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Whats the Difference Between Big Data and Big Content?

Whats the Difference Between Big Data and Big Content?

 

With the ever-changing nature of digital information, system and network designers need to think bigger than Big Data when it comes to information management.

Decision makers inherently understand that information assets can yield potentially make-or-break value to any organization. Great efforts are expended to extract as much of that value in a timely and cost-effective manner. However, the massive amount of information that system and network designers must manage, which is growing every day, changes and shapes new behaviors related to accessing and harnessing this information.

The management challenges stem from both scale and complexity. Digital information assets include large quantities of structured data residing in database applications such as ERP, CRM, and other enterprise systems, as well as numerous disparate repositories of unstructured content assets (e.g., documents, images, engineering drawings, illustrations, audio and video files, etc.).

As a result, the information-management challenge has now become an issue spanning Big Data (structured data) and Big Content (unstructured content), since both must be harmonized to extract maximum value.

Today’s Digital Information Landscape

In the past, the Big Data challenge put the spotlight on operational systems and data warehouses. Data mining and reporting emerged with the promise of improving decision-making and results. Today, the realities of multiple and disconnected information silos make it harder for employees to quickly find the most current versions of the right information assets and understand contextual relationships between different information assets in different silos. Information economy workers need easy access to data and content that’s spread among network file folders, CRM and ERP systems, SharePoint sites, email servers, and more.

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In response to the complexities and scale of present day Big Data and Big Content challenges, information management has become a priority initiative for many enterprises. Forrester Research, in a recent survey of technology management professionals, found that 73% of respondents had a formal information-architecture (IA) practice. However, in the firm’s information strategy and architecture survey, it discovered that only 13% of respondents tackled the integration of structured data and unstructured content. The majority of organizations and businesses lack a formal information-management strategy that can span both structured data and unstructured content.

Many industry watchers agree that the sheer quantity and complexity of structured data and unstructured content has created an environment of content chaos. IT teams express frustration with their lack of control at times when the cloud and non-sanctioned file-sharing applications are adding new dimensions to already chaotic attempts at information management.

Not surprisingly, systems bogged down by this chaos impose a huge productivity hit on users. Large teams and collaborative processes are especially affected, particularly when geographically dispersed teams and other complexities factor into information-sharing and management requirements.

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Content chaos also degrades the users’ overall confidence in information assets. When organizations cannot ensure the accuracy, efficacy and timeliness of structured data and unstructured content, the trustworthiness of decisions made from this information drops significantly.

Emerging Information-Management Alternatives

Enterprise-information-management (EIM) solutions bring a layer of information intelligence to system and network architectures. Leading-edge EIM systems address the need for solutions that foster consistent, harmonious management of structured data and unstructured content assets that are integrated to better deliver content in context.

The newest generation of information-management solutions makes it possible for information systems to provide end users with a more holistic view of information by moving away from folder-based organization methods. Instead of forcing users to manually define and manage hierarchical folder structures, and requiring everyone to learn and remember where information resides, intelligence is attached to information assets. End users can then access structured data and unstructured content based on what it is, instead of where it is.

Anticipating Future Requirements

Today’s EIM platforms must evolve to anticipate the future behaviors surrounding Big Data and Big Content. Besides the changing nature of digital information, EIM, and end-user behaviors, system architects should keep an eye on the larger-scale issues that will intensify Big Data and Big Content challenges. Globalization, unprecedented economic fluctuations, increasing regulations, and pressures to go paperless all shape the way that information systems are evolving. In addition, advances in the areas of artificial intelligence (AI), machine learning, and text analytics help system and network designers add intelligence to their repositories.

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Collectively, the demands of the modern workplace are elevating the need for higher performance, more robust, and more holistic platforms for information management. Big Data and Big Content continue to call for Big Innovation from the ground up.



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