Ready Access, Effective Analytics: The Foundation of a Successful Enterprise

An industry analyst once articulated the now mainstream definition of big data as the three Vs of big data – volume, velocity and variety.

Volume: Many factors contribute to the increase in data volume. Transaction-based data stored through the years. Unstructured data streaming in from social media. Increasing amounts of sensor and machine-to-machine data being collected. In the past, excessive data volume was a storage issue. But with decreasing storage costs, other issues emerge, including how to determine relevance within large data volumes and how to use analytics to create value from relevant data.

Velocity: Data is streaming in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time. Reacting quickly enough to deal with data velocity is a challenge for most organizations.

Variety: Data today comes in all types of formats. Structured, numeric data in traditional databases. Information created from line-of-business applications. Unstructured text documents, email, video, audio, stock ticker data and financial transactions. Managing, merging and governing different varieties of data is something many organizations still grapple with.

Goober, Head – Solutions, Huawei Enterprise Business, shares, “The core of every successful business is to have an organized, secure and extensive database of information. This information ranges from basic file work in a company right up to extensive business-centric or service-centric data. In the era of burgeoning information technology has evolved to deliver the benefits of Big Data. The simplest explanation of the big data phenomenon is that, on the one hand, it is all about large amounts of data, while on the other hand it is also almost always about running analytics on those large data sets. On the face of it, neither the volume of data nor the analytics elements are really new. For many years, enterprise organizations have accumulated growing stores of data. Some have also run analytics on that data to gain value from large information sets.”

Simple examples of this are web giants which analyze user statuses or search terms to trigger targeted advertising on user pages. But big data analytics are not restricted to these web giants. All sort of organizations, and not necessarily huge ones, can benefit – from finance houses interested in analyzing stock market behaviour, to police departments aiming to analyze and predict crime trends. At root, the key requirements of big data storage are that it can handle very large amounts of data and keep scaling to keep up with growth, and that it can provide the input/output operations per second (IOPS) necessary to deliver data to analytics tools. To depict it pictorially, one does not simply require a very large cabinet, but a cabinet with shelves and drawers to organize and access the contents of that cabinet with great ease.

Largescale Big Data
Hyperscale computing environments have been the preserve of the largest web-based operations to date, but it is highly probable that such compute/storage architectures will bleed down into more mainstream enterprises in the coming years. The appetite for building hyper-scale systems will depend on the ability of an enterprise to take on a lot of in-house hardware building and maintenance and whether they can justify such systems to handle limited tasks alongside more traditional enterprise environments that handle large amounts of applications on less specialized systems.

Analytics for the small firms
But hyperscale is not the only way. Many enterprises, and even quite small businesses, can take advantage of big data analytics. They will need the ability to handle relatively large data sets and handle them quickly, but may not need quite the same response times as those organizations that use it push adverts out to users over response times of a few seconds.

So, to sum up, big data storage needs to be able to handle capacity and provide low latency for analytics work. One can choose to do it like the big companies in hyper-scale environments or adopt object storage in more traditional IT departments to do the job. In either way, it is necessary for organizations to understand and leverage the benefits of an asset like Big Data and to tap into it in whatever manner is most beneficial and cost-effective for their business. In a cluttered market, it is then imperative for IT decision-makers to select wisely and make the best bet for a successful future.