In Advocates of big data often argue that the big data has transitioned beyond hype and that it is for everyone. However, the value it brings in is to analyse how it matters to the user and how the traditional three Vs of big data--such as volume, velocity, variety--add value. Most vendors believe that volume is of less importance; what matters is the variety of data generated and the value it ushers in.
This is what big data vendors have to say
Deepinder Singh Dhingra, Head of Product & Strategy, Mu Sigma, says value, one of the Vs, has been added recently; this indicates mining valuable pieces of data from the other data that does not matter; as a corollary to this, measurement of big data is relative as it indicates how well one can handle these three big data activities. These include:
-- Store: Can you store all the data, whether persistent or transient?
-- Process: Can you cleanse, enrich, calculate, translate, or run algorithms, analytics, or otherwise, against the data?
-- Access: Can you retrieve, search, integrate, and visualise the data?
Ramendra Mandal, Country Manager, QlikTech India Pvt. Ltd finds that customers are laying more thrust on the variety aspect of the data, which would help their business. Mandal reiterates that variety--and how to integrate varied data from different sources pose the greatest challenge to companies now. Some examples are sales groups who wish to study the RoI concept for their marketing campaigns, and customer-buying pattern in the retail via RFID technologies.
The recent US elections have been a testimony to how big data is effectively generated and harnessed with a wide section of data scientists hired to generate the massive data in various forms and through varied sources, says Mandal.
The McKinsey Global Institute estimates that data volume is growing 40 per cent per year, and will grow 44X between 2009 and 2020. Although volume of data is the most visible parameter, it is not the only characteristic to measure big data. Sheshagiri Anegondi, Vice President, Technology, Oracle
Volume: Machine-generated data is produced in much larger quantities than nontraditional data. For instance, a single jet engine can generate 10TB of data in 30 minutes. With more than 25,000 airline flights per day, the daily volume of just this single data source runs into Petabytes.
Velocity: Social media data streams--while not as massive as machine-generated data--produce a large influx of opinions and relationships valuable to customer relationship management. Even at 140 characters per tweet, the high velocity (or frequency) of Twitter data ensures large volumes (over 8 TB per day).
Variety: Traditional data formats tend to be relatively well described and change slowly. In contrast; non-traditional data formats exhibit a dizzying rate of change. As new services are added, new sensors deployed, or new marketing campaigns executed, new data types are needed to capture the resultant information.
SrikanthKarnakota, Director Server & Cloud business, emphasises that big data is getting to be a reality to be reckoned with Data explosion is driving the need to measure data, which is increasing by 10X in every 5 years. IT heads face the task of using the volume of unstructured data and allocate it logically to make it structured information, says Karnakota.
Measuring Opportunities
According to Maneesh Sharma, Head - Database and Technology, SAP India, big data presents a number of opportunities, with the optimists maintaining it has the power to revolutionise business and drive growth over the next decade; the more cautious claim that companies who successfully mine big data stand to derive significant business benefits. However, there are fundamental obstacles that must be surmounted.
Firstly, big data calls for new infrastructures that can store data differently and process it regardless of the format--infrastructures that allow you to slice the data in different ways. Secondly, big data requires specific tools that can search, understand, analyse and model the data downstream from the system, says Sharma.
IDC defines big data technologies as a new generation of technologies and architectures designed to extract value economically from very large volumes of a wide variety of data by enabling high-velocity capture, discovery, and/or analysis. This definition encompasses hardware, software, and services that integrate, organise, manage, analyse, and present data that is characterised by "four Vs" volume, variety, velocity, and value.
To substantiate further, it is not size that matters, as the various seemingly smaller applications may still require intense and complex information processing and analysis that characterise big data applications.
The second most important measurement is to understand the combination of data sources and formats which matter to determine the validity of an application to be put under the big data bracket, Sharma opines.
Another measuring capsule is the speed at which information arrives and is analysed and delivered. The velocity of data moving through the systems of an organisation varies from batch integration and loading of data at predetermined intervals to real-time streaming of data. The former can be seen in traditional data warehousing and is also today the primary method of processing data using Hadoop.
The value of big data revolves around capital, operational, and business benefits it brings to the enterprise.
The value is measured taking into account both the cost of technology and the value derived from the use of big data; the cost variable is important because it is a key defining factor of what's new with big data, says Sharma.
Vivekanand Venugopal,Vice President and General Manager of HitachiDataSystems India, points out that performance characteristics differ based on data types and storage infrastructure needs to be fast enough to manage and process this data regardless of its type.
The true Value of big data comes from the analysis of data across disparate types to generate a competitive advantage for organisations, says Venugopal.
Venugopal reiterates that data is becoming more complex today, and current storage architectures are unable to support these evolving user requirements. In order to extract true value from data, organisations will need to adopt new approaches.
Vishnu Bhavaraju, Regional Manager - Sales atEMCGreenplum emphasises the fact that the dust is settling down with regard to big data, as customers are no longer apprehensive about the value it can bring in.
The vital factor is that discovering the Vs and value in big data is not vague now, as there are used cases and every business group in an organisation is leveraging the data to measure their RoI, Bhavaraju.
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