Socializing Access to Business Data for Greater Productivity

Self-service analytics has many benefits, but it’s also caused the data landscape within many companies to become like the Wild West, said Jon Pilkington, chief products officer at Datawatch, during a presentation, titled "Revolutionizing Self-Service Data Preparation & Analysis," at Data Summit 2017.

The secret to better data and analytics outcomes, he said, is “data socialization.”  This new approach integrates traditional self-service data preparation benefits with key attributes common to social media platforms, enabling data scientists, business analysts, and even novice business users to search for, share, and reuse prepared, managed data to achieve true enterprise collaboration and agility.

The biggest change in the analytics space over the past 20 years, is that nobody knows what anyone else has done, and there is little resusability of datasets leading to wasted time and duplicated efforts.

Citing data from IBM, Pilkington said that the annual cost of poor data quality in the U.S. is $3.1 trillion and analysts waste 50% of their time finding and correcting data, resulting in lost time and lost opportunities.

According to Pilkington, there are too many autonomous, one-off projects; too many compliance requirements; it takes too long to get, clean and organize the data; organizations lack operational repeatability; and there is an overall lack of trust in the data.

What is needed, he said a process for data reuse, enhancement, collaboration, and “blessing” by a governance officer to enable data preparation for the masses.

Social media and mobile applications have dramatically increased end user expectations on the availability and timeliness of enterprise data. Similarly, adopting a social approach to data, can benefit users as well, said Pilkington.

By “socializing data,” said Pilkington, organizations can effectively create an enterprise data marketplace that is secure, governed, trustworthy, and sourced by the business an data experts.

Purpose-built for analysts seeking datasets to meet their analysis and data reporting needs, “data shoppers” can contribute to categorization, curation, cataloging, and crowdsourcing to enable vetting and reuse of data assets.

The result for IT and business teams is centralized access and certified views of data assets, data usage and user activity analysis, operationalization of business processes, and stronger governance.

For analysts, it means higher productivity through certified, reusable datasets, better decisions using cognitive analytics, greater quality and trust. And, for information workers, the result is the ability to search, find, recommend, publish, and share; data acquisition from on-premise and cloud; and Excel-like data preparation.

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