There is a major change going on in the marketplace.  Because news circumnavigates the globe so quickly, and innovative ideas commoditize rather quickly thanks to our over-communicative global economy, your business requires a keen ability to meet the demands of an accelerated rate of change just to sustain your current market positioning.  Unfortunately, many of your information resources, required to chart the course for this accelerated rate of change, haven’t kept pace.  This is largely due to the time-consuming processes used to ingest new information and the high degree of complexity in the information models and transformation processes used to ingest current information.

The old practice of ensuring all information is represented in the information model and assuring organizational stakeholders that “it’s like Prego, it’s in there” just doesn’t surface welcome reactions any longer.  A better means of providing focused, relevant, actionable information is mandatory. This new method, which can be thought of as Post-Discovery in contrast to the inflexible nature of pre-discovery, shifts the focus of information delivery to agility.

Post-Discovery is accomplished with new processes and information storage and presentation tools that can accommodate a Post-Discovery architecture.  These tools generally do not demand the presence of a data model used for navigation, but rather use flexible named pairs of data for searching for intelligence.

These tools possess the following attributes:

• They have a simple means of representing the navigation of information, normally through named pairs (subject, predicate, object standard as used in Wikipedia and dbPedia), which allows the ability to store content of diverse type and format rather than conforming to a single, standard model.

• They use an agile presentation layer that can be changed rapidly and often.

• They use a workflow engine to ingest and consume information.

• They can ingest batches of content as well as streamed content, and for streamed content, be able to query it in near-real time, perhaps as an alert or filter.

• They depend on a well-devised, repeatable process that has at its heart an information catalog as a means to ensure they don’t quickly become unmaintainable.

JUST HOW DOES THE POST-DISCOVERY PROCESS DIFFER

There is a fundamental difference between the traditional pre-discovery based solutions for delivering intelligence and the Post-Discovery approach.

Post-discovery solutions:

• Can automatically generate the necessary data structures they require for storing and navigating information.

• Have a highly decoupled architecture with little or no hard wiring in the entire architecture, thereby allowing an agile environment much more suited to the highly adaptive world we live in.

• Use standard web-based tools for the presentation of information.  Since the user interfaces do not have to be data-model aware, the options open to the developer allow a much more rich internet application solution to be adopted for their intelligence-focused applications.

• The slice-and-dice mechanism introduced for traditional BI has been replaced with intelligent search, which provides a much richer navigation paradigm than slice and dice affords.

• There is a decoupled obligation to perform data discovery for the purpose of building the data catalog.  This catalog offers the consumer of intelligent search a directory of what data is available in the application environment and provides the developer the means to avoid creating a convoluted mess.

HOW TO GET STARTED

To get started constructing a Post-Discovery intelligence applications environment, there are several prerequisites that must be adopted:

• A willingness to take a different approach. One of the primary drivers toward a Post-Discovery approach is the inability for current processes to meet the demands of our stakeholders to remain adaptive in our highly communicative global economy.  Shared commitment to adopting a different approach is mandatory for achieving different results.

• Adoption of a suite that accommodates a Post-Discovery approach.  If you try to implement a Post-Discovery environment using traditional tools, you will also need a translation layer and an additional level of complexity that will further distance stakeholders and further support the common image that this is all a black box with no vision into its accuracy or lineage.

• Adoption of processes and tools that elevate the trustworthiness of data.  Much of the time spent using intelligent systems today is wasted validating the information for accuracy prior to its use.  To achieve the value returns available from a Post-Discovery approach, you need to change the opinion shared by many stakeholders that the data you publish is not trustworthy.  Only by being proactive in shattering this lack of trust in information do you stand a chance of succeeding.

In addition, you are likely to benefit from including these steps:

• Enlist the services of a coach for the first couple of Post-Discovery applications you tackle.

• Be sure to determine which information demands heightened focus by your stakeholders and prioritize the intelligent applications that use this information.  There is too much information floating in your enterprise not to short list the initial applications to be tackled.  Do not assume that you can take a best guess and get this right.

• Devise a means to communicate the incremental value achieved through the adoption of Post-Discovery intelligent applications in terms that have meaning to your stakeholders. Not only does this approach ensure stakeholders’ expectations are set, it also provides feedback for identifying areas for improvement.

• Employ a process that will ensure a heightened degree of re-usability of the initiatives you deploy.

UNIFIED INFORMATION ACCESS: AN APPROACH TO POST-DISCOVERY

The fastest route to Post-Discovery is Unified Information Access (UIA), which offers a schema-less method of delivering all types of information – data and content – in response to ad-hoc user requests. UIA derives its indexing and retrieval capabilities from enterprise search, but unlike enterprise search UIA also includes data in the index and in responses to a single query.

When a UIA platform ingests content and data into a single index, it retains the relational aspects of the data. A UIA platform also extracts data from content, identifying entities, such as names and products, so the content acquires the qualities of data. In other words, users and processes can issue a natural language, ad-hoc query to access data (and content) without a pre-existing data model.

Learn more about the Post-Discovery Approach by downloading the full whitepaper "Post-Discovery Intelligent Applications: The Next Big Thing"

About the Author

Mark Albala is President of InfoSight Partners, LLC, a services firm that provides technology and accounting services focused on obtaining value by creating focused insight into trustworthy, actionable information, devised to meet the demands of this highly communicative global economy. Mark has spent the past 25 years in a variety of leadership positions in services firms and industry, focused on helping organizations elevate their use of information they already possess.

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