Dan Woods is CTO and founder of CITO Research. He has written more than 20 books about the strategic intersection of business and technology. Dan writes about data science, cloud computing, mobility, and IT management in articles, books, and blogs, as well as in his popular column on Forbes.com.
An important recent trend has been the move toward self-service analytics. Here’s why I think you should take this a step further. Let your business analysts do their own data wrangling.
Analysts know what they’re looking for in their data, so they are in the best position to extract meaning from it. And given the right tools, they can do it much faster than the staff in charge of ETL and big data initiatives, whose time is already stretched.
In order to reap value from big data, business users must explore data on their own terms. They must be able to discover what is in the data and how it might be useful for various analyses. They must be able to identify the unique elements in data, such as value distributions and outliers, and use them for the data transformation and analysis process. But they can’t do any of this without access to the data.
A self-service data preparation platform like Trifacta enables business analysts to discover, wrangle and visualize complex data quickly and intuitively. Within the course of a day, a business analyst can explore and experiment with multiple datasets – possibly going down several dead ends before discovering a valuable insight – all without having to put in a single request to the big data team. At the Royal Bank of Scotland, self-service data wrangling is a reality, and today, 300 business analysts wrangle raw data in Hadoop to help the bank meet its goal of providing customers with personalized banking.
Enabling business analysts to explore data also gives them the opportunity to identify new types and sources of data that would be valuable to their analyses. And more data can lead to greater discoveries, more insights, and better-informed decisions. After all, that’s the point of big data initiatives.