Is big data just a fancy word that people like to use or is it actually something of substance? While majority of the internet is still on debate over this topic, the truth is that big data is actually a thing, and it’s happening right now, as we speak!
Imagine a multinational corporation like Nestle that has products distributed to most parts of the world. Now Nestle will have a large database of distributor, retailer, and consumer data, right? Now add to that the social media data from the fans and followers of Nestle, reports generated from surveys conducted by Nestle and their distributors, and the hoards of online mentions of Nestle.
Now what you have is a large volume of data filling at high velocity. For Nestle to make decisions based on the stored data, the company has to figure out a framework on which relevant data can be derived.
Big data is quite easy to understand, but very difficult to implement and integrate with your current operational workflow. In fact, the successful implementation of big data is what helps your performance conduct analytics that can give you better insight into how you should market your products and services. Here are some ways that can help you create a refined approach to incorporate big data analytics.
Cloud Networking & Architecture
Supporting customer data to perform big data analytics goes beyond the conventional challenges of storage in your cloud based system. Cloud providers today are aligning their solutions to support big data analytics, even for SMEs. However, they do advise entrepreneurs that big data requires a more holistic approach to implementation that oversees the networking and architectural aspects of the cloud.
In other words, you have to acknowledge that the current onset of the cloud technology may not necessarily support big data analytics. That being said, a set of hosting solutions to complement the primary cloud system can always be helpful. Jonathan King, the VP of Cloud Solutions at Savvis claims that,
“You’ll have pieces of the big data engine that are running full-tilt all the time, which means that really is ideal for dedicated infrastructure, unlike other components, which are going to be variable, [and] that’s ideal for cloud. A lot of these jobs are batch — you’re going to run them in four- or eight-hour increments at different times — so having a burst-up from dedicated to virtual is really table stakes.”
Networking issues are quite common with big data in the cloud. However, some cloud providers like CloudSigma have figured out an ecosystem strategy that allows its customers and partners to gain access to large repositories that can compute data quickly at low costs. This singular approach, however, still requires you to customize which data should be derived. For instance, if you want to discover what products are more popular in online mentions, you should develop an infrastructure that only gives data from relevant sources. If not, then your decision making process could be compromised with misleading data.
Imagine having hoards of data filling up your repositories at a greater volume than you prefer. The obvious outcome from this is increasing running costs of growing your database, and subsequently updating the framework on which data is derived. This brings in more challenges than opportunities. This is why you should seek to discuss these details elaborately with your cloud service provider to understand how much room you have to work with.
Security and Compliance
Sony is one of the many multinationals that have some sort of big data analytics framework in their workflow. However, Sony has been on the receiving end of backlash from customers who had their personally identifiable information (PII) and sensitive data compromised because of hacks. In 2013, the Sony PlayStation network was hacked, leaving the personal information and credit card details of more than 1 million users exposed. Earlier this year, the database of Sony Pictures was compromised, and the hackers managed to expose salaries of executives, private emails, and much more.
These attacks on Sony not only led to consumer backlash, but hefty lawsuits as well because of failure to comply with data privacy regulations. With the Digital Rights Act in effect, the last thing you want is getting your database compromised because of poor incorporation of big data.
While the elasticity of cloud technology makes it highly practical for big data analytics, you still need to address the security framework to make sure that consumer and corporate data is protected at all times. One ideal approach is to seek managed service providers that can work with your cloud system. If your cloud service provider has security applications and systems in place, consider deploying them. However, make sure to run cost and risk assessment before investing in add-on solutions from your provider. As it is, with large database, the costs of security are high, so make sure you do your homework and determine the best available security systems that you can integrate with the cloud.
At the same time, you have to realize that security has to remain absolute in your business workflow, not just the cloud. Having a safe and secure repository is good, but having loopholes in your operational workflow may still compromise your data. A good approach is to curate your existing systems and determine the effects of big data on them. This can help you realize which systems need a more foolproof framework.
Encryption and data redundancy is a good way to keep information secure that your business does not actively use. Encryption gives you the benefit of having data that is unreadable. A good approach to encryption is giving different levels of accessibility to employees based on their status in the organization. This prevents any internal or external threats. Data redundancy, on the other hand, is creating a backup of backups to make sure you have your data up and running in case your database gets compromised. Data redundancy allows you to keep your live repository light and responsive, and keeps sensitive data protected.
Incorporating big data can have a significant impact on your business, for the good or the bad. Unless absolutely needed for improved decision making, avoid big data analytics, especially if you sell a range of products or services.