Enterprise Search Takes Its Place at the Big Data Table

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Stephen Baker Head ShotIn this special guest feature, Stephen Baker, CEO of Attivio, gives his thoughts about how search is becoming a larger component of Big Data. In leading Attivio, the Data Dexterity Company, Baker brings more than 15 years of experience as a top executive within the enterprise software industry. Baker holds an MBA from the University of Pennsylvania – The Wharton School as well as a BS in Music and Marketing from Hofstra University.

Enterprise search has been around for more than two decades. And in that time, it’s grown from an ineffective and not-very-user-friendly tool to become a business critical application for enterprise knowledge workers to find the information they need to do their jobs. As enterprise information stores have grown to enormous volumes, search vendors have worked hard to make their solutions fast, accurate, and easy to use – like Google for the enterprise.

Enterprise search finds unstructured content housed in file shares like SharePoint and other content management systems, in email archives, and in the content repositories of applications like customer relationship management. The same approach to helping knowledge workers find information parallels a thorny, emerging problem for business analysts: finding data. And as a result, it’s clear that the convergence of BI, Big Data, and enterprise search is fueling the future of analytics.

Enterprise search has benefited from new data processing frameworks like Hive, Pig, HBase, Presto, Impala, Spark, and others. These frameworks can connect structured, semi-structured, and unstructured data to a range of natural language search tools. The notion of search applied to data is very powerful. And if a search solution can combine data with unstructured content, the potential for game-changing insights soars.

The Consumerization of Analytics

Data-driven applications can embed ubiquitous search—untethering ordinary business users from the constraints of the expert mindset of data architects and data scientists. And a new generation of enterprise search applications often carry with them a data visualization layer that makes them even easier to use and pushes analytical processing to the data source, which reduces latency.

Just like the “consumerization” of IT, we’re seeing the consumerization of analytics. The Big Data market rewards data visualization apps that are simple to use and designed for a broad range of users—not just the one percent of highly skilled data geeks. Speed to insight; insight at the speed of thought; that’s what businesses are looking for now. The term often heard to describe these new data users: citizen data scientists.

Enterprise Search for Accelerated Data Discovery

At Attivio, we’ve turned enterprise search into a platform for accelerating data discovery. We believe data discovery is the single most important piece of the puzzle to enable truly agile BI and speed time-to-insight. We call this data dexterity. Data dexterity is the ability to skillfully and gracefully get your hands on the right data and work with it to get it quickly to the point of informing decisions.

In meetings with our clients and other large companies, we constantly hear that companies want to do more with their data, but time constraints present a significant barrier. Fortunately, the renaissance of enterprise search has started to address the increasingly complex and time consuming data discovery needs for business intelligence (BI), Big Data, and predictive analytics initiatives.

And that’s good news because there are more data sources than ever before from which to gather insights. Email and social media as well as traditional databases, streaming data, and other third party data sources can be goldmines.

Reducing Time to Insight

According to Forrester, Inc., organizations spend 80 percent of any analytics initiative on data integration. That means only 20 percent remains for developing business insights. And it can be even less.

Not only that, but many enterprises can only “see” 10 percent of their data. The other ninety percent remains hidden—dark data. Data is often locked in silos, and it’s just too time-consuming to get it out. And making connections across structured, semi-structured, and unstructured information to serve to a BI tool is a completely manual, slow process – although highly valuable for developing strategic insights. Organizations that can cross this chasm will be poised to transform productivity, mitigate risks, and seize market opportunities.

For data-driven organizations, the ability to apply search to the data discovery problem – in other words, to provide self-service data discovery – couldn’t have come at a better time. It impacts the entire ecosystem of BI, Big Data, and predictive analytics. As more connected devices and the Internet of Things (IoT) send us ever larger volumes of data, we can’t neglect its importance. That would mean ignoring critical insights that improve decision making—and missing an opportunity to compete on analytics.


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