Research, planning and specific visual (visual-aid) examples are keys to de-mystifying predictive analytics for non-techies.
Analytics is a very broad topic.
Although considered by many to be commoditized, data warehousing, master data
management and business intelligence (BI) are still evolving. In a
"non-techie" way, predictive analytics uses large amounts of
asynchronous and sometimes not apparently related data, cultivate it into
information, and identify key drivers that can be monitored and recalibrated to
impact change within the enterprise.
Predictive analytics enables decision
makers to predict with confidence what will happen next so that they can make
smarter decisions, solve problems and improve outcomes. Here are a few
perspectives to articulate predictive analytics:
Research, planning and specific visual (visual-aid) examples are keys to de-mystifying predictive analytics for non-techies. To really show a potential customer how predictive analytics
will benefit them requires an understanding of what data is available from
their product usage and consumer behavior.
Sometimes, the conversation requires a starting point that explains how measuring
product performance can improve their business. If you are past that point,
providing creative and visual examples of how their data insights can guide and
contribute to the product management and business development processes is the
next step. There are a number of use cases available for demonstration, but the
most effective step would be to show exactly which parts of their product and
their customer experience could be impacted, as well as indicating how revenue
in each area could grow.
Predictive analytics is the powerful output of IoT (Internet
of things) and big data analytics. It is that kind of output that makes
people understand the power of big data analytics. It is power of distributed computing.
It is the power of the IoT and BIG data that is available all over and the
infrastructure that helps to aggregate all possible data and derive patterns
among them. Understanding the data, business and building good models are all
very significant in predictions.
Good statistician and a good programmer can build and predict
almost anything. For some, that's
analogous to forecasting, while for others it is a set of metrics used to measure
their progress or performance. Ask "what is measured?" in these
forecasts or metrics (the data collected and measured -- usually a variety of
data and data ranges (numerical or textual). Then ask "how are these
forecasts / metrics formulated?" – there are usually different
combinations of data and data ranges (these represent predictive models in the
organization). One can then move on to the "analytics" ... If an
organization has a sufficiently sound and complete corpus of data, one can
apply a variety of analytics to calibrate the models previously discussed ( using
more or less data types, or different ranges) in order to change the fidelity
of the metrics or forecast; start discussions regarding the fidelity of the
metrics from the "false positives" and "false negatives"
perspectives, since most people can relate to these concepts. And once you get
through the deterministic aspects of predictive analytics, one can start
conversations about applying stochastic analysis to discover new aggregations
and clustering -- which can lead to new or improved predictive models the
organization never thought of.
Predictive Analytics is a great space to come up with new
business for any kind of customers. In
non-technical perspective, you can come up with practical use cases and with
sample data, you need to make them understand the benefits that would be
derived from the output that you generate. So first, you need to demonstrate how
accurate these models are. Can
you make the future trend data actionable, or is it just another appealing but
irrelevant visualization? when you don't see the wood for the trees it is
difficult to see what the question is. Where does predictive work hit a
company's bottom line? In production? Distribution? HR? Etc. Basing major
decisions on predictive Analytics seems like walking a tightrope 9000 feet in
the air. You could make it across and land in the Fortune 500, or of course,
you could fall, and another senior manager will be left with the same dilemma.
Getting accurate predictive results will be the key to
selling to customers. The main concern of top execs is ACCURACY. Any exec would love to be able to predict future trends based on current and historical data. Are your customers
ready? Do they need education? Are you ready to invest in education? For example, if the predictive
financial forecast for a month or so ahead is not close to actual, it will
undermine the credibility of forecasts over a longer period. Here is external
system called predictive analytic that identifies the customer/prospect
category. Now salesman with self intuition and also with additional info of
prediction from system decides the approach to engage/connect with
prospects/customer. When you feel that you have answered the value for money
question you can visualize it, write it up and or present via multiple digital
and face to face channels. The main consideration when identifying how to
demystify real technical breakthroughs for non-technical audiences is their own
level of sophistication and maturity. You must understand the application of
technology across their own business and where is it in the spectrum of the digital technologies.
With regard to ethnicity and language preference, the best
possible methodology is predictive. It
provides an unbiased view of data and predicts ethnicity, language and a number
of other related data elements based on proprietary predictive software. The
software contains name tables, naming rules and algorithms, enhanced
neighborhood analytics. The first step is to understand what you are trying to
get across - if their question is philosophical - that is how you explain in
general about any complex idea, structure or product, then you can expect the
plethora of answers you have seen. But if they are value driven and customer
focused, then the question is how they can present their solution to their
customer base. For example, in Education, you can generate a model based on the
history of scores that students obtained. Based on the analysis on the scoring
patterns, it can be derived to predict the students scoring aspects in future.
Further, it also helps on determining which students would need little
supervision, more supervision or complete supervision. Another case in Retail
segment, similar model can be injected and come up with Sentiment Analysis of
customers and correlate with the cost and accordingly take business decisions
to boost the sales. Similar things can be applied in almost all industries
possible.
Predictive Analytics is an emerging
arena and the broad topic, it plays significant role in both bottom line cost
efficiency and top line business growth. Therefore, it has been put to the top
project priority list in any forward-looking organization’s agenda, and it has
full of potential.
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