5vs Of Big Data

1. Big data is about volume. Volumes of data that can reach unprecedented heights in fact. It’s estimated that 2.5 quintillion bytes of data is created each day, and as a result, there will be 40 zettabytes of data created by 2020 – which highlights an increase of 300 times from 2005.
2. The definition of Big Data is most commonly based on the 3-V model and it is now time to add another two crucial factors.

1. Big Data Definition
2. 5vs Of Big Data Services

Information Catalyst delivers expertise and innovative solutions in the field of information and data interoperability.

ICE has been instrumental in the Big Data Value Public-Private Partnership (BDV PPP) / Big Data Value Association (BDVA) as described further below. It was the lead author of the proposal to create a 1B€ Big Data Value Public Private initiative. Mr Stuart Campbell (ICE’s Director) acts as the inaugural Secretary General of the Big Data Value Association (BDVA) including Board partners such as SAP, ATOS, Philips, Telecom Italia, TNO, Indra and Thales, to name a few, and acted to grow a membership of over 130 organisation, commercial and academic, within 12 months.

IBM data scientists break big data into four dimensions: volume, variety, velocity and veracity. This infographic explains and gives examples of each. For updated figures, please refer to the infographic Extracting business value from the 4 V's of big data.

It has been said that data is the ‘new oil’. This is untrue. Data, or rather information, is the old, new and future elixir of life providing ‘eternal life and eternal youth’ for application software through to business decision making. By interoperating information within organisations and between them, in-cloud and on-premise, it enables business’ to better and more efficiently function – to live and stay young and fresh. The more information is structured, accessed, described, and shared, the more the value of the data is amplified throughout the entire information supply chain whilst still of course considering privacy and security concerns. The pertinent use and exploitation of information can minimize costs and maximize revenue and profit throughout the information chain.

To quote the European Commission: “Big Data is one of the key economic assets of the future”. Mastering the generation of Value from Big Data will create a significant competitive advantage for European industry, creating economic growth and jobs. Value is considered as one of the 5 Vs (currently) of Big Data with the others being Volume, Velocity, Variety and Veracity and so Big Data is not just ‘big’ as in, for example, exabytes of information, but also considers big as in high-speed, high variety of types, or the high ambiguity of the information.

The volume of data is rapidly growing: It is expected that by 2020 more than 16 zettabytes of useful data will exist (16 Trillion GB), which implies an equivalent growth of 236% per year from 2013 to 2020. This data explosion is a reality that Business must both face and exploit in a structured and aggressive way to create value for itself and its customers and in all sectors. Powerful tools are available and have been, and will be, developed to collect, store, analyse, process, and visualize huge amounts of data. Open data initiatives have been launched to provide broad access to data from the public sector, business, and science. Linked data initiatives have been established to help try to make sense of the different data sets.

The following table and graphics have been presented by the recently established Big Data Value Public Private Partnership (BDV PPP) and the Big Data Value Association (BDVA) which represents the private side of the partnership along with the European Commission representing the public side. It shows the impact that Big Data is having or will have.

Big Data Definition

There are many challenges to establish a Big Data ecosystem and these can be represented in the following diagrams. The hexagons represent the challenge areas which range from applications which can increasingly take advantage of Big Data, to the need to re-skill and educate the workforce of the future.

The multiple dimensions of Big Data

Big Data and Big Data Value represents an extremely strategic and profitable opportunity for geographies and companies. But to drive innovation and competitiveness, it is necessary to foster the development and wide-scale adoption of Big Data Value technologies, establish successful use cases, and seek data-driven business models. At the same time it requires dealing with many different aspects of an increasingly complex landscape:

• Data: Availability of data and the access to data sources. There is a broad range of data types and data sources: structured and unstructured data, multi-lingual data sources, data generated from machines and sensors, data-at-rest and data–in-motion. Value is generated by acquiring data, combining data from different sources, and providing access to it while ensuring data integrity and preserving privacy. Value is added by pre-processing, validating, analysing augmenting and ensuring data integrity and accuracy
• Skills: In order to leverage the potential of Big Data Value, a key challenge for Europe is to ensure the availability of highly and rightly skilled people who have an excellent grasp of the best practices and technologies for delivering Big Data Value within applications and solutions. There will be the need for data scientists and engineers who have expertise in analytics, statistics, machine learning, data mining and data management. These technical experts will need to be combined with domain experts with strong industrial knowledge and the ability to apply this know-how within organisations for value creation
• Legal: The increased importance of data will intensify the debate on data ownership and usage, data protection and privacy, security, liability, cybercrime, Intellectual Property Rights (IPR) and the impact of insolvencies on data rights. These issues have to be resolved in order to remove adoption barriers and enable a favourable European regulatory environments that is needed to facilitate the development of a true Big Data market
• Technical: Key aspects including real-time analytics, low latency and scalable data processing, new and rich user interfaces, data interaction and linking data, information and content, all have to be advanced to open up new opportunities and to sustain or develop competitive advantages. Interoperability of datasets and data-driven solutions are essential for a wide adoption within and across sectors. De facto standards are a primary mechanism to avoid any long negotiation process which could slow down Big Data market interoperability
• Application: Business and market ready applications need to be a core target to allow activities to have market impact. Novel applications and solutions must be developed and validated based technologies and concepts in ecosystems that provide the basis for Europe to become world-leader in the creation of Big Data Value.
• Business: A more efficient use of Big Data and understanding data as an economic asset carries great potential for the economy and society. The setup of Big Data Value ecosystems and the development of appropriate business models on top of a strong Big Data Value ecosystem must be supported in order to generate the desired positive impact on economy and employment
• Social: Big Data will provide solutions for major societal challenges, such as the improved efficiency in healthcare information processing or reduced CO2 emissions through climate impact analysis. In parallel it is critical for an accelerated adoption of Big Data to increase awareness on the benefits and the Value that Big Data can create for business, the public sector, and the citizen

5vs Of Big Data Services

If you would like more information on Big Data and what it can do for your business…

To define where Big Data begins and from which point the targeted use of data become a Big Data project, you need to take a look at the details and key features of Big Data. Its definition is most commonly based on the 3-V model from the analysts at Gartner and, while this model is certainly important and correct, it is now time to add another two crucial factors.

Big Data definition – the three fundamental Vs:

• Volume defines the huge amount of data that is produced each day by companies, for example. The generation of data is so large and complex that it can no longer be saved or analyzed using conventional data processing methods.
• Variety refers to the diversity of data types and data sources. 80 percent of the data in the world today is unstructured and at first glance does not show any indication of relationships. Thanks to Big Data such algorithms, data is able to be sorted in a structured manner and examined for relationships. Data does not always comprise only conventional datasets, but also images, videos and speech recordings.
• Velocity refers to the speed with which the data is generated, analyzed and reprocessed. Today this is mostly possible within a fraction of a second, known as real time.

Big Data definition – two crucial, additional Vs:

• Validity is the guarantee of the data quality or, alternatively, Veracity is the authenticity and credibility of the data. Big Data involves working with all degrees of quality, since the Volume factor usually results in a shortage of quality.
• Value denotes the added value for companies. Many companies have recently established their own data platforms, filled their data pools and invested a lot of money in infrastructure. It is now a question of generating business value from their investments.

As we wrote in our previous blog post, defining Big Data is not so easy since the term relates to many aspects and disciplines. And for many people the most important thing is companies’ success (Value), the key to which is gaining new information – which must be available to many users very quickly (Velocity) – using huge amounts of data (Volume) from highly diverse sources (Variety) and of differing quality (Validity), in order to be able to quickly make important decisions to gain or maintain competitive advantage.

In the book “Big Data – Using smart Big Data analytics and metrics to make better decisions and improve performance” Bernard Marr writes that if Big Data ultimately did not result in an advantage then it would be useless. We could not agree more.

Here is something else that may interest you:
Where does Big Data begin? – Many perspectives, one classification
The next big things in the data world (Part 1) – Data Science on scale
The next big things in the data world (Part 2) – Machine Learning/Deep Learning as a ServiceLearning/Deep Learning as a Service
The next big things in the data world (Part 3) – Human Data Interfaces (HDI)Interfaces (HDI)
Radioeins broadcasts re:publica special – *um explains Big Data