5 Misconceptions the Retail Industry Still Has About Data Analytics

data analytics misconceptions in retail industryData analytics is permanently changing the business landscape for the better. This is because the insights afforded to companies utilizing this technology makes them far more prepared for both proactive and reactive decisions. Data doesn’t lie. Most professionals who are already using data in their organization see how it increases profitability, and opens new opportunities. Despite this, there are still some people who are resistant to get on board with the data revolution. Professionals in more traditional industries, such as retail, often fall into this category. Here are five misconceptions the retail industry still has about data analytics.

1. It Will Cost the Company a Lot of Money

No one wants to spend money unnecessarily. However, there’s a reason why so many people have repeated this popular phrase: You have to spend money to make money. Many executives in the retail world fear that the amount of capital investment for data analytics will be too great. This is largely due to a misconception about the nature of modern analytics. You don’t need to have rooms full of expensive hardware and massive IT teams in order to build and maintain a data analytics department. In today’s world, many of the best analytics tools operate through the cloud. This usually involves a reasonably priced subscription fee, and requires no expensive hardware because the program runs through the Internet. The capital investment for data analytics is actually quite reasonable—especially considering that it will likely make your company money.

2. Data Itself Is the Only Important Element

Some people might think that the raw data is the only thing that matters in data analytics. They then conclude that if they can collect their own data, they can do the rest on their own. This is a dangerous mistake. Data isn’t worth anything unless it can be synthesized into a usable model. This is where data analytics comes into play. Without some level of expertise, and proper programs, you’re not going to develop any groundbreaking insights with your data.

3. It’s Too Late to Start Using Data Analytics

Due to its growing popularity, there are individuals who think it’s too late for them to start using retail industry data analysis. This is a mistake. It’s never too late for an organization to get on board with data analytics. In fact, the longer a company waits, the more it will miss out on potential benefits. Competitors in the retail industry are wising up to the overall positives of incorporating data analytics. This is an essential step for businesses that want to be successful in the world of tomorrow.

4. You Need to Have Concrete Questions before Approaching Data

It’s also common for people to think that they can only use data analytics if they have specific questions in mind already. While it helps to have concrete ideas, it’s not always necessary. Modern data analytics tools like relational search allow workers to instantly integrate data sets. If something pops into your head, it’s now possible to get an answer for it right away as opposed to just forgetting about it because there’s too much work involved. These spontaneous insights can be some of the most productive, and produce the greatest overall benefit to a company.

5. You Already Know Enough

There are also business owners who believe the purpose of data is just to validate things. While data can and should be used for that purpose, it’s not at all its only function. At its core, data analytics can uncover key patterns and trends that would have been totally discrete to the human eye. It’s arrogant to believe that there’s no new knowledge that can be gained from processing raw numbers in new ways. The organizations that get over this hump are the ones that will see the most success in the modern economy.

There are still many misconceptions out there about data analytics. Fortunately, many business owners are starting to see the immense applicability of this data revolution.

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