Most companies nowadays understand how important it is to collect data from a variety of sources. They know that valuable insights can be harnessed from these information that can potentially help managers and business owners to make the best decisions for their company.
However, there are also those who are wary about analyzing data because of the many misleading information about what it involves. If you are one of those who are still on the fence about data analytics, read on and see if any of the 5 myths below is what’s holding you back.
Data analytics is only for the big boys.
Many mistakenly believe that data analytics are just for enterprises and the small businesses will not benefit from it. Some small business owners also get intimidated about it because they lack the knowledge on how to do it or think that they don’t have enough data in their company to analyze. This is simply just not true.
Companies big and small need to collect and analyze data in order to grow. Even as a start-up company, you must already think about the types of data you need to collect and from which channels. Do not automatically think that you don’t have enough of it. Most companies these days record transactions in a database or an accounting system, in-house or outsourced data entry services. These data can already be interpreted and give you the results that you need.
Data analytics is only for complicated problems.
Data analytics can give your company answers to a wide variety of questions, whether they’re complicated or not. While it is true that there are many powerful analytics tools that can do more in-depth analysis, such as fraud analysis, it doesn’t mean that it can’t be used to gain insights on less complicated questions.
What really matters with data analytics is being able to have a good business question and have the data, the tools and the technique that will help answer the question.
Analytics takes up too much time.
Data analytics doesn’t have to take all of your time. Some marketers think it does because they think that they have to report on every single metric. However, this doesn’t have to be the case.
Before conducting the analysis, you must already set the questions you want answered. Then decide on which metrics are necessary to measure in order to get the answers that you need. Finally, figure out how to track them in your software. Knowing exactly what you need will significantly cut down the time you spend on analyzing.
You must have a mathematical background to get the job done.
Many business owners automatically think that analysis needs an extensive background in math, which immediately puts off those who don’t. While it’s true that a mathematical background would be helpful, you don’t have to be a math whiz to conduct data analytics successfully.
There are many different sources of information, such as classes or even articles online, especially geared towards beginners that can teach you the essential things you need to know. Aside from that, there are many different data analytics software available in the market these days that are easy to understand and to use. Take this visualized data for example: a UK contact lens distributor created a well-designed infographic on road safety that used relevant data for their target market.
You need to have expensive tools to conduct analysis.
This might be true for huge companies that have massive amounts of data and complicated problems to solve. However, a lot of businesses don’t really have a need for those powerful, expensive software.
The truth is, more than the software, the most important is the technique if you already have simple tools in your disposal. As long as you have data recorded in a database or a spreadsheet, a tool that can create charts and graphs is all you need assuming you already have a working knowledge of the techniques you can utilize to solve the problem.
Don’t immediately be scared by everything you hear about data analytics. Some of them, such as these myths presented above, are simply just not true. Do your research thoroughly first before you say no.