“Digital transformation,” as commonly discussed, sounds simple: Revamp the business strategy by adopting digital technology. However, the challenges organizations face today and what they must do to meet those challenges is far more complex. By 2017, only 12 percent of the companies listed in the 1955 Fortune 500 were still on the list. That's a lot of attrition, and it’s largely due to the failure of Fortune 500 companies to reinvent themselves while new, tech-savvy upstarts transformed the competitive landscape.
Today, many companies fail to sustain their market leadership because their market gets disrupted by more advanced, tech-savvy companies. Amazon is a great example. It has disrupted several industries, even the technology industry, by introducing completely new digital-based business models. It started out with books, then moved to general retail, then to cloud computing, each time inventing new ways to connect with customers and deliver what they really want: more choice, free shipping, simpler and more scalable IT infrastructure, etc. That’s what digital transformation is really about.
Achieving digital transformation requires not just an embrace of digital technologies, but an entirely new way of thinking.
New Tech-Enabled Business Models
Technology used to be about streamlining existing processes. Companies generally viewed technology as something they had to cost-justify. Can we produce more widgets at a lower cost? Can we make our employees more productive? Can we automate routine tasks to reduce errors and lower costs? The role of technology for the past 50 years was largely about productivity improvement and cost reduction.
But new thinking is leading the digitally transformed business of the future. It’s no longer about making our existing businesses more streamlined through the use of technology. It’s about reinventing our businesses around what technology makes possible. That means using technology to gain competitive advantage and pursue new business opportunities. Most companies begin by rethinking their customers’ experience by, for example, leveraging the fact that virtually all consumers carry smart phones with GPS, accelerometers, and “always-on” connectedness. What kind of new digital businesses can we create around that? And when such connectedness is embedded in our company’s products, new possibilities for monetizing those products emerge.
Take for example an industry most of us consider non-digital: automobile manufacturers. On the surface, automakers make big clunky machines that clog our highways. But under the covers, they are becoming pretty much what the iPhone is to Apple: mobile devices that connect customers to a broader digital platform enabling new business models and revenue streams. Connected cars now keep track of where we are, how fast we’re going, how hard we press our brakes and even when our blinkers are on. Very soon, automakers will be presenting us with coupons to the nearest gas station when our tank nears empty. Automakers predict that monetizing data from connected cars will add $100 per year of revenue for every car sold, and by 2030, revenue from this digital business will reach $750 billion.
That’s a digital transformation.
And digital transformation isn’t happening just with cars or industrial equipment or enterprise business models. It’s happening everywhere, in every aspect of our lives. Smart cities can now adapt in real time to changing conditions. For example, thanks to the prevalent use of strategically-placed sensors, such as on lampposts, cities can now automatically route first responders to a problem as quickly as possible, ensuring they avoid traffic congestion. Cities can also deploy traffic control systems, including using signs that automatically reroute traffic based on situations that are occurring, such as accidents or the end of sporting events. They can even deploy solutions like automatic lighting systems and gunshot triangulation.
Digital health monitors can now go beyond tracking an individual’s health and fitness. Today, hospitals can use multiple sensors to track the condition of home-bound patients – thousands of them at the same time. From eliminating the expense of a doctor visit or the inconvenience of a trip to the hospital, to ensuring the fastest possible response to an emergency, home-based patient monitoring can dramatically improve the quality of care and the quality of life.
Jet engine manufacturers have developed “digital twins” of their physical engines. By adding sensors to their engines, manufacturers can gain near real-time visibility into how individual — and indeed entire fleets of engines — are operating. These digital twins provide manufacturers full visibility into the operation and health of their engines, including the ability to predict failures and prevent costly unscheduled maintenance. This has enabled a new business model for manufacturers: rather than selling engines to airlines and competing with third parties for the most profitable part of their business, engine maintenance, manufacturers can now offer “pay-by-the-hour” pricing that 1) aligns incentives of both airlines and engine manufacturers by only charging for actual usage, and 2) eliminates third-party competition for maintenance.
Even the insurance industry is looking at new digital business models. Many insurance companies already have pilot programs based on aggregating data about an individual’s activities – at home, in the car, at work – and then offering policies based on how people actually live: how much they drive, how much they exercise, their heart health, etc. In this model, the premium could even fluctuate depending on the route someone takes when they drive to work. The insurance company could recommend a different route that would cost less because of the lower chance of accidents.
And if the insurance companies don’t figure this out, another company like Apple or Amazon or a raw tech startup will.
Becoming a Tech Company
One consequence of digital transformation is that every company must now become a tech company. CEOs across many industries have been quoted as saying, “We want to be a tech company with a license to do X.” That is, a tech company with a retail license, or a banking license, or an insurance license, you name it. And it turns out that a license to do one thing actually gives you the license to do many other things. Once you're a technology company, you can engage in a lot of related businesses – remember Amazon’s evolution from books to IT infrastructure.
That’s digital transformation.
The Technology Foundation of Digital Transformation
Becoming a digitally transformed company requires a technology overhaul. Consider the amount of data the automaker must collect and analyze in real time from millions of connected cars. Consider a healthcare organization collecting and analyzing real-time data on thousands of patients. Consider a credit card company analyzing data from millions of transactions to detect signs of fraud or abuse.
Today’s businesses just aren’t set up for this. They have operational systems built on disk-based databases that require that data be transferred via ETL into data analytics systems for analysis. This means they are always analyzing stale data – a day old, a week old, a month old – which means any business decisions based on the analysis are already out of date. This is not digital transformation.
The reason for separating operational systems from analytics is that traditional architectures are not powerful enough to support transactions and analytics on the same data set in real-time. And the only way to unify them – to make real-time decisions as transactions are happening – is to speed up and scale out the existing architecture with in-memory computing. With a well-designed distributed in-memory computing architecture, you can combine everything on a single system that processes transactions and performs instant analysis with automated decision making, even incorporating continuous machine learning or deep learning.
This hybrid transactional/analytical processing (HTAP) capability – or what Gartner calls “in-process HTAP” when it includes continuous learning, is an essential foundation for digital transformation and modern business models.
The In-Memory Computing Platform
Various forms of in-memory computing have been around for decades – caching, in-memory data grids, in-memory databases, in-memory processing engines like Spark, etc. The difference today is, first, memory is far less expensive than it used to be, making it practical for businesses of all sizes to benefit from in-memory computing. Second, multiple in-memory technologies are merging into comprehensive in-memory computing platforms with a unified API. This results in a best-of-breed stack that works together seamlessly and makes the in-memory computing infrastructure simpler to program, deploy and maintain. And in the past decade, in-memory computing platforms have gained the maturity to serve as highly available systems of record that companies can rely on to run their business.
IDC says that by 2019, next year, $2 trillion will be spent on digital transformation. Gartner says that by 2019, 75 percent of cloud native application development will utilize in-memory computing and that by 2022, 40 percent of large global enterprises will be using in-memory databases to reduce the proliferation of physical data stores and the publication of data. It’s no surprise these two trends are moving in sync – because in-memory computing is a foundational technology for digital transformation. Companies can now have thousands or even millions of digital touchpoints and can begin innovating new digital businesses and revenue streams around them. Businesses can now gain instant situational awareness about their customers, products and supply chains to make real-time decisions that capitalize on short-lived opportunities as they happen.
This changes everything about a business. This is business reinvention. This is digital transformation.
Abe Kleinfeld, CEO at GridGain
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