dimension data article 1

Big data is more than a buzzword. It’s what drives transformation at corporations around the globe. It helps you understand your customers’ buying behaviour. It can help businesses close targeted sales. There’s so much data now that many CIOs have stopped calling it “big.” It’s enormous and it’s growing fast.

But how should businesses approach this surge in data strategically? Peter Prowse, Group Data Centre Vice President: Strategic Partnerships at Dimension Data Australia, says the underlying principles of data are “fundamentally changing the way businesses will be able to react to, or even anticipate, business opportunities.” Essentially, the impact and value of data shouldn’t be underestimated.

“In simple terms, big data refers to data sets that can’t be handled easily through traditional methods such as dedicated servers driving a traditional database or data warehouse structure (like Oracle or Teradata), and the associated analytics toolsets like Cognos that would drive interrogation and analytics,” he says. He offers three characteristics of this new data environment:

  • Volume: the massive amount of data generated and collected by organisations
  • Variety: the array of different types of collected data, from text, to audio, video, web logs, social media and more
  • Velocity: the speed at which data is collected, analysed and some even say anticipated

Kevin Leahy, Group General Manager for Next-generation Data Centres at Dimension Data, says it’s all about identifying patterns from raw information, the essence of data mining. For example, it might entail finding a connection between beer and diapers. “By analysing cash slips, one might discover an unexpected correlation between the sales of beer and diapers. This could be because fathers on an errand to buy nappies also conveniently purchase beer at the same time,” he says. The newly discovered information can then be used to motivate a change in sales strategy that could drive higher sales. “Imagine what’s possible with all of the new data generated from web browsing, online transactions, even tracking movements within shopping malls via mobile devices,” he adds.

“In the financial services sector, for example, banks and insurance organisations use big data to identify fraud by spotting patterns that would indicate the likelihood of fraudulent transactions.” Prowse offers another example from the world of telecommunications: “A large US mobile phone operator let’s call it X Telecoms - was suffering significant customer churn across its mobile customer base. By using traditional data analytics tools and processes, the organisation was able to quantify the amount of churn quite accurately, but not the reasons for it. In desperation, X Telecoms turned to a group of data scientists to identify the underlying cause of the churn. Using unstructured data the company captured every day, it was able to provide the following insights:

  • Every time one person switched a mobile plan to a competing provider, five friends would closely follow, which then meant that each of those five friends would have another five friends leaving the network - a snowball effect.
  • This behaviour was driven by a bundling offer from mobile companies, offering free phone calls and texts to five friends.

New tools

While unstructured data can’t be easily converted into actionable intelligence by traditional databases, these examples show how the tools for gleaning knowledge and insights from it are developing fast. “We’re seeing rapidly advancing techniques of artificial intelligence, such as natural-language processing, pattern recognition and machine learning. These artificial-intelligence technologies can be applied in many fields,” says Prowse. For example, Google’s search and advertisement business and its experimental robot cars - which have navigated over 1 million miles on their own - use a bundle of artificial-intelligence tools that analyse vast quantities of data and enable instant decision-making.

These developments are ushering in massive opportunities for businesses. In turn, CIOs are coming under increasing pressure to provide the necessary tools and processes to enable a big data strategy for their businesses in order to capture market opportunities and/or prevent reputational damage.

The ability to capture and effectively utilise data from multiple sources, in multiple formats, and in real time (volume, velocity, variety), touches almost every aspect of the IT ecosystem, from storage to security. However, it’s important to define an underlying information policy first before new infrastructure or processes are implemented.

Organisations should decide which types of information they want to keep and for how long, how and where they need the information stored, and how they would access the information. This will help guide further activities such as network infrastructure optimisation, leveraging both traditional and new data-mining toolsets, or making structural and process improvements to streamline the way decisions are made.

“CIOs need to ensure that their house is in order,” says Prowse. “Consider the impact on the network. We’re reaching the point where the effectiveness of networks is inversely proportional to the volume of information they contain. Organisations need to make sure that all the elements used to build the network work together well. The traditional approach - keeping networks running by adding more bandwidth - will no longer do. The data-ready network needs to be developed with overarching business objectives in mind, by a team that comprises representatives from multiple technology domains and business units.”

Start With Security

Concerns about security change with big data. It’s precisely the unstructured nature of big data that makes it more vulnerable to security breaches than corporate information generated by the usual applications.

“Unstructured data makes security professionals nervous. This is because it’s not ‘tagged’ to a specified risk profile or category, and it’s not yet clear as to what its value is to the business,” says Matthew Gyde, Group General Manager for Security Solutions at Dimension Data. “The result is that it can’t be mapped to your corporate governance policies and remains a weak spot in your security posture. With data flowing into the organisation in an unstructured way, there’s also a higher risk that it may contain malicious content.”

That said, organizations don’t need to be frightened of big data, but they should pursue it in the right way. Businesses that are currently investing only about 7 percent of their data budget on security. This suggests they may be falling into the same trap with security as in years past. Security solutions shouldn’t be “bolted onto” whichever new solution you’ve purchased as an afterthought, but should be built into the solution itself. Security should be part of the big data conversation right from the start.

Start Now

Organisations should know they don’t need large investments in infrastructure and resources to start

“You can start by installing a low-cost, simple platform to gather the data, and from there, begin to identify useful patterns that would almost immediately drive returns, if followed up with proactive activity,” says Leahy. “A small investment in such a platform can be funded from the benefits gained by its use. This is possible across all business sectors where a broader range of patterns may become relevant.”

These could include quality control patterns in manufacturing, patient re-admittance patterns in hospitals, bookings versus cancellations patterns in travel, and many other processes. Even small entry points are showing business returns that fund business growth and allow IT to build the skills needed to take this to the next level.

Gyde advises that, from a security perspective, it’s important to consider a few measures that could help make big data safer. “Importantly, this should involve file-level and database-level monitoring, which in turn create the need for greater management to respond to alerts generated by the monitoring applications,” he says.

For example, perhaps there’s a requirement to implement a managed security service delivered by a third party in order to cope with the added workload and ensure consistency and responsiveness in securing big data. It’s important, however, to partner with a security services provider that understands the broader effects of big data on the data centre and networking environments, and has the relevant integration skills, expertise, vendor relationships and global footprint to match your organisation’s requirements.

Copyright Dimension Data 2016