In order to understand at what point ‘data’ transitions into being ‘big data’, and what its key elements are, it is imperative that we study the 5 Vs associated with it: Velocity, Volume, Value, Variety, and Veracity.
“Big data” is a relatively modern field of data science that explores how large data sets can be broken down and analyzed in order to systematically glean insights and information from them. Earlier, conventional data processing solutions are not very efficient with respect to capturing, storing and analyzing big data. Hence, companies with traditional BI solutions are not able to fully maximize the value of it. In order to successfully understand what big data means, we need to take a look at the 5 V’s of big data.
In the year 2001, the analytics firm MetaGroup (now Gartner) introduced data scientists and analysts to the 3Vs of 3D Data, which are Volume, Velocity, and Variety. Over a period of time, data analytics as a field saw a rampant change in how data is captured and processed. As part of this evolution, data was growing so rapidly in size that it came to be known as big data. With the astronomical growth of data, two new Vs —Value and Veracity— have been added by Gartner to the data processing concepts.
Velocity refers to the speed at which the data is generated, collected and analyzed. Data continuously flows through multiple channels such as computer systems, networks, social media, mobile phones etc. In today’s data-driven business environment, the pace at which data grows can be best described as ‘torrential’ and ‘unprecedented’. Now, this data should also be captured as close to real-time as possible, making the right data available at the right time. The speed at which data can be accessed has a direct impact on making timely and accurate business decisions. Even a limited amount of data that is available in real-time yields better business results than a large volume of data that needs a long time to capture and analyze.
Several Big data technologies today allow us to capture and analyze data as it is being generated in real-time.
Big data volume defines the ‘amount’ of data that is produced. The value of data is also dependent on the size of the data.
Today data is generated from various sources in different formats – structured and unstructured. Some of these data formats include word and excel documents, PDFs and reports along with media content such as images and videos. Due to the data explosion caused to digital and social media, data is rapidly being produced in such large chunks, it has become challenging for enterprises to store and process it using conventional methods of business intelligence and analytics. Enterprises must implement modern business intelligence tools to effectively capture, store and process such unprecendented amount of data in real-time.
Although data is being produced in large volumes today, just collecting it is of no use. Instead, data from which business insights are garnered add ‘value’ to the company. In the context of big data, value amounts to how worthy the data is of positively impacting a company’s business. This is where big data analytics come into the picture. While many companies have invested in establishing data aggregation and storage infrastructure in their organizations, they fail to understand that the aggregation of data doesn’t equal value addition. What you do with the collected data is what matters. With the help of advanced data analytics, useful insights can be derived from the collected data. These insights, in turn, are what add value to the decision-making process.
One way to ensure that the value of big data is considerable and worth investing time and effort into, is by conducting a cost Vs benefit analysis. By calculating the total cost of processing big data and comparing it with the ROI that the business insights are expected to generate, companies can effectively decide whether or not big data analytics will actually add any value to their business.
While the volume and velocity of data are important factors that add value to a business, big data also entails processing diverse data types collected from varied data sources. Data sources may involve external sources as well as internal business units. Generally, big data is classified as structured, semi-structured and unstructured data. While structured data is one whose format, length and volume are clearly defined, semi-structured data is one that may partially conform to a specific data format. On the other hand, unstructured data is unorganized data and doesn’t conform with the traditional data formats. Data generated via digital and social media (images, videos, tweets, etc.) can be classified as unstructured data,
The sheer volume of data that organizations usually collect and generate may look chaotic and unstructured. In fact, almost 80 percent of data produced globally including photos, videos, mobile data, and social media content, is unstructured in nature.
The Veracity of big data or Validity, as it is more commonly known, is the assurance of quality or credibility of the collected data. Can you trust the data that you have collected? Is this data credible enough to glean insights from? Should we be basing our business decisions on the insights garnered from this data? All these questions and more, are answered when the veracity of the data is known.
Since big data is vast and involves so many data sources, there is the possibility that not all collected data will be of good quality or accurate in nature. Hence, when processing big data sets, it is important that the validity of the data is checked before proceeding for processing.
Data is the oil of the 21st century and organizations today in different industries are realizing this quickly. Insights derived from high volume, high velocity and validated data collected from varied sources can add value to the overall decision-making of the company. While most organizations today do have the intent to use data, many are struggling to effectively capture, store, process or harness it.
Acuvate helps medium and large sized businesses in effectively leveraging data with its suite of Business Intelligence and Analytics services. We help companies right from building a robust BI strategy, setting up a data warehouse, integrating real-time data to leveraging advanced analytics.