Bridging the Great Divide: Data at Rest and Data at Motion

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Steven_NoelsIn this special guest feature, Steven Noels of NGDATA makes the case for coupling “data at rest” and “data in motion” to make for better customer experiences. Steven Noels is co-founder and CTO at NGDATA, a Belgium based firm using big data technology to provide customer relationship management (CRM) solutions. He  has 15 years of product management experience, delivering solutions in data reporting applications, content management and publishing systems, and large-scale legal databases.

When we talk about big data, we hear a lot about data lakes—or data pools—where companies collect data and it sits there … essentially doing nothing. We refer to this as data at rest, where a company has so much available data but is gaining no guidance or ability to derive analysis from it. By collecting and putting your data into these ‘lakes’ or ‘pools,’ you’re essentially saying R.I.P. to your company’s most valuable asset.

That’s where data in motion comes into play. Data in motion focuses on using context while doing real-time analysis. It takes the historical data from the ‘lakes’ and folds in real-time contextual data to realize value so that tactical actions are applied to all of it.

But, make no mistake—data at rest and data in motion are not mutually exclusive. Rather, each provide value and complement each other in important ways. It’s like a tango that when coupled together, data at rest and data in motion make for better customer experiences.

Here’s how.

As I mentioned earlier, data at rest is all about collecting historical facts. It allows companies to learn from the past and, using historical data, helps predict the future. Take your company’s website, for example. Your company collects a myriad of details from its website, such as profiles of who is visiting and all the activity visitors are doing on the site—i.e. which pages are visited, who visits when, how often, for how long, and more—and this information may tell you that you’ve converted and acquired five percent of customers. What’s great about data at rest, is that companies can look at this historical data to learn what behaviors prompted and led to this conversion.

But, while data at rest is great at learning from the past to help predict and anticipate future actions, all of this is for naught without the ability to act on this data in real-time while applying context to it. After all, what good is your data if it’s not actionable?

When companies collect data, it is critical to process it in real-time to find characteristics that will help better engage with customers. I’m talking about going from a pure analytical mindset to one that is actionable—analyzing data in real-time driving action through it.

You can do this by mapping out data in such a way that you can learn from the past, understand what parameters to watch for, and look out for changes in these parameters in real-time to see which customers are becoming more or less active/inactive. This type of visibility shows whether a customer is likely to churn, a key challenge for many organizations. When a customer becomes increasingly inactive, that is a telltale sign that customer is likely to churn. For seeing that, you need to combine facts from the past with real-time analysis of the present.

Similarly, contextual information tells you important details such as when a customer visits your website—is it on the weekend? At night? First thing in the morning? Access to this information (again, in real-time!) tells an organization when the best time to engage with this customer. A good analytics solution should provide this level of detail to companies to allow them to be more actionable, for instance by deeply personalizing the customer experience with concrete understanding of the context.

Like all things, the devil is in the details. Companies are collecting data quickly, but only by having full context and visibility into this data, coupled with the tools to take real-time action on it, will make for great customer experiences.

And remember, realizing the benefits of data at rest and data in motion is a two-pronged approach. While historical data analysis alone can be helpful for deciphering a customer’s stability and pattern tendencies, this combined with the most recent interactions between a customer and a company will likely bring greater benefits to both the company and the customer, and data in motion makes this possible.


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