More data is better, but more structured and clean data is much better. It might appear to be an urban myth that a data scientist spends around 80% of her time cleaning and preparing data. I have been working with data for more than fifteen years, and trust me; it is - unfortunately - a reality. In a world where corporations are becoming more and more analytically competitive, any extra datapoint can theoretically be translated into a competitive advantage. That is why most companies are mining data. However, they are struggling to make sense out of tons of data scattered around different internal sources. Data cleaning and preparation are costly, not to mention the time spent understanding the databases' content. We cannot turn back time, and there is little to be done concerning historical data; however, there is a lot we can do concerning new data. Spending time to collect new data in a structured way is a profitable investment for a company. Having a clear and structured company database is like having an organized library for a university instead of accumulating books in the basement. Storing books by throwing them in the basement is, without question, a very time efficient short-term solution. Still, it does not take much imagination to guess which university will achieve success by making the most out of its information. Again, the point is a cultural one: companies need to invest in finding the right strategy to collect data so that tomorrow or ten years down the road, people can easily find the information they need. If this statement seems obvious, the grim consequences of foregoing this investment are not. Data scientists are and will be a scarce resource for a long while, and becoming more analytically competitive will mean relying on more "plug-and-play" A.I. driven software solutions. In plain terms, companies that have invested in structuring their data will have access to "off-the-shelf" software solutions that will be faster, cheaper, and very often more powerful than in-house solutions. Other companies will be struggling to make sense of their data, spending resources on scarce human capital while their competitors overtake them. It is clear that investing time and resources to structure any new datapoint is just as important as having an ordered warehouse for a logistic company. The question is then how to minimize the cost of structuring and storing each new datapoint so that company data can quickly and efficiently fuel the data-driven transformation. At Quick Algorithm, we believe the successful way of doing so is adopting a new generation of software solutions that are A.I. compatible, i.e. that have been designed to collect data for future integration with A.I. powered analytics software. To go even further, we believe in adopting software solutions with an integrated approach to data generation, collection, and A.I.-driven analytical insights. This solution delivers the highest efficiency because it reduces the need for internal data-scientists and engineers as the data generation and analysis happens in a pre-packaged solution. That is why we have created Scops, a revolutionary tool for industrial and facility asset management that generates and collects tons of structured data and delivers A.I.-driven insight about anomalies and new trends straight away. Of course, all companies have a need for customized solutions. That is why Scops is equipped with APIs that allow for seamless integration with other solutions. In plain English, adopting an integrated solution is equivalent to having an ordered library with expert insight and a librarian to point you in the right direction. The alternative is to get your flashlight and start searching your basement while other companies innovate in their Ivy League style libraries. Jacopo Piana, CEO & Founder of Quick Algorithm
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It was not long ago that we were almost entirely blind. There was little data available that was only updated occasionally, and often with long delays. Most of the time was spent putting all this information in some slides that were doomed to be outdated. We were almost blind in managing businesses. The amount of data collected has now skyrocketed in both the service and the industrial sectors. Paradoxically, we are currently running the risk of becoming blinded by data.
From the darkness of a little data to an overwhelming ray of light generated by tons of data points, we cannot say that our management ability has improved at the same pace technology has. Therefore, the next challenging technological milestone is not the ability to collect and process even more data. This is something we can do. Instead, the challenge is to blend data from thousands of sources and distill the relevant information for the user. We need to get information, not data, from digital devices. We need to gain actionable insights, not information that requires hours of analysis to become useful. On top of this, different observers should receive different insights from the same dataset depending on their jobs, responsibilities, and perspectives. The interest and focus of a production manager is different from that of the HR manager, but the database is unique in the ideal smart factory. Augmented Analytics, which relies on Machine Learning and AI, can free people from data preparation and analysis and provide them with actionable insights. Also, Augmented Analytics has the objective of making data information accessible to anyone, without requiring technical knowledge in the realm of data. Our vision at Quick Algorithm is that, in the Data-Driven Economy, the benefits of processing a large amount of data should be accessible to anyone; that is why we invest in making Augmented Analytics a reality with our software Scops. After all, the greatness of a technology is not measured by its complexity, but by the number of people that can benefit from it. Jacopo Piana, CEO & Founder of Quick Algorithm As many of you might have noticed, the relevance of advanced analytics and A.I. has increased dramatically in our economy.
I want to stress that this is not merely a curious side effect of technological progress, but it is "THE" 21st century's technological progress. As was the case with steam power and electricity, the advent of Machine Learning, which is the building block of A.I., has brought a technological quantum leap. Like any new technological paradigm, this is having a large effect on both society and the economy. At school, we learned about the industrial revolution, and now we have the opportunity to live the "Data-Driven Economy" revolution. As with any important technological change, there are opportunities and challenges. The opportunities are new businesses, more production efficiency, and better and smarter services that are accessible to a larger share of the population. However, new challenges arise from fiercer analytics competitiveness in many sectors, including services and manufacturing.
We may not have realized it yet, but since a few years ago, our economy undertook a profound transformation. We probably all remember studying at school about the industrial revolution or the advent of the printing machine. Well, something similar is happening now. Something that will transform our economy forever and will profoundly influence our future.
We have now entered what experts call the Data-Driven Economy. As the name correctly synthesizes, this paradigm transformation stems from the increasing availability of data and are always improving the ability to analyze it. As with any other paradigm shifts, the Data-Driven Economy is drastically reshaping the business landscape. In this blog post, I will briefly summarize some of the main characteristics of the data-driven economy. Stay tuned on our blog for a deep dive on each one of them! 1. Big Data First things first: Big Data! Vast amounts of data coming in different forms and at high speed. The classic definition of big data entails four other characteristics (commonly known as the four Vs of Big Data): volume, velocity, variety and veracity. Big Data and our ability to analyze big data are at the building blocks of the Data-Driven Economy. For more on this, I invite you to read our article on “What makes Data Big Data”. 2. Analytics Competitiveness The increasing availability of data, storage capacity and more efficient algorithms are reshaping the competitive landscape. In the Data-Driven Economy, to stay competitive in their market, companies need to start exploiting the power of data. Perhaps even more importantly, this shift does not involve only tech giants such as Google and Facebook that have always laid their competitive advantage on data. Even the most traditional companies in the least digitalized sectors will be impacted. This will profoundly change the dynamics of entire industries. Smaller companies may even find themselves overcoming bigger competitors thanks to their ability to better using data. 3. Machine Learning Possibly the most crucial technology behind the Data-Driven Economy. Simplifying a bit, Machine Learning is a big family of algorithms coming from statistics and applied math that learn from data to extract evidence from it. For more on Machine Learning and its connection with Big Data and Artificial Intelligence, I suggest you read our article on “If Artificial Intelligence is an (intelligent) Car”. 4. Information asymmetries In economic theory, we talk about information asymmetries every time one party in an economic transaction has more information than the other. The Data-Driven Economy is creating significant information asymmetries. In fact, in recent years we have assisted to the centralization of increasing amounts of information in the hands of a few big companies. 5. Systemic risk Such centralization of power brings along also a high systemic risk. It’s enough to think that 60% of the cloud services of the planet lay in the hands of three companies. If something happened to one of them, the consequences would be devastating for the entire industry (and economy) similarly to what happened during the financial crisis of 2008 to those that were considered “too big to fail”. This is why we talk about systemic risk. 6. New forms of trade Last but not least, the Data-Driven Economy is creating new business opportunities. New business models that derive their strength from gathering and analyzing data. Consider examples such as Netflix or Amazon, starting from a traditional form of trade; those companies were able to leap their industries thanks to data. Stay tuned for more on Data-Driven Economy and Data-Driven Organisations! It sounds strange that a tech company who is mainly developing Data-Driven A.I. solutions titles an article "Why A.I. is doomed to fail". That's because we know better than others why we have seen A.I. applications failing to succeed in the industrial and manufacturing sector. A.I. is not doomed to fail. To the contrary, it is doomed to take companies to success when implemented correctly.
So, what are the leading causes that jeopardise the adoption of A.I. inside industrial companies? In this short post, we are mentioning the top three reasons for that. 1) Starting Big Implementing A.I. in any company, it's a somewhat disruptive process. People from different departments and functions are involved in the project (e.g. implementing predictive maintenance solutions) and the amount of data necessary to feed the algorithms increase almost exponentially with the machines involved in the process. As complexity increases, the time to adoption increases and people quickly lose momentum and the project risks being trashed with the excuse that either the company is not ready or the technology is not advanced enough. To avoid hitting a brick wall during the adoption phase, we have seen that smaller projects led by a task force of highly motivated people that uses the most favourable setting in the company to adopt A.I. is returning the best chances of success. This follows the Kaizen approach, according to which small changes can activate and lead to tremendous results if transformation is conducted progressively and not with a quantum-leap approach. Small projects, have faster adoption time and lead to quicker productivity reward that can convince the rest of the company to adopt the new solution. 2) Use only some data Would you run for the first time a rugged trail with a brand-new bike and an eye-patch? I would say that it is not the easiest way to succeed, and I would not blame myself if I couldn't spot an anomaly or something going-on on my blindspot. That is to say that the adoption of A.I. powered solutions in the industrial and manufacturing sector often fails because the algorithms have blindspots on what's going on in the factory. It happens that not all the data are made available, and this nullifies the potential of the algorithms. Machine Learning needs many examples to understand what is going on, for instance, to spot a machinery anomaly in advance. Not feeding all the data necessary to learn is making the process lengthier at best if not impossible. That's why it is so essential moving to A.I. implementation once all data can be made available to the software. 3) Not considering human activity Last but not least, one of the top three reasons why companies fail to adopt A.I. solutions in the industrial production environment is that algorithms are only fed with data coming from sensors and human activity is not considered at all. To the contrary, sensors data are of little help if not coupled with the information coming from the production and maintenance activity. Not collecting information about how people in the plant interact with the machines and not keeping track of their actions often make anomaly detection very tricky for A.I. because these data blindspot causes algorithms to mistake anomalies for maintenance activities and vice versa. That is why recently we have launched Scops Q-Track, which allows very quickly to keep track of all the human interactions with machines in a way that creates a 360-degree view on what's happening in the plant. This tremendously empowers industrial A.I. because for the first time it makes available not only IoT data but also a wealth of people interactions with machines, such as maintenance activities, usage and human-based anomaly signalling. Conclusions A.I. is doomed to make companies more successful only when appropriately implemented. In this short article, we have summarised the top three mistakes that make A.I. adoption doomed to fail. Starting from big instead of small pilot projects increases the risk that A.I. implementation runs aground because the level of complexity and disruption is too large to be managed. Also, not making available all the data necessary and creating data blindspots for the algorithms make A.I. not successful in spotting anomalies and understanding trends. This makes A.I. driven results disappointing and slows down the adoption process, even if A.I. is not directly to blame. Last but not least, A.I. needs human in the loop to learn and perform at best. Sensor data are not enough to understand the complexity of a production plant, and tracking human activity is crucial to give A.I. all the information needed. Keeping records of human-machine interactions is vital, but at the same time, too time-consuming if the right digital solution is not adopted to make this process very efficient and smooth. Scops Q-Track aims exactly at keeping track of all the human-machine activities carried out in the plant. It does not require people to record data but instead offers a platform so handy and useful to employees and engineers to share information that they will be creating data-logs without even noticing. |