Home ›› 28 Sep 2021 ›› Editorial
The survey of C-executives from leading healthcare and life sciences companies included established leaders such as Anthem Health, Bristol-Myers Squibb, Cigna, CVS Health, Eli Lilly, Glaxo Smith Kline, Humana, Merck, Pfizer, Sanofi, and United Health, as well as emerging entrants like Cerevel. The findings dramatize the extent to which firms in the healthcare and life sciences were more aggressive, more optimistic and more successful in achieving data-driven business outcomes than their peers in other industry sectors during 2020. For example, while only 17.9 per cent of financial services companies reported that they had created a data-driven organization, more than twice as many—40.9 per cent—healthcare and life sciences firms reported having achieved this milestone. The urgent embrace of data is reflected in other findings as well. While only 38.8 per cent of financial services firms report that they are managing data as a critical business asset of their organization, a startling 57.1 per cent of healthcare and life sciences firms report having achieved this outcome. These numbers are consistent down the line—66.7 per cent of health and life sciences firms report driving innovation with data, versus 46.3 per cent in financial services; and 45.5 per cent of health and life science firms report having achieved transformational business outcomes, compared with 22.4 per cent in financial services. Let us explore some key benefits of Big Data in retail industry:
By 2025, Gartner predicts that the top 10 retail giants will make use of real-time pricing.
Big Data Analytics will help achieve real-time pricing to adjust in-store prices for the customers. Here retailers try to analyze the impact of change in prices of the various products. “What-if” analysis helps in understanding the impact of price on sales, customer’s purchasing decision, product selection, etc. Big data analytics help retailers estimate the optimal price, which will increase sales and thereby generate maximum revenue. Long term decisions like where to open a retail outlet, how various stores are performing, etc. can be taken through Big Data Analytics. Big Data Analytics also helps in taking various short term strategic decisions like offers/discounts, product merchandising, product display, etc. Analyzing customer’s data helps in customizing the discounts/offers for the focused customers. This data is related to purchase history, search history, average bill value, frequency of visit to the retail stores, etc. Customized SMSs and emails related to offers/discounts are generated through big data analytics.
From census records to birth registers, we’ve been collecting data for centuries. However, the amount of data we create and collect has exploded to phenomenal proportions since the dawn of the internet. In 2013, it was claimed that 90 per cent of all the data in the world was created in the previous two years. And that figure has vastly multiplied since.
First coined in 2005, the term “big data” is used to describe these huge quantities of information—datasets so vast that they defy traditional analysis. Today, governments, private companies, and public service providers are all trying to tap the potential of big data. However, while it has many potential benefits, it also comes with some risks.While it’s easy to get caught up in the opportunities big data offers, it’s not necessarily a cornucopia of progress. If gathered, stored, or used wrongly, big data poses some serious dangers. However, the key to overcoming these is to understand them. So let’s get ahead of the curve.Broadly speaking, the risks of big data can be divided into four main categories: security issues, ethical issues, the deliberate abuse of big data by malevolent players (e.g. organized crime), and unintentional misuse.
The more data an organization collects, the more expensive and difficult it is to store safely. This is already a problem. According to the Risk-Based Security Mid-Year Data Breach report, 4.1 billion records were exposed through data breaches in the first half of 2019 alone. This highlights just how important data security is, but also the challenges organizations face in keeping our data safe. The more data a company holds, the higher the cost and practical burden of keeping it secure. Related to this is the issue of privacy. Governments, social media giants, insurance companies, and healthcare providers are just a handful of organizations that have unprecedented levels of access to our data. While they’re bound by data protection laws (with the potential for huge fines) the increasing number of high profile data breaches in the last few years shows that more action is needed. Organizations—especially big tech—may have information on where we live, where we go, how we spend our money, and so on. With personal bank details and other sensitive information under their protection, and cyberattacks on the rise, this begs the question: just because companies can store vast amounts of data, does that mean they should?
Another danger with big data is if third parties get their hands on sensitive information. In 2020, it’s estimated that we’ll produce 2.5 quintillion bytes of data every day. That’s tough to visualize, but you can trust that it’s an immense amount—far more than any organization can easily manage or analyze. Nevertheless, hackers and cyberattackers can target this data to sell on the DarkNet.
Phishing, bank fraud, and insurance scams are all common examples of how big data can be deliberately misused by organized crime groups. The days of try-their-luck emails offering you a million dollars if you just send through your bank details are long gone! If you’ve recently been the victim of a scam, you’ll know just how sophisticated they can be.
Big data also plays a big part in the misinformation and spread of fake news that has characterized public debate for the last half-decade. Nefarious organizations can use big data to target ads or fake news that aims to influence our ideas, beliefs, and even who we vote for. The reason so much fake news is successful is because it is well targeted and preys on people’s fears—all of which can be tracked (or at least inferred) from big data. With the risks of data theft growing by the day, this issue remains to be solved.
While those deliberately seeking to abuse big data are one problem, not all dangers are necessarily premeditated. Enter machine learning. This is a crucial tool for analyzing and extracting insights from big data. However, while machine learning algorithms learn on their own, they must first be programmed how to learn, which allows human bias to sneak into the algorithm. Human bias, as well as bad practice in data analytics, or even just poor quality data, can lead to bad insights. If these insights are used to make important financial or safety decisions (for example) there are going to be negative effects.
The writer is MD and CEO of Community Bank
