Big data analytics seems to be everywhere these days, but its not one of those trends that simply pops up without reason. Big data analytics has proven to be crucial to discovering new insights for businesses, giving them new capabilities and improving on their existing operations. To actually get down to using that data is the big challenge many organizations are facing. Its easy to simply say that companies need to gather data and analyze it, but in practice the process can be complex.
Thats where data wrangling comes in. Before businesses can even think of fully implementing results from big data analyses, they need to engage in a bit of data wrangling. Perhaps this is your job, making your role the first step in a larger strategy. While many may dismiss the role of data wrangler as mere janitorial work, when done well, it can make all the difference between successful big data efforts and continuing struggles. But first, you need to ensure your data wrangling skills are up to par.
So what exactly makes a good data wrangler? If its a job thats often thought of as less than glamorous, why even bother honing your data wrangling skills? To be truthful, without good data wrangling, the rest of the big data process simply cant progress in any meaningful way. Organizations may try to get by with the barest effort in data wrangling, but theyll quickly find they have little idea what to look for from their data sets. Yes, data wrangling takes a lot of time and requires a lot of effort, but its all worth it in the end. The next leap in acquiring excellent data wrangling skills is all about keeping your own efforts efficient and consistent.
Having further knowledge of your role is extremely important as well. Get to know all the facets of your responsibilities as it can make your job a lot easier. One of the best places to start is mastering what Trifacta calls the six core activities of data wrangling. Those activities include discovering, structuring, cleaning, enriching, validating, and publishing. Each one requires special attention, and each feeds into the other in special ways. For example, the cleaning activity involves taking the collected data and getting rid of any information within it that could end up distorting analysis results. Or in the case of validating, it encompasses the process of identifying any issues with the data sets consistency and overall quality. Every single one of the core activities is crucial to the big picture, and failing at any one of them can end up impeding progress.
But getting these activities down is only one part of being a good data wrangler. You also need to know the tools of the trade, and one of the most important ones is Hadoop. For all the tasks youll be doing in your data wrangling duties, Hadoop acts a natural fit. Much of the data now used by organizations is unstructured, making it difficult to manage and analyze with the more traditional databases and SQL. Hadoop overcomes these challenges by being versatile enough in its data preparation design. In fact, Hadoops feature set is somewhat simple, but it gives data analysts the ability to code their own data preparation requirements. This accelerates the busywork involved while still delivering on the results. That makes these activities related to data wrangling much easier to manage.
Beyond tools and activities, there are many other avenues you can explore to get your data wrangling skills to that next level. Online tutorials can help you master new techniques and think of new solutions to problems that you might not have thought of before. Use cases are plentiful and can provide real world examples that you can then apply to the issues your organization is facing. And dont forget to practice. Theres lots of public data out there for you to practice on before you try it on the real thing.
Data wrangling may not be as flashy as other aspects of data scientists, but that doesnt make it any less crucial. Whether calculating the cost benefits of installing a flash storage array or creating detailed profiles of prospective customers, data wrangling cannot and should not be dismissed. As long as you work hard at making sure your skills are honed for the tasks you encounter, youll be an indispensable part of any data team.
