IBM and Datawatch, a maker of self-service data preparation and visualization tools, have teamed up to help users quickly gain insight from structured and unstructured data using Datawatch Monarch and IBM’s Watson Analytics and Cognos Analytics.
As part of the partnership, IBM will resell Datawatch Monitor, which enables so-called citizen analysts by making it easy to access, manipulate and blend data from a variety of sources.
“This partnership is driven by some interesting patterns that are occurring in the industry,” Grosset told eWEEK. “What we’re seeing is while organizations really love their enterprise data warehouses and using evidence-based decision-making, what’s happening is they want to start pulling in more types of information. And often the key differentiation or key business insights are held in types of storage or unstructured means that aren’t in their enterprise data warehouse. And they’re not part of the corpus of information that’s easily acceptable to everyday people.”
Data that had previously been inaccessible or locked away can now be rapidly prepared for analysis using Datawatch, Grosset said.
“What we’re seeing is people want to use different types of data in combination to come up with novel insights,” Grosset noted. “And what Datawatch and their Monarch tool enables is your line-of-business, your knowledge worker to pull in data that could be on their desktop in a spreadsheet. It could even be in a PDF document or a JSON document, and they want to pull that in and start to do analysis on it. That’s become a really predominant use case.”
Datawatch Monarch enables users to place a file or document on a prep canvas, and the tool will make the data available in rows and columns. It features more than 80 pre-built functions to transform and manipulate data. The tool also enables users to combine disparate data types for analysis.
“Datawatch’s intuitive self-service data preparation technology was specifically designed for the everyday business user and is a perfect complement to IBM’s modern business intelligence system,” Michael Morrison, president and CEO of Datawatch, said in a statement. “Users can now spend their time in high-value analytic activities and harness the full power of IBM’s cognitive capabilities to derive new insights that have the potential to innovate and transform their business.”
Meanwhile, Grosset stated that the pattern in the industry with data discovery is that more and more it’s not the professional analysts doing the initial analysis; it’s the everyday people in an organization. These are often managers of departments. They have some data and they want to understand it better. These types of individuals—these knowledge workers—have an idea but they need to base their decision-making on some evidence. So they pull all the data together with their corporate information and they can start to make correlations to prove or disprove their hypotheses, he said.
IBM, Datawatch Team Up on Data Preparation for Watson Analytics
Moreover, IBM statistics show that people spend a lot of time on data preparation—disproportionately more time than on data analysis, Grosset said.
“We want to enable this mixture of different types of data, which is taking people a lot of time today to do this in a conventional enterprise context,” he added. “This is why tools like Datawatch Monarch are so important because they do enable your everyday person to just pick up a tool, shape their data and load it and make it ready for analysis really quickly. This is taking something like 80 percent of people’s time when they’re doing analysis. So people are not spending time analyzing the important things—they’re spending time working on their data.”
According to Forrester Research, business analysts and data scientists typically spend up to 80 percent of their time manually preparing data, and it’s estimated that only 12 percent of enterprise data is used today to make decisions.
Grosset explained that while the data required for analysis is quite often structured and easy to use, often the data is not in a convenient form. It could be survey data from an online source. It could be open government data, or it could be any one of a number of different sources. Yet people are often mixing data from different sources together to gain new types of insights, he said.
“Imagine you’ve got your sales data and you want to understand whether the weather had some impact on your sales,” said Grosset. “I could go get some weather data and combine that with my sales data and I could start to look for any type of correlation using analytics to see whether my sales went up or down based on weather. We actually know that things like social media activity go up when the weather is bad.”
The ability to simply send data accessed and prepared in Datawatch directly to Watson Analytics and Cognos Analytics will enable businesses to select any data source quickly and automatically convert it into structured data for analysis, Marc Altshuller, vice president of Watson Analytics and Business Intelligence for IBM Analytics, said in a statement. “This will not only expedite time to insight, but it increases the likelihood of uncovering new insights that have the potential to transform the business,” he said.
Clark Carpenter, infrastructure supervisor at Southeastern Med, an acute health care center, said the combination of Datawatch and IBM Watson Analytics is “well suited” for self-service analytics because of the ability to send data directly into IBM Watson Analytics.