Big Data and its Bigger Impacts
Big data is a term that in its simplest form means: incredible volume of data (structured and unstructured) that inundates a business on a daily basis. Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them anymore. But it’s not just the expanse of data that’s important. Its what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and help address business problems that couldn’t have been tackled before.
Classifying data as “Big Data” happened in the early 2000s when data got defined by the 3 Vs: Big data is data with greater variety arriving in increasing volumes and with ever-higher velocity:
- Volume: Organizations have to deal with huge volumes of unstructured data that may run into terabytes or petabytes from a variety of sources, like business transactions, social media and machine-to-machine data. New technologies like Hadoop (an open-source framework created specifically to store and analyze big data sets) are helpful in the storage of the same.
- Variety: from traditionally structured, numeric data that could fit in relational DBs to unstructured and semi-structured data types such as emails, video, audio, text documents, financial transactions, that may require pre-processing
- Velocity: the unprecedented speed at which data is streamed received and must be dealt with in a timely manner. RFID tags, sensors and smart metering warrant the need to deal with data gush in real-time or near-real time manner.
In the last decade or so, users have been generating crazy amount of data through Social Media, YouTube, and other online services. The development of open-source frameworks like Hadoop, NoSQL and more recently, Spark have made big data easier to work with and cheaper to store. In fact, it’s not just humans who have been at it. With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance. The emergence of machine learning has produced still more data. While big data has come far, its usefulness is being established gradually. Cloud computing has expanded big data possibilities even further.
Why Is Big Data Important?
The success of big data is actually determined with how effectively and efficiently a business uses the data. The sources to gather the data might significantly vary but once they’ve gathered, they can make significant improvements. You can take data from any source and analyze it to find answers that enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smart decision making. Big data when combined with high-powered analytics, can help accomplish business-related tasks like:
- Determining root causes of failures, issues and defects in near-real time.
- Recalculating entire risk portfolios in minutes.
- Detecting fraudulent behaviour immediately.
Big Data Applications
Big data can present an abundance of new growth opportunities, from internal insights to front-facing customer interactions. Three major business opportunities include:
- Automation: Huge amounts of real-time data can be immediately analyzed and built into business processes for automated decision making. With scalable IT infrastructure and decreasing cloud computing costs, automating data collection and storage is fairly manageable.
- In-depth insights: Big data has helped organizations discover hidden opportunities that were unknown before the ability to review large sets of data. Complex data sets can even be used to develop new products or enhance existing ones.
- Faster, better decision making: With the speed of data analytics technology, paired with the ability to analyze new sources of data, businesses are now able to analyze information instantly and make smart, informed decisions.
Where is Big Data Used?
Big Data is rampantly used in the following setups and is helping them deal with clients in a much more streamlined manner:
Big Data Challenges
While big data is promising, it has its own challenges:
- Big keeps getting bigger: data volumes are doubling in size about every two years.. Organizations still struggle to keep pace with their data and find ways to effectively store it.
- Data analysis to make data relevant and organized is an extremely tedious process. Data scientists spend 50 to 80 percent of their time curating and preparing data before it can actually be used.
- Finally, big data technology itself is changing at a fast pace. Few years ago, Apache Hadoop was the popular technology used to handle big data, followed by Apache Spark. Today, a combination of the two frameworks appears to be the best approach.
As the Volume, variety and velocity of data keeps enhancing, more research and development shall be ongoing for efficient data handling, storage and apt usage.