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 Amazon.com 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.
References:
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 http://snowplowanalytics.com/analytics/recipes/catalog-analytics/market-basket-analysis-identifying-products-that-sell-well-together.html
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.
Dr. Skylar (Sky) Hernandez 