Big data, as the name suggests, is data in huge amounts, and the process of its acquisition, retention, and analysis is very old. In modern times, it gained popularity in early 2000 when industry analyst, Doug Laney introduced its definition with three V’s namely Volume, Velocity, and Variety. Volume. The huge volume of data originates from business transactions, social media research and other machine-to-machine data. Velocity. The data is being created at an enormous rate and is needed to be dealt with in real-time. Variety. Data comes in many formats numeric data, emails, forms, structured documents, and raw databases.
Users of Big Data
Such big data is being created at an enormous rate and it keeps on growing. There is a huge potential that if all this data is analyzed, it can bring great operational and economic benefits for the businesses in every industry.
Banking: Big data, if analyzed properly, can help banks come up with financial solutions for customers while maintaining legal compliance and keeping the risk of fraud to a minimal level.
Education: Educators who are equipped with well-analyzed big data can reduce the risk of failure of students and can have a significant impact on the learning process by making adequate changes to curriculums.
Government: If Government agencies apply big data analytics appropriately, they can produce solutions for managing utilities, traffic monitoring and control, and reducing crime.
Health Care: With big data analytics, health care providers can get insight into the seasonality of different diseases and patient trends and fixes can be devised quickly and effectively.
Manufacturing: With a deeper insight to the data, manufacturing industries can amend their business process aiming at waste reduction and better production control while catering to customer demands.
Retail: Having the proper insight, retailers can better market their products to customers thus enhancing customer relationships and boosting the business.
Trends of big data
1. Big data becomes fast and approachable
In previous years, there have been several technologies on the rise which are aimed at fulfilling the needs of analytics on big data. With the growing complexities, larger corporations are now in need of more than one tool for data analytics and data sourcing. They can get this data from a wide variety of sources like warehouse clouds which may be structured and non-structured.
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2. Establishment of manmade data lakes
It is similar to the concept of building a dam on a river and then utilizing the water for different purposes. First, a cluster, a data reservoir is built and data is gathered in it. Once a sufficient quantity has been gathered, this data may be used for various purposes. At this point, the data analytics tool walks in to play its role.
Lately, it was common for every organization to have its own data lake but this trend will be changing now. At this point, the corporations more critically analyze their data needs before investing in any infrastructure – be it personnel or data. Companies now demand a repetitive, efficient, and effective use of data lakes for better information. It will lead to stronger collaborations between business and IT.
3. Variety is a key driver of big data investments
Big data is defined by the three V’s, volume, velocity, and variety. Currently, all three V’s are registering enormous growth. But in the near future, variety will be the key driver behind big data investments. This trend will be on the rise as more and more businesses will be aiming to consolidate the data coming from different sources, therefore incorporating more variety of high-quality data. There are a lot of new data formats like schema-free JSON, nested type databases, and non-flat data. In the future, the performance of all the data analytics tools shall be measured by their ability to provide real-time connections to data sources.
5. Big data is shaped by the end-user
There is going to be a rise in the self-service data analytics platforms. Keeping it in mind, business users are now demanding a reduction in the complexity and time needed to prepare the data analysis as they have to deal with the data from a number of variety sources. This data is often in a lot of formats. Intelligent, smart, and powerful data analytics tools have the capacity to present data as snapshots, which reduces the time and sources needed to interpret the data.
The rise of self-service analytics platforms has improved this journey. But business users want to further reduce the time and complexity of preparing data for analysis, which is especially important when dealing with a variety of data types and formats. Many new companies like Paxata, Alteryx, and Trifacta are focusing on end-user data prep.
6. Rejection of same-size frameworks.
Currently, many organizations are working toward catering to the hybrid data need. It is being done by utilizing case-specific architecture designs. An organization’s data strategy now depends on a number of factors like the user profiles, the volume of data, questions, timings, and frequency of access, data speed, and the level of consolidation. These are not standardized architectures but are driven by the need. Due to the flexibility of these designs, technology is now reaching new horizons.
