How Data Science is used in Business..??
Uber has reinvented the transportation all over. This is an
overstatement if we do not look behind the scene to see how Uber has created
this unexpected change. This company made simple for a user to book an Uber –
To make this happen, the company collects the big data and employs Data Scientists.
In light of what Uber has achieved, businesses utilizing their valuable assets,
data, and continuously employing Data Scientists are streaming ahead to beat
the competition by a mile.
From making better decisions, defining goals, identifying
opportunities and classifying target audience to choose the right talent, Data
Scientists offers immense value to businesses.
How do such companies gain industry-specific insights from Data
Science ?
Data Science
is creating insight-driven manufacturing. The interesting Data
Science story of Ford indicates how manufacturers take advantage of the data.
From wireless connections to in-vehicle sensors, Ford is consolidating advancements
to gain insights into driver behaviour and improve its production times.
Manufacturers use high-quality data from sensors placed in
machines to predict the failure rates of the equipment; streamline inventory
management and optimize factory floor space. For long term, manufacturers have
been seeking to address the equipment’s downtime. The advent of IoT has allowed manufacturers
to make machines talk with one another – the resulting data is leveraged
through Data
Science to reduce unplanned equipment’s downtime.
Dynamic response to market demands is an another challenge
faced by this industry – Line changeover is at the heart of assuring dynamic
response; manufacturers now use the blend of historical line changeover data
analysis with product demand to determine effective line transitions. The
combination of statistical models and historical data has helped expect inventory
levels on the shop floor – Manufacturers can determine the number of components
required on the shop floor.
Data
Science Master’s program, in Brillica Services, is a vast field which is becoming
more valuable to the organizations, whether of Small, Mid & Large size. The
Harvard Business Review has labelled data science as the "sexiest job of
the 21st century". If they mean that jobs in Data
Science are increasing drastically, also data scientists can work in fields
as diverse as health, retail, or ecology, and that data scientists are
commanding high salaries, then they were spot on. After all, we have been creating
more than 2.5 Exabytes of data every day.
DATA SCIENCE
IS NOW IN EVERY OPERATION IN RETAIL
The retail industry is picking up the nuggets of wisdom from
data that is growing exponentially by consolidating Data
Science. Data Scientists at Rolls Royce determines the right time for
scheduling maintenance by analysing airplane engines data. L’Oreal also have a
team of Data Scientists working to find out how several cosmetics affect
several skin types.
Retailers now lean on predictive analytics to improve
customer experience across the devices and channels. Sentiment analysis of
product reviews, call centre records and social media streams also allows the
retail industry to gain market insights and customer feedback.
On the Merchandizing front, retailers make good use of video
data analysis to identify cross-selling opportunities with shopping trends as
well. They also learn behavioural patterns from heat sensors and image analysis
for promotional displays, improved layouts and product placements. With the
product sensors, they gain insights on post-purchase use.
When it comes to marketing, retailers are consolidating Data Science
to ensure personalized offers to reach every customers’ mobile phone. Retailers
promote real-time pricing, run targeted campaigns to segmented customers
through appropriate channels and provide tailored offerings through web
analytics and online behavioural analysis.
Data Science
also helps retailers benefit from real-time inventory management and tracking.
GPS-enabled big data telematics help optimize routes and promote efficient
transportation. Retailers are ill-treating unstructured and structured data to
support demand-driven forecasting.
EFFECT OF
DATA SCIENCE ON FINANCIAL SERVICES SECTOR
Financial service companies are turning to Data Science
for answers – leveraging new data sources to build predictive models and
simulate market events, using NoSQL, Hadoop and Storm to exploit
non-traditional data sets and store different data for future analysis.
Sentiment analysis has risen into another valuable source to
achieve several objectives. With sentiment analysis, banks track trends,
respond to issues, monitor product launches and enhance brand perception. They make the most of the market sentiment
data to short the market when some unforeseen event occurs.
Real-time analytics serve financial institutions’ purpose in
fighting with the frauds. Parameters like spending patterns, account balances,
employment details and credit history among others are analysed by banks to define
if transactions are fair and open. Lenders also get a clear understanding of
customer’s business operations, assets and transaction history through credit ratings
that are updated in real time.
Data
science also helps financial institutions to know who their customers are –
in turn, offer customized products, run relevant campaigns and build products
to suit customer segments. Where cutting down risks is an imperative for
financial institutions, predictive analytics serves their purpose to the handle.
TRAVEL
INDUSTRY’S JOURNEY WITH DATA SCIENCE
We have moved on from the time when travel companies used to
create customer segments. Today, they get a 360-degree view of every customer
and create personalized offers. How is this possible? Travel companies use a combination
of datasets from social media, itineraries, predictive analytics, behavioural
targets and location tracking to arrive at the 360-degree view. For instance, a
customer visiting Facebook pages in Zurich can be attracted with discounted
offers on flights to Switzerland.
Big data creates a significant difference for travel
companies which promotes safer travels. The sensors from trains and other
automobiles provide real-time data on various parameters along the
journey. This way, companies can predict
problems, and more importantly, prevent them by using precautions against them.
By integrating historical data, advanced booking trends as well as customer’s
behavioural data, travel companies ensure maximum yield, with no vacant seats.
Predictive algorithms are also proving itself useful to send drivers to the
available parking stations. Data from sources on wind, weather and traffic are
being used to predict fuel needs and delays.
In the present scenario, businesses use Data Science
in a number of ways. Data Science
is here to give a better picture of the business– move from the static to
dynamic results.
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