Social media has worked its way into almost every aspect of our daily lives – both personally and professionally. Its on-demand nature has dramatically increased end-user expectations of the availability and timeliness of enterprise data. And the data collection methods inherent in social media platforms, such as crowdsourcing, are the envy of business users and analysts who desperately seek to share information and curated data sets across their organization (let’s face it, no one wants to re-invent the wheel). It’s time for organizations to transform the way they think about business data, and this means “getting social” with enterprise data to maximize analytics and business outcomes.
An Evolution in Data Accessibility and Self-Service
Self-service data preparation has significantly advanced in the last 12–18 months. Modern tools deliver access to dark data locked in semi-structured and unstructured data repositories, automated and pre-defined data preparation functions, direct exports of analytics-ready data to visualization tools, and business intelligence (BI) platforms, built-in automation and governance functionality for security and compliance, and more. However, despite all of these innovations, for most business users and analysts, data access is still limited to personal data sources, historical reports or data controlled by IT and BI gatekeepers – which can be outdated by the time they receive it. Far too many people are building Excel spreadsheets and reports in seclusion, using sources they can’t completely trust and/or making business decisions based on incomplete information.
But, data preparation and analytics don’t have to be this difficult, and there’s been an evolutionary leap to expedite, simplify and improve these processes by using data socialization.
Data socialization takes the fundamentals of social media – creating and sharing information – and brings it to the business world. In technical terms, it involves a central data-management platform that unites self-service visual data preparation, discovery, cataloging, stewardship, automation and governance with key attributes common to social-media platforms. It leverages popular social media and crowdsourcing features to make data readily accessible and easily sharable across an organization. It empowers business users and analysts to:
– Understand the relevancy of data in relation to how it’s used by different user roles, and follow key users and data sources (à la Twitter)
– Find, “like” and share data, and receive automatic notifications when new relevant content becomes available (à la Facebook)
– Build a network of influencers and collaborate to better harness the tribal knowledge that too often falls to the wayside (à la LinkedIn)
– Create a marketplace of enterprise and public data sets. As users work with requested data, machine learning technology identifies patterns of use and success, performs data quality scoring, suggests relevant sources and automatically recommends likely data preparation actions based on user persona (à la Amazon).
– Leverage intuitive search capabilities for cataloged data, metadata and data-preparation models indexed by user, type, application and unique data values to quickly find the right information for analysis (à la Google)
– Receive crowdsourced reviews and user ratings on data quality and relevancy (à la Yelp)
– Know what data to avoid and glean insight into alternative data management approaches based on previous user experience (à la Waze)
In a nutshell, data socialization allows individuals and teams to search for, access, share and reuse prepared, managed data, as well as leverage user ratings, recommendations, discussions, comments and popularity to make better decisions about which data to use in analytics processes. Certified curated and raw data sets are readily accessible in a centralized data ecosystem created by the user. Individuals can learn from each other by sharing sources, findings and experiences with their teams. And, by incorporating elements of gamification, they can learn how their contributions are being used by colleagues and better understand their value in the analytics process. Overall, with data socialization and crowdsourcing, business users and data analysts can be more productive and better connected as they source, cleanse and prepare data for analytical and operational processes.
Governance Still a Priority
At this point, you might be thinking that while data socialization may sound fantastic, having crowds of people publishing, sharing and enriching data sets can make data governance and security unmanageable – but it’s actually quite the contrary.
Thanks to the recent advancements in self-service data preparation governance mentioned earlier, features such as data masking, data retention, data lineage and role-based permissions are applied throughout data access, preparation and analytics processes. Additionally, similar to how social media platforms (e.g. LinkedIn) employ an editor to review content before it’s published, with data socialization, data scientists and IT professionals can serve as data stewards, monitoring shared content and determining whether it should be certified as an enterprise data source. With these measures in place, data quality is improved, and this means better analytics and outcomes for everyone.
If these capabilities aren’t enough to get you thinking about the utopia created by data socialization, consider this: Users gain the ability to acquire and prepare data from any source, eliminate or automate redundant work across different silos, and share techniques and curated data with their peers. Organizations can maximize their investments in self-service analytics, improve operational processes, optimize their human resources and tap their data’s full potential to make faster, better business decisions. These results are worth “liking” and “sharing” in my book – don’t you agree?
As Chief Product Officer, Jon Pilkington brings more than two decades of business analytics experience to Datawatch, including 18 years in the business intelligence market. Jon joined Datawatch from Sonian Systems, a public cloud email archiving vendor, where he served as vice president of marketing and product management.
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