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Business Professionals Need Data
Business runs on data, especially the core financial, sales, human resource and supply chain transaction systems that form the backbone of business processes. In addition, every business has key professionals who rely on data as they help the enterprise make money, save money, and reduce risk. Some examples include:

• Sales managers working to grow revenue
• Customer service managers ensuring customer satisfaction
• Research scientists and engineers accelerating new product development
• Petroleum engineers maximizing yields at wells and refineries
• And hundreds more…

Not only do these business professionals need up-to-the-minute data from across a wide range of sources, they need different data every day as they face issues that are often new and unanticipated. For example, deciding where to send sales representatives to maximize the sales of a new add-on product is completely different from deciding where to send these sales reps to counteract a new competitive offering.

Today’s Data Landscape
Delivering data to business professionals is proverbially easier said than done. Data growth is through the roof. And complexity is the norm as data sources and types proliferate. On the structured data side, enterprises continue performing transactions; building physical warehouses, marts and stores; and extending their business models through both partnerships and mergers and acquisitions. Semi-structured data growth is accelerating as well, as XML documents are rapidly becoming mainstream. Unstructured data including text, audio, and video is also growing at a fast pace.

To meet these changing demands, the IT tools designed for accessing and analyzing business data also continue to grow both in their variety and complexity. These include business intelligence (BI), performance management analytics, reporting, portals, dashboards, mash-ups, as well as fully functional custom and composite applications. Figure 1 depicts a typical data landscape. The diagram addresses structured and semi-structured data. Unstructured data presents unique challenges distinct from structured and unstructured data.

Where Today’s Solutions Fall Short
Enterprises have invested millions in solutions to help key business professionals answer questions, solve problems, and make decisions. By and large, these solutions have done a good job addressing common, frequent, and well-anticipated information requirements. But with business pace accelerating, and agility one of the last defensible competitive advantages, these traditional approaches are falling short. Common complaints include:

• No access to all the relevant data needed
• Current tools too complex to learn and navigate
• Too much dependency on IT for new reports and report enhancements
• IT is too slow to react to changes

Data Discovery – The Solution for Business Professionals
A new category of application, data discovery, is emerging to address these complaints. Data discovery applications are end-to-end solutions that let business professionals “do it themselves” with little to no IT assistance. Complementing existing reporting and analytic solutions, data discovery opens the door to all the enterprises’ structured and semi-structured data.

For example, a customer service manager needs to resolve a recurring missed delivery problem for a key customer. The manager knows the customer’s name and customer status (“red”). The manager needs to figure out the cause of the missed deliveries to recommend and implement a solution and return the customer to “green status.”

Table 1 shows the end-to-end process the manager might use to resolve this problem using a data discovery product.

IT’s Role in Data Discovery
Although one of data discovery’s benefits is to reduce the burden on IT, IT still plays an important role “behind the scenes.” At setup, IT installs the data discovery server and sets the credentials for users’ accounts and privileges. IT also grants access to the discovery server for all the source data domains. IT also runs the data indexer as well as the relational inferencing tool. At runtime, IT runs periodic indexing and inferencing updates to ensure the freshest possible data. Further, IT administers users, adds new data sources, and keeps up with organizational and system changes. In addition, IT may customize data discovery, making it easier and more productive for business users by adding annotations, aliases, domains, synonyms, and views.

IT’s incremental support efforts are relatively minor when compared with typical scenarios today. Data discovery products keep data security risks low by leveraging and conforming to existing security paradigms and controls, down to the row and column level. They are non-invasive, placing little additional burden on existing architectures or operations. Finally, IT’s overall workload is typically reduced because of the elimination of a large percentage of new reports and other requests from the newly more self-sufficient business professionals.

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