Business intelligence (BI) tools first appeared on the enterprise technology scene several decades ago, at birth clumsy and difficult to use but ultimately improving the flow of data through organisations from their operational systems to decision support. Data warehousing cut the time it took to access data, but even at their full maturity, BI systems could do little more than produce data and reports in a traditional organised way. The rules-driven software wasn’t actually providing intelligence at all.
But with the advancement of artificial intelligence and, more importantly, machine learning, true business intelligence is actually on its way to the enterprise. Such self-learning software will run on servers, be built into bots, drive decision-making systems, be embedded into cars or aircraft, and become the beating heart of mobile devices.
Increased data-processing power, the availability of big data, the Internet of Things, and improvements in algorithms are converging to power this actual business intelligence. To be clear, this will be an evolution rather than a revolution. There are a number of factors that could limit the progress of machine learning and its integration into business, from quality of data and human programming to cultural resistance. However, the question is when, not if, the BI tools of today become a quaint relic of earlier times and real business intelligence emerges.
Beyond sci-fi AI
Artificial intelligence (AI), a term dating back to the 1960s, is tossed about quite a bit these days. It’s an umbrella description that refers to computers capable of doing things that a human typically would. It’s often inaccurately used interchangeably with machine learning. Machine learning however, is a specific subset of AI that uses statistical methods to improve the performance of a system over time. Any programmer can write code to develop a program that more or less acts like a human. But it’s not machine learning unless the systems is learning how to behave based on data. Machine learning comes in several flavors, sometimes referred to as supervised learning (the algorithm is trained using examples where the input data and the correct output are known), unsupervised learning (the algorithm must discover patterns in the data on its own), and reinforced learning (the algorithm is rewarded or penalised for the actions it takes based on trial and error). In each case, the machine is able to learn from data, structured and increasingly unstructured in the future, without explicitly being programmed to do so, absorbing new behaviors and functions over time.
Gartner recently placed machine learning at the height of “inflated expectations” in its report, noting that this emerging capability is two to five years from mainstream adoption. But those immersed in machine learning development are grounded in reality. And the reality is that they are making significant strides. Machine learning mimics human learning; it takes time.
The big advantage machines have over us is that they can handle massive amounts of data, take advantage of ever-faster processing power, and run (and thereby) improve 24 hours a day. Over just the last four years, the error rate in machine learning-driven image recognition, for example, has fallen dramatically to near zero, practically to human performance levels.
Still, every instance of machine learning is different. Just as, for us, learning to play piano is different from learning how to crawl, each instance of machine learning is different. It may take longer for a computer to learn to analyse text than it takes it to recognise the meaning of a furrowed brow.
Machine learning for the rest of us
Digital giants are leading the way in machine learning development. Google has more than 1,000 machine learning projects underway, including its Google Brain project. IBM continues to make headlines with Watson. Microsoft uses neural networks to powers its search rankings, photo search, and translation systems while Facebook translates 2 billion user posts in more than 40 languages each day in the same manner. In the last year alone, venture capital firms have poured approximately $5 billion into machine intelligence startups.
At this early stage, there are no concrete baselines for machine learning adoption rates in the rest of industry. Consumer adoption of machine-learning technologies has taken off with the success of Amazon’s Echo and Apple’s Siri. It’s an important component in fraud detection and surveillance, image and voice recognition, and product recommendations. But, as a recent report from 451 Research pointed out, but enterprise adoption is less pervasive. To broaden the enterprise use of machine learning, some of the biggest tech players in the field, such as Google, Microsoft, Intel, and Facebook make their older machine learning systems and designs available to the open source community.
Machine learning could bring significant value to the business: improving the core functionality of existing software and analytics, uncovering previously inaccessible insights hidden in large data sets unstructured data formats, and taking over tasks like image recognition, text analysis, and repetitive knowledge work. The potential use cases are seemingly endless, from supply chain and risk detection to logistics and technical support to behavioral analysis and customer support.
Machine learning is not a silver bullet and there are a number of issues that companies must address. Because it is based on algorithms that learn from data rather than relying on rules-based programming, effective machine learning is dependent on relevant and reliable data, and lots of it. Business leaders must take a hard look at available data (the quality of it, the gaps in it, the silos around it) to extract the value of self-learning capabilities.
What’s more, machine learning is ultimately guided by human decision making. Humans will decide what problems the technology will be used to solve. Humans will develop the algorithms to employ. And humans don’t necessarily operate on logic.
Perhaps most importantly, the adoption of machine learning is going to be determined more by organisational and cultural forces than by technical factors. Humans are not yet machine ready. Machine learning will need to be designed with the man-machine interaction in mind. Fear, uncertainty and doubt about how these self-learning systems will impact our roles and our livelihoods must be addressed, with significant investment made in change management as business processes and models are reworked to integrate self-learning systems.
The rise of the machines in business and beyond
Business leaders have been talking about the importance of context-sensitive systems to the enterprise for several years. Machine learning could finally bring that concept to life, from smart software to smart vehicles to intelligent machines and robots, to machine learning-enabled digital assistants and to smart grids that can learn to understand their environment and adapt on their own.
Smart machines will become an integral part of business, and daily life, creating insight from data in ways that humans on their own never could. That will lead to new levels of automation, cost savings, and process change. Gartner predicts that in 2018, 45 percent of the fastest-growing companies will have fewer employees than instances of smart machines and customer-facing digital assistants will recognise individuals by face and voice across channels and partners. Self-learning algorithms will introduce unprecedented levels of efficiency in business systems taking over highly repetitive work. On a personal level, smart assistant technology could turn our mobile devices—already capable of voice response, into interactive learning assistants tasked with helping us navigate our daily lives. Machine learning could uncover new efficiencies in our complex and overstressed infrastructure systems including energy, logistics, healthcare, IT, and even education.
The value that machine learning can deliver will be dependent on the degree to which these systems can deal with structured and unstructured data (which remains a challenge) as well as the availability of useful data and quality algorithms. Taking over the mundane and repetitive tasks within business systems and for consumers is all but guaranteed. Organisations are starting to collect unstructured and unprocessed data in so-called data lakes. If companies open up more of their self-learning data and designs, that shared insight will result in ever better algorithms and more accurate and effective machine learning capabilities.
If machine learning matures to the point that it can handle unstructured data, organisations openly share data, and algorithms begin to interact with each other more freely, machine learning will be embedded in all systems, devices, machines and software. That will enable highly context-sensitive insight at both the large scale and individual level. We can only guess about the level of automation and support that will result, but the impact on business, and society, will be significant.
However this evolution plays out, it will take time. But business leaders can prepare now for the rise of machine learning, taking a hard look at data structures and availability, freeing up information from siloed systems, identifying the richest areas for machine-fueled insight and improvement, and addressing the cultural and change management challenges that will be required to take advantage of this real business intelligence.
Article taken from Digitalist magazine by SAP – You can read the original article and others, here.
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