In practice
So how can organisations actually put this into practice? First and foremost, before even looking at your data, you need to identify the business problem you want to solve, whether the business is a global enterprise, or an agile start-up.
Technology by itself is not the silver bullet - and there is no benefit to collecting lots of data just because you can. Big Data needs robust analysis that is relevant to the business; technology is a critical enabler only after you have figured out the first part of the equation.
Typically, businesses want to better understand customers, make financial predictions and improve sales forecasting. These are of course obvious uses and ones which remain hugely important and fundamental to business success.
However, beyond these typical uses, businesses are starting to have interesting conversations and think more strategically about how they can further use the information they gather to better enhance their business and decrease risks.
To do so, a good understanding of a business' data and information is critical. At the outset, knowing the structure and location of the data is essential. This is often not taken into consideration but some solutions may not work with certain storage formats.
The way that data is stored and accessed differs according to the end goal, especially when it comes to real-time analytics. Policies and processes must then be put in place to provide transparency around data management, quality and governance before an organisation can start to mine its big data for value.
Is everyone a data scientist?
Getting access to - and making sense of - data has, until recently, been seen as a complex and highly-skilled task, delivered by people with advanced degrees in statistics and prior analytical experience.
This dynamic simply can't scale with businesses, especially if companies want to embed predictive analytics into all areas of the organisation, from point of sale to the call centre. We could (and should) be in a situation in a few years where up to half of all employees in organisations are using predictive analytics in some capacity as part of their daily tasks.
Does this mean we all have to become data scientists for businesses to get the maximum return from the big data they collect? The answer is no.
Whilst analytical skills are becoming more and more important, and employers will start to look for evidence of this on CVs of people hoping to join their organisation, the fact is that newer predictive analytics and business intelligence technologies are making analytics much more accessible for the average worker.
More intuitive technology with easy-to-use interfaces that reflect the trends in consumer technology mean there is not always a requirement for specialist data science skills to allow individual lines of business to interpret data, and feed that insight back to the wider business.
The challenge for organisations then is to put the right investment into developing a data-driven workforce, alongside data-driven processes and applications.
Final thoughts
So where to go from here? With the networked economy in which we now live and work, companies are faced with an explosion in the amount and type of datasets now available on everything from customers to sales.
Those that fail to utilise this rich information are also likely to fall behind competition; demand for customer centric (rather than product centric) businesses is more prevalent than ever before. Combine this with the fact that data is increasingly visual, current and actionable, and it's clear to see that analytics has the potential to drive real value.
But what will really set the competition apart will be the ability to affect a cultural shift to one of greater collaboration. Performing more comprehensive analysis of data and, most importantly, avoiding information silos by encouraging integration across the business is where analytics will come into its own. Data should not be segregated as the responsibility of one individual or department; it should be front of mind of everyone.
- The author, Irfan Khan, is the Chief Technology Officer for SAP GCO (Global Customer Operations), a 25,000 people strong field organisation. He has overall responsibility for SAP's vision, strategy and technology leadership across the company for all SAP solutions. In partnership with SAP's product development organization, Irfan works to ensure our technology direction is in line with the needs of our customers and supports SAP's leadership and long-term vision for growth.
