Analytics Predictions 2013 by Alberto Roldan
Posted December 31, 2012
Keywords: Analytics, Text Analytics, Sentiment Analytics, Social Media Analytics, Workforce Analytics, Predictive Analytics, Web Analytics
This year I would like to do something different and address trends in the business analytics industry that, in my opinion, will affect all industries in the next 1-3 years. The purpose of these predictions is to provide some guidance so that the companies we represent today make informed decisions in the area of analytics that could affect their profitability and revenues in the future:
1. Electric power transmitted through the air. Could you imagine the implications of this technology on all industries? This will eliminate the need to plug in to restore electric power to any device. This would include cars, computers, laptops, and mobile devices. The opportunity for smart-energy solutions is going to transform business analytics with a potential of a ten-fold increase in the amount of business opportunities. Key skills will be experience in smart-grid analytics for short-term forecasting of electricity utilization, big data, and machine learning algorithms like principal component analysis (PCA).
2. Embedded semiconductor analytics. This technology is currently used in automobiles as sensors (e.g., check oil, gas gauge). Could you imagine the implications in the business-to-business (B2B) market for this technology? Right now alerts that are generated (e.g., retailer fraud, institutional compliance for anti-money laundering, or pharmaceutical utilization) need to be programmed and maintained at a substantial operational cost. This embedded technology can produce some measurable costs improvement in most industries.
3. Executives will become more conversant in analytics methodologies and technologies. The larger the investment in analytics, whether technologies or solutions, the more important it will become for executives to have a deeper understanding in this area of investment. This type of conservation will facilitate moving analytics from the realm of the data scientist to permeate the operational side of any company.
4. Reliance in machine-learning algorithms for big data analytics. The concept that analytics methodologies that work in small data sets but not in big data analytics will come to the forefront. Utilization of machine-learning algorithms to provide business insights and forecast future trends that have an impact on revenues or cost levers will become make significant inroads in the next 36 months.
5. Operational analytics. The term operational analytics will be used to have a good or bad implication depending on the experience of each person. It will be bad for those that have spent millions of dollars in data warehousing and technology tools but have not obtained a measurable cost savings as a consequence of their investment. It will be good for those that have made a prior investment that has had a measurable impact in revenues or costs. The ability of consulting and analytics companies to explain the measurable value of operational analytics will become the cornerstone of whether a company perceives analytics as a good or bad investment.
6. Analytics reporting will undergo a transformation to visualizations that can accommodate multiple dimensions involved in big data. Reporting will undergo a transformation that allows end users to simultaneously visualize multiple dimensions in real-time big data situations. The advent of big data, machine-learning algorithms, and the need to prove measurable business value will require 3D visualizations for reporting purposes so that companies have a 360-degree view that will capture business insights as well as the impact on profitability of multiple what-if scenarios.
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