How To Use A Data Warehouse For Customer Analysis

Over the past decade, the rate of data generation has reached a level in which the vast majority of companies are generating more data than they know what to do with. In fact, around 73% of all data generated by companies goes unused, simply due to the fact that there is more data than they can handle at once.

Yet, data analysis is a leading method of improving the success of business in general. Especially when it comes to customer satisfaction, businesses that use customer data and actively tailor their content to suit their specific audience will have 131% more sales and 126% more profit than competitors that don’t make use of customer analysis.

However, how can companies deal with the increasing amount of data, moving through it and selecting which aspects are most important for their analysis investigations? The simple answer is to use a data warehouse, with these aspects of data infrastructure providing invaluable centers for data storage, processing, and analysis.

In this article, we’ll explore how data warehouses are used to make customer analysis more efficient, providing a faster, easier, and more scalable method of putting customer insight to use.

Let’s get right into it.

Data Warehouses Make Data Easier To Analyze

Data warehouses act as central locations where absolutely all the data that a company produces can be sent to. Instead of different departments holding onto different data sets, a data warehouse ensures that everyone knows where to put data. As a result of this collection strategy, all the data that analysts need to effectively do their jobs becomes readily available.

Alongside simply storing data in one location, data warehouses can also clean up data and regulate its structure. This means that while data might come into the warehouse in a range of formats and files, the warehouse can effectively catalog it, transforming it into a format that is much easier to analyze.

With this, data warehouses effectively tame the mass of data that companies have to deal with, turning an indistinguishable mess into something clear that follows logical constraints. When it comes to analyzing customers, all the data is already collected and in a usable format.

This means that data scientists will then spend less time cleaning, structuring, and selecting data, and more time actually conducting analysis. With this, companies can access insight from their data much more easily, turning the stream of data into something they can read and act upon.

Quite simply, data warehouses ensure that data becomes useful once again, allowing analysts to rapidly access needed data at scale.

Data Warehouses Improve the Speed of Analysis

The introduction of AI tools into data warehouses has increased the speed with which analysis can be undertaken significantly. When conducting manual analysis, data scientists need to create specific databases, work out which questions they want answered, and then discover how to incite answers from the data itself.

While this process would take days or even weeks of work, depending on complexity and specificity, the introduction of AI has made this into something that takes mere minutes. Data warehouses can rapidly move through all of the data involved, creating analysis that far exceeds the scope and detail that is possible with human analysis.

What’s more, looking at a comparison of Apache Druid vs Pinot, two leading cloud data warehouses, the speed of queries is simply unmatched. What would take humans days of building databases and inputting custom formulas takes only a few seconds within these leading data warehouses.

The speed with which analysis can be conducted is breathtaking, allowing businesses to access more specific insight at a greater level than ever before. 

Alongside allowing data scientists to conduct analysis without having to structure the data themselves, the speed with which data warehouses permit them to act has radically changed this industry. Equally, the movement to automatic analysis has allowed for real-time analytics to enter into the world of customer analysis.

With this, companies can actively track customer movements, discovering trends in their behavior in real-time which can help to create strategy going forward. Insight into the customer journey allows a business to better understand how their customers act, what they focus on, and what products are most popular.

Availability Has Lead To Scalability

Alongside the benefits of data warehouses, with the modern movement toward data and data tracking as a whole, cloud data warehouses have become more available than ever. With this in mind, businesses are no longer constrained by their own local facilities.

In the past, the scope of data that could be collected and analyzed was directly related to the amount of storage space that local data centers could handle in a business.

However, with the rise of cloud data warehouses, businesses can now partner with any number of online services and use their data stored. If a company needs to increase the amount of data they are analyzing, they can simply scale their plan, providing a limitless method of increasing both storage and analysis of data.

The connection between accessibility and scalability is perhaps one of the most important benefits of data warehouses for businesses. With this change, there is no longer a limit to what can be done with data, with customer analysis being an ongoing part of strategy that warehouses enable.

As this industry continues to develop, we’re likely to see a range of further incorporations between business analysis and data warehouses, helping to push the bounds of what is possible with customer analysis even further.

Final Thoughts

Although customer analysis is vital for companies that want to continually engage and please their consumer base, it is increasingly difficult to do due to the sheer amount of data that companies produce on a regular basis.

However, with the introduction and mass adoption of data warehouse tools, a method of staying on top of data production and management has become easily accessible.

By turning to data warehouses as a single source of truth for data, companies are able to conduct analysis on a huge scale, pulling together potentially years of customer data, as well as industry averages, in order to reveal more about their customer base.

While effectively using data warehouses, companies can thrive within this digital age, using the array of tools and structuring techniques offered to instantly understand their customers and enable better future planning. In this age of data production, companies must turn to the very best tools to stay ahead of the curve and continue to deliver unmatched user experiences to their customers. 

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