Big data is everywhere, and behind every organized solution, you face on the daily. The term refers to massive sets of data that inundate businesses during day-to-day operations– but it’s not the data dump itself that matters to businesses, but the goldmine of insights it reveals once it’s sifted through, analyzed and put into plain and simple words.
The amount of data an average business sees in a day is torrential. Big data scientists find themselves having to deal with the ‘three V’s’ as they’re called:
- Volume: tonnes of data from a dozen different sources including social media and daily transactions
- Variety: structured and unstructured data; numeric or stock; video or audio
- Velocity: Breakneck speeds at which data flows in from all channels into the dump
Big data is highly complex and interrelated, which means sifting through and making sense of it can be quite the herculean task. However, the insights gathered through the process of going through the dump can enable reductions in costs, effort and time. It can also open up new revenue streams, enable the development of new products and bolster analytical and strategic business decision-making.
How is big data implemented in business?
The traditional method of storing data is by using relational database software, built for Structured Query Language (SQL). However, the future of big data began looking too complex for businesses to be able to control, which led to the introduction of NoSQL.
NoSQL is customizable and scalable, making them ideal solutions for businesses both big and small. It’s made specifically for big data, and stores data in the following ways:
- Document storage
- Graph storage
- Key-value storage
- Column family storage
NoSQL provides real-time, super-quick access to data, without the need for schemas and columns. This allows the running of real-time programs towards furthering business processes. Without the schema middleman, data scientists can directly interact with tonnes of data, which in turn saves any business a lot of effort, time and money.
Why is big data important in business?
Industry professionals and students alike are looking to learn big data analytics and science because of the plethora of job options it opens up in the world of business.
Access to information
Bug data opens up new avenues for businesses to explore, be it in terms of generating revenue, introducing new products or strengthening marketing. It enables real-time data monitoring and allows for A/B testing where necessary without too much of an impact on ‘business as usual’ if the strategy doesn’t work out.
Faster decision-making
Hadoop and other in-memory analytics software allow businesses to conduct analyses on information immediately, further enabling them to come to crucial decisions faster and based on data instead of speculation. Big data can also be leveraged to lookup more updated and dynamic data, allowing decision-making to be accurate as well.
Conclusion
As a good data analytics course will show you, big data is in use across several burgeoning industries, each with their own means and end goals. Be it manufacturing, pharmaceuticals, retail or even governments, there is no place big data can’t be implemented– which means there is no place big data specialists can’t go.
