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August 2, 2015

Top 5 Big Data myths

It's time to make sure what you know about Big Data isn't just a myth.

By James Nunns

Before you go out tonight and chat to your friends about Big Data (I know it’s a cool topic) make sure you know what you’re talking about.

Don’t make the mistake of declaring something about Big Data only to be proven wrong. Ok, so you may not be talking about it at the bar, but you should be talking about it at work.

To help clear up any misunderstandings about Big Data, CBR has compiled a list of the top 5 myths.

1. Everyone is ahead of us in adopting Big Data

This isn’t true, while there are many companies talking about Big Data, Gartner found that only 13% of companies is surveyed had actually deployed any Big Data solutions.

This is compared to 73% declaring investment, or plans to invest in some form of Big Data strategy. So don’t worry, you aren’t lagging behind everyone, but you might want to start acting rather than just talking about it.

Maybe start small, roll out a proof of concept and see how it can benefit your business to make sure you aren’t left behind by your competitors.

2. Lots of data means good data

No it doesn’t. Your data could be absolute rubbish that may negatively impact your business if you could get it to tell you anything.

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Poor quality data could be riddled with errors and missing data, which could be misleading. Perhaps photographs or videos are incorrectly tagged.

Don’t make the assumption that just because you have a lot of data it’s going to be automatically great. You need to have an intelligent model that can sift through the data to make sense of it and advise what to keep and what to chuck.

3. Big Data can tell you the future

Big Data can guide you to a more accurate prediction of what the future might be, but it’s not going to be 100% accurate.

The best solutions will be able to tell you the probability of a number of events based upon the historical data that you have fed to your solution.

Not only is this dependent on your data, but it is also dependent upon the questions you ask. If you apply analytics with a lack of precision or detailed hypothesis then you could be lead astray.

Like asking three wishes of a Genie, be careful what you ask for.

4. You need a Data Scientist

Yes and no. In an ideal world every company could have a Data Scientist to guide your hand with decision making, but this isn’t the case.

Many tools have been created to make you SAP, Salesforce or Tableau platform capable of making intelligent and informed decisions that can guide you without needing a Data Scientist.

While some may predict the death of the data analyst all together, it is unlikely this will happen. Speaking to Big Data bosses from the tech community will end in them informing you of the importance of the Data Scientist.

One way to get around not having a Data Scientist is to outsource your analytics to someone that can do the work for you.

5. Big Data means big costs

Not really. Of course this is relative and depends upon what kind of solution you go for but in most cases it isn’t expensive.

Technologies like Hadoop can be very affordable, while outsourcing your analytics is one way to avoid costs such as integrating with legacy systems.

Virtual Hadoop platforms such as Qubole in the cloud can eliminate the expensive of physical servers and warehouse space.

The pay as you use model is one that is becoming more common with Big Data platforms, so you only need to pay for what you use, when you use it.

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