Posted by Ellen Wilson on January 10, 2017

In our blog post Healthier, Happier Reporting: Part One, we recommended the New Year’s resolution of working smarter, not harder. This may sound easier said than done, but adopting new data preparation habits could save you hours of time and thousands of dollars in 2017. In fact, it’s said that only 20% of an analyst’s time is spent truly analyzing data- which means that an average of $22,000 per year per analyst is wasted. Currently, most analysts are spending too much time connecting to the relevant sources, exporting those to some common format, relating and joining that data, and then manually manipulating, filtering, and summarizing.

 

Sound familiar? If so, consider adding more value to your organization in the coming year with a self-service data preparation solution. Data preparation tools can help you to spend less time preparing data by simplifying your data wrangling and data blending processes. These processes are essential because they allow you to easily clean, standardize, and combine your data sets, allowing you can get a holistic view of your data. Both data wrangling and data blending reveal a “deeper intelligence” within your data, but are two separate aspects of data preparation that are enabled in a self-service data preparation solution.

 

Data Wrangling

 

With the amount of data and data sources rapidly growing and expanding, it is getting more and more essential for the large amounts of available data to be organized for analysis. This typically requires analysts to manually convert and map data from one format into another. However, this manual process is prone to human error and can be extremely time consuming to complete, depending on the size of the datasets and the frequency in which the data must be captured. Utilizing a data preparation tool with data wrangling capabilities could solve this problem, by reducing the time spent extracting and organizing unruly data.

 

Data wrangling, the process of accessing and cleaning messy and complex data sets, enables analysts to focus on analyzing the data, rather than capturing and organizing it. With Datawatch Monarch, data wrangling is a simple process that only involves connecting to the relevant data sources and then organizing the data one time. Monarch captures all the data cleansing and transformation actions in the Change History, which can be reused for future datasets to save hours of data preparation.

 

Data Blending

 

After your data has been wrangled into your ideal format, data blending can be used to combine data from multiple sources into a functioning dataset. The process of data blending is gaining attention among analysts because it is a straightforward method of extracting value from multiple data sources. It can discover correlations between the different data sets without the time and expense of traditional data warehouse processes, and provides actionable, holistic data to analysts for better decision-making.

 

In today’s world, it is critical for businesses to adopt and integrate tools that will allow their users to visually combine their data so that they will have a competitive edge when it comes to responding to big data flows. Datawatch Monarch’s Join Analysis enables business users to easily discover the best ways to join their datasets with a common field. Monarch also allows for Fuzzy Matching, meaning that slight differences in naming conventions can be easily resolved, and the information can still be appended correctly within seconds.

 

 

2017 is just beginning, so don’t miss this chance to form new habits around your data preparation. Start working smarter, not harder and make your data wrangling and data blending processes easier than ever before. Get your free trial of Monarch, and begin transforming your data preparation processes today!

 

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