When business executives hear the term "big data," they instinctively think of the enormous volumes of data that are currently available. This data is produced by e-commerce and omnichannel advertising systems, IoT-connected gadgets, or business apps that provide increasingly accurate data about transactions and activities. And these are only a few examples.
The sheer volume of information is frightening and, in some cases, even overwhelming. But analysing sizable big data collections can significantly benefit businesses. We'll discuss a number of these benefits in more detail below, but first, let's define our words precisely because there is more to this than just data volume.
Why Should Businesses Use Big Data?
Better Customer Insight
When attempting to comprehend its clientele, whether they are categorised or individual, a modern firm has a range of data sources to choose from. Clickstream research of e-commerce activity is particularly useful for understanding how customers navigate through a company's numerous web pages and menus to find products and services in an increasingly digital economy. Businesses can see which things people add to their carts but later remove or abandon without checking out; this provides important clues as to what consumers may want to buy, even if they don't.
Increased market intelligence
Big data can help us gain a deeper and broader understanding of market dynamics as well as help us analyse customers' intricate purchase patterns in greater detail.
Just a few of the market segments for which social media is a typical source of market information are breakfast cereal and vacation packages. For almost every form of business transaction, you can imagine, people share their preferences, experiences, suggestions, and even photos. Yes, even of their breakfast fare. These accumulated perspectives are invaluable to marketers.
Big data can aid in both the development of new products and the analysis of the competition, for instance, by giving different client preferences priority.
Agile supply chain management
You should be aware by now that contemporary supply networks are alarmingly susceptible to occurrences like shipwrecks in the Suez Canal, shortages brought on by pandemics of essentials like toilet paper, or the trade disruption brought on by Brexit.
It surprises me because we frequently ignore our supply networks until a severe interruption has occurred. Because of big data's ability to enable predictive analytics, which is typically done in close to real-time, our worldwide network of demand, production, and distribution generally operates efficiently.
Big data systems are able to give a level of knowledge that hasn't been seen before because of their capacity to combine data on customer trends from e-commerce platforms and retail apps with supplier data, real-time pricing, and even transportation and weather information.
Being inspired is only one part of innovation. It takes a lot of work to identify themes that have the potential for novel initiatives and experiments.
Accessible big data technologies and approaches can enhance R&D, typically leading to the development of new products and services. On occasion, data that has been prepared for distribution might stand alone as a product after being cleansed, organised, and managed. For instance, the London Stock Exchange now makes more money selling data and research than it does from trading stocks.
The finest big data technologies won't be able to produce unique insights from data on their own. As the human aspect, data scientists, BI analysts, and other analytics specialists still need to contribute their expertise and creativity.
Diverse use cases for data sets
In my professional expertise, I've witnessed instances when well-modeled and prepared data was completely improper for another business goal.
For instance, a credit card company's marketing division was curious to know how customers used the numerous cards they carried. The research was made more difficult by the numerous failed swipes and canceled transactions that were common at the time, either as a result of problems with the payment terminal's connection or flaws in the cards' magnetic stripes. As a result, the failed transactions were meticulously deleted from the database.
The result was a data collection that was ideal for the initial marketing application. The fraud prevention team was unable to utilise it, though, as they had to look into the failed transactions that would have left evidence of card fraud. The destroyed data was stored on tapes, which made it challenging to retrieve.
We may now store all of the raw data in a data lake in the big data era and only analyze it when specific analytics apps require it. Next, we can either build data pipelines specifically for each use case or simply run ad hoc queries to feed the analytics workflows. As a result, a wider range of applications can be employed with the same data collection.
Improved business operations
All business operations can be improved by using big data. It supports the optimization of company processes to provide cost savings, boost productivity, and enhance customer satisfaction. Both hiring and HR management might be improved. By enhancing risk management, fraud detection, and cybersecurity planning, organisations can reduce financial losses and avoid potential risks to their operations.
One of the most exciting and productive applications of big data analytics is enhancing physical processes. For example, using big data and data science to develop predictive maintenance schedules can help reduce the cost of repairs and downtime for crucial systems and equipment. Start by looking at the details regarding the age, state, location, warranty, and servicing.
In order to effectively project the future of a company's operations using data, statistical and predictive modelling approaches might be used. It is feasible to improve corporate analytic capabilities by collecting and combining data from outside the organisation. Since many companies anticipate that the worldwide market for big data will reach $61 billion by 2020, they are increasingly taking this into account.
Simply put, CEOs should concentrate more on enhancing company strategy while analytics provides superior insights for improved data-based decision-making.