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. His previous works from his signal hunting series include ‘why you should empower analysts to wrangle their own data‘ and ‘what is signal hunting‘.
Big data is changing the nature of data analysis. In order to succeed at finding big data insights, you must become a signal hunter. And like any hunter, you need tools that enable you to do your job.
As we discussed in our last post, signal hunting is the act of mining big data for useful nuggets or “signals.” Signal hunting takes place during the data wrangling process, which is supported by software expertly designed to speed up the process of data preparation. Signal hunting, aided by a self-service data preparation platform like Trifacta, consists of several important steps:
- Discovery – Explore your data and assess the potential value of datasets, including how they might be used. This is also the time to familiarize yourself with the unique elements of the data that may inform the transformation and analysis process.
- Structure – Aggregate and format data for analysis.
- Clean – Replace or standardize data, such as a null value, that may distort the analysis.
- Enrich – Add value to your data wrangling efforts by enriching the dataset with further data that might be useful to the analysis. This often involves joins and complex derivations.
- Validate – Identify data quality and consistency issues and verify that they are properly addressed by applied transformations.
- Publish – Load the output of your data wrangling efforts into a data analytics package or publish it in a central area for future project needs.
Trifacta helps automate the data wrangling process to make signal hunting easier and faster. For example, business analysts can set a variety of different options when defining transformation logic to cater to their technical ability. Visual representations of transformation suggestions allow users to work in the framework that best suits their skill level.
A tool like Trifacta can empower you to be more effective and productive, enabling you to apply your domain knowledge and find valuable signals in the data. It’s used for wrangling social data at LinkedIn, for finding fraud in Medicare and Medicaid claims, and for wrangling media data at GoPro. Handling big data efficiently frees up time for curiosity, exploration, and more effective analytics. Try out Trifacta and start signal hunting.