Data In The Context of Digital Transformation
In case you weren’t aware, we are on the cusp of a full-on data tsunami. Thanks to the digital transformation movement, there are a growing number of systems and devices in use in any given business, and they’re throwing off data which needs to be stored, protected, and often analyzed. Businesses are reaping huge rewards from their digital transformation efforts in the form of a better understanding of their customers, better ways of engaging with them, and increased operational efficiency due to the insights that modern digital tools can provide.
The downside, however, is that the variety of tools that IT teams use to empower their businesses cast off a massive amount of data that can be tricky to deal with. Many wise businesses understand that more value lies beneath the surface of the data collected from this swath of systems. For example, when it comes to IT, beyond just the immediate value that a monitoring system can provide, mining the data over time to discover interesting correlations, anomalies, and patterns can lead to complete revolutions in IT strategy (and even overall business strategy) that are ultimately profitable to the business.
Until the point where those discoveries are made, however, the storage of that data is an expense without an ROI. Beyond the obvious cost of active storage capacity for working data, the full data set also needs long-term retention and backup. It must also be dealt with from a compliance perspective – if the data contains personally identifiable information (PII), there may be GDPR implications or other compliance-related requirements for anonymization, data masking, encryption, and other security measures.
The Value Equation
Holding on to this data and spending the money, staffing resources, and opportunity cost on solving the above problems quickly becomes a problem if we don’t start extracting value from the data. To derive value from data, I believe that in an abstract sense, the formulas are as follows:
Data + Context = Meaning
Meaning+ Action = Value
All the data in the world is meaningless without context. As well, a solid understanding of how the various data points are interrelated is important. Once the data is contextualized and meaning is established, something can be done with the conclusion to ultimately derive value. The faster and more reliably a tool can provide context to the correlations and patterns that appear in my data, the more quickly I can act and do something useful, which is the reason I’m storing said data in the first place.
I think that a SolarWinds product I was briefed on illustrates this concept perfectly. SolarWinds acquired Pingdom in 2014 to roll in their suite of cloud monitoring products. The product line is all SaaS-based and includes other tools such as AppOptics, Papertrail, and Loggly (all of which are also either acquisitions or combinations of acquisitions).
Data + Context = Meaning
Meaning + Action = Value
It’s been years since I’d thought about Pingdom; the last time I looked at it was pre-acquisition. Honestly, my impression of Pingdom was that it was a glorified uptime monitoring tool that just sat there and pinged your website to make sure it was online. And maybe back in 2008 I’d have been right; but as I came to understand during Tech Field Day 16, it does a whole lot more than that today.
Data + Context = Meaning (+ Action = Value)
Pingdom’s Visitor Insights module looks at the end user experience of a website (things like page load time) and and tells me how those technical metrics correlate with behavior metrics my business would care about (such as bounce rate). Pingdom’s Visitor Insights can tell me that page load times are up and in a statistically significant way, an increased bounce rate correlates with the increased page load time.
In a less unified set of tools (perhaps using Google Analytics plus PageSpeed Insights), it is up to you to draw these correlations. This is data without context. The beauty of a tool like Pingdom is when the data comes together in context. Understanding how these different metrics affect each other ultimately leads me to a meaning: my web page loading slowly is causing visitors not to stay.
Having understood the meaning of the data – that visitors aren’t hanging around while the page loads – I can choose to investigate and correct the issues causing page load time to increase. As a result, my bounce rate is likely to drop, more visitors will stay and interact, and hopefully somewhere down the line I profit.
The ActualTech Take
Getting data is not the problem that it once was. We’re inundated with data at this point. The problem is drawing out meaning from that data with enough direction to take action and create value. The two keys: 1) how quickly we can mine that meaning out of the data, and 2) how accurate that identified meaning actually is. If we can improve in those two areas by leveraging tools like Pingdom (and the others in the SolarWinds Cloud portfolio), we’ll be well on our way to truly achieving the digital transformation of lore.