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The Three Secrets to Successfully Monetizing IoT Data

The global business landscape is being transformed by the Internet of Things. In the emerging Connected Economy every business will have to become an IoT business, and enterprises that don’t adapt, innovate, and transform their models will risk falling behind. However, the reality is that IoT monetization is going to be difficult, especially IoT data monetization. In fact, I believe that more that 80 percent of companies that begin IoT implementations will squander their transformational opportunities.

For any company that has customers, suppliers, employees or assets, IoT monetization can be transformational. But success in IoT monetization comes down to more than just technology; it requires careful strategic planning and shifting your entire corporate mindset to treating data as the valuable asset that it is. To learn more, I encourage you to check out the article I wrote for BetaNews, where I detail my top three strategies for ensuring that your IoT initiative is part of the 20 percent that actually succeed.

Oh, and one last thing; as 2016 draws to a close, I want to take this opportunity to wish you Happy Holidays and a fantastic 2017 from all of us here at GoodData!

– Roman Stanek

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How AI Will End Offshoring

We are living in the Rise of the Machines. No, Skynet and its hordes of Terminators haven’t materialized just yet, but there are many that fear that advances in automation and Artificial Intelligence are a serious threat to humanity in at least one aspect: jobs. In America, more jobs are already lost to robots and automation than to China, Mexico or any other country. But the global impact of the these systems will be felt even more strongly. I believe that the proliferation of ‘virtual talent’ will have a profound effect on intellectual offshoring and business process outsourcing, one that will be especially pronounced for emerging countries.

Traditionally, outsourcing and offshoring has primarily been conducted as a cost reduction measure. After all, why spend more on an expensive local worker sitting at a computer when the same tasks can be performed at a dramatically lower cost by an overseas worker sitting at the same computer, all while maintaining the same level of quality? In the pre-AI world, that thinking made perfect sense. But what if you could make the computer do that same job, without a human operator? The cost savings would be massive, and the business decision obvious.

Advances in AI technology are rapidly making this hypothetical a reality. Recent research from Gartner found that by 2018, 40% of outsourced services will leverage smart machines, “rendering the offshore model obsolete for competitive advantage.” And this market is only going to grow. The same report states that over $10 billion worth of these systems have already been purchased from the more than 2,500 companies providing this technology, while more than 35 major service providers are investing in the development of “virtual labor” options that leverage AI-driven service offerings.

All of this means that the intellectual offshoring we’ve seen since the 90s will no longer be needed or even viable, as there won’t be any business requirement for these services or economic incentive to move these tasks overseas. AI and advanced analytics allow for the automation of many tasks that are currently outsourced. That’s an extremely attractive option; automating tasks that are currently performed by hundreds of overseas employees will enable businesses to hire more expensive local talent who can focus on the difficult tasks and strategic decisions that make bigger business impacts.

AI not only softens the incentive of cheap foreign labor, it also negates the advantages of lower offshore operational costs. If you can locate the machines that are performing these tasks anywhere on earth for basically the same cost, why not keep them close to your home base of operations and save a bundle on travel, audit and compliance costs?

These shifts might take a few years as technology develops, but they are coming, and they will fundamentally change the way the world does business. Intelligent machines are here, and companies that continue to rely solely on outdated offshoring models out of fear of the risks and challenges of being early adopters of these systems do so at their peril. Virtual labor technology can offer potential cost savings of between %10-%70, so business leaders must begin planning now for how to adopt this technology and adapt their organizations to maximize its potential in order to survive and remain competitive.

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Systems of Insight and AI: The Future of BI

Lately, I’ve been thinking a lot about how I define Business Intelligence and where I see the industry heading. More and more, this connects back to embedded AI, machine learning, predictive analytics, data enrichment and other AI methods, but at the end of the day we all need to be on the same page.

At its core, BI is all about demonstrating profitable activity with analytics and enabling businesses to use their data to drive actions and outcomes. The popular thinking is that self-service BI tools are easy to use and good for the business, and business users like the idea of having their own data analysis tools. But what organizations are failing to realize is that they will be far more productive and profitable if they use AI and machine learning to automate the mundane decisions that most people use current BI tools to make and allow their employees to focus on more strategic problems.

I don’t believe that the concept of self-service analytics is a scaleable one. Employees should be spending their time on their core job functions, instead of slicing and dicing data. Embedding analytics at the point of work and automating mundane decisions with machine learning and AI enables business users to take immediate actions to improve business outcomes. Employees can focus on their day-to-day responsibilities, while letting machine learning take care of automating tasks that don’t require the expertise, experience and context that only a human can provide.

I’ve been inspired by JP Morgan’s COIN program, it’s an outstanding example to showcase how a business can leverage machine learning to automate decisions, and in the process save themselves millions of dollars a year. COIN is a learning machine that automates in seconds the mundane task of interpreting commercial-loan agreements that, until the project went online in June, consumed 360,000 hours of work each year by lawyers and loan officers. This perfectly illustrates the business value that machine learning can have.

Both startups and some of the world’s leading technology companies alike are pouring investment into AI technologies and machine learning capabilities, and those that aren’t are behind the curve. Tractica predicts the market for enterprise applications of AI to surpass $30 billion by 2025, with a focus on better, faster, and more accurate ways to analyze big data for a variety of purposes. Those investing in advanced AI and machine learning capabilities will lead the BI industry as it moves more and more towards automation.

Right now, the BI industry remains focused on self-service capabilities, but I think there should be less emphasis on self-service flexibility and more on automation and AI. At GoodData, we are focused on creating solutions that support embedding AI at scale to automate the basic business decisions that people mostly use BI tools for, which is why we will keep investing in predictive analytics, machine learning, data enrichment, and other AI methods.

GoodData wants to be part of the production environment, and we believe that the best way of doing that is deploying Smart Business Applications that harness the above technologies to make organizations more productive by allowing employees to spend less time analyzing data and more time focusing on daily tasks. Smart Business Applications involve the seamless integration of BI tools with day-to-day business applications and workflows. They work by delivering relevant, timely, and actionable insights within the business applications where work is already being done, so decision makers no longer have to stop what they’re doing and open another app to get the insights they need. By putting these intelligent insights right in front of them the moment they need them, GoodData is ushering in the next generation of BI.