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What’s Big Data Missing? Accessibility.

November 14, 2016 by admin

Big DataBig data has made a big impact on the business landscape, but not as big an impact as it could. Despite the astonishing amount of data being generated and gathered for analysis, in the US, fewer than 60 percent of companies are routinely generating value or revenue from data.

What accounts for this? In order to be acted upon, data can’t just be available to the people making the decisions. The term “big data” came into existence to refer to data sets so large that available analysis and storage methods were unable to effectively handle them. With this sort of an overload, decision makers can’t be expected to find actionable information in the noise.

User-Unfriendliness

Available analytics tools can also fall short, because becoming skilled in using an analytics tool is also outside the purview of many decision makers. If big data is simply too large for marketers, project managers, C-level executives, and others to handle effectively, then the available tools are often too user-unfriendly. They may require nontechnical employees to learn new vernacular, uncomfortable interfaces, or even elements of database management, formal logic, or query design in order to get the information they need. This accidental barrier may reduce the amount of usable data that reaches decision makers, or eliminate their engagement with data entirely.

Natural Language Processing 

Fortunately, there are non-technical endpoints for users who need to interpret and investigate massive data sets. Consider the case of Google.

In 2014, big data and prediction researcher Mikael Huss estimated that Google processed 100 petabytes (100 million gigabytes) of data per day – over three times as much as the NSA. At the time, they were also estimated to be storing 15,000 petabytes of information. But the average Google user isn’t writing queries to mine the data: they’re more likely to type in “how do I maximize ROI” or “cats jumping in boxes”. These searches produce usable results because Google has devoted extensive time and effort into natural language processing research.

Natural language processing allows Google to abstract extremely complicated search algorithms and hide them from the end user. To be sure, this abstraction protects their proprietary algorithms, but it also reduces the barrier to user engagement. And in the majority of cases, the end user isn’t interested in the process. They’re interested in an actionable result.

In short, Google has designed their computer systems to understand the language of humans – rather than training humans to speak the language of computers.

If big data is to see mass business adoption, or to reach its full potential in the businesses which do adopt, businesses should take a page from Google’s handbook and make querying the datasets as painless as possible.

Filed Under: Big Data Tagged With: accessibility, big data, data analytics, natural language processing, user experience

Big Data for Small Business

June 13, 2016 by admin

The growth of data is changing the operational landscape for businesses of all sizes. Until recently, the advantages of big data have been the domain of big businesses with the staff and infrastructure to capitalize on it.

But now, small and medium businesses (SMBs) and even startups can integrate big data into their operations and take advantage of the same benefits large businesses have. Big data integration can put small businesses on equal footing with their larger competitors and help them differentiate themselves from the crowded market.

Big businesses have long known that big data can increase efficiency, speed up processes, and improve customer interactions. They have been able to harness these benefits because they have IT departments to support and maintain the infrastructure necessary to integrate big data capabilities.

It may appear impossible for smaller companies to absorb the requirements in terms of staff and infrastructure needed to deploy big data processes. But with attention to a few key details, SMBs can harness big data advantages.

Find the Right Price

It goes without saying that smaller businesses are generally more sensitive to the cost of products and solutions. Big data systems can be expensive — sometimes prohibitively so. The higher the price of a product or system, the harder it can be to justify its cost against budgets.

But that doesn’t mean SMBs can’t tap into big data. It is important for companies to examine their current needs and then look for a solution that cost-effectively matches those needs. Finding a solution that can scale as needs scale gives an SMB an affordable entry point that can later grow as the company grows and can afford more robust services.

Aim for Flexibility

Often when big companies transition to a big data solution, it involves overhauling all of their systems. SMBs typically can’t afford the expense and disruption of such a transition.

Instead, SMBs should evaluate which departments or processes would benefit from the introduction of a data gathering system. Then look for a solution that allows for more of a piecemeal deployment approach rather than an entire system overhaul. This type of approach would allow systems that meet the company’s needs to remain in place and work with the new data system where necessary.

Search for Simplicity

Larger companies have the personnel and resources to implement more complex big data systems, but SMBs often don’t have that luxury. SMBs need big data solutions that are relatively simple, easy to deploy, and straightforward to use and maintain.

As the company’s needs evolve following deployment, new applications also should be easy to integrate with minimal disruption to operations.

Because SMBs are less likely to have dedicated staff to implement and provide maintenance, these functions should be simple and not require large demands on personnel in terms of training and ongoing support.

Conclusion

SMBs must take careful steps to make sure the deployment falls within budget and doesn’t involve costly downtime or maintenance. A key first step is to evaluate current needs to determine where a big data system might add value to the company’s operations. Then research the available options, taking care to look for solutions that solve immediate needs without placing costly demands on personnel and resources. Finally, place extra value on solutions that can solve both current and future needs through a scalable architecture.

Filed Under: Big Data

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