No Biggie: Data As A No Brainer

February 17, 2014   //   Business Intelligence, , , , , , , , ,

The Data, Information & Knowledge Paradigm

As information scientists, our goal is to understand the structural and/or functional relationships among data, information and knowledge. Typically, information is defined in terms of data and knowledge in terms of information [1], so it is easy to characterize the three concepts as a continuum. Data is merely raw facts [2], observations and by itself is not usable until it is structured in a usable, relevant form. We call this relevant form information – the meaning we attribute to raw facts. Information is useful because it answers questions such as “who,” “what,” “where,” etc. [2]. Knowledge however is an elusive concept because it originates and is applied in the minds of “knowers,” [3] and then embedded in documents and repositories as well as in organizational routines, processes, practices and norms.

Business Intelligence and the Big Data Bottleneck

Business Intelligence studies data and information that play a key role in the strategic planning process of a corporation. Companies consume and process information, creating the knowledge to improve decision making, cut costs and identify new business opportunities or inefficient business processes that are ripe for re-engineering.

As technology advanced, however, it brought a key problem: collections of data sets so large and complex that it became difficult – if not impossible – to process them using traditional database management tools and data processing applications. You may have heard of it. It’s called Big Data [4]. Some of the challenges of Big Data involve determining whether the quality of information is good and refining the information into usable knowledge.

Machine Learning: One Step Further

Big data requires exceptional technologies to efficiently process large quantities of data. One of the suitable technologies is called Machine Learning, a branch of artificial intelligence concerned with the construction and study of systems that can learn from data. For example, a system could be trained to understand a user’s past behavior as well as similar decisions made by other users; then use this model to predict the rating or preference that a user would give to a movie or book.

As the number of individuals that are active on a social networking continues to grow, the ability to understand what people say online by the application of data analytics becomes a “no-brainer.” Sentiment Analysis uses social media algorithms to understand the sentiment of customers in real time, knowing how they perceive new products, services or campaigns. It uses Natural Language processing to extract the subjective information with respect to some topic – the attitude of a speaker or writer and whether it is positive, negative or neutral.

Sentiment Analysis as a Strategic Advantage

Sentiment Analysis is strategic to any business. Imagine the potential of automating the task of analyzing what is said online providing insights into what customers are really looking for. It enables retailers to develop the product customers want instead of the product marketing departments think they want. Businesses can test hypothesis before spending any capital on product development by observing how customers feel about your brand, how they react to your marketing campaigns and more.

Machine Learning is the holy grail of analytics. It takes Business Intelligence to the next level, bridging the gap between data and real knowledge. It learns from experience automating the task of understanding contextual information, eliminating the need for human intuition and providing insight into the minds of knowers – the customer. There are a wide variety of learning tasks and successful applications. On my next post I will further explore the task of analyzing sentiment and once more try to persuade you that the pressure to exploit analytics as a strategic advantage is increasing for an obvious reason – the high cost of not knowing.

To learn more about business intelligence and all of the exciting new features SWC has for Big Data and the BI community; please join us for our next informative Business Intelligence event.

Chicagoland Business Intelligence Meetups

The SWC Business Intelligence Community is also hosting monthly Chicagoland BI meetups. These complimentary events are open to those wanting to learn more and network with other BI users in a fun and social setting. Our next meetup is Thursday, February 20th, 2014 at Pinstripes in Oak Brook. The topic is Tableau and “R” Integration, led by Chad Dotzenrod. The event starts at 5:30 PM, the presentation will be at 6:00 PM, followed by bowling, bocce and lots of other fun discussions around BI.

If you can’t make this event, but are interested in future Chicagoland Business Intelligence events, please Events.

Additional Business Intelligence Posts

If you enjoyed this post from Patricia, please check out a few of our past posts on business intelligence:

Ask SWC: What Is The New R Feature For Tableau 8.1?
My Search For The Business Intelligence Chupacabra
An Agile Approach to Business Intelligence
OAuth 2.0 – Google API Business Intelligence Implementation
Ask SWC: What’s So Great About Tableau?
If They Only Had Tableau
How to Fast Track Business Intelligence
Can’t afford BI? Try the BI Analytics Tools in Everyday Software
Do Tableau And MDS Make Strange Bedfellows?
Technology Meetups Make A Difference
Ask SWC: What Is A New Technology That You Find Interesting?
[1] Jennifer Rowley. The wisdom hierarchy: representations of the DIKW hierarchy Journal of Information Science April 2007 33: 163-180. doi:10.1177/0165551506070706
[2] Henry, Nicholas L. (May/June 1974). “Knowledge Management: A New Concern for Public Administration”. Public Administration Review 34 (3): 189. doi:10.2307/974902
[3] Wallace, Danny P. (2007). Knowledge Management: Historical and Cross-Disciplinary Themes. Libraries Unlimited. pp. 1–14. ISBN 978-1-59158-502-2.
[4] White, Tom (10 May 2012). Hadoop: The Definitive Guide. O’Reilly Media. p. 3. ISBN 978-1-4493-3877-0