A top reason customers choose Heap is for the ability to automatically capture customer behaviors on their website and integrate them with the rest of their customer data stack: shopping carts, payment platforms, CRM, email marketing, and A/B testing suites, to name a few. Heap makes sure that you have all the data you need to make a decision, automatically and retroactively, even as the scope of your questions changes over time. However, capturing the data is just step one; you need to be able to cleanly and flexibly interact with it for any analytics to be useful and viable. To make data useful for teams across your organization and for questions of all shapes and sizes, you cannot manipulate the raw data directly — it’s fragile and inflexible. Data models break weekly and, eventually, only the most technical members of your organization can interact with the data at all. We’ve heard this story a countless number of times, and it’s something we want to solve once and for all.
So today, we’d like to present a solution to data woes like these: data virtualization. To provide an introduction to the topic and highlight the impact data virtualization can have on the major problems that hamstring analytics today, I’ve written our newest whitepaper, “Why Data Virtualization is an Analytics Game Changer.” Check out a teaser below!
Let’s say you’ve just written a letter using a typewriter. As you’re reading it over, you notice a typo. You grab some correction tape, paint over the error, and carefully draw in the right letters. As you keep reading, you notice that a particular sentence is phrased poorly, and another paragraph might make more sense if it were moved up in the document. Now the Wite-Out won’t cut it; the edits are too complicated to make on the fly. You could re-type the entire page from scratch, or just leave it as-is and hope it’s good enough.
This was the state of writing and editing before the word processor. Today, the edits described above would take a few seconds rather than hours of re-work. And it’s more than just time savings: word processors have fundamentally changed the way we write. Instead of carefully pre-planning every keystroke, we’re free to type as quickly as thoughts come to mind, knowing that we can refine, edit, delete, and rearrange our work later as needed.
The modern analytics stack is still stuck in its typewriter phase. Modern data and engineering teams spend countless hours of work on data collection and schematization only to have to re-instrument over and over again. Instead of spending their time building predictive models or finding the signal in the noise, data teams spend 60% of their time cleaning and organizing data in preparation for analysis.
Even worse, all that time spent cleaning isn’t necessarily leading to the promised land of trustworthy, complete data. Only 38% of organizations have high confidence in their data and analytics for customer insights.
However, a powerful new technique called data virtualization is drastically reducing the time that data teams spend on data preparation. Data virtualization is not only saving time, but, like the word processor, it’s shifting the way that people interact with data.In this white paper, we’ll begin by giving a technical overview of what data virtualization is. Then, we’ll discuss the biggest problems related to data organization and cleaning that arise in a modern analytics infrastructure and how data virtualization can address those issues. Finally, we’ll discuss the state of the data virtualization market today via real-world examples of data virtualization being used successfully.
From this point, the whitepaper details the major data problems that limit data and analytics teams today, as well as why data virtualization is well suited to address them. Additionally, it covers the current landscape of data virtualization tools. If you’re spending most of your time cleaning data, or find that your data initiatives are stalling and delivering sub-par returns, then you’ll want to check out the rest of the whitepaper to get a primer on data virtualization and the problems it solves.
To read the rest of the whitepaper and learn how data virtualization can free up your data team and greatly enhance your data initiatives, download it now.
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