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Attunity Blog

Vice President of Sales
Attunity

Firms' Big Data Solutions Must Meet Both Technical and Business Users' Needs

January 7, 2013

When it comes to emerging technologies, it is often difficult to separate the hyped items from legitimate game-changers. New technologies that may seem to hold all the promise in the world may turn out to be busts, whereas others deliver all that they promised and more.

At this point, it is safe to say that big data analytics falls squarely into the latter category. When the technology first began to enter the public consciousness, individuals and organizations hoped that it would be able to turn unstructured and semistructured information that would otherwise yield little value into usable insight. Over the past few months and years, big data analytics has done just that.

Now that the utility of big data analytics has been fairly well established, the questions concerning the technology have moved more toward the specifics. What priorities should firms hold when developing and implementing big data analytics systems?

As Wired recently pointed out, one of the most important points for firms to focus on in this regard is ensuring that the system meets both technical and business users' needs.

Varied requirements
The news source predicted that 2013 will see a rise in demand for big data analytics tools. This is a widely shared sentiment, as numerous industry experts foresee that big data analytics use will rise across virtually every industry and region as more companies realize the potential benefits.

As this trend continues, the number and variety of workers eager to gain access to big data analytics systems will likely increase. This is why Wired asserted that firms will need to ensure that the solutions they deploy are satisfactory for a range of end-users, including business users. Organizations from numerous different industries will find they have workers who can benefit from direct access to big data analytics.

This marks a change from the past, when big data systems were relegated primarily to the domain of data scientists and other IT specialists. Use of the technology would be handled solely by these individuals, and the results would then be distributed to the relevant personnel within the organization.

Now, however, big data analytics systems exist which are much easier to use. This can greatly benefit an organization, as it is far more efficient for end-users throughout the business to play an active role with big data then to wait for results from the IT department.

To enjoy these benefits, though, firms must make a concerted effort to ensure that the big data systems selected and processes developed are easy to use and mesh smoothly with users' job functions. This will encourage adoption and promote productivity and efficiency.

Resources needed
However, even the most user-friendly big data analytics system will fail to yield optimal results if organizations do not focus on another critical aspect of technology: the ability to move data sets easily and quickly. When it comes to big data, timeliness is of the utmost importance, as information begins to lose value almost immediately. In some cases, only real-time analytics can produce the desired quality of insight.

This presents a major challenge for firms. While it is very easy to quickly transmit a single file or even multiple files from one part of an organization to another, the same cannot be said of the massive data sets typical of big data. Without tools in place, transmitting such stores of information can pose a tremendous drain on the organization and take a long time to complete, undercutting the utility of analytics technology.

This is already an issue for businesses utilizing big data analytics, but it is poised to become significantly more severe if, as Wired predicts, the number and diversity of big data analytics users increases. This will require the frequent movement of massive amounts of data through an organization. Otherwise, users will not have the raw data necessary to perform analytics operations.

Storing big data in the cloud presents a potential solution to this problem, as data stored in this manner can be accessed easily from any location. However, moving the data into the cloud in the first place can still present a major challenge for firms, as this can be a slow, demanding process for legacy solutions. The issue is exacerbated by the fact that most data is created on the premises, rather than in the cloud.

Fortunately, tools exist which can help organizations to overcome these obstacles. With a dedicated cloud-based platform for moving and synchronizing data, businesses can reduce the time and expense behind moving big data sets. This maximizes the data's availability, ensuring that more users can access the necessary information and enjoy big data analytics' benefits.