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 ?
MANUFACTURING INDUSTRY’S GAIN 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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