If you’re looking for ad hoc reporting tools, I hate to break it to you, but you’re as late to the game as a navigator trying to find the New World in 1493.
If you want to discover value in your data the same way Columbus discovered the New World, it’s time to go beyond ad hoc reporting tools and look for software with augmented data discovery.
Augmented data discovery (formerly called “smart data discovery“) is business intelligence software that automates the time-consuming, error-prone, manual parts of discovering value in your data.
In this article, I’ll do three things:
- Tell you why ad hoc reporting tools alone will put you behind the curve
- Tell you how to get ahead of that curve with augmented data discovery
- Explain this via a holiday appropriate metaphor, comparing navigational advances in Columbus’ day (a day after Columbus Day) to business intelligence software advances in our own
To paraphrase that great New World philosopher Bugs Bunny, I think when you read tech journalism, you should learn something.
What is ad hoc reporting?
Ad hoc reporting is the ability to explore and get answers from your data, when you want it.
You might be tempted to say, “Well, when else would I want it?” For a long time, however, that was whenever the IT department, or your business intelligence analysts, could get around to exploring the data for you.
Older, traditional business intelligence software was too complicated for average business users, so they had to rely on their IT department to query their data for them.
Ad hoc reporting tools changed all that. Ad hoc reporting tools were far easier to use than traditional business intelligence software. You didn’t have to know how to “speak” a computer coding language to use them. You could drag and drop information like you do on a desktop, and get the same results.
Ad hoc reporting tools were, and still are, solid ways to explore your data and find insights. But, they’re not as sophisticated as augmented analytics. Even visual-based data discovery tools that turn complicated spreadsheets into consumable visuals have their flaws. It takes a long time to crunch all the numbers, and you could miss out on connections and associations thanks to human error.
Ad hoc reporting tools are an unreliable means of navigation, much like dead reckoning was in Christopher Columbus’ day.
Ad hoc reporting tools: A dead reckoning of data’s value
Dead reckoning is somewhat reliable, low-budget navigation. You figure out where you are by guessing at what direction you’re going, and how far you’ve gone.
You do this by checking your course with a compass, checking your time with an hour glass (or water clock), and checking your speed by throwing trash over the side of the ship, and seeing how fast it moves. Add that speed to your compass direction, and voila! You know where you are. Sort of.
A navigation system that relies on pitching logs over the side of the boat seems a tad rudimentary
Dead reckoning is a lot like ad hoc reporting tools. You can discover a lot of insights with ad hoc reporting tools, and dead reckoning was reliably used by more than one explorer. But, both are comparatively messy and imprecise.
Dead reckoning couldn’t tell you longitude, for instance. And if a “sea-sick cabin boy” forgot to turn the hourglass, so much for a precise reading.
Similarly, ad hoc reporting tools can be useful, but they’re also messy and imprecise. And, not only are “data volumes increasing,” (full research available to Gartner clients only), but that data is also “becoming more complex.”
That means there are more variables, and it’s harder to determine how all of them interact (should I cross-ref zip code with age? Or P&L with the last two weeks of the quarter?)
The increasing number of data points, and possible connections, will overwhelm analysts, causing them to inevitably miss connections and rely on biases. That’s not to say anything against analysts—a tool or system with built-in imperfections breeds mistakes. Ad hoc reporting tools depend on a limited human mind (and schedule) to analyze all the possibilities.
But where ad hoc reporting tools depend on subjective human interpretation (and error), augmented analytics depend on objective machine learning algorithms. In that sense, augmented analytics can do for your business what celestial navigation did for Christopher Columbus.
Augmented data discovery is your way to a new world
There’s some argument, actually, over whether Columbus used celestial navigation. For the purposes of this article, though, I’ll assume he did some celestial navigation, because 1) his journals frequently refer to the positions of the sun and stars, 2) he took celestial navigation tools (an astrolabe and quadrant) with him, and 3) the comparison between celestial navigation and augmented analytics fits too darn well.
Augmented data discovery and celestial navigation are similar:
- both are new methods that offer huge benefits
- both can help get you to a New World, whether that’s the Americas, or a data-driven analytics culture
Celestial navigation uses a more reliable method (reading heavenly bodies) to get more accurate results, and help you discover more, faster.
Augmented data discovery uses a more reliable method (machine learning algorithms that are less likely to make human errors) to get more accurate results, and help you discover more value in your data.
Celestial navigation: just a bit more complicated than dead reckoning
To quote Gartner, augmented data discovery is “a next-generation data and analytics paradigm that uses machine learning to automate data preparation, insight discovery and insight sharing for a broad range of business users.”
In plain English, that means that tools with augmented data discovery capabilities can check more data points, and suggest more connections between them, than a person could do.
It also means that augmented data discovery can communicate those connections to more people, because it makes its observations, and recommendations, in plain English. In other words, the software can give you the same insights that professional data scientists get.
Traditional ad hoc reporting tools can only handle so many variables at once. Celestial navigation, contrarily, adds more variables—namely, the position of the stars and planets. It also uses more advanced tools, like astrolabes, to turn those variables into discoveries. Augmented analytics can also factor in more variables, thanks to the machine learning algorithms it uses.
Augmented data discovery: early discoveries
Some business intelligence vendors are already pioneering augmented analytics capabilities. Microsoft Power BI, IBM’s Watson Analytics, DataRPM, and Answer Rocket are all vendors that have invested in augmented data discovery features.
DataRPM, for instance, used augmented analytics capabilities to help a medical manufacturer better test their products.
A medical transducers manufacturer (the parts that transfer information from an ultrasound to its computer) needed to cut down on the number of defective products rolling off their assembly line. The company makes 60 kinds of transducers. Each transducer takes 15 steps to assemble. In addition, there are also 150 smaller steps that go into creating parts. That’s far too many variables for an analyst to check over with an ad hoc reporting tool. The room for human error is dizzying.
Data RPM’s machine learning algorithms gave the medical company a way to turn that dizzying number of variables into a lower failure rate. Data RPM’s Cognitive Predictive Maintenance platform isolated defective products, then found which steps in the assembly process made those transducers defective. It used this information to also figure out the main sources of failure so they could be prevented in the future.
The result? A failure rate that dropped from 4% to 1.5%.
Augmented analytics and you
If you’re using ad hoc reporting tools, consider making the move to an augmented analytics tool.
To learn more about software vendors developing augmented analytics capabilities, check out the vendors mentioned in Gartner’s “Other Vendors to Consider for Modern BI and Analytics” (Gartner content available to Gartner clients only).
If you’re interested in learning more about augmented analytics, check out one of these Gartner Digital Markets posts: