The 4 Vs of Big Data

Although Big Data presents inevitable opportunities, many organisations are challenged by the complexity of Big Data programmes - where to start, how to manage the projects efficiently, and how to ensure that outcomes are aligned with strategic business objectives.

Despite the wide-ranging benefits of implementing a Big Data strategy, it is a significant step for any organisation and so requires careful planning and execution.

So how do companies ensure they can integrate and exploit new and legacy data sources as velocity, variety, and volume continue to grow?

Here are five key areas to consider when you’re planning or examining your Big Data programme:

1. Have you aligned your organisation at all levels? Define ownerships, centralise data management and break silos.
2. Have you chosen the right technology, build and manage the infrastructure needed to work with Big Data?
3. Can you share the data internally and with third party organisations?
4. Are you able to build, nurture and train teams to ensure seamless adoption?
5. Have you assessed internal capabilities and considered bringing third party experts on board to help you to manage programme complexity and deliver maximum ROI?

The Fourth V

Many people know that the term ‘Big Data’ is commonly coupled with the three Vs: Volume, Velocity, and Variety.

However, while solving the challenges around the 3 Vs is the focus point of many Big Data solutions, the realisation of a fourth V - Value - is key. Without extracting tangible business value from your Big Data solution, it is irrelevant how well the other 3 Vs are managed.

To get maximum value from a Big Data strategy, you need to be organised internally, and to discover the full potential of your Big Data strategy, your entire organisation needs to have at least a baseline understanding of its potential.

Most people in your organisation will understand that Big Data encompasses the ability to become more efficient, proactive and predictive when trying to overcome well-trodden business issues. However, many will have worked at organisations that have failed to find value in Big Data, and which may have spent vast amounts of money on powerful data warehouses and relational database management systems, to analyse data and answer pre-determined questions.

The shift to data science

To resolve complex business problems, drive innovation and growth, companies must shift their focus from traditional business intelligence (BI) to data science.

Data science is an interdisciplinary field of processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics.

Transforming retrospective data analysis to predictive, proactive and empirical analysis of data, is a necessity. Data science is a fundamental part of becoming a data-driven organisation, but adoption requires a cultural change within organisations.

Data science changes the game for virtually all industries. When used in conjunction with business analytics, it can:

  • Develop and monetise deeper business and customer insights
  • Expedite decision making, time to market and answering key questions
  • Enable greater analytics capabilities and creating a data-driven culture

Go big or go home

While there are endless examples of Big Data success in business, the pan-industry success stories below illustrate the multiple gains to be had from adopting the shift to data science:

Amazon

One of the early adopters in the use of data analytics, Amazon was the first company to patent the shipping of goods before an order is even been placed. A genius strategy to increase delivery efficiency and cost savings.

Mercedes-AMG

By analysing large volumes of data from disparate sources in real-time, Mercedes-AMG has been able to introduce a greater number of model and customisation options, which have supported its market share and profit growth.

Burberry

Burberry Group plc is using radio frequency identification (RFID) tags in its stores to create a richer shopping experience. Customers can now view a video of how the item was made, and also browse complementary products – a valuable way of optimising sales.

Facebook

The unrelenting use of analytics and the fact that Facebook’s business model is built entirely around the extraction of our data is the cornerstone of its monumental success.

Financial gain is not the only benefit of Big Data, however. There is enormous potential for enterprises to share data in a federated way that increases efficiencies across all business functions.

For example, Gatwick Airport has been a leading light. By inviting third-party organisations, including consumer services, to add their data to the analysis, the airport showed that improved check-in speeds would lead to passengers spending more on purchases. This has increased the number of passengers passing through security every hour from 165 to 600, which has, in turn, increased the productivity of the runway.

Big Data does indeed offer amazing opportunities, as both structured and unstructured data can be combined and viewed from multiple perspectives, revealing new insights and helping organisations find novel solutions to complex problems. Leading organisations will increasingly scale their programmes to be cross-functional, combining data analytics with other applications and embedding intelligence in every process.

Mike Merritt-Holmes is Co-Founder and Chief Strategy Officer at Big Data Partnership