As discussed in the previous
blog, every business has a risk and needs to find how to decrease the risks.
Each negative outcome that occurs presents an opportunity from which to learn
new things. In a small scale business, these decisions could be easy and more
mind driven being controlled by a set of managers only. As the businesses grow
beyond a size, predicting the future course of action and avoiding the past
mistakes becomes important.
The enterprise integrates the predictive model’s
scores in order to act upon what has been learned. At each step, the predictive
scores foresee where a “blunder” may incur unnecessary risk, thereby guiding
the organization to avoid it. In this way, predictive analytics delivers a
complete data-driven system for risk management.
modeling capabilities are scientifically proven and have benefited from decades
of advancement. With a tenured track record of success, predictive analytics
boasts mature software solutions that deliver this technology to – and
integrate it with – the modern enterprise.
All businesses are run at a
risk. Risk is the way business is managed. Every decision an organization takes
impacts the risks an enterprise can withstand like the risk of customer
defecting, of not responding to an expensive glossy mailer or offering a huge
retention discount to a customer who was not leaving even otherwise and in turn
missing out on a critical customer who leaves.
The data driven means to
compute risk of any type of negative outcome in general is predictive analysis.
Insurance companies have used this very well. Insurance companies are
augmenting their practices by integrating predictive analysis in order to
improve pricing and selection decisions.
actuarial methods that enable an insurance company to conduct its core business
perform the very same function as predictive models: Rating customers by chance
of positive or negative outcomes.
Predictive modeling improves on standard actuarial methods by incorporating
additional analytical automation, and by generalizing to a broader set
Predictive analytics is an
enabler of big data: Businesses
collect vast amounts of real-time customer data and predictive analytics uses
this historical data, combined with customer insight, to predict future events.
Predictive analytics enable organizations to use big data (both stored and
real-time) to move from a historical view to a forward-looking perspective of
For example, historical
preferences of consumer can be analyzed from their usage patterns and
promotional offers can be planned accordingly. The historical data can help
predict which promotional offer would be most useful.
are a few solutions available that help achieve this. They combine the
capabilities of data mining solutions and predictive analysis to provide a
single solution for predictive analysis. Vendors
may offer proprietary solutions or solutions based on open source technologies.
Predictive analytics software can be deployed on-premises for enterprise users
or in the cloud for small businesses or for project or team based initiatives.
are no more asking for just analysis of their data. Traditional Business
Intelligence tools have been doing that for all for a long time now.
What we are
exploring is getting more useful insights from our data. Organizations are
looking for visualization tools and predictive analysis to explore data in new
ways and discover new patterns. Big data analysis is not only restricted to
processing large volumes of data on a software, it needs equivalent hardware
processing capabilities too.
Analysis is the practice of extracting information from existing data to
determine patterns and predict the future outcome & trends. It helps
forecasting what might happen in the future with an acceptable level of
reliability and includes what-if scenarios and risk assessment.
Applied to business, predictive analysis models are used to analyze current
data and historical facts in order to better understand customers, products and
partners and to identify potential risks and opportunities for a company. It
uses a number of techniques, including data mining, statistical modeling and
machine learning to help analysts make future business forecasts.
Simple words: A practitioner of data science is a data scientist.
They apply their skills to achieve a broad spectrum of end results. They have the ability to find and
interpret rich data sources, manage large amounts of data despite hardware,
software and bandwidth constraints, merge data sources together, ensure
consistency of data-sets, create visualizations to aid in understanding data,
build mathematical models using the data, present and communicate the data
insights/findings to specialists and scientists in their team and if required
to a naive audience.
Data science is emerging to meet the
challenges of processing very large data sets i.e. "Big Data"
consisting of structured, unstructured or semi-structured data that large
enterprises produce. A domain at center stage of data science is the explosion
of new data generated from smart devices, web, mobile and social media. Data
science requires a versatile skill-set. Many practicing data scientists worldwide
specialize in specific domains such as the fields of marketing, medical,
security, fraud and finance.
Many people ask the
question that is there actually a need for real-time analysis. How would it
help them in their business and is it worth the kind of investment it needs to
get into real-time analysis?
You can analyse
your business to see how you can use it for your business development. We can
site a few small examples of different streams that organizations have used to
see the benefits. Let us start with healthcare – a small device worn on the waist
belt injects desired quantities of Insulin to a diabetic patient while monitoring the sugar
level through the device.
enter a hotel and the person at reception knows your name, booking details and
your preferences. While the second part is already in control through club
memberships, the camera on the door takes your picture, searches for the record
and by the time you reach the reception, your details are on the desk.
near the store gets a message on additional offers on their favourite product
for the next 30 minutes. If you reject a
product from the shelf, you get a message of additional offers on the product. Always
get a win-win situation for both the store and the consumer.
Human race has
been playing with data for ever. Large volumes of data being generated, and
reports created of these, have been the traditional approach the way business
have been run and expanded. The future prospects were based on some trends of
your own organization, market and competitors and all this was based on the
been analysing this data to help organization plan the above.
changed now and businesses cannot only rely on past performances to plan their
future. You need to capture the current trends and the needs of the consumer
today. Analytics have changed now from analysing past data to perform real-time
data. Data generated not only from the structured in-house databases but also
from non-structured data generated through social media and consumer behaviour.
available today to collect data from these sources and put them together
through what is called the process of “Data Conditioning” into databases to
help analyse them. All this data gets processed real time to produce near
instant results helping the businesses serve their consumers better.
While most of us believe Data Science is
handling large volumes of data effectively and efficiently, Data Science is not
restricted to this. Merely using data isn’t really what is meant
by “data science.” A data application acquires its value from the data itself,
and creates more data as a result. It’s not just an application with data; it’s
a data product. Data science enables the creation of data products.
Several organizations have used this well and have given the
industry several products that help handle Data Science. Key examples are from
Google itself who realized the importance of a search engine and further made
it more effective using tracking links. Spell checks with suggestions and
speech recognition are other examples of Google’s way of creating products out
of data. Facebook and Linkedin have used patterns of friendship relationships
to suggest other people you may know or should know, sometimes with great
The thread that ties most of these applications together is that
data collected from users provides added value. Whether that data is search
terms, voice samples, or product reviews, the users are in a feedback loop in
which they contribute to the products they use. That’s the beginning of data
When theatre veteran Noel Coward said this, he was talking more about the economics of theatrical world. But very soon many realized that this applies to everything in life. When we transitioned from DAT drives to DLT, many questioned the fact that the data size would keep growing and DLTs won't help. Technologists did not think that way and they got ready with the LTOs followed by disks and now deduplication. Technology is always ready for the next level to resolve the next problem you might face. It is important to be adaptable to the new things. Anything can become obsolete and every new thing brings in new and fresh energy to overcome the obsolete. Never hold yourself to the current technology. It has to become obsolete soon and you have to be ready to accept the new order. At some point, obsolete has to go out and the new stuff would be good for the new situation. So Always Be Ready.
Because the show will go on, with or without your participation.