Big Data Analytics
Big data analytics can create significant value for businesses. The cloud is the perfect platform to economically generate insights from big data facts.
Apple’s recent purchase of Topsy, a Twitter analytics platform, stirred up a storm of speculation as to Apple’s motivation for the acquisition. To the casual observer, Topsy and Apple would appear to be strange bedfellows. But for anyone with an understanding of how big data analytics can impact revenue, help businesses understand their customer base, and find new business opportunities, the acquisition seems like a very shrewd move. Apple’s purchase of Topsy gives it access to two potentially lucrative assets: a huge amount of data in the form of Twitter’s firehose, and employees with the expertise to use that information to Apple’s advantage.
How Can Big Data Analytics Support Business Intent?
Big data has brought business intelligence into the 21st century. In 2013, we stand on the cusp of a revolution in our ability to gather data, analyze it, and integrate the insights it brings into decision-making processes. There is a torrent of potentially useful and exploitable data created every day. Twitter’s data is just one example, but it’s an example that Apple thought it worth paying $200 million for.
In a recent study from Bain & Company that surveyed 400 companies with revenues largely in excess of $1 billion, those companies that demonstrated big data analytics expertise were twice as likely to be in the top quartile of financial performance within their industry, three times more likely to execute decisions as intended, and five times more likely to make decisions quickly.
However, as has often been said, big data is the name of a problem, not a solution. Within the petabytes of existing data, there are insights to be gleaned that can guide almost every aspect of business development. But, the vast mass of data is practically useless. To make use of it, businesses need to collect it, filter it for relevance, analyze it with the application of sentiment analysis, geolocation tools, and other techniques, and generate information that usefully contributes to furthering the goals of the business.
The Cloud As An Enabler Of Big Data Analytics
Forrester has defined big data as “Technologies and techniques that make capturing value from data at an extreme scale economical.” The key word here is economical. If the costs of extracting, processing, and making use of data exceed the advantages to be gleaned, then it’s a pointless exercise. Fortunately, as data volumes have grown, so has a technology that helps bring its use into the ambit of most businesses. Cloud technology, whether public, private, or some combination of the two, is an essential component in enabling businesses to generate a substantial ROI from big data analytics.
Collection And Filtration
As I said earlier, most data out there is useless, but the mass of data still needs to be filtered for relevance and stored in a form that’s useful. There’s very little benefit in investing in huge amounts in in-house or collocated infrastructure to temporarily store data, the vast majority of which will be discarded. There is also nothing to be gained from bringing the data behind a company’s firewalls and into their internal network, with all the IT management headaches that entails.
This stage of the big data funnel is a perfect application of public cloud platforms, which can provide scalable on-demand compute and storage resources.
Once data has been rendered into a usable form, it has to be analysed to generate actionable information. It is rarely necessary to keep the raw data that is fed to analytics applications over the long term, but what is usefully stored is the results of the analytics process. Public or hybrid cloud technology can be used for the analytics phase, employing Hadoop or an alternative for the storage and processing of data sets. In the case of hybrid cloud users, the raw analytics phase can be carried out with public cloud infrastructure, and the processed and actionable information brought in-house with a private cloud component.
Visualization, Integration, And Collaboration
This is the stage at which we actually have useful information that can be used to guide decision making, but it still has to be made available to users in a form that is interpretable and integrated into existing business systems, such as enterprise resource planning and customer resource management applications. Software as a Service applications running in the cloud and taking advantage of the data developed in earlier stages powerfully enables integration and allows users to collaborate.
Apple’s purchase of Topsy is simply a logical consequence of a recognition of the benefits big data – in this case of social media derived insights – can provide to business. The cloud is the perfect platform for making economical use of that data.
By Moazzam Adnan,
Moazzam Adnan joined Atlantic.Net in December 2000 as a Product Manager. He currently holds the position of Director of Business Development. His approach to business development is innovative and active, and he is always looking for pioneering strategies to grow the business via novel partnerships and revenue opportunities. Google + Moazzam Adnan
- The Five Rules of Security and Compliance in the Public Cloud Era - October 18, 2016
- 5 Ways Cloud-based Tools Can Help Accountants Escape The IT Treadmill - October 17, 2016
- RCS In Emerging Markets Means A Step Forward For Cloud Computing - October 10, 2016
- Cloud Native Trends Picking Up – Legacy Security Losing Ground - October 6, 2016
- Introducing and Implementing Voice Biometrics in Call Centers - October 6, 2016