7 Common Misconceptions About Big Data Analytics
Big Data has been a buzz word for a little while now, yet there are still several common misconceptions about Big Data, and how exactly Data Analytics works.
No matter what industry your business is in, chances are they’re going to be processing a lot of data. Whilst data analytics have already changed how businesses process and translate their raw data into actionable insights, companies have started gathering large volumes of data from a variety of sources including mobile, transactional, and behaviour. After processing this data through a reliable and flexible data analysis software solution, the results can bring about drastic transformation to your business. Still, many misconceptions have arisen about the value and strength of big data:
1. Big Data is Inherently Valuable
Raw, unadulterated data holds no inherent value to you, or your organisation. In order for the data your organisation receives to hold any value, it must first be sorted, processed, and then distributed. In order to do this effectively, you must know what questions you want answering before you start looking through the data. Only then will anything of value come to light.
2. Deliver Self-Learning Algorithm
For over a decade now companies have incorporated the results of their big data analyses into their service offerings, to varying degrees of success. Whilst some leading eCommerce brands give purchase suggestions closely related to the product a given customer is searching for (Product X); these suggestions may not necessarily accurately reflect the customer’s requirements (Product Y).
Careful analysis of accompanying purchases made by customers who previously bought Product X would better indicate which products are usually bought alongside or shortly after the initial purchase, thus providing a much higher chance of success in on-selling these products.
3. Lots of Data is Required
With a name like big data, you’d be forgiven for thinking that you need ridiculously large pools of data in order to glean any actionable insights; that’s simply not the case, Whilst large pools of data can be useful, much more important is the quality of the data. Large data sets are prone to duplicate or redundant entries, and whilst a solid data analysis software solution can easily identify and discount any duplicate entries you might encounter, your time would be much better spent if you ensure the data you are receiving is of a high quality from the onset.
4. Bring Major Changes
One of the most common misconceptions about big data is that analysis of it always results in big changes for an organisation. As mentioned in our first point, big data in and of itself is not actionable. Even after your data scientist has picked out the important information, you’ll still need to make sure you’re asking the right questions of said data before anything of value can be gleaned.
5. Big Data is Only for Big Business
Every single one of the FTSE 500 companies uses data analytics for their predictive analysis, but you don’t need to be a large organisation in order to make effective use of big data. Even smaller companies can utilise it to gain profits for their business. The use of big data has successfully expanded across different industries and created benefits for companies of every size.
6. It’s a Solution for Everything
As my colleague, Ricky, is keen on saying, “data analysis is not a wonder weapon.” Whilst the benefits of effective data analysis to an organisation are significant, they only truly become apparent when used in conjunction with other activities employed by said organisation. Ensuring your organisation has an effective process for translating the data you obtain after your analysis into something actionable is key to ensuring a high return on investment.
7. It Will Replace Your Data Warehouse
Some will believe that Big Data and Data Warehouse are mutually exclusive, but that’s not the case. Once you’ve obtained and processed your customer data, it will enter your data warehouse – a storage system that holds all of your customers’ big data. It is from here that the data is then available to be interrogated, and any insights from it uncovered. One cannot operate without the other, and so it is unlikely one will ever replace the other, either.
What do you think? Can you think of any other common misconceptions about Big Data? Leave a comment below.