More Data, More Problems: Analyzing the 5 Cons of Big Data

5 Cons of Big Data

Referred to as data mining in a past life, big data has all of a sudden become its own thing, backed by its own hype and lasting buzz. It has picked up tremendous steam as more organizations collect and analyze swarms of data in attempt to leverage insights that enable them to make decisions based on preferences and behaviors. While this trendy practice of advanced number crunching has proven to have quite the upside, it also comes with some drawbacks. On that note, here are five big data cons to think over.

1. Big Data Planning and Execution

Big data isn’t all about gathering and dissecting information. Centralization and actually knowing how you’re going to use that data is also essential. It sounds trivial, but having a strategy that entails how you plan to approach a big data deployment will make your goals much easier to achieve, particularly when it’s devised in a language that data scientists, managers, senior executives, and other key parties can understand. Exactly what that plan consists of will depend on a variety of factors, but in general, it should`outline what data sources, IT personnel, and tools offer the most value to the project.

2. Expanding Existing Infrastructure

Some businesses approach big data with the thought that their current infrastructure is strong enough to support their needs. As if they can simply load up on analytics or other solutions and thrive right out of the gate. Later they often realize that storage capacity, scalability, security and a host of other factors they never considered are also vital to success. Organizations of all sizes must understand that evaluating their existing infrastructure and anticipating its growth is the key to continually maximizing what big data has to offer. This is where things can get mighty costly.

3. Gathering the Right Tools

There is a lot of power in in the structured and unstructured jewels at your fingertips, but  data is merely that – data, bits of information. To the naked eye, this information does not provide the type of visibility that allows analysts to jump right in and start making monumental discoveries. Needless to say, the right tools go a long way in helping organizations make sense of their data. A good set of analytics and visualization tools will help you understand what the data is telling you and put those insights into the proper context. Unfortunately, the fast growing mix of data-driven apps on the market can make finding the right tools quite the challenge.

4. Assembling the Human Resources

According to IDC’s Digital Universe study, the total amount of data in the world could reach a whopping 40 zettabytes of data by 2020. Who knows how spot on such a projection will be, but the point is that new data is being produced rapidly – at a rate that far exceeds the number of IT specialists equipped to keep it under wraps. With adequately skilled data scientists in limited supply, one of the biggest big data challenges an organization will face is finding the right people for the job. From predictive modeling to general analysis, there are many levels, so you may find that you require a diverse team of data masters with unique skillsets to make sure all critical points are covered.

5. Budding Privacy Concerns

The true beauty of big data is that potentially valuable data may reside almost anywhere. You may find a healthy helping in your sales transactions, a few pieces in your social media messages, and even more in other public sources. The sensitivity of this data and unease on the consumer end are the motivation behind the FTC’s “Reclaim Your Name” program, an initiative that calls for companies in the data broker industry to provide information detailing how they collect and use consumer information. While the FTC only singled out one industry so far, privacy is a major concern for organizations in IT, manufacturing, financial services, and other fields that handle vast amounts of digital information. A single slip up could have major repercussions for any company.

The disadvantages of big data are significant to spooky degrees and should be carefully considered before any investments are made in analytics, storage, and staffing of new talent. Fail to give them the attention that they deserve, and you could end up with one huge big data problem on your hands.

More Data, More Problems: Analyzing the 5 Cons of Big Data
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