Tue. Jun 18th, 2024
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Data analytics is vital for any business, for a whole host of reasons, but the ultimate reason is this: data holds the key to optimizing your processes and performances. Data can come in all shapes and forms, including both numerical and written, and the way that you analyze it can also vary from context to context. So, if you’re just starting out as a business data analyst or looking to move into this career, it might seem quite daunting. 

This blog post will explore what data analytics is, and how it can be deployed in a business context to make your firm operate in a more efficient manner. It will also delve into the different types of data analytics, and look at how to capture it as well as what to do with it once you have it. In short, if you’re looking to transform your data analytics for business, this is the place to do it. 

What are data analytics?

Put simply, data analytics is the science and practice of capturing and looking at large amounts of information in order to extract insights into what happens and why. In an age of big data, and with so much data about customers, suppliers, and others available around every corner, it’s becoming increasingly more important to analyze data – and if your business is failing to do so, then it could be falling behind competitively.

Data Analytics

Data analytics can be used for a whole host of purposes. It can be used at a macro level within organizations to see trends: a brand might measure how many people visit its website and leave within a certain amount of time, for example, because this so-called ‘bounce rate’ can have an impact on sales. This doesn’t tell you everything, of course, and other data sets might then have to be acquired or tapped into in order to look at why. By pairing up different sets of analyzed data, a company can work out different parts of the puzzle.

Another way that data analytics can be used is to give individual customers what they need – but at scale. A company might, for example, personalize the customer’s engagement with the brand by capturing data on what pages of the brand’s online merchant catalog they look at, and then using this to remarket either on the same platform or elsewhere. That way, the customer gets an individualized experience – but the company can use the same technology to rinse and repeat the process.

Raising profits, cutting costs

It’s also worth thinking about the aim behind using the technology of data analytics. Usually, it’s to increase revenue and profits. Some of the most cutting-edge forms of data analytics tools take current data and apply artificial intelligence to generate potential data about the future. For example, a company that produces computers might run its current output through a predictive data analytics system, which will then contextualize it and identify how easy – or otherwise – it might be to scale up. If this suggests that scaling fast and big is likely to go well and not hit snags, then a company owner might use this new-found, data-powered knowledge to open a new plant. 

While the profit-boosting, or at least revenue-boosting, benefits of data analytics are relatively obvious, there can also be an impact on the cost side. Data analytics can enrich management accounts by showing in a dynamic fashion where any inefficiencies in production line systems lie, for example. This can in turn allow effective choices to be made by senior managers when it comes to procuring technology, allocating staff resources, and more. 

It’s easy to see, then, why institutions such as Walsh University are starting to offer courses that supply the labor market with the people required to make this sort of change happen. Doing an online master’s in data analytics is fast becoming a useful way to get the knowledge to work in this sector, and it’s helping to ensure that there are enough people with the required skills. 

Qualitative data

Finally, it’s also worth exploring the fact that not all data is numerical. In the data analytics world, the term ‘quantitative’ refers specifically to numerical data. ‘Qualitative’, on the other hand, refers to data in which the insights can be measured in words. This might include data collected through written information, or perhaps through sentiments or opinions expressed in verbal assessments.

The world of data analytics for business tends to focus on the former sort of data, and on the way that it can provide cold, hard evidence of trends in a particular direction. However, it’s also worth investigating the possibility of analyzing qualitative data. Qualitative data can tell you things that quantitative data often fails to pick up on, such as how people feel when they buy a particular product, or what their particular pain points are when they contact customer service. This might require a specialist professional to come in and advise your business on how to approach the question, or it may require a certain sort of software that is capable of word analysis. 

Final thoughts

Ultimately, data analytics is becoming increasingly important to businesses. It’s essential for companies to make sure that they are using the latest technology to capture information, whether it’s to extract macro-insights into the way that system-wide assets such as websites are working, or simply to ensure that the user experience is customized. In the end, all data analysis can and ought to be used to keep costs low and profits high – and so it should be integrated squarely and firmly into your practices. Whether it’s quantitative or qualitative data you’re planning to use, it’s important to make sure that you have the right software or human resources on hand to get to where you need to be.