of the bad data so that they can finally get around to utilizing the good data. After all, bad data can alter results, leading to incorrect and inaccurate insights. The costs of bad data are high, with some research stating it costs a typical business more than $13 million every year. So data cleaning is important, but it’s time-consuming and not all that fun.
The amount of time actually spent on data cleaning varies depending on the survey. 31 percent of data scientists who responded to an O’Reilly Media survey say they spend one to three hours every day doing it. Other reports reveal much larger numbers, with some showing that 50 to 80 percent of their time is taken up in the data cleaning process. Some data scientists even go so far as to say 90 percent of their time involves cleaning up bad data. No matter how you look at it, the results seem to echo what a CrowdFlower survey discovered, where two-thirds of data scientists say data cleaning is among their most time-consuming tasks, and 40 percent say they simply don’t have enough time to actually do big data analysis. The numbers are far from encouraging, revealing the big data bottlenecks that many data scientists have to confront. While some will argue that this isn’t necessarily a waste of their time, many can’t help but think that there are better ways for data scientists to be spending their day. To gain insights from data, the whole data cleaning process will need to be streamlined, freeing up data scientists to engage in more creative and inventive tasks.
So what can you do to help in this effort and empower data scientists? Interestingly enough, the same CrowdFlower survey asked data scientists that question. The number one answer (at 54 percent) was for businesses to provide the right tools to help them do their jobs. One of the most common big data solutions used by organizations to speed up data cleaning involves heading to the cloud and using Big Data as a Service (BDaaS). This involves automating many of the necessary tasks that data scientists find mundane or too time-consuming. By employing the right machine learning solutions to this problem, the efforts will lead to higher quality data and more actionable business intelligence. Businesses should also make sure to hire enough data scientists to actually achieve their goals in good time. If more data scientists are working on a set of data, the results will be reached in a short order.
When insights are slow in coming, it’s not always the fault of your data scientists. They enjoy being inventive and would like nothing more than to spend their time coming up with creative solutions based on the data they analyze. Data cleaning, however, can take up too much of their time. Once you know what the problem is and how to fight against it, you’ll be in a good position to help data scientists reach their full potential and unlock the possibilities of big data.
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