Do you know you create a piece of data every time you enter a query on search engines? According to a statistics, we create an average of 40,000 every second and approx. of 1.2 trillion pieces of such data in a year on Google alone.
Likewise, we create ample amount of complex data types which are difficult to process with traditional data-processing application and software. In order to process and streamline these huge sets of data, Big Data technology was introduced.
To simply put it, Big Data refers to these huge and complex data sets. The term is also used to explain its usage in predictive analytics, user behaviour analytics and other advanced data analytics methods. Big data helps in extracting value from the any particular size of data set. The data processed by big data technologies are either structured or unstructured.
If a data has defined length and format then it falls under the category of structured data. For example, dates, numbers and strings in the form of group of words and numbers. However, an unstructured data is the one which doesn’t have proper formatting. It often goes beyond the traditional rows and columns. The examples of unstructured data would include emails, audios and videos files, social media pages, etc.
There are various other attributes of big data which define the term and technology as a whole. To further explain this, let us understand certain Vs of big data.
- Veracity: Veracity stands for the meaningfulness of any data. All our data has some amount of clutter in it. Before the data is streamed for storage in the database, it gets scrutinized and all the abnormalities, noises and biases are filtered. It’s only the valuable data which is stored.
- Validity: A data is valid if it’s correct and accurate for its intended use. The data sifting process evaluate the validity of any data and process further analysis before streaming it. Any data is helpful only when it is valid.
- Volatility: Big data volatility refers to the relevancy of any data in terms of time duration. Under volatility, data should be stored only for the span of time for which it is relatable.
- Variability: Variability in a data has a few different meanings. One is to figure out the inconsistencies in the data by anomaly and outlier detection methods and letting pass the useful data.
Another aspect of data variability includes the multitude of data dimensions resulting from multiple disparate data types and sources.
Today, big data has a huge impact on several industries. It is influencing their growth, method of undertaking tasks, attracting the customers and so on.
- Impact on Marketing: Big data has changed the inside out of marketing. A synchronized data is helping marketers evaluate customer engagement, retention, loyalty and better ways to optimize their performance.
- Impact on Business: Any business taking up the big data technique can secure and safeguard their data from numerous threats lurking over it. According to a survey:
➢ 64% of IT companies are heavily investing in big data.
➢ 69% of respondents ‘confirmed that big data is crucial and high priority’.
➢ 75% of CIOs have revealed that big has positively impacted their productivity and overall efficiency.
➢ 70% of participants have revealed that their businesses have seen a positive impact on account of their big data investment.
- Impact on Society: Society is driven by technology and every new technology is making life simpler and convenient. With big data, consumer behaviour is often observed to deliver/offer relevant promotions and products. For example, Amazon observes a consumer’s behaviour to prompt the potential choices for easy buy.
- Impact on healthcare: According to a report, big data would reach $34.27 billion worldwide by 2022 in the healthcare section alone. The technology is being used to analyse and streamline the huge data of patients and improve on the quality of service.
These impacts make it apparent that big data is getting bigger and bigger with each day and is acquiring a huge room in each of our lives, organizations and institutions. The big data technique is being used in almost all sectors, which lays its foundation for future growth and its scope to become an all-inclusive technology used by layman in everyday life.
- App based approach: Some handy tools are likely to emerge in the coming years which wouldn’t require analyst to perform the task of analysing data. Microsoft and Sales force have announced features which will let non-coders to view business data via apps. These kinds of apps will allow everyone to have access over data and its storage.
- Predictive Analytics: With predictive analytics, organizations get an insight into customer behaviour which helps them in generating new customer responses, purchases and promote cross-sell opportunities. Predictive analytics is particularly
helpful for industries like finance, healthcare, aerospace, hospitality, automotive, retailing, manufacturing industries and pharmaceuticals.
- Quantum Computing: Quantum computing is in the pipeline for tech giants like Microsoft, IBM, Intel and Google. They are racing against each other to develop the first quantum computing which would encrypt data, predict weather, and solve complex medical problems in an enhanced manner.
These are some of the future aspects of big data. It is certain to grow exponentially and bring a huge change in every sphere of life. For this reason, it becomes even more important for everyone to know the in and out of big data in today’s times.
To explore more on big data, you can get in touch with the DBSOM experts!