Data analytics: From studying the past to forecasting the future

To compete in today's competitive market place, it is critical that executives have access to an accurate and holistic view of their business. The key element to sifting through a massive amount of data to gain this level of transparency is a robust analytics solution. As technology is constantly evolving, so too are data analytics solutions. 

In this blog, three types of data analytics and the emerging role of artificial intelligence (AI) in processing the data are discussed:

Descriptive analytics

As the name suggests, descriptive analytics describe what happened in the past. This is accomplished by taking raw historical, whether from five minutes or five years ago, and presenting an easy-to-understand, accurate view of past patterns or behaviors. By understanding what happened, we can better understand how it might influence the future. Many businesses use descriptive analytics to understand customer buying patterns, sales year-over-year, historical cost-to-serve, supply chain patterns, financials, and much more.

Predictive analytics

This is the ability to accurately forecast or predict what could happen moving forward. Understanding the likelihood of future outcomes enables the company to better prepare based on probabilities. This is accomplished by taking the historical data from your various silos such as CRM, ERP, and POS, and combining it into one single version of the truth. This enables users to identify trends in sales, forecast demands on the supply chain, purchasing and inventory level based on a number of variables. 

Prescriptive Analytics

This solution is the newest evolution in data analytics. It takes previous iterations to the next level by revealing possible outcomes and prescribing courses of actions. In addition, this solution will also show why it will happen. Prescriptive analytics answers the question: What should we do? Although this is a relatively new form of analytics, larger retail companies are successfully using it to optimize customer experience, production, purchasing and inventory in the supply chain to make sure the right products are being delivered at the right time. In the stock market, prescriptive analytics can recommend where to buy or sell to optimize your profit.

All three categories of analytics work together to provide the guidance and intelligence to optimize business performance.

Where AI fits in

As technology continues to advance, AI will become a game-changer by making analytics substantially more powerful. A decade ago, analytics solutions only provided descriptive analytics.  As the amount of data generated increased, solutions started to develop predictive analytics. As AI evolves, data analytics solutions are also changing and becoming more sophisticated. BI software vendors are currently posturing to be the first to market with an AI offering to enhance prescriptive analytics. 

AI can help sales-based organizations by providing specific recommendations that sales representatives can act on immediately. Insight into customer buying patterns will allow prescriptive analytics to suggest products to bundle which ultimately leads to an increase in the size of an order, reduce delivery costs and number of invoices.

Predictive ordering has enabled companies to send products you need before you order them. For example, some toothbrush or razor companies will send replacement heads in this way. They predict when the heads will begin to fail and order the replacement for you. 

Improving data analytics for your business

If you are considering enhancing your data analytics capability and adding artificial intelligence, we encourage you to seek out a software vendor that offers you industry-matched data analytics that is easy and intuitive for everyone to use. This means dashboards, scorecards, alerts developed with the standard KPIs for your industry, pre-built.

Collaborating to customize the software to fit your business and augmenting with newer predictive analytics and machine learning-based AI happens next.

Source: Phocas Software

“Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten.”

These words, articulated by Neil Armstrong at a speech to a joint session of Congress in 1969, fit squarely into most every decade since the turn of the century, and it seems to safe to posit that the rate of change in technology has accelerated to an exponential degree in the last two decades, especially in the areas of artificial intelligence and machine learning.

Artificial intelligence is making an extreme entrance into almost every facet of society in predicted and unforeseen ways, causing both excitement and trepidation. This reaction alone is predictable, but can we really predict the associated risks involved?

It seems we’re all trying to get a grip on potential reality, but information overload (yet another side affect that we’re struggling to deal with in our digital world) can ironically make constructing an informed opinion more challenging than ever. In the search for some semblance of truth, it can help to turn to those in the trenches.

In my continued interview with over 30 artificial intelligence researchers, I asked what they considered to be the most likely risk of artificial intelligence in the next 20 years.

Some results from the survey, shown in the graphic below, included 33 responses from different AI/cognitive science researchers. (For the complete collection of interviews, and more information on all of our 40+ respondents, visit the original interactive infographic here on TechEmergence).

Two “greatest” risks bubbled to the top of the response pool (and the majority are not in the autonomous robots’ camp, though a few do fall into this one). According to this particular set of minds, the most pressing short- and long-term risks is the financial and economic harm that may be wrought, as well as mismanagement of AI by human beings.

Dr. Joscha Bach of the MIT Media Lab and Harvard Program for Evolutionary Dynamics summed up the larger picture this way:

“The risks brought about by near-term AI may turn out to be the same risks that are already inherent in our society. Automation through AI will increase productivity, but won’t improve our living conditions if we don’t move away from a labor/wage based economy. It may also speed up pollution and resource exhaustion, if we don’t manage to install meaningful regulations. Even in the long run, making AI safe for humanity may turn out to be the same as making our society safe for humanity.”

Essentially, the introduction of AI may act as a catalyst that exposes and speeds up the imperfections already present in our society. Without a conscious and collaborative plan to move forward, we expose society to a range of risks, from bigger gaps in wealth distribution to negative environmental effects.

Leaps in AI are already being made in the area of workplace automation and machine learning capabilities are quickly extending to our energy and other enterprise applications, including mobile and automotive. The next industrial revolution may be the last one that humans usher in by their own direct doing, with AI as a future collaborator and – dare we say – a potential leader.

Some researchers believe it’s a matter of when and not if. In Dr. Nils Nilsson’s words, a professor emeritus at Stanford University, “Machines will be singing the song, ‘Anything you can do, I can do better; I can do anything better than you’.”

In respect to the drastic changes that lie ahead for the employment market due to increasingly autonomous systems, Dr. Helgi Helgason says, “it’s more of a certainty than a risk and we should already be factoring this into education policies.”

Talks at the World Economic Forum Annual Meeting in Switzerland this past January, where the topic of the economic disruption brought about by AI was clearly a main course, indicate that global leaders are starting to plan how to integrate these technologies and adapt our world economies accordingly – but this is a tall order with many cooks in the kitchen.

Another commonly expressed risk over the next two decades is the general mismanagement of AI. It’s no secret that those in the business of AI have concerns, as evidenced by the $1 billion investment made by some of Silicon Valley’s top tech gurus to support OpenAI, a non-profit research group with a focus on exploring the positive human impact of AI technologies.

“It’s hard to fathom how much human-level AI could benefit society, and it’s equally hard to imagine how much it could damage society if built or used incorrectly,” is the parallel message posted on OpenAI’s launch page from December 2015. How we approach the development and management of AI has far-reaching consequences, and shapes future society’s moral and ethical paradigm.

Philippe Pasquier, an associate professor at Simon Fraser University, said “As we deploy more and give more responsibilities to artificial agents, risks of malfunction that have negative consequences are increasing,” though he likewise states that he does not believe AI poses a high risk to society on its own.

With great responsibility comes great power, and how we monitor this power is of major concern.

Dr. Pei Wang of Temple University sees major risk in “neglecting the limitations and restrictions of hot techniques like deep learning and reinforcement learning. It can happen in many domains.” Dr. Peter Voss, founder of SmartAction, expressed similar sentiments, stating that he most fears “ignorant humans subverting the power and intelligence of AI.”

Thinking about the risks associated with emerging AI technology is hard work, engineering potential solutions and safeguards is harder work, and collaborating globally on implementation and monitoring of initiatives is the hardest work of all. But considering all that’s at stake, I would place all my bets on the table and argue that the effort is worth the risk many times over.

Source: Tech Crunch

The BI trends your business cannot neglect in the near future

According to the World Economic Forum’s Future of Jobs Report, the top five trends set to positively impact business growth through 2022 are (1) the increasing adoption of new technology, (2) the increasing availability of big data, (3) advances in mobile internet, (4) advances in AI, and (5) advances in cloud technology.

This nexus of these and other trends, and their accelerated innovation and development (as an example, think of how fast we’ve gone from rotary phones to smartphones, to the dematerialization of other devices onto smartphones, and now to 5G), raises the imperative for organizations to focus their next-decade vision and investment strategy now.

Consider these 2020 and beyond assertions for enterprise analytics and mobility from Ventana Research:

• By 2020, analysis of streams of IoT event data will be a standard component of nearly all big data deployments.
• By 2021, two-thirds of analytics processes will no longer simply discover what happened and why, they will also prescribe what should be done.
• By 2022, one-half of organizations will re-examine the use of mobile devices and conclude the technology being used does not adequately address the needs of their workers, leading them to examine a new generation of mobile apps.

And that’s just a start. In '10 Enterprise Analytics Trends to Watch in 2019', Ventana Research CEO Mark Smith notes that in addition to 5G, enterprise organizations’ mobility strategies must absolutely address accelerating technologies and capabilities, such as:

• Device proximity features that can provide environmental context and suggest where to take action based on location.
• Gestures and camera-based input that make it even easier and faster to engage with business applications.
• Biometrics, from facial recognition to fingerprints, that enable significantly better device, data, and enterprise security.
• High-quality device cameras that make it easy to capture, share, and use photos and videos and their data within business processes.
• Augmented reality (AR) that enables the use of a mobile device’s camera to digitally interpose virtual objects to enhance work experiences.
• Speech recognition and voice assistants on mobile devices that make it simpler for users to access information and act quickly.

The future is here. Is your organization ready to take advantage of the accelerated innovation around enterprise analytics and mobility?

Source: MicroStrategy

The data management issue in the development of the self-driving car

Self-driving cars and trucks once seemed like a staple of science fiction which could never morph into a reality here in the real world. Nevertheless, the past few years have given rise to a number of impressive innovations in the field of autonomous vehicles that have turned self-driving cars from a funny idea into a marketing gimmick and finally into a full-fledged reality of the modern roadway. However, a number of pressing issues are still holding these autonomous vehicles back from full-scale production and widespread societal embrace. Chief amongst them is the data management challenge wrought by self-driving vehicles.

How should companies approach the dizzying data maze of autonomous vehicles? Here’s how to solve the data management of self-driving cars, and what leading automotive companies are already doing.

Uber and Lyft want to release self-driving cars on the public

Perhaps the most notable development in the creation of autonomous vehicles over the past few years has been that Uber and Lyft have both recently announced that they’re interested in releasing self-driving cars to the general public. In other words, these companies want autonomous vehicles that are navigating complex city environments by themselves and without the assistance of a human driver who can take over in the event of an emergency.

Uber has already spent a whopping $1 billion on driverless cars, perhaps because the ridesharing app relies heavily on a workforce of freelancers who aren’t technically considered full-time employees. It could be that Uber and other companies see a financial imperative in automating their future workforce so that they don’t have to fret about providing insurance and other benefits to a large coterie of human employees. Whatever the company’s motivations, Uber has clearly established itself as a leader in the self-driving car space where investments are concerned and will continue to be a major player for the foreseeable future.

Other companies like Ford may have the right idea, as they’re moving in the opposite direction of Uber and trying to take things slowly when debuting their autonomous vehicles. This is because Ford believes that solving the data management challenge of self-driving cars takes time and caution more than it does heavy spending and ceaseless innovation. Ford’s approach opposite to Uber's approach to self-driving cars could pay off too, as the company has avoided the disastrous headlines that have followed Uber everywhere when it comes to testing and general brand PR.

We can learn from Ford in one regard: haste. Though important when delivering a product to market, it often results in shoddy production that leads to costly mistakes. The company is deciding to take things slow when it comes to collecting and managing data from auto insurance companies, which is a standard others should be following if they don’t want to get in over their heads. Ford’s focus on creating data 'black boxes' not dissimilar to those on airplanes, which can be consulted in the event of a major crash or incident for a data log of what occurred, is going to become a standard feature of autonomous vehicles before long.

It’s a matter of trust

It’s going to become increasingly obvious over the next few years that solving the data management challenges wrought by the advent of self-driving cars is going to be a matter of trust. Drivers need to be certain that their cars aren’t acting as surveillance devices, as does society broadly speaking, and manufacturers need to be taking steps to build and strengthen trust between those who make the car, those whose data the car collects, and those who analyze and utilize such data for commercial gains.

The fierce competition between Tesla and Waymo is worth watching in this regard, largely because the profit incentives of the capitalist marketplace will almost assuredly lead both of these companies to throw caution to the wind in their race to beat one another via self-driving cars. We will only be able to solve the data management challenge issued by autonomous vehicles if we learn that sometimes competition needs to be put aside in the name of cooperation that can solve public health crises like deaths resulting from self-driving vehicles.

The data management challenge posed by self-driving cars demands that that auto and insurance industries also take ethics into consideration to a hitherto undreamt-of extent. Modern vehicles are becoming surveillance hubs in and of themselves, with Tesla’s newest non-lidar approach to self-driving car data collection proving to be more accurate, and thus necessarily more invasive, than nearly any other technique that’s yet been pioneered. While this may help Tesla in the sense that it’s propelling the company ahead of its adversaries technologically speaking, it poses immense ethical questions like what the responsibility of the market leader is when it comes to fostering innovations which necessarily surveil the public in order to function.

It’s a self-driving world now

The data management challenges being generated by the ceaseless advance of self-driving vehicles won’t go away anytime soon, as we’re now in a self-driving world where automation, data collection (another term for surveillance), and programmatic decision-making is the new standard. While we’ve grown so used to always being the one doing the driving, humans are now being put in the backseat and must trust in the capacity of machines to deliver us to a brighter future. In order to arrive at our destination unimpeded, we need a new focus on ethics across the automotive and insurance industries that will ensure this new technology is primarily used for good.

Additional regulation will be needed in order to protect the privacy of everyday people, and modern infrastructure must be constructed in order to alleviate the sensory-burden being placed on autonomous vehicles if they’re to succeed in the long-term. The good news for those who love self-driving cars is that the profit incentive is enough to make companies plow ahead regardless of the data management challenges they’re facing. This could result in huge ethical dilemmas later on, though, so those interested in self-driving cars can’t allow humans to become unmoored from the driver’s seat if we want our values to be represented on the roads of tomorrow.

Author: Steve Jones

Source: SmartDataCollective

Unexpected convergent consequences...this is what happens when eight different exponential technologies all explode onto the scene at once.

This post (the second of seven) is a look at artificial intelligence. Future posts will look at other tech areas.

An expert might be reasonably good at predicting the growth of a single exponential technology (e.g., the Internet of Things), but try to predict the future when A.I., robotics, VR, synthetic biology and computation are all doubling, morphing and recombining. You have a very exciting (read: unpredictable) future. ​ This year at my Abundance 360 Summit I decided to explore this concept in sessions I called "Convergence Catalyzers."

For each technology, I brought in an industry expert to identify their Top 5 Recent Breakthroughs (2012-2015) and their Top 5 Anticipated Breakthroughs (2016-2018). Then, we explored the patterns that emerged.

Artificial Intelligence — Context

At A360 this year, my expert on AI was Stephen Gold, the CMO and VP of Business Development and Partner Programs at IBM Watson. Here's some context before we dive in.

Artificial intelligence is the ability of a computer to understand what you're asking and then infer the best possible answer from all the available evidence.

You may think of AI as Siri or Google Now on your iPhone, Jarvis from Iron Man or IBM's Watson.

Progress of late is furious — an AI R&D arms race is underway among the world's top technology giants.

Soon AI will become the most important human collaboration tool ever created, amplifying our abilities and providing a simple user interface to all exponential technologies. Ultimately, it's helping us speed toward a world of abundance.

The implications of true AI are staggering, and I asked Stephen to share his top five breakthroughs from recent years to illustrate some of them.

Recent Top 5 Breakthroughs in AI: 2011 - 2015

"It's amazing," said Gold. "For 50 years, we've ideated about this idea of artificial intelligence. But it's only been in the last few years that we've seen a fundamental transformation in this technology."

Here are the breakthroughs Stephen identified in artificial intelligence research from 2011-2015:

1. IBM Watson wins Jeopardy demo's integration of natural language processing, machine learning (ML), and big data.

In 2011, IBM's AI system, dubbed "Watson," won a game of Jeopardy against the top two all-time champions.

This was a historic moment, the "Kitty Hawk moment" for artificial intelligence.

"It was really the first substantial, commercial demonstration of the power of this technology," explained Gold. "We wanted to prove a point that you could bring together some very unique technologies: natural language technologies, artificial intelligence, the context, the machine learning and deep learning, analytics and data and do something purposeful that ideally could be commercialized."

2. Siri/Google Now redefine human-data interaction.

In the past few years, systems like Siri and Google Now opened our minds to the idea that we don't have to be tethered to a laptop to have seamless interaction with information.

In this model, AIs will move from speech recognition to natural language interaction, to natural language generation, and eventually to an ability to write as well as receive information.

3. Deep learning demonstrates how machines learn on their own, advance and adapt.

"Machine learning is about man assisting computers. Deep learning is about systems beginning to progress and learn on their own," says Gold. "Historically, systems have always been trained. They've been programmed. And, over time, the programming languages changed. We certainly moved beyond FORTRAN and BASIC, but we've always been limited to this idea of conventional rules and logic and structured data."

As we move into the area of AI and cognitive computing, we're exploring the ability of computers to do more unaided/unassisted learning.

4. Image recognition and interpretation now rivals what humans can do — allowing for imagine interpretation and anomaly detection.

Image recognition has exploded over the last few years. Facebook and Google Photos, for example, each have tens of billions of images on their platform. With this dataset, they (and many others) are developing technologies that go beyond facial recognition providing algorithms that can tell you what is in the image: a boat, plane, car, cat, dog, and so on.

The crazy part is that the algorithms are better than humans at recognizing images. The implications are enormous. "Imagine," says Gold, "an AI able to examine an X-ray or CAT scan or MRI to report what looks abnormal."

5. AI Apps proliferate: universities scramble to adopt AI curriculum

As AI begins to impact every industry and every profession, there is a response where schools and universities are ramping up their AI and machine learning curriculum. IBM, for example, is working with over 150 partners to present both business and technology-oriented students with cognitive computing curricula.

So what's in store for the near future?

Anticipated Top AI Breakthroughs: 2016 – 2018

Here are Gold's predictions for the most exciting, disruptive developments coming in AI in the next three years. As entrepreneurs and investors, these are the areas you should be focusing on, as the business opportunities are tremendous.

1. Next-gen A.I. systems will beat the Turing Test

Alan Turing created the Turing Test over half a century ago as a way to determine a machine's ability to exhibit intelligent behavior indistinguishable from that of a human.

Loosely, if an artificial system passed the Turing Test, it could be considered "AI."

Gold believes, "that for all practical purposes, these systems will pass the Turing Test" in the next three-year period.

Perhaps more importantly, if it does, this event will accelerate the conversation about the proper use of these technologies and their applications.

2. All five human senses (yes, including taste, smell and touch) will become part of the normal computing experience.

AIs will begin to sense and use all five senses. "The sense of touch, smell, and hearing will become prominent in the use of AI," explained Gold. "It will begin to process all that additional incremental information."

When applied to our computing experience, we will engage in a much more intuitive and natural ecosystem that appeals to all of our senses.

3. Solving big problems: detect and deter terrorism, manage global climate change.

AI will help solve some of society's most daunting challenges.

Gold continues, "We've discussed AI's impact on healthcare. We're already seeing this technology being deployed in governments to assist in the understanding and preemptive discovery of terrorist activity."

We'll see revolutions in how we manage climate change, redesign and democratize education, make scientific discoveries, leverage energy resources, and develop solutions to difficult problems.

4. Leverage ALL health data (genomic, phenotypic, social) to redefine the practice of medicine.

"I think AI's effect on healthcare will be far more pervasive and far quicker than anyone anticipates," says Gold. "Even today, AI/machine learning is being used in oncology to identify optimal treatment patterns."

But it goes far beyond this. AI is being used to match clinical trials with patients, drive robotic surgeons, read radiological findings and analyze genomic sequences.

5. AI will be woven into the very fabric of our lives — physically and virtually.

Ultimately, during the AI revolution taking place in the next three years, AIs will be integrated into everything around us, combining sensors and networks and making all systems "smart."

AIs will push forward the ideas of transparency, of seamless interaction with devices and information, making everything personalized and easy to use. We'll be able to harness that sensor data and put it into an actionable form, at the moment when we need to make a decision.

Source: SingularityHub

Your company needs a unique vision on the future, on multiple levels

Does your organization have a unique point of view about the future?

If your answer to the question is no, Gary Hamel says that you do not have a strategy. So, how does one establish their own distinct vision for the future?

Working on trends and scenarios gives your organization opportunities to anticipate future events, to explore new possibilities and build optionality. Understanding business environment and industry trends is a crucial starting point for gaining all kinds of vital strategic foresight. But what are these trends and how do you get a hold of them?

The Four Levels of Trends You Need to Know

On level 1 are megatrends.

Megatrends are a part of a larger line of development, a recognizable whole consisting of phenomena with a distinct history and a development direction. These are things that we all know that do not entail any surprises. Examples of megatrends include climate change, digitalization, and urbanization. Megatrends should always be taken into consideration in strategic planning.

Recommended reading for making sense of megatrends: Hans Rosling: Factfullness (2018).

On level 2 are trends.

A trend is a change from something into some specific, clearly visible direction or a direction that has just started to emerge. Trends have a trajectory. Two rising trends can strengthen one another. Trends can be only local phenomena or tied to a single industry.

In a perfect situation, one company can be a trendsetter and, for example, shape the whole industry’s business logic. An example of this is Tesla who revolutionized both the technological solution (an electric car) and its sales (direct to consumer via online sales channel). Most organizations, however, just strive to notice relevant trends in time and to sufficiently answer and adapt to them.

Recommended reading for the COVID-19 pandemic’s trend context: Scott Galloway: Post Corona (2020).

On level 3 are weak signals.

A weak signal is a new and surprising event or phenomenon that can be seen as the first sign of change or a new course of development. Weak signals have no recognizable history, and they can remain singular. Discerning level 2 trends calls for a lot of work, but weak signals demand even more discipline due to their high signal-to-noise ratio. In a perfect situation, an organization practices systematic business environment monitoring that takes emerging events on the edges of the industry into account.

This sort of monitoring is the most cost-effective as a paid service and/or tool. For a smaller organization with a limited budget, this may mean collecting the signals themselves. Even this requires a systematic approach, and for example regularly reviewing observations made by employees in team meetings (for example, a peculiar comment from a customer, an interesting post on LinkedIn, etc.).

Recommended reading for getting better at spotting signals from peripheries: Rita McGrath: Seeing Around Corners (2019).

On level 4 are the so-called black swans.

Black swans are surprising and highly unlikely events that have significant influence and that change the course of development quickly, causing uncertainty. They are an “unknown unknown”, a radical context of ambiguousness where we cannot know things for sure.

A major part of decision-making in business involves uncertainty. Strategic choices must continuously be made in situations where there is only limited information available. There are no quick and easy formulas with which we can remove the uncertainty or identify the black swans waiting for us on the horizon. At worst, forecasts and Excel calculations taken from historical data give us a dangerous illusion of control.

Instead, amidst radical uncertainty, all of us on every level of our organizations must be able to say “I don’t know”. Admitting to not knowing something is a prerequisite for learning, renewing, and making discoveries. Combining things creatively, using abductive analysis and taking leaps of imagination, we can at least start to chart possible black swans, testing, preparing and building resilience.

Recommended reading for anticipating black swans: Nassim Taleb: Black Swan (2007) and John Kay & Mervyn King: Radical Uncertainty (2020).

Renew Your Business to Ensure Future Success

The coronavirus pandemic, when viewed in a trend context, is a black swan that has accelerated and strengthened many trends that were prominent before it (such as e-commerce, remote work, innovations in transportation). The pandemic made decades happen in weeks, as Scott Galloway aptly sums up.

In turbulent times, Gary Hamel sees strategy work as ever more significant in order for an organization to stay relevant. The most important question for management to ponder is: “How are we going to re-invent ourselves and the world around us during the next five years”? To answer this question, your organization needs to recognize and understand the trends that have an impact on your business environment, and what all this means to you.

Recommended reading for the new normal: Gary Hamel & Michele Zanini’s Humanocrazy (2020).

Author: Nora Kärkkäinen

Source: M-Brain