In today’s digital age, we generate an enormous amount of data every second. Literally, almost everything you do online leave various digital trails and footprints. From posting a photo on Instagram, browsing websites, emailing your colleagues, buying things on Amazon – everything falls under this umbrella. Big data analytics refers to the methods of extracting insights and actionable information from this vast amount of data.
What is Big Data Analytics in simple terms
Let’s use a simple hypothetical scenario. Imagine you run an online store that sells homemade candies. You notice your sales are gradually dropping recently, but you can’t figure out why. You can collect and analyze data from various sources, e.g., your web traffic, social media, visitor demographics, purchase history, etc. Then, you let big data analytics do its magic, and it will serve up patterns and trends that can guide your decision-making.
For instance, if your sales are declining, you might discover that your website is not mobile-friendly, many customers abandon their carts because the checkout process is complicated, or maybe someone else is now offering similar candies at a better value. You might also notice that your audience is primarily children and parents, and they are interested in specific types of treats. Now that you know, you can act on it! How about optimizing your website for mobile, simplifying the checkout process, and offering more personalised approaches to your customers at fair prices?
On the flip side, one day, if you notice a massive spike in your sales, you’ll not only just count your blessings but, using big data analytics, you will figure out perhaps, one of your competitors has closed shop. This way, you are always informed and aware of your next best move.
It’s not just for businesses
Big data analytics is not exclusively useful for businesses. Governments can use it to monitor public health trends and detect potential disease outbreaks. Healthcare providers can use it to identify patients at high risk of developing certain conditions and offer preventive measures. Schools and colleges can use it to identify areas where students need more support and resources.
Where Big Data Analytics made a positive mark
There are many real-world instances of big data analytics being used to achieve significant benefits. Let’s take a brief stroll across several domains.
Healthcare: The healthcare industry has been reaping the benefits of big data analytics to improve patient outcomes and reduce costs. According to a report by McKinsey & Company, the use of big data analytics in healthcare can save approximately $300 billion annually. One example is using predictive analytics to identify patients with high risk of being readmitted to hospitals. This has led to a 15-30% reduction in read missions and saved an estimated $100,000 per hospital annually.
Sports: Sports leagues have adopted big data analytics to gain a competitive edge and ensure players’ safety. For example, the NBA uses big data analytics to analyze player performance and identify potential injuries. Most teams have an analytics department now, and leagues like NBA, NFL, and NHL – all have substantial investments in big data analytics. For instance, according to an article in Forbes, the Toronto Raptors had the highest rate of player injuries in the league during the 2012 season. After that, they began using wearable technology to keep an eye on athletes during practices and games for the first indications of soft tissue injuries. The Raptors had the fewest injuries of any club in the league in the 2014 season.
Retail: Shops and merchants – both big and small are using big data analytics to curate streamlined customer experience, optimize inventory management, and increase sales. Walmart leverages it to optimize its supply chain and reduce waste, according to research from Harvard Business Review. By analyzing data from its stores, suppliers, and customers, it reduced food waste by 16% and saved $1 billion annually.
Finance: Banks and financial institutions leverage big data analytics to improve risk management, detect fraud, and personalize services. According to a survey by Accenture, 79% of financial institutions believe that big data analytics and privacy are crucial to their business. One example is using machine learning algorithms to detect fraudulent transactions in real time. According to another study by Accenture, this has led to a massive reduction in fraud losses for some banks.
As data continues to grow, the opportunities for using big data analytics will only increase. But it’s not just rainbows and sunshine.
Concerns over Big Data Analytics
Undoubtedly, big data is an incredible tool, but how one uses it decides its impact. Therefore, it can be misused as well. History has a trail of such narratives, and let’s dig through the salient cases.
Privacy: One of the biggest apprehensions around big data analytics is privacy. Personal information might get exposed or misused in the process of collecting and analyzing large amounts of data by bad actors. For example, as reported by the New York Times, in 2018, millions of Facebook users’ personal information was taken by Cambridge Analytica without their permission. It was alleged to be for political influence.
Security: Another concern, perhaps overshadowing privacy, is security. Large amounts of data are valuable targets for cybercriminals, and there is a risk that sensitive information could be stolen or compromised. In 2017, a data breach at Equifax exposed the personal data of 143 million people. This breach was attributed to a vulnerability in the company’s web application software and raised concerns about the security of large databases. Therefore, all judicious companies and governments are investing highly in state-of-the-art cybersecurity. With decades of experience precisely in this domain, consider checking Technuf’s portfolio for you’re the safety and security of your business.
Ethical issues: Bias and discrimination have been contentions in automation in big data analytics. For example, in 2018, it was reported that Amazon had developed an AI recruitment tool that was biased against women. The tool used data from resumes submitted to Amazon over 10 years to make hiring decisions, but it was found to discriminate against women because the data was biased toward male candidates. This raised concerns about the use of AI in recruitment and the potential for bias and discrimination.
Legalities: Big data analytics can also raise legal issues, such as compliance with data protection laws. In 2019, Google was fined €50 million by the French data protection regulator for violations of the EU’s General Data Protection Regulation (GDPR), according to The Times. The regulator found that Google had not obtained valid consent from users for personalized advertising.
Big Data Analytics is not just for Futureproofing but Essential Right Now
To sum up, big data analytics is no longer just a fancy strategy for futureproofing a business but has become an essential tool for businesses to stay competitive in the ever-evolving market. With the vast amounts of data generated daily, businesses need to be able to extract valuable insights from this data to make informed decisions, optimize operations, and enhance customer experiences. Big data analytics provides a way to do just that, offering businesses a means to capture, store, analyze, and interpret data promptly and effectively. By leveraging the power of big data analytics, businesses can gain a competitive edge, identify new opportunities, and drive growth in an increasingly data-driven world. As such, businesses that are yet to embrace big data analytics risk being left behind in the race for success and growth. In addition, while big data analytics can be an incredibly powerful tool for businesses, it is vital to use it judiciously to preserve privacy, security, and ethics.
At Technuf, we are big data analytics specialists from multiple areas of predictive solutions to business intelligence. Stay ahead of the competition while safeguarding your enterprise’s data with our solutions.