{"id":9948,"date":"2016-02-16T10:55:57","date_gmt":"2016-02-16T16:55:57","guid":{"rendered":"http:\/\/www.mrc-productivity.com\/blog\/?p=9948"},"modified":"2016-02-16T10:27:05","modified_gmt":"2016-02-16T16:27:05","slug":"5-common-big-data-myths","status":"publish","type":"post","link":"https:\/\/www.mrc-productivity.com\/blog\/2016\/02\/5-common-big-data-myths\/","title":{"rendered":"5 common big data myths"},"content":{"rendered":"

\"Education\"Summary: Big data is surrounded with so much hype, the misconceptions often get confused for facts. How can business leaders separate big data myths from reality? In this article, we identify some of the biggest myths surrounding big data, and explain why they’re false.<\/em><\/span>
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photo credit: geralt<\/a> via pixabay<\/a> cc<\/a><\/figcaption><\/figure><\/p>\n

One of the biggest trends over the last few years, the concept of big data is surrounded with hype. Everywhere you look, there\u2019s someone touting a new way to capitalize on big data. There\u2019s some new article explaining how big data will change business.<\/p>\n

Now, don’t get me wrong. I’m not saying that big data is just hype. Quite the opposite. Data volumes are growing, and will continue to do so. This ‘big data’ offers plenty of advantages to businesses of all types and sizes. It’s a trend that you can’t ignore.<\/p>\n

However, the problem with an overly hyped topic (like big data), is the fact that not every bit of information surrounding it is true. Misconceptions get mixed in with fact. After a while, businesses start believing these myths.<\/p>\n

Today, let\u2019s explore some of these common misconceptions, and explain why they\u2019re false. Here are 5 of the most common myths surrounding big data.<\/p>\n

1. We don’t\/won\u2019t have Big Data<\/h3>\n

If you\u2019re like most companies, you don\u2019t generate terabytes of data. So, you can safely ignore the idea of \u201cbig data\u201d altogether…right?<\/p>\n

Not so fast. The idea that you can just ignore big data altogether is one of the most common myths out there.<\/p>\n

Here are two reasons why you can\u2019t ignore it. First, as explained in this article<\/span><\/a>, all businesses have access to more data than they realize. Just because you don\u2019t generate \u201cbig data\u201d doesn\u2019t mean you can\u2019t access (and capitalize) on it. <\/p>\n

Second, consider the recent trends. Over the last few years, we\u2019ve seen data volumes explode–and this trend isn\u2019t slowing down any time soon. It\u2019s estimated that data volumes will rise 50x by 2020<\/span><\/a>. While you may not have big data now, who\u2019s to say you won\u2019t have it in the near future? Wouldn\u2019t you rather get ahead of the trend now, before you\u2019re stuck playing catch-up?<\/p>\n

\n\u201cMany non IT related companies feel that they don’t have Big Data and hence conclude that they don’t have to deal with it (Ostrich hiding itself with its head in the sand),\u201d says Dj Das, Founder & CEO of Third Eye Consulting Services & Solutions, LLC<\/span><\/a>. \u201cThe truth is that, the rate at which Data is growing in every field, every domain, everyone would have to deal with the data deluge one day or the other. The earlier someone can get their arms around it, understand how to deal with it and make best use of Data for business decisions, the sooner they will be prepared for the future.\u201d\n<\/p><\/blockquote>\n

2. Big Data is only an IT problem<\/h3>\n
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photo credit: geralt<\/a> via pixabay<\/a> cc<\/a><\/figcaption><\/figure>\n

Many make the mistake of assuming that big data is all about technology. The \u201cbig data project\u201d starts in the IT department. They get all the technology in place before exploring ways it will benefit their business.<\/p>\n

In reality, it should be the other way around. <\/p>\n

Big data is not a technology project. It\u2019s a business project. You must first figure out what business problem you want to solve before exploring the technology. Of course, the IT department will be involved implementing the technology. But, it must stem from a business need if you want it to succeed. <\/p>\n

\n\u201cMost engineers are of the view that non-tech senior management somehow expect the IT teams to create magic by using the “Big Data” trick,\u201d says Binny Mathews, Co-founder of DeZyre<\/span><\/a>. \u201cHowever for Big Data to be effective it needs buy in from various teams including senior management and domain teams like marketing, finance etc.\u201d\n<\/p><\/blockquote>\n

3. Big data are always correct<\/h3>\n

George Burns once said, “If you live to be one hundred, you’ve got it made. Very few people die past that age.”<\/p>\n

Statistically speaking, he\u2019s correct. But if you take the data at face value, you\u2019d be wrong.<\/p>\n

I share that quote to make a point. Big data has something of a \u201cmagical\u201d feel to it. Somehow, it pulls answers out of overwhelming amounts of data. Because there\u2019s so much data, you can\u2019t possibly go through and verify that everything is correct.<\/p>\n

So, should you just have to assume it\u2019s accurate? Can you blindly take that data at face value? Not so fast.<\/p>\n

That\u2019s when problems occur. If you don\u2019t understand your data, or the process of gathering\/refining that data, you will draw inaccurate conclusions.<\/p>\n

Just like regular data, big data\u2019s effectiveness depends on multiple areas. As explained below, the accuracy of the answers you draw from big data depend largely on four important factors.<\/p>\n

\n\u201cIn learning about the space, one of the most striking myths is that “big data” hold the unequivocal answer – that \u2018big data are right,\u2019\u201d says Dave Crusoe, Director of Technology & Academic Success, Boys & Girls Clubs of America<\/span><\/a>. \u201cIn fact, “big data” are prone to a variety of conceptions and misconceptions that can serve to reinforce preexisting stereotypes or behaviors. What we gain from “big data”, in turns, is only as good as (a) the accuracy of the sources for data; (b) the interrelationship of the data, or the assumptions that play into the interrelationship; (c) the assumptions that are used to analyze the data and (d) the coarse generalizations that are used to make sense, and act upon, the data. Errors and assumptions are injected all along that activity chain.\u201d<\/p>\n

\u201cSo, the key take-away is that although “big data” can provide insight about all sorts of behaviors, they can only do so at a coarse, rough level. In fact, the insights that “big data” provide are only as good as the effort that went into understanding it, and even those are limited at best. “Big data” provides a signal, but not the answer; the answer, instead, must come from the synthesis of a variety of sources, “big data” and otherwise.\u201d\n<\/p><\/blockquote>\n

4. Big Data will solve almost any business problem<\/h3>\n

\"photo
photo credit: nvokicka<\/a> via pixabay<\/a> cc<\/a><\/figcaption><\/figure>Over the last few years, big data has been touted as the solution to many different problems. For instance, it’s presented as a way to get more customers, reduce churn rate, improve employee satisfaction, and so much more.<\/p>\n

The problem is, it’s turned into something of a ‘catch-all’ business solution. As a result, many businesses hold unrealistic expectations for big data.<\/p>\n

Can big data answer important questions for your business? Of course. Will it solve every problem you have? Of course not. <\/p>\n

Big data isn\u2019t a magical solution that will automatically provide business insights. It helps different businesses in different ways. What’s the key? As explained below, if you want big data to provide the right answers, you must first ask the right questions.<\/p>\n

\n\u201cOne of the biggest myths about big data is that it can solve almost any business problem,\u201d says David Ferguson, CEO & Founder, 5000fish, Inc<\/span><\/a>. \u201cThe reality is that generally the answers you get from analyzing big data is only as good as the quality of the data being analyzed (junk in \u2013 junk out) and if you happen to be asking the right question (which is more difficult than it sounds). Companies that buy into the \u201cmagic 8-ball\u201d of big data analysis tend to quickly realize that the hype around big data also means big cost to their bottom line.\u201d\n<\/p><\/blockquote>\n

5. Everyone else is way ahead of you in big data adoption<\/h3>\n

Based on the hype surrounding the topic, you\u2019d think that everyone has already jumped on board. You\u2019d think that businesses across the globe are pulling insights from their next-gen big data analytical platforms as we speak.<\/p>\n

The reality: According to a recent survey<\/span><\/a>, 17% of companies use big data in their organization today. <\/p>\n

Now, does that mean you should ignore it all together? Does that mean it can\u2019t help your business? Of course not. <\/p>\n

Rather, view it as an opportunity. Businesses get discouraged because they feel they’re falling behind. But, that’s just not accurate. Don\u2019t assume you\u2019re getting left in the dust. Many organizations are still warming up to the concept. They\u2019re in data-gathering mode, but they haven\u2019t figured out how to turn this data into actionable insight.<\/p>\n

\n\u201cAccording to Ginni Rometty, CEO of IBM, Big Data is the \u2018next oil\u2019,\u201d says Tom Smith, Research Analyst at dzone<\/span><\/a>. \u201cHowever several executives pointed out that this is unrefined oil for the next 10 to 20 years. The future of Big Data is providing real-time data to connect people, machines, experiences, and environments in a way to improve life experiences in a more personal way – from fewer traffic jams to better quality of life throughout life.<\/p>\n

However, there are very few companies really doing Big Data right now because there\u2019s so much data in repositories that the next challenge is to figure out how to aggregate the data so it can be analyzed. We also need to figure out the right questions to ask to transform business and the customer experience. Companies with sufficient talent are already doing these things for their clients; however, they see many more possibilities as more people learn how to prepare and analyze data across disparate databases.\u201d\n<\/p><\/blockquote>\n

Summary<\/h3>\n

While this list could certainly go on, the points listed above are just a few of the common myths surrounding big data. What do you think? Would you add anything to the list? If so, please feel free to share in the comments.<\/p>\n","protected":false},"excerpt":{"rendered":"

Summary: Big data is surrounded with so much hype, the misconceptions often get confused for facts. How can business leaders separate big data myths from reality? In this article, we identify some of the biggest myths surrounding big data, and explain why they’re false.<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-global-header-display":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","slim_seo":{"title":"5 common big data myths - mrc's Cup of Joe Blog","description":"Summary: Big data is surrounded with so much hype, the misconceptions often get confused for facts. How can business leaders separate big data myths from realit"},"footnotes":""},"categories":[8],"tags":[79],"class_list":["post-9948","post","type-post","status-publish","format-standard","hentry","category-education","tag-big-data"],"_links":{"self":[{"href":"https:\/\/www.mrc-productivity.com\/blog\/wp-json\/wp\/v2\/posts\/9948","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.mrc-productivity.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.mrc-productivity.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.mrc-productivity.com\/blog\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.mrc-productivity.com\/blog\/wp-json\/wp\/v2\/comments?post=9948"}],"version-history":[{"count":11,"href":"https:\/\/www.mrc-productivity.com\/blog\/wp-json\/wp\/v2\/posts\/9948\/revisions"}],"predecessor-version":[{"id":9967,"href":"https:\/\/www.mrc-productivity.com\/blog\/wp-json\/wp\/v2\/posts\/9948\/revisions\/9967"}],"wp:attachment":[{"href":"https:\/\/www.mrc-productivity.com\/blog\/wp-json\/wp\/v2\/media?parent=9948"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.mrc-productivity.com\/blog\/wp-json\/wp\/v2\/categories?post=9948"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.mrc-productivity.com\/blog\/wp-json\/wp\/v2\/tags?post=9948"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}