A significant challenge for big business is finding and implementing the latest technologies to stay competitive. In their role, chief technology officers spend reams of time researching new products, and the larger the organization is, the more they have to test-drive each one to make sure the technology integrates with its existing systems and meets business goals.
A new proof-of-concept (PoC) as-a-service platform called prooV aims to assist big business in this activity. The platform deploys deep learning (or “deep mirroring”) to mimic a company’s IT infrastructure. This enables a business to test-drive multiple technologies in a secure, virtual environment (prooV’s platform). prooV has additionally set up a type of “App Store” for enterprises, allowing them to discover hundreds of technologies in areas as diverse as blockchain, cybersecurity, mobile and chatbots.
To understand how this platform works in practice, Digital Journal spoke with he co-founder and CTO, Alexey Sapozhnikov, about how deep learning can be used to connect enterprises/startups, what are the most popular technologies being tested by big business, and the challenges in business technology innovation.
Digital Journal: How important is implementing technology for businesses to stay competitive?
Alexey Sapozhnikov: The mantra “innovate or die” is truer than ever – businesses know that if they don’t use technology to their advantage, they’ll stagnate and perish. That’s the reason why companies around the world are creating innovation labs to find and implement the next big technologies.
Technology is at the core of every organization’s innovation and growth, no matter how old the company is, its size, or its industry. What was cutting-edge a mere five years ago is already considered obsolete – and the innovation cycle continues to shrink each year.
For example, within the last decade cloud computing – delivering servers, storage, databases, software, and other services over the Internet rather than on your computer’s hard drive – was considered new and novel. Today, the cloud is currently not designed for the volume, variety, and velocity of data for devices like self-driving cars, drones, and Internet of Things (IoT) systems. Companies are now looking at using “edge computing” – processing data closer to the device to make split second decisions – to power these devices.
DJ: Are some types of technology more important that others?
Sapozhnikov: It depends on the organization and its industry. In general, however, the types of technologies that are most important are those that save time, increase return on investment, and can help companies grow and gain a competitive advantage.
Critical technologies are those that serve as the underlying foundation of an organization’s operations – such as cloud computing, data centers, IT networks, security software, analytics, mobile applications and devices, and other operating systems.
DJ: What are the most popular technologies being explored by big businesses right now?
Sapozhnikov:The most popular technologies depend on the industry, but one technology that has generated a lot of attention is artificial intelligence (AI), and how machines can “learn” to do certain actions based on being exposed to reams of data. A basic type of AI is a chatbot, which is used by banks, retailers, tourism companies among others to interact with customers and answer basic information, such as provide details of an upcoming trip or your bank balance. AI can also be used to power technologies behind the scenes, such as determine if a financial transaction if fraudulent, or recognize consumer sentiment to improve customer satisfaction levels.
Blockchain is another type of technology that is being explored by enterprises. While many people immediately associate blockchain with cryptocurrency, it can be used as an distributed ledger to record transactions between multiple parties in an effective and permanent way. Examples of specific blockchain applications include digital voting, tracking food and other goods in a supply chain, and managing electronic health records for patients.
Edge computing is a popular technology being evaluated by big business. By processing data near the “edge” of your network, where the data is being generated, instead of transmitted to a datacenter, applications and devices can respond to data almost instantly. This is critical for technologies such as autonomous vehicles, which can’t delay in knowing when to stop or go while driving on freeways and down busy streets.
Other key technologies that continue to be popular on prooV’s platform including cyber defense, Internet of Things (IoT), business intelligence, application performance monitoring, and container technologies.
DJ: How can CIOs assess the latest technology trends effectively?
Sapozhnikov:It’s not a simple issue, as no CIO can be an expert in the latest innovations and trends. Understanding how new systems can then be measured in both business and technology terms can also be a very challenging and intricate matter.
Traditionally, CIOs have learned about technology trends by reading/researching online, learning about them at industry events, or talking to other CIOs and peers. However, this process has remained stagnant, despite technology advancements, and isn’t always the best way to learn about the best products and services to meet their business goals.
At prooV, we saw a clear opportunity to change this time-consuming, age-old practice, by providing a platform where big businesses can learn about relevant technologies, and test-drive them ahead of purchase. One of our key features is providing benchmarks for every new PoC in our platform, based on having already built a comprehensive testing environment for the technology and our own in-house expertise. This saves a lot of time for the CIO.
DJ: How does prooV deep mirroring work?
Sapozhnikov:Deep mirroring is a way to automatically generate data that mimics (or mirrors) the patterns and behaviors of the data of a company that is essential to its day-to-day business tasks and processes. Particularly for companies that are in highly regulated industries and don’t want to inadvertently expose any confidential customer data – such as financial services or healthcare – prooV gives businesses the benefits of running a PoC with real data, but with none of the risk.
With as little as a company’s sample set of data, prooV can generate millions of records that simulate a company’s data. Deep mirroring is a type of artificial intelligence that helps companies run PoCs securely and virtually on prooV’s platform, and ensures that businesses have an accurate picture of how the technology will perform before it is implemented in the organization.
DJ: Does deep mirroring differ to deep learning?
Sapozhnikov:Deep learning is a branch of machine learning. It’s like a “digital brain”, so to speak, that is trained to perform various tasks by using knowledge from previously solved problems. For example, think of a baby learning the difference between an apple and an orange, or between a cat and a dog. The baby wasn’t born with pre-existing knowledge of the two – rather its parents told the baby over and over again. This repetitive “training” is so effective that eventually, the baby will recognize the object without its parent’s help.
Deep mirroring is based on sophisticated, self-trained deep learning techniques to learn the structural design of sample data for the purpose of generating more data. This makes it possible for the platform to look at rows and rows of data and generate a similar set. In short, deep mirroring is the ability to take small, independent sample data and create more data similar to its logic and rules. This capability is one of the key aspects of breaking down the barriers hindering big business innovation.
By using deep mirroring, big businesses don’t need to undertake the PoC process one at a time in their IT environment. Rather, businesses can run multiple PoCs at once securely and remotely on the prooV platform, accelerating innovation.
DJ: How did you go about developing prooV?
Sapozhnikov:prooV was founded by me and my cofounder, Toby Olshanetsky, after over 20 years of us building and selling software to big businesses. As serial entrepreneurs, we realized that the inherent issues in the traditional PoC process between corporations and technology vendors. Vendors can struggle to get their foot in the door with CIOs at large companies who may not have heard of them before, or have stringent processes around the PoC process.
We spent about a year talking to CIOs around the globe to understand their challenges before developing prooV, and also learned more about their own roadblocks in finding, piloting and evaluating new technologies. prooV was created to bring technology vendors and companies together to test new software.
DJ: How do you ensure prooV remains up-to-date?
Sapozhnikov:Because we are exposed to the latest and greatest innovations, staying up-to-date is relatively simple. We are in a prime position to market ourselves directly to the over 1,000 customers on our platform, and our strong product team also constantly shares many groundbreaking ideas to our users.
DJ: Which companies are using prooV?
Sapozhnikov:We have two types of customers. Firstly, we work with large corporations that are interested in finding, testing and evaluating the latest solutions to address their business goals. We also work with technology vendors who want to expose their innovations to big businesses across any industry, and hopefully have them successfully try and implement their software.
prooV is a simple idea that’s a win-win for both sides. Companies can discover the next big technology at low risk and low cost, while vendors can let companies realize the full potential of their solution.