Why every business needs data analysis

Making decisions 

“... the more you know about it, the wiser and better you will be”.  

This has never been truer than in the global economy we exist within today. As businesses seek to grow across various markets and expand into other economies, they will encounter challenges as the customers, the products and the cultures change.  

By collecting data, a business mitigates the speculation and risk of their important decisions, whilst achieving a much greater understanding of demographics and behaviour. In turn, this will lead to a better approach across the board, as well as increased profitability. 

 

Marketing  

Information has always been key to the marketing team. When is the best time to send an email? What topic gets most engagement? Are we sourcing customers in the right way?  

Historical data is essential to analysing what works exceedingly well, and what misses the mark. This is why every social media platform now comes with a business analysis option, so you can see when people are clicking on your posts. Google Analytics has become one of the most valuable tools for anyone with a website – and who doesn’t? 

Would Coca Cola have made their ill-fated recipe change with a better understanding of their customers’ preferences? Or would McDonalds’ Arch Deluxe burger have failed so badly with a better marketing message built on the data of previous campaigns? 

 

Efficiency 

Some might see this as a way of clamping down on the workers, but far from it. This is about making an employee’s workflow more streamlined, removing the extraneous operations that don’t contribute to their work or well-being.  

A study in 2022 by Harvard Business Review, found that 70% of all meetings keep employees from completing their tasks and projects. Without the data to prove otherwise, it can be difficult to shake traditions and practices, even if they’re obviously detrimental everyone except that one manager. 

Data can also spot bottlenecks in workflows and optimise the process to improve output. This doesn’t just necessarily mean manufacturing, this could be energy project that requires a lot of moving parts to work together, from software development to sales. 

 

Research 

What works now probably won’t do so forever. Demand, trends and technology change at a rate so rapidly that it’s barely possible for the consumer to keep up, never mind the businesses that relied for so long on a specific product. Consider how Kodak, which ruled the photography domain for decades, almost seemed to disappear overnight with the advent of digital cameras and smartphones. 

Using data to predict trends and study their competitors' movements allows a company to stay ahead and stay relevant, a difficult task in this modern era. Tracking behavioural and psychographic trends will ensure that an organisation can launch a product with far less risk than it might otherwise have done. 

 

The types of analysis and example industries: 

Descriptive analysis 

What happened in the past? How did we market the product and what were the sales like? Did we have more meetings in the past, and has there been more productivity in the office since? 

Descriptive data is essential to every industry because only by learning from the past can we best plan for the future. Especially across sales-lead companies, this type of data gives stakeholders the ability to clearly see what they need to achieve to continue growth and push targets. 

 

Diagnostic analysis 

So, we know what happened before. Yet, the big question is always, why? Our marketing socials had more engagement than ever last month, but was that due to the content, the medium, or the time that they were posted? 

Diagnostic analysis is part of the overall package that every business needs to use data effectively. Across banking and finance services, learning why a stock dropped or rose is more important for the future than just knowing that it did. If production levels fell last quarter, then what was the reason why, and how was it different from the month before? 

 

Predictive analysis 

Now that we understand what happened and why, we can begin to make forecasts for the future. Predictive analysis is used by businesses to see all possible outcomes and events, and determine which ones are the most likely. 

This type of analysis is used across atmospheric and hydrographic sciences to provide the information we see on our weather apps. Banks use it for interest rates and the government for economic policy. Football teams will use statistics and predictive analytics from that when considering training regimes and buying players. 

 

Prescriptive analysis  

What do we do now?  

Prescriptive analytics is the realm of data-driven proposals and actions. The other analysis types have all led to this moment, what do we do with what we know? Assuming the data has been handled correctly through the first three processes, businesses should have a clear understanding of what options are available to them and will provide the best outcome. 

A marketing team will plan to launch their campaign at a specific time of year depending on their customer’s behaviour. A financial institution will decide to buy or sell based on their predictive analysis, whilst a manufacturer can improve on efficiency based on the smallest of predicted tweaks to their process.