Business Intelligence: Predictions

According to the Association of Professional Futurists (n.d.), “A professional futurist is a person who studies the future in order to help people understand, anticipate, prepare for and gain advantage from coming changes. It is not the goal of a futurist to predict what will happen in the future. The futurist uses foresight to describe what could happen in the future and, in some cases, what should happen in the future.” In my opinion, I will discuss what the future might hold for Data Mining, Knowledge Management and comprehensive BI program and strategy.


The future of …

  • Data mining:

o    Web structure mining (studying the web structure of web pages) and web usage analysis (studying the usage of web pages) will become more prominent in the future.  Victor and Rex (2016) stated that web mining differs from traditional data mining by scale (web information is much larger in number, making 10M web pages seem like it’s too small), access (web information is mostly public, whereas traditional data could be private), and structure (web pages have unstructured, and semi-structured data, whereas traditional data mining, has some explicit level of structure).  The structure of a website can contain: Page Rank, Page number, Damping factor, Number of pages, out-links, in-links, etc.  Your page is considered an authoritative piece if there are many in-links, or it can be considered a hub if it has many out-links, and this helps define page rank and structure of the website (Victor & Rex, 2016).  But, page rank is too trivial of calculation.  One day we will be able to not only know a page rank of a website, but learn its domain authority, page authority, and domain validity, which will help define how much value a particular site can bring to the person.  If Google were to adopt these measures, we could see

  • Data mining’s link to knowledge management (KM):

o    A move towards the away from KM tools and tool set to seeing knowledge as being embedded into as many processes and people as possible (Ferguson, 2016). KM relies on sharing, and as we move away from tools, processes will be setup to allow this sharing to happen.  Sharing occurs more frequently with an increase in interactive and social environments (Ferguson, 2016).  Thus, internal corporate social media platforms may become the central data warehouse, hosting all kinds of knowledge.  The issue and further research need to go into this, is how do we more people engaged on a new social media platform to eventually enable knowledge sharing. Currently, forums, YouTube, and blogs are inviting, highly inclusive environments that share knowledge, like how to solve a particular issue (evident by YouTube video tutorials).  In my opinion, these social platforms or methods of sharing, show the need for a more social, inclusive, and interactive environment needs to be for knowledge sharing to happen more organically.

o    IBM (2013), shows us a glimpse of how knowledge management from veteran police officers, crime data stored in a crime data warehouse, the power of IBM data mining, can be to identifying criminals.  Mostly criminals commit similar crimes with similar patterns and motives.  The IBM tools augment officer’s knowledge, by narrowing down a list of possible suspects of crime down to about 20 people and ranking them on how likely the suspects committed this new crime.  This has been used in Miami-Dade County, the 7th largest county in the US, and a tool like this will become more widespread with time.

  • Business Intelligence (BI) program and strategy:

o    Potential applications of BI and strategy will go into the health care industry.  Thanks to ObamaCare (not being political here), there will be more data coming in due to an increase in patients having coverage, thus more chances to integrate: hospital data, insurance data, doctor diagnosis, patient care, patient flow, research data, financial data, etc. into a data warehouse to run analytics on the data to create beneficial data-driven decisions (Yeoh, & Popovič, 2016; Topaloglou & Barone, 2015).

o    Potential applications of BI and strategy will affect supply chain management.  The Boeing Dreamliner 787, has outsourced 30% of its parts and components, and that is different to the current Boeing 747 which is only 5% outsourced (Yeoh, & Popovič, 2016).  As more and more companies increase their outsourcing percentages for their product mix, the more crucial is capturing data on fault tolerances on each of those outsourced parts to make sure they are up to regulation standards and provide sufficient reliability, utility, and warranty to the end customer.  This is where tons of money and R&D will be spent on in the next few years.


  • Ferguson, J. E. (2016). Inclusive perspectives or in-depth learning? A longitudinal case study of past debates and future directions in knowledge management for development. Journal of Knowledge Management, 20(1).
  • IBM (2013). Miami-Dade Police Department: New patterns offer breakthroughs for cold cases. Smarter Planet Leadership Series.  Retrieved from
  • Topaloglou, T., & Barone, D. (2015) Lessons from a Hospital Business Intelligence Implementation. Retrieved from
  • Victor, S. P., & Rex, M. M. X. (2016). Analytical Implementation of Web Structure Mining Using Data Analysis in Educational Domain. International Journal of Applied Engineering Research, 11(4), 2552-2556.
  • Yeoh, W., & Popovič, A. (2016). Extending the understanding of critical success factors for implementing business intelligence systems. Journal of the Association for Information Science and Technology, 67(1), 134-147.

Big Data Analytics: Future Predictions?

This is a world that is constantly going through change, especially technological change. There are many predictions regarding where we will be as a society as a result of leveraging big data. This post will, focus on what my prediction on where society will be in 10–15 years as a result of big data analytics.

Big data analytics and stifling future innovation?

One way to make a prediction about the future is to understand the current challenges faced in certain parts of a particular field.  In the case of big data analytics, machine learning analyzes data from the past to make a prediction or understanding of the future (Ahlemeyer-Stubbe & Coleman, 2014).  Ahlemeyer-Stubbe and Coleman (2014), argued that learning from the past can hinder innovation.  Although Basole, Seuss, and Rouse (2013), studied past popular IT journal articles to see how the field of IT is evolving, and in Yang, Klose, Lippy,  Barcelon-Yang, and Zhang, (2014) they conclude that analyzing current patent information can lead to discovering trends, and help provide companies actionable items to guide and build future business strategies around a patent trend.  The danger of stifling innovation per Ahlemeyer-Stubbe and Coleman (2014), comes from when we consider a situation of only relying on past data and experiences and not allowing for experiencing or trying anything new.  An example is like trying to optimize a horse-drawn carriage; then the automobile will never have been invented (Ahlemeyer-Stubbe & Coleman, 2014).   This example is a very bad analogy.  We should not focus on only collecting data on one item, but its tangential items as well.  We should focus on collecting a wide range of data from different fields and different sources, to allow for new patterns to form, connections to be made, which can promote innovation (Basole et al. 2013).

Future of Health Analytics:

Another way to analyze the future is to dream big or from a movie (Carter, Farmer, and Siegel, 2014). What if we could analyze our blood daily to aid in tracking our overall health, besides the daily blood sugar levels data that most diabetics are accustom to?  The information generated from here can aid in generating a healthier lifestyle.  Currently, doctors aid patients in their care with their diet and monitor their overall health.  When you are going home, this care disappears.  But, constant monitoring may help outpatient care and daily living.  Alerts could be sent to your doctor or to other family members if certain biomarkers get to a critical threshold.  This could aid in better care, allowing people’s social network to help them keep accountable in making healthy life and lifestyle choices, and possibly lessen the time between symptom detection to emergency care in severe cases (Carter, Farmer, and Siegel, 2014).

Generating revenue from analyzing consumers:

Soon, it is not enough to conduct item affinity analysis (market basket analysis).  Item affinity (market basket analysis) uses rules-based analytics to understand what items frequently co-occur during transactions (Snowplow Analytics, 2016). Item affinity is similar to the current method to drive more sales through getting their customers to consume more.  However, what if we started to look at what a consumer intends to buy (Minelli, Chambers, and Dhiraj, 2013)? Analyzing data from consumer product awareness, brand awareness, opinion (sentiment analysis), consideration, preferences, and purchases from a consumer’s multiple social media platforms account in real time can allow marketers to create the perfect advertisement (Minelli et al., 2013).  Establishing the perfect advertisement will allow companies to gain a bigger market share, or to lure customers to their product and/or services from their competitors.  According to Minelli et al. (2013) predicted that companies in the future should be moving towards:

  • Data that can be refreshed every second
  • Data validation exists in real time and alerts sent if something is wrong before data is published in aiding data driven decisions
  • Executives will receive daily data briefs from their internal processes and from their competitors to allow them to make data-driven decisions to increase revenue
  • Questions that were raised in staff meetings or other organizational meetings can be answered in minutes to hours, not weeks
  • A cultural change in companies where data is easily available and the phrase “let me show you the facts” can be easily heard amongst colleagues

Big data analytics can affect many other areas as well, and there is a whole new world opening up to this.  More and more companies and government agencies are hiring data scientists, because they don’t just see the current value that these scientists bring, but they see their potential value 10-15 years from now.  Thus, the field is expected to change as more and more talent is being recruited into the field of big data analytics.


Ahlemeyer-Stubbe, A., & Coleman, S.  (2014). A Practical Guide to Data Mining for Business and Industry. Wiley-Blackwell. VitalBook file.

Basole, R. C., Seuss, D. C., & Rouse, W. B. (2013). IT innovation adoption by enterpirses: knowledge discovery through text analyztics. Decision Support Systems V(54). 1044-1054.

Carter, K.  B., Farmer, D., Siegel, C. (2014). Actionable Intelligence: A Guide to Delivering Business Results with Big Data Fast!. John Wiley & Sons P&T. VitalBook file.

Minelli, M., Chambers, M., Dhiraj, A. (2013). Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses. John Wiley & Sons P&T. VitalBook file.

Snowplow Analytics (2016). Market basket analysis: identifying products and content that go well together. Retrieved from

Yang, Y. Y., Klose, T., Lippy, J., Barcelon-Yang, C. S. & Zhang, L. (2014). Leveraging text analytics in patent analysis to empower business decisions – a competitive differentiation of kinase assay technology platforms by I2E text mining software. World Patent Information V(39). 24-34.