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.
No comments:
Post a Comment