Most of us are now aware on some level that information is being collected about us all the time. Whether that's mobile usage, buying behaviour, web-browsing habits…the list really does go on.
We live in an "always on" culture in which everyone is digitally empowered. There are officially more mobile devices than people in the world - over 15 billion of them in fact, connecting us to the people and information we need to share, shop and consume.
And with the internet of things permeating both the workplace and the home, this "networked economy" is set to widen, connecting everything from your fridge, to your car, to the office coffee pot.
As a result, data is becoming more and more deeply embedded in our lives, and is exploding - doubling about every 18 months. It's unavoidable.
Not just our future, but our present
To take one example - try booking a flight abroad without big data. For a boarding pass to be generated, your itinerary needs to pass through a number of massive databases - from ticketing to no-fly lists - before you get confirmation from whatever online booking system you use.
You may then want to check what the weather's like in your intended destination, perhaps organise a hotel, and make sure you'll have wireless access once you arrive. Interestingly, but perhaps not surprisingly, Big Data plays a major role in completing each of these fairly routine tasks.
Now, extrapolate this from the perspective of an individual to cover a whole department, line of business or market unit, and then add customers, suppliers and partners into the equation. All of a sudden you have an astronomical amount of information being generated on a daily basis which, if analysed and understood, can deliver a 360 degree picture of your business at any point in time.
It's important to consider upfront why you are collecting this data, and what it is you want to achieve. Once that's established, the benefit - and the real competitive advantage that can be gained - is that responding to this information as its filtered back into the business enables you to become more agile to change; responding to market fluctuations or an increase in demand.
This presents an opportunity for businesses to rethink the way they operate, and drive unprecedented change. It's also requiring them to move faster than ever. To get - and stay - ahead of the competition, companies must not only sense the present, but see the future and proactively shape it to their advantage. The key to this lies in the deluge of data that they are sitting on.
An age-old practice
Human beings have collected and stored information since the dawn of our species, and it is this habit that has allowed us to pass on knowledge from one generation to the next, so that we can avoid duplicating previous efforts and ultimately, develop beyond our forerunners.
From markings on cave walls, to slate, paper scrolls, punched cards, the ever-faithful floppy disk, USB, and more recently, the cloud. The evolution of data storage to where we are today means that we can now theoretically collect unlimited data - ideal for a world in which many terabytes of information is now being generated every single second.
Structuring the unstructured
For many years, organisations have been accumulating information, but not necessarily doing much with it. This stockpiled information is often rather ominously termed 'dark data' - neglected data that accumulates in log files and archives that nobody knows what to do with.
Although it never sees the light of day, no one wants to get rid of it because it might prove useful at some point. Much of this data is also 'unstructured' - unorganised and raw, in fact, Gartner estimates that roughly 80% of all corporate data is unstructured.
The term Big Data seems an apt description for this rapidly growing supply of information, and if we take Wikipedia's definition, is "a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing application".
This has driven the growth of new technologies to do this job, such as NoSQL, Hadoop and MPP databases. These technologies in turn power the business intelligence and real-time software that, in a nutshell, make sense of big data to turn it into understandable, actionable insights for businesses to make future decisions based upon. Enter, predictive analytics.
Finding order from the chaos
Consumers come across predictive analytics on a daily basis, albeit unknowingly. Between weather forecasts, betting odds, insurance premiums and credit analyses, the touch-points are numerous.
Alongside this trend, businesses too are keen to cash in, and rightly so! Predictive analytics technology is the core 'enabler' of big data, allowing businesses to quickly interpret information - sometimes from many different data sources - in real time, and respond accordingly.
This could be anything from anticipating customer needs, forecasting wider market trends or managing risk, which in turn offer a competitive advantage, the ability to drive new opportunities and ultimately increase revenue.
Take retail for example. Now that we're currently experiencing a summer heatwave, many retailers will be looking at how they can exploit this to their advantage using predictive analysis. If a customer bought a gas barbeque last month, will they need more gas canisters this week?
And what is the purchase frequency to anticipate demand when they do buy? How can promotional cross-sell and upsell be incorporated to increase transaction size, number of items, and revenue?
This information provides the business with the ability to forecast, in real-time, the likelihood that a customer will buy, abandon or indeed go to a competitor - which gives them the power to save the situation by providing targeted deals, or offering informed services if needed.
While currently a fledgling market, the use of predictive analytics is set to rise. With growing data volumes, predictive analytics is firmly on the agenda; in fact 2013 research from SAP found that for 60% of businesses, predictive analytics is already an investment priority. Additionally, over two-thirds of companies think that predictive analytics will be an investment priority for them within the next five years.
The use of this technology is placing data firmly at the heart of organisational decision-making, across all sorts of sectors and industries. Businesses are now able to not only 'crunch the numbers' and make sense of the swathes of information that they are gathering, but also use historical data to make predictions and extrapolate actionable insight as they try to navigate an uncertain future.
Introducing in-memory
When discussing the future of storage in 2006, computer scientist, Jim Gray was famously quoted as saying the "tape is dead". The hard disk was then cited as taking over tape; equally the role of the hard disk was then to be taken over by main memory. Jim made these predictions based on the way in which hardware was continuing to diversify; all of which were the first steps to realising in-memory computing. He was right.
With in-memory, information is stored in the RAM of dedicated servers rather than in databases operating on slower disk drives. In-memory computing helps businesses to quickly detect patterns, analyse massive data volumes on-the-fly, and perform their operations much more quickly than was previously possible.
Existing in-memory technology can process data up to 10,000 times faster than legacy databases. These tremendous advances have increasingly placed pressure on software development; however there are lots of opportunities and directions in-memory can be taken. We're already seeing this in several key industries, for example sport.
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.
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