Self-service data preparation is one of the key components in an effective analytics strategy; however in many instances an organization’s source data is diverse and rarely presents itself in a form that is accessible or in the format needed to perform analytics. Data sources come in a wide range of report formats, which often require IT intervention to make it usable to the average line of business user or citizen analyst. In fact, business analysts and data scientists typically spend up to 80 percent1
of their time manually preparing data, and it’s estimated that only 12 percent2
of enterprise data is used today to make decisions.
With Datawatch Monarch, IBM Watson Analytics and Cognos Analytics users can now rapidly prepare data from virtually any information source, from traditional databases to multi-structured documents, such as PDF and text reports, Web pages, JSON and log files that had previously been locked away. Data can now be prepared for analysis in a fraction of the time that it takes using spreadsheets and other manually-intensive measures.
“Many organizations still do not have access to the underlying data needed for critical business insights and as a result are spending entirely too much time preparing data and not enough time analyzing,” said Marc Altshuller, Vice President, Watson Analytics and Business Intelligence, IBM Analytics. “The ability to simply send data accessed and prepared in Datawatch directly to Watson Analytics and Cognos Analytics will enable businesses to quickly select any data source and automatically convert it into structured data for analysis. 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.”
Additional features of Datawatch Monarch for Watson Analytics and Cognos Analytics include:
- Simple data preparation – Drop a file or document on a prep canvas, and data is instantly available in rows and columns. More than 80 pre-built functions are available to transform and manipulate data with simple mouse clicks.
- Disparate data integration – Quickly combine dissimilar data using a joint analysis recommendation engine with fuzzy matching.
- Sharing and automation – Achieve transparency and reuse leveraging data preparation steps that are automatically saved in human-readable format.
- Security and information governance – Leverage features such as data masking to protect sensitive information, including patient names and social security numbers, and meet regulatory requirements.