A company’s enterprise content management strategy is a complex approach involving many layers. At the core of ECM is the attempt to maximize business intelligence. The challenge is, while businesses grow and the intelligence within that business becomes more complex, the needs for BI evolve as well. As more data is being stored in both structured and unstructured formats, within the organization and externally, ECM strategies have to incorporate technologies to not only locate information, but locate the most appropriate data in a timely manner.

When searching on the Web, everyone knows which search engines to call upon. But when it comes to searching internally for information, we ask around the office and then run a search on the company network ¬– fingers crossed and hoping for the best. Is that really the best way to locate necessary data? In order to locate and analyze all of the structured and unstructured forms of data, BI needs to be taken up a notch. This is where semantic intelligence comes in. By incorporating semantics into the mix, solutions can not only locate all the available pieces of data, but analyze the information to make sure the user gets only the most appropriate data. Semantic Intelligence is the incorporation of semantic technology into the traditional business intelligence practices, resulting in finding and extracting knowledge and information from all data sources, including structured and unstructured, and internal and external sources—ultimately allowing for more informed decision making.

As companies grow, data is often scattered amongst departments and divisions, all with their own separate documents. These groups are often unaware of how their use of data affects the rest of the organization. Technology is helping many businesses solve the challenge, allowing them to implement several different systems that contain similar information but do not necessarily interact with each other. The problem is that duplicate data may be processed in different systems, creating replicated work and different data structures.

The second leg of this problem is the plethora of external unstructured information that may also be crucial to a company’s internal business decisions. External data, including consumer comments, independent reviews and online market reports have an equally important role in the ever-changing strategy to meet a company’s business goals. Applications, blogs, social networks and forums where content creation, sharing and understanding takes place should all be included in the umbrella of BI. By leveraging semantic intelligence, companies can get a clear analysis of consumer sentiment, internal and external trends and competitive information. Uncovering this data not only from within an organization’s network and also from the the Web paints a clear and more accurate picture.

Consider the auto industry, for example, where a strong semantic analysis tool could have assisted in the prevention of and reaction to recent blunders from the top auto companies. Statistical data, sales volumes, growth percentages and other internal data all serve to provide a clear picture of where a company stands – yet give no indication of a brand's true reputation.

For many auto companies, that internal data was alarming, but it still begged the question, why? Through semantic technology across the Web, companies could have identified early on what their customers were saying, what their complaints were, where they wanted to see changes and with which products they were satisfied. Many intelligence tools identify dissatisfaction with one feature of the car, but satisfaction with another – calling that an overall neutral sentiment. Proper semantic intelligence technology should not only call out the overall sentiment of each of those features, but home in on the negative ratings and comments. Twitter, blogs, Facebook and other social media outlets have given customers a stage and a megaphone – and they cannot be ignored.

Relative to other businesses, the auto industry has some of the most Web-vocal consumers. The gap in consumer/manufacturer communication has created a huge negative aftermath, as evidenced by the recent downturn of U.S. auto companies. The voice expressed online is crucial, and keyword search simply is not going to find the data-rich content that manufacturers need to improve their brands.
 
Unlike a normal Google search, which leverages a simple keyword search, semantic intelligence uncovers the meaning that words express, in their proper contexts. Technically speaking, semantic intelligence understands the word and its context, whether it’s singular or plural, masculine or feminine, indicative or imperative or in past, present or future tense. When managing and locating external data, semantic intelligence can decipher if consumer sentiment about products or brands is positive or negative, and what specifically they are raving or complaining about. Semantic intelligence provides an early indicator of trends, news and warning signs to look out for.

Semantics incorporates morphological, logical, grammatical and natural language analysis, which ultimately translates into higher precision and recall in searches. This means that only the most important and accurate data is delivered to the user. Semantic intelligence helps organizations strategize, analyze and make predictions more accurately because it delivers the most appropriate data. This means that duplicate information is eliminated, and crucial information can be identified across departments to ensure that decisions are being made on the basis of the most robust and accurate information.

There is real business value in incorporating semantic intelligence into an overall ECM strategy. By extracting knowledge from these sources and combining them with BI, semantic intelligence arms companies with crucial information from structured and unstructured data forms – and in a volatile economy, this is a fundamental component to efficiency, productivity and essentially customer satisfaction.