Customer 360 and the Data Integration Paradox

The more customer information you have, the better you can understand your customers and achieve “Customer 360.” But there’s a catch, which is that more data means more complexity – especially with mainframes in the mix. How do you cope? Keep reading to find out.

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What is Customer 360?

Customer 360 is a concept that emphasizes the importance of knowing your customers from every angle. It means gathering as much data as you can about your customers’ preferences, purchasing histories, technological platforms and so on.

Having access to this data is essential if you want to gain a truly deep understanding of your customers. Without it, you’d only be able to paint an image of your average customer in broad strokes. You’d be unable to cater to customers as individuals or understand the nuances that exist within your customer base.

By achieving Customer 360, you can personalize the experience you provide to each customer because you know that individual customer’s traits and preferences. You can also maximize the effectiveness of predictive analytics in your marketing efforts by making decisions based on the deepest, most detailed data available about your customer base.

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The Paradox of Customer 360

Achieving Customer 360 should be the goal of any modern business that wants to market to and engage with customers and potential customers in an optimal way.

But from a technical perspective, Customer 360 comes with a big caveat: The better you know your customers – meaning the more customer data you gather about them – the harder it becomes to manage that data.

That’s because, in order to know your customers truly well, you have to leverage all of the data sources available to you. You need to collect information like traffic data from your websites, purchasing histories from your inventories and transaction information stored on mainframes.

These are all disparate types of systems. In order to leverage the customer data they store, you need a way to collect data that is strewn across your organization and exists in many different formats.

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Mainframes and Customer 360

Because mainframes are so often written off as outdated legacy systems that no longer offer much value to businesses, it’s worth emphasizing just how important mainframes are to the Customer 360 equation.

Mainframes process 30 billion transactions each day. They collect vast amounts of data on a per-customer basis.

If you exclude this transaction data from your analytics, you overlook a crucially valuable source of insight. That’s why it’s essential to include mainframe data as part of your data lake when analyzing customer data using platforms like Hadoop.

Solving the Paradox

But again, integrating data from mainframes with information stored on commodity servers and other environments is not a trivial task. The data is stored in different formats, and it can take a long time to transfer information from tape storage to magnetic disks.

That’s where solutions like Syncsort’s DMX-h come in. By automating and speeding the process of migrating mainframe data into Hadoop, DMX-h helps businesses rise to the challenge of complete customer data integration in order to achieve Customer 360.

Plus, Syncsort has now added data quality solutions to its portfolio following its acquisition of Trillium. Trillium’s data quality tools help businesses get even better results from Customer 360 – as recognized by their leadership position in Gartner’s Data Quality Magic Quadrant – by making sure the customer data they analyze is not only as complete as possible, but also as accurate as possible.

 Customer 360 and the Data Integration Paradox

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