Big data is, well, big. A hot topic in the business world for its massive potential, big data strategies are being brought into companies all over the world. At this point, businesses not interested in leveraging big data are moving far away from the norm. Using big data and predictive analysis, a massive amount of information is available to businesses about their customers, business growth potential, and internal processes, which can provide valuable insights to spur growth and limit inefficiencies. Despite these benefit, we’re still on the precipice of a big data revolution. Why? Because simply keeping up with all the data and using it effectively is something that many businesses struggle with. It takes a lot of commitment and resources to integrate big data into corporate culture and hire the right big data team. With these challenges in mind, what’s next for big data? How will data wrangling affect how we use data to implement positive changes in every industry?
Massive Growth of Data
By 2015, we had created over 90% of all our world’s data in the previous 2 years. The amount of data that was used to send men to the moon represents only a tiny sliver of the data available on a single laptop today. With all this data available, businesses have used it to become more efficient, win over new customers, and plan for the future. However, it’s virtually impossible to use all of the available data to its full potential. Currently, only about 5% of data is ever analyzed. That number will get even smaller as we continue to accumulate more and more data. We’re constantly moving faster and faster. By 2020, it’s estimated that over 44 zettabytes of data will exist—that’s over 44 trillion gigabytes. Smart devices will become more common, and an estimated 50 billion of them will be in use by 2020, collecting data in many different ways. The next step in big data is figuring out new ways of efficiently analyzing the information—which is where data wrangling comes in.
What is Data Wrangling?
For businesses to gain relevant insights from big data and predictive analytics, the first step is for the big data team to ask a question. Asking questions is how data scientists narrow down the data that is needed. The most time-consuming part of gaining benefit from collected data is “wrangling” it, to make it useable. Unfortunately, this process takes up about 66.7% of a data scientist’s time, and simply makes the data more structured, cleaned up, and suitable for use or storage. In order for big data to progress, this process must be refined to become more efficient, automated, and streamlined.
Strategies to Improve Data Wrangling
So what can be done to make the dirty work of data wrangling a bit easier? First, bring in more help. There’s currently a shortage of professionals trained in how to work with big data, but many employers are investing in training for existing employees. It’s a win-win: companies have a cost-effective method of gaining data talent, and employees have access to new opportunities. Companies can also help improve the data wrangling process by providing the necessary resources for data teams to succeed, and set clear goals and objectives to help the team ask the right questions and work more efficiently.
Currently, some companies are working to make data wrangling a more streamlined process–Trifacta, a company born out of an academic research initiative, is trying to automate data wrangling using existing tools. In the future, this could help companies save time and resources on data analysis, making them more efficient.
Organizational Impact of Data Analytics
Big data can transform businesses. Analysis results from McKinsey Global Institute (MGI) estimate that retailers could increase operating margins by 60% and healthcare costs could be reduced by 8% using big data. That’s just scratching the surface of what’s possible—big data has a place in nearly every industry. While it’s possible for almost any company to decrease costs and boost profits using big data strategies, few businesses have reached the maximum potential, due to implementation and execution challenges. While large Internet-based companies like Google and Amazon have analytics at the core of their business models, other organizations are struggling to fit data into their existing cultures. As more solutions for easy data wrangling become available, it will be easier for companies to leverage the data they’re collecting and put it to use. However, the companies who are making an effort to implement comprehensive big data solutions now are gaining a huge competitive edge as we move toward the future of big data.
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