On a broad spectrum, big data is a term applied to data sets whose management is beyond the abilities of traditional data-processing software. Typically, big data is defined as the three V’s: high volume, velocity, and variety.
Data that streams rapidly from many different sources and is formatted in different ways is called big data. These sources include (but is not limited to) most public information stored on social networks, information collected via trackers on search engines, purchases made via your credit cards and what you watch on online streaming websites.
What is Big Data Analysis?
Even before ‘Big Data’ became a household term, manufacturers regularly scrutinised business patterns to identify problem areas and profit points. Big Data Analytics is analogous to this but on a much larger scale.
Considering the rate of flow of input, it is a near-impossible task to manually organise, sort, filter, and culminate valuable data in a limited time. Big Data Analytics relies on machine learning, data mining, and statistics to selectively organise valuable data to optimise business productivity.
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Importance of Big Data Analysis
If all the data available to a business is captured and analysed, patterns and trends are obtained. These can help the business target its marketing or point of focus to maximise profits. Big Data Analytics help businesses make more intelligent moves, increase efficiency, and help bridge the producer-consumer gap.
Big Data Analysis first developed into its current form around the year 2012. Any firm in any industry can cash in on this data-driven economy to optimise their business in today’s world.
Consider Macys.com, effectively a single store of the giant retailer. It places great emphasis on customer-oriented service using personalising advertisements, emails, and products. The analytics team has groups dedicated to Data Science, Business Insights, and Customer Insights. Due to this thorough and effective analysis structure, Macys.com is growing at a highly remarkable rate of 50% per year.
Big Data can reduce net expenditure by decreasing storage and shipping costs and help industries identify the most profitable moves. Rather than risking failed campaigns, companies can predict success rates to a reasonably accurate extent.
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Everyday applications of Big Data
Whether or not we are aware of it, Big Data surrounds us. Whether it’s government, media, healthcare or other service providers, Big Data is slowly beginning to play a major role in effective functioning.
Big Data Analytics can play a major role in smoothly running cities. For example, traffic flow can be regulated with real-time traffic information, weather data, and social media. If every unit of infrastructure can access the same data, we may never again need to worry about missing a train due to a delayed bus or getting late to work due to traffic.
E-commerce websites like Amazon and eBay can highly personalise product recommendations and emails based on data collected from search history, viewed products, and previous purchases. Analysing the data collected can help improve the website’s accessibility and customer-friendliness and optimise business by listing products in a most likely-to-buy order.
Even streaming services such as Netflix, Amazon Prime or YouTube make use of big data in order to provide recommendations to its users.
Insurance fraud can be detected with significantly greater ease using Big Data Analytics. Fraud can cost the medical industry up to $5 billion a year. The current fraud detection system involves complex SQL queries that can take several weeks to yield results, causing huge losses. With Big Data, billions of billing and claim records can be easily analysed by fraud investigators. Moreover, machine learning and predictive analyses assist greatly by alerting users when a pattern homologous to known fraud schemes is noticed.
Big Data could play a crucial role in maintaining law and order. For example, national security agencies use Big Data to keep abreast of terrorist activity. In addition, big Data can improve cybersecurity. For example, police forces often use Big Data tools to locate and apprehend criminals and predict criminal activity. In addition, credit card companies use it to detect fraudulent transactions.
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Key players in Big Data
- Apache Hadoop: This project develops open-source software for the distributed processing of large data sets. It is gradually being used more and more by high-profile companies as it allows for the processing and transfer of large quantities of data at a fraction of original prices.
- Tableau: With humble origins as a research project at Stanford University, Tableau is currently one of the biggest names in Big Data. It helps in the creation of valuable insights from raw data by providing interactive visualisations.
- Dataiku: Rated by employees as the best Big Data company to work for in 2018, Dataiku specialises in developing data science software meant specifically to handle Big Data. They emphasise collaborative work to create highly effective products.
- Google: Famous for the search engine, Google also has robust data and analytics services. Google is focused on making its products as personalised and user-friendly as possible by incorporating machine learning and data science at the grassroots level.
- IBM: With a whole blog dedicated to their advances in the field of data and analytics, IBM certainly makes it abundantly clear that they are a pioneer of Big Data management. Emphasising acquiring accurate results in less time, IBM constantly updates and remodels current tech to optimise the user experience.
What could go wrong?
As with any other field, the advent of Big Data comes with its own set of flaws and fears.
First and foremost, there is no guarantee of privacy. As it usually proceeds behind the scenes, there is no way for users to know how much of their data is being used. At this point, not even a complete boycott of technology can fully protect all personal details.
Secondly, Big Data analytics isn’t always accurate. Despite trying their best to improve insight quality, machine learning and data scientists cannot yet keep up with the variability of incoming data. As a result, the reliability of Big Data is questioned as even mild inaccuracies can cause losses for businesses.
Thirdly, data could facilitate discrimination. When Big Data is used to check someone’s candidacy for a particular post, the qualification markers are set by the person in power. If they are biased, the verdict will be partial. For example, suppose you are applying for a job. Your conservative potential employer may be able to access data that indicates that you have highly liberal views. Hence, he could reject you for the position on completely arbitrary grounds even though you are perfectly qualified.
Of course, as Big Data continues to grow, dialogue and discussion regarding ethics will arise. Steps have already been taken at the global level to ensure privacy. But rest assured, laws will be made to protect people before Big Data can reach its maximum potential.
Also read: How Huawei phones might be compromising your privacy and security
What to expect?
Big Data and affiliated fields such as analysis, storage, transfer, and privacy are expanding rapidly. Global Big Data revenue is projected to rise from $42 billion in 2018 to $103 billion in 2027. The compound annual growth rate of Big Data is expected to be a whopping 10.48%
Big Data Analytics is mostly a background mechanism, so no drastic changes ought to be superficially visible. However, prepare to be surprised and impressed by how well your search engines and favourite brands seem to know you. Though the changes may not be apparent all at once, the world will change to such an extent that we may look back on this year and wonder how we ever managed to exist this way.
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