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By Jeff Wing
March 29, 2017
About 20 years ago, the digital revolution descended on the business world like a storm front. Software solutions flooded the marketplace, making all kinds of excited promises about technology’s ability to tune up business processes, iron the wrinkles out of the workflow, and generally save the day. Forward-thinking construction outfits (some of them) saw the potential and snapped up these efficiency-boosting software products like crazy.
When the fever broke and cooler judgments began to prevail, early tech-adopter business folk realized none of these hastily purchased software solutions could talk to each other. The solutions were siloed. There was lots of copy/pasting of company data to get the new “software solutions” to work as a unified whole. And if you wanted to analyze your data for clues to bettering workflows, you had to buy still more software (analytics this time) to help you find hidden clues to improving efficiency. The office junk drawer continued to fill up with install discs. Everyone had a hunch there must be a better way.
Then SaaS came along and changed everything. SaaS (Software as a Service) produced and offered its own tools, parked them in a collaborative Cloud where you and your team could drop in and make use of them 24/7, and provided the tools as a service. Now you didn’t have to fuss with installs and upgrades. The tools in the Cloud were effectively rentals. The server room and IT team were happily moved to another office––your software vendor’s cloud––and software became, of all things, a subscription. With SaaS, the era of the cluttered server closet had ended, and not a minute too soon.
Now, something extraordinary is headed our way that is going to open SaaS’ hood and throw in enough high-octane fuel to kick the whole game to a previously unimagined new level.
Data-Driven Software (DDS) is a new way of achieving Business Intelligence. Data-Driven Software products will integrate with your SaaS to deeply analyze, and draw strategic direction from, the richly accrued (and anonymized) data your SaaS has been collecting and warehousing in the course of your regular business practices. If the phenomenal new breed of software is half as smart as its promoters and early adopters assure us it will be, there is a huge sea-change coming in the way we approach the strategic business landscape.
These new programs will crunch the numbers and extract from this extremely granular data the mathematical truths buried in your years of routine record-keeping. DDS promises to replace conventional business wisdom with the truth-telling purity of math, using statistical models to be both predictive and prescriptive; the software will tell you what is likely to happen next and what to do with that information. In some proposed Data-Driven Software models which use something called “machine learning,” the software will even act autonomously to carry out the action its own findings recommend. If you ask it to.
It’s the kind of drenching storm that sweeps in without commotion and quickly makes you realize you’ve been living in drought conditions. The strategic opportunities inherent in DDS are enormous. But the change will be big, and for some, unmanageable. It’s going to take some prep in our collective thinking. As companies of every kind make eager use of this new business intelligence tool, the competitive advantage will go to those who have the clearest understanding of what the new tool can do, and how best to use it. This new analytics-based phenomenon uses the tenets of Big Data to provide answers to questions we don’t even know enough to ask yet.
Here are a few ground-preparing notions to keep in mind as this new culture-changing business tool approaches and rewrites expectations:
Data-Driven Software will be mining your SaaS-aggregated data so deeply the conclusions drawn from the process will likely not match your business intuition. DDS will be using statistical models that look for deeply buried, non-evident relationships and connections between things that would not ordinarily be considered related at all. What’s key here is the user’s ability to overlay the DDS findings with his or her own niche expertise in the business environment. A light will go on when the deep data results are seen in the context of your own deeply understood expertise.
You will use Data-Drive Software to gain insights on the best direction forward and vastly improve results with every iteration. As an example, Google Ads uses data loop intelligence on its AdWords service, and we all intuitively understand how they’re achieving their great, and greatly focused, AdWord results. When you “Google” something and you get your result, there will be AdWords accompanying that search result. The Google Adwords that show up at that point are not only based on the advertiser's’ bid amounts, but on Google’s prediction as to which ad is most likely to get clicked by a user––in this case, you. That prediction is based on Google having noted how often searches similar to yours have then gone on to click the accompanying AdWords. It’s as if Google is using every search as a sort of data-learning loop to teach its algorithm where it could do better.
This “data-learning loop” can be applied to your use of data-driven software. As you get your results, you learn where they could be bettered and you feed those lessons back into the data-mining operation. When you use already successful data mining results to further refine, you are piling “better” upon “good” to climb an exponential ladder to much higher accuracy and stunningly clear decision-making. You will quickly close in on the best practice, whether it’s predicting sales, understanding the best times to purchase materials, anticipating a labor shortage, or knowing where and when to relocate the business. The beauty of data-driven software is that applying its business intelligence has an observable impact, and from that you can accurately mold and shape and improve your decisions.
While DDS (and the spirit of Big Data that fuels it) look as though they might be able to actually keep their big promises, the rewards should not all be turned inward. When you are able to advance the interests of a customer or client by leveraging the benefits of DDS, you have won that customer forever. Make sure that a healthy portion of your data-driven riches are shared out to your customers in the form of an enhanced customer experience. When your DDS mining tells you, for instance, when a certain type of wood is likely to be in surplus in the marketplace, share that data-mining intelligence with your wood supplier and fold those savings into the bid. In the end, all your data-driven business practice and customer experience enhancements are brought to you by––your massed customer data.
The data as used is truly customer-agnostic; identity-free, and anonymized. Amazon, Uber, Google––these popular and beloved companies use their customers’ own analyzed data to give their customers predictive joy; they recommend uncannily perfect reads, pull up to the curb so soon after your call it’s almost as if they KNEW you were going to need a ride––which is not far from the truth. When Uber pulls up within minutes, it’s because they have successfully made you a single drop in their statistical ocean. In today’s wary “privacy culture,” we tend to dislike data-collecting in general, but we cherish it in the particular – as when Amazon crunches your ordering history to recommend the perfect book. Give your customers these sorts of surprising solutions.
Regression analysis, big data crunching, and the other futurist data trends coming down the innovation highway are more than just exciting concepts. DDS is going to put real power under Saas’s hood, and it will change the competitive environment. Incredibly lean building? Yeah, for starters. The intricate business intelligence drawn from these new data-driven software products in the construction sector will inform everything from predictive (lean) materials ordering, to improved interior design flow in new hospitals, to urban planning that looks at demographic patterns to offer infrastructure and services that cater to those populations. There is no horizon where the data-driven possibilities are concerned.
Classical SaaS isn’t going anywhere anytime soon. Quite the contrary. This new generation of analytical software solutions seeks to filter SaaS’s massive data sets through enough high-level algorithmic math to drive much of the guesswork out of doing business.
But how cool would it be if SaaS caught the Data-Driven wave and began evolving its own native analytics functions? Can you imagine? SaaS Tools + Data + Analytics + Human-powered Expertise. What does that equal? Something hugely game-changing. Stay tuned.
The Anatomy of a Request for Information (RFI)
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