While plenty has been written about big data, it seems that misconceptions of what it is and how it can affect a business are still running rampant. True, there are plenty of myths about nearly every topic in existence, but failing to address inaccuracies can cause business owners to make incorrect decisions or assumptions when investing in big data. To help keep decisions sound, Gil Allouche is here to identify and debunk five of the most common myths around.
Unstructured data only
This myth has been generated mainly by the imprecise use of the word 'unstructured'. While one of the huge advantages of big data technology is that it can store and process unstructured information, the technology is really built for multi-structured data. In other words, it can store a wide range of data types, including structured data, text strings, audio files, email messages and social media. What really characterises big data sets from traditional ones is that the collection will consist of varied data types that don't need to be adapted in order to be captured and stored.
Low quality data
While big data can suffer from low quality (just like every other set can), that does not mean that it will inherently contain low quality data. In fact, a large set of information can provide greater accuracy than a smaller sample contaminated by human assumption and bias. If you take proper data quality assurance with all of your large data sets, your outcomes should turn out just fine.
Only large companies produce such huge data volumes
The inclusion of the word 'big' causes many small business owners to assume that big data is purely for large companies with huge amounts of information. Nothing could be further from the truth. Although volume is often discussed as a key attribute of big data, there is no set amount that qualifies. What matters is that everybody's data is growing in size. In addition, enormous data sets are generally not analysed all at once anyway, so the amount of information you have really isn't such an important factor when it comes to big data.
The size of the organisation shouldn't matter either, since every business should strive to run based off data-driven insights rather than gut feelings or intuition. In fact, big data may be the key that allows a small business to outpace their larger competitors.
Fresh talent is needed
When a business considers investing in big data, they often make the assumption that they'll also need to hire a scientist already familiar with complex algorithms and running a big data system. When they discover that these individuals are difficult to find, they presume that the technology will be too hard to implement. However, there are many big data startups that offer tools that don't require special skills or expertise, through a SaaS or DaaS solution. These package solutions will create the algorithms you need and are often specifically built with small businesses and limited resources in mind. Big data as a service is particularly helpful in providing the technology that makes it simple to prepare, integrate and explore big data in the cloud.
Machine learning removes human bias
While machine learning has the potential to do many great things, it is not the solution to removing human bias. Machine learning still requires humans to make key decisions, such as which use-case to pursue or which machine-learning methods to use, which introduces bias into the process. In addition, it is ultimately a human that interprets the models until they provide (what the individual believes to be) reliable output. Machine learning can be a powerful tool, but human interference must still be accounted for.
Hopefully debunking some of these myths has made you more confident in your investment in big data technology. Should your organisation choose to use a big data service, remember that this is a new field and some of the services will be much better developed than others, so do your homework on which one is best for you.
Gil Allouche is the vice president of marketing at Qubole. Gil began his marketing career as a product strategist at SAP while earning his MBA at Babson College and is a former software engineer.
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