BIG DATA. The multiplier effect of analytics

“It was the best of times, it was the worst of times.”

—Charles Dickens, A Tale of Two Cities

It took almost 120 years for electric lights to reach homes and become a profitable business.

The initial idea of electric light came from Humphry Davy in the early 1800s. But it was not till 1879 that Thomas Edison found a way to make incandescent light bulbs, and it was only in the 1920s that electric utilities reached homes and became a profitable business.

Today we don’t need a century or even a decade to translate a good idea into reality. Thanks to the advancement in technology, the pace of innovation and transformation in the last decade has been unprecedented in human history.

These are, however, challenging times as well because innovative ideas and inventions do not have a long shelf life. Every new idea, every great innovation becomes outdated within a few years. If a business has to survive in today’s competitive, fast-paced, connected world, it has to constantly innovate. To gain a competitive advantage, companies that have timely, accurate and relevant information — and the ability to act decisively on it — are best placed to win.

But how do you zero in on accurate and relevant information with data volumes reaching such dizzying heights?

Experts say there are as many bytes of data as stars in the universe and, like the universe, data is expanding.

Ninety per cent of this data has been created in the last two years, thanks to digitisation, increased penetration of the internet and mobile as well as the growth of social media. The data coming from non-traditional sources, such as blogs, social media, email, sensors, photographs and video footage, holds the promise of giving enterprises deeper insight into their customers, partners and businesses.

New technologies allow easy data wrangling and analysis of big data on Hadoop. Organisations can understand when they should spend time and money to explore data and when not to proceed.

So, how do you make your way through this maze to find the golden nugget? Big Data and Analytics help capture information from across and beyond an organisation, and analyse it, to get the right input at the right time. This enables executives make intuitive decisions and turn data into business value.

Take for example the retail industry. It’s a low margin, highly competitive sector. A retailer has to constantly find ways to create competitive differentiators and develop products that are in keeping with changing customer preferences. Today. loyalty cards have become so commoditised that consumers see them as an entitlement and not a lure that will make them stick to a brand.

To find innovative ways to attract the consumer, the retailer today uses the card to gather a huge amount of information about consumer demographics and psychographics such as purchase pattern, economic profile, likes and dislikes, members in the family, etc. This information is combined with data gathered from other channels like customer call logs, social media, web logs etc to provide personalised offers and develop new services and products. Data from loyalty cards helped UK retail giant Tesco decide on the 24-hour-shop concept. The company found that when in-store capacities were highest, business actually went down — rather than plateauing. Customer data analysis revealed that when shoppers arrived at the car park and saw the store close to full, they went back. So Tesco started its 24-hour shops which allowed customers the flexibility to shop anytime. As a result, capacity crunches were less likely and did not turn customers away.

The banking and finance sector has been at the forefront of adopting analytics solutions. Banks are using historical data to make probabilistic decisions — such as what may happen to revenues if an account manager quits. Analytics is helping them find patterns in customer behaviour and act to address a concern or grab an opportunity.

Fraud is one of the biggest nightmares for a bank. Technology helps banks predict potential fraud, reviewing data from multiple systems. These range from savings accounts, mutual funds, deposits, credit cards and loans to social media feeds, point of sale data, internet transactions, bill payment data and web behaviour analysis. All these are crucial for banks and financial institutions.

Analytics is also speeding the pace of discovery because it makes it possible to run powerful statistical and data mining algorithms on a billion row datasets in a matter of seconds to deliver intelligent insights, especially in the field of translational research and personalised medicines.

Finding a treatment based on a patient’s genes, proteins and environment is essentially a signal-detection exercise. Clinicians are using analytics to integrate a patient’s genetic data and environmental data with his or her electronic medical record to determine the effectiveness of personalised treatments in areas like cancer treatment. They are also analysing genetic codes to determine an individual’s predisposition to a disease and advise preventive measures.

Increasingly, analytics is becoming real time. The growth in mobile and other connected devices makes it possible to capture and process information as it happens, when it happens, to gain real time insights and take real time action.

Businesses can capitalise on the opportunity to build new services. For example, telecom operators can capture a customer’s location when entering a certain area (“geo-fencing”) and correlate it to create targeted offers and promotions for business partners in the vicinity.

Scientists say we are at the dawn of an analytics revolution. Data is the new raw material of social and economic development. These are the best of times for those who harness the power of big data and the worst of times for those who don’t.

The writer is Programme Director, EPM, BI & Exalytics, Oracle APAC

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