Making predictions with Big Data
Technology is playing a ubiquitous role in our daily lives—whether it’s policing a city, speeding up financial transactions or transforming supply chains
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At first glance, the letter from the Delhi police commissioner’s desk could have easily been dismissed as another routine laundry list of his department’s “achievements” in the previous year.
A closer look at the letter, written a little over two years ago, would have sprung a pleasant surprise in the context of the city police’s technology prowess.
The Delhi Police, according to the letter, had partnered with the Indian Space Research Organisation to implement CMAPS—Crime Mapping, Analytics and Predictive System—under the “Effective use of Space Technology-based Tools for Internal Security Scheme” initiated by Prime Minister Narendra Modi in 2014.
CMAPS generates crime-reporting queries and has the capacity to identify crime hotspots by auto sweep on the Dial 100 database every 1-3 minutes, replacing a Delhi Police crime-mapping tool that involved manual gathering of data every 15 days. It performs trend analysis, compiles crime and criminal profiles and analyses the behaviour of suspected offenders—all with accompanying graphics. CMAPs also has a security module for VIP threat rating, based on vulnerability of the potential target and the security deployed, and advanced predictive analysis, among other features.
A prototype of the standalone version was installed at the Delhi Police control room in June 2015. The software’s statistical models and algorithms today help the police carry out “predictive policing” to forecast where the next crime is likely to occur, much like in cities such as London, Los Angeles, Kent and Berlin.
That’s just one example of how technology is playing a ubiquitous role in our daily lives—whether it’s policing a city, speeding up financial transactions or transforming supply chains.
Fintech start-up Lendingkart Technologies has developed tools based on big data analytics to help lenders evaluate borrowers’ creditworthiness. Using these tools, its sister company Lendingkart Finance Ltd aims to transform small business lending by providing easy access to credit for small and medium enterprises.
The “technology platform has helped create a highly operational efficiency model that enables swift loan disbursement within 72 hours of loan application. Over 120,000 SMEs (small and medium-sized enterprises) have till date reached out to Lendingkart Finance for their credit needs,” the company said.
Accenture Labs and Akshaya Patra, the world’s largest NGO-run midday meal programme, said on Thursday that they had partnered in a project to “exponentially increase the number of meals served to children in schools in India that are run and aided by the government”.
Using “disruptive technology”, they hope to potentially “improve efficiency by 20%, which could boost the number of meals served by millions”.
Accenture Labs began the project with a “strategic assessment and design thinking, then developed a prototype for improving kitchen operations and outcomes”. An example of Akshaya Patra’s transformation, according to Thursday’s statement, was its move “from manual collection of feedback from children and schools to a more efficient technology-based solution” that involved the use of blockchain (the underlying technology of cryptocurrencies like bitcoin) and sensor-enabled devices to gather feedback digitally, and use artificial intelligence (AI) technologies to “predict the next day’s meal requirements”.
Consider another example. Until even early 2015, the thousands of distributors of consumer goods firm Marico Ltd in Mumbai used to place orders and wait “almost a day” before getting the goods delivered. Now it takes just 10-15 minutes for an order to be delivered, helping them stock fewer goods. In turn, the lower inventory helps them cut down on warehouse space and pare costs, besides reducing the waiting time for trucks. All these distributors have benefited from an analytics-driven Order Management Execution System that the company launched in December 2014.
What exactly is big data analytics?
Big Data and the so-called Internet of Things (IoT) are intimately connected: billions of Internet-connected “things” will, by definition, generate massive amounts of data. By 2020, the world would have generated around 40 zettabytes of data, or 5,127 gigabytes per individual, according to an estimate by research firm International Data Corp. It’s no wonder that in 2006, market researcher Clive Humby declared data to be “the new oil”.
Companies are sharpening their focus on analysing this deluge of data to understand consumer behaviour patterns. A report by software body Nasscom and Blueocean Market Intelligence, a global analytics and insight provider, predicts that the Indian analytics market will cross the $2 billion mark by this fiscal year.
Companies are using Big Data analytics for everything—driving growth, reducing costs, improving operations and recruiting better people.
A major portion of orders of e-commerce firms now come through their analytics-driven systems. These firms record the purchasing behaviour of buyers and customize things for them. Travel firms, on their part, use data analytics to understand their customers—from basic things like their travel patterns, the kind of hotels they like to stay in, who their typical co-travellers are, their experiences—all geared to giving the customer a personalized experience the next time the customer visits the website.
In hospitals, intelligence derived from data helps improve patient care through quicker and more accurate diagnoses, drug dosage recommendations and the prediction of potential side effects. Millions of electronic medical records, public health data and claims records are being analysed.
Predictive healthcare using wearables to check vital medical signs and remote diagnostics could cut patient waiting times, according to a 13 January report by the McKinsey Global Institute. International Business Machines Corp.’s Watson, a cluster of computers that combines artificial intelligence and advanced analytics software and works as a “question answering” system, is being used for a variety of applications, most notably in oncology, the branch of medicine that deals with cancer. Watson for Oncology helps physicians quickly identify key information in a patient’s medical records, sift through tons of data and come up with most optimal medical choices.
Many companies globally and in India, including some start-ups, are using machine-learning tools to infuse intelligence in their business by using predictive models. Popular machine-learning applications include Google’s self-driving car, online recommendations from e-commerce companies such as Amazon and Flipkart, online dating service Tinder and streaming video platform Netflix.
Railigent, Siemens AG’s platform for the predictive maintenance for trains, listens to the trains running over its sensors and can detect, from the sound of the wheels, which wheel is broken and when it should be replaced.
Predictive algorithms are used in recruitment too. Aspiring Minds, for instance, uses algorithms powered by machine learning that draw on data to address complex issues—for instance, to accurately gauge the quality of speech in various accents against a neutral accent (also using natural language processing). This helps companies improve recruitment efficiency by over 35% and reduce voice evaluation costs by 55%.
Artificial intelligence, machine-learning-based algorithms and anomaly-detection techniques will need to be used to monitor activity across networks and real-time data streams, consulting firms point out. These technologies will, for instance, let banks in India identify threats as they occur while maintaining low false positive alarm rates even for new types of threats.
There are still challenges in bringing about wider technology adoption.
“Our survey showed that only about 4% of companies across industries have the capabilities to use advanced data analytics to deliver tangible business value. While some oil and gas companies have invested in their analytics capabilities, many struggle to get their arms around this powerful new opportunity,” said a March 2014 note by Bain and Co..
“We often find that senior executives understand the concepts around Big Data and advanced analytics, but their teams have difficulty defining the path to value creation and the implications for technology strategy, operating model and organization. Too often, companies delegate the task of capturing value from better analytics to the IT department, as a technology project,” the note pointed out.
In the 2006 movie Deja Vu, law enforcement agents investigate an explosion on a ferry that kills over 500 people, including a large group of party-going sailors. They use a new program that uses satellite technology to look back in time for four-and-a-half days—to try to capture the terrorist.
Predictive policing is surely not as advanced today. And advances in predictive analytics can certainly raise ethical issues. For instance, the police may in the future be able to predict who might become a serial offender, and make an intervention at an early stage to change the path followed by the person, as is the case in Deja Vu. Or an insurance firm may use predictions to increase the premium or even deny a user an insurance.
Any disruptive technology needs checks and balances in the form of good policy if it is to deliver to its potential.