#ai art still needs human intervention and even if it no longer needs it in the future it needed it to start out

[the-dear-skull]

I see and whole heartedly agree with the criticisms with AI art, but both sides have people acting like it's the end of human made art, and it's like,,,,,,, no.

[dataoutsourcingcompany]

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[tastydregs]

Will COVID-19 accelerate an automated future?

Millions of Americans have started to work from home amidst the current COVID-19 pandemic. Retailers have struggled with supply while nervous consumers are hoarding everything from toilet paper to hand soap. Across the globe, Chinese e-commerce giant JD began testing a level-4 autonomous delivery robot in Wuhan and running its automated warehouses 24 hours a day to cope with a surge in demand.

Suddenly, autonomous machines need to be better than just proof of concepts. They can no longer depend on on-site engineering support for edge cases. They must be robust enough to work independently across various real-life situations.

In some ways, the COVID-19 epidemic accelerates an automated future that’s already on its way. It has exposed problems that have long existed in the artificial intelligence (AI) venture scene: buzzwords and hype cloud people’s judgment, making it difficult to see real progress.

The industry needs to take on much-needed reforms towards real-world autonomous systems in the following three areas.

1. Rethink metrics

As more autonomous machines are deployed in the real world, conventional metrics such as speed, cycle time, or success rate can no longer represent the full picture. We need to measure the reliability of the system under uncertainties with robustness metrics such as the average number of human interventions.

We need more tools and industry standards to evaluate overall system performance across a wide range of scenarios because real life, unlike a controlled environment, is unpredictable.

If a delivery robot can reach a max speed of 4 MPH but cannot complete a single delivery without human support, the robot is not creating much value to its users.

DevOps emerged a few years ago to shorten the development cycle and continuously deliver high-quality software. In comparison to software engineering, AI or machine learning (ML) is much less mature. 87% of ML projects never go into production. However, recently we’ve started to see MLOps or AIOps appearing more and more.

This marks a crucial transition from AI/ML research to actual products that are used and tested every day. It requires a significant change in mindset to focus on quality assurance instead of state-of-the-art ML models. I’m not saying we can’t have both at the same time, but to date, we’ve seen much more emphasis on the latter.

Starsky Robotics, a once promising self-driving truck company founded in 2015, shut down in March. Founder Stefan Seltz-Axmacher said its biggest challenge was that “supervised machine learning doesn’t live up to the hype.” | Credit: Starsky Robotics

2. Redesign error handling and communication

The recent shutdown of Starsky Robotics reminds us that we are still years away from fully autonomous solutions. That doesn’t mean robotics cannot bring immediate values to humans. Even if humans need to handle edge cases 15% of the time, that still means companies can reduce significant labor and integration costs.

However, AI companies currently tend to spend much more resources on building autonomous systems and much less time thinking about error handling and seamless hand-off between machines and humans.

We need a better way of handling and communicating errors, especially for ML products because ML is more probabilistic and less transparent. Therefore, showing the confidence level of model predictions or framing your predictions as suggestions instead of decisions are ways to gain trust with users.

We need to categorize errors into different levels, design different protocols accordingly, and prioritize minimizing fatal errors that stop the system and require human intervention. If fatal errors occur and the system isn’t working anymore, can we respond quickly and troubleshoot remotely?

The most difficult part is to identify the unknown unknowns, errors that systems cannot detect. Therefore, it’s also crucial to have two-way communication and allow users to flag errors or choose to activate the previously agreed fallback plan.

3. Redefine human-machine interaction

The novel coronavirus forces companies to more rapidly adopt automation and shift to the cloud. As fewer people control a larger number of robots, do we have the right tools and technologies to pass all the relevant information to that decision-maker promptly? Are there enough sensors on each robot to provide a full picture?

Today, we rely on tactile input like computers or tablets to control robots. Are these still the best interface as the amount of information soars and response time remains short? Should we reconsider human-machine interfaces that go beyond tactile, for example, voice, VR/AR or brain-machine interface?

We also need to decide who should be in control. As machines get smarter, should we always make the final call?

For example, who should be controlling an autonomous robotaxi? The car itself? The human safety driver? Someone who monitors a fleet of robotaxis remotely? The passengers? Under what situation? Or should it be a co-decision with weighted judgment by both humans and machines? What’s the ethical implication? Can the interface support multi-step co-decision making?

Ultimately, how do we design human-centered AI to make sure autonomous machines make our lives better, not worse? How do we automate the right use cases to augment humans? How do we build a hybrid team that delivers better outcomes and allows humans and machines to learn from each other?

There are still a lot of questions that we need to answer. And the current COVID-19 pandemic is pushing us to answer them more quickly so that would-be autonomous systems can deliver on their promise. If the makers of these systems can focus on the three areas I’ve outlined above, they’ll be better positioned to reach key conclusions more quickly. And that will ensure we’re heading in the right direction.

About the Author

Bastiane Huang is a Product Manager at OSARO, a San Francisco-based startup building AI-defined robotics. She has worked for Amazon in its Alexa group and with Harvard Business Review, as well as the university’s Future of Work Initiative. She also writes about ML, robotics, and product management.

The post Will COVID-19 accelerate an automated future? appeared first on The Robot Report.

[opticien2-0]

GUEST COMMENT The art of selling: AI in retail

There have been a number of buzzwords and defining technology trends in retail over the last decade: from Big Data, to omnichannel, and the ubiquitous, omni-present Cloud. And now the internet of things (IoT) and artificial intelligence (AI) have seemingly become the latest crazes and talk of the town. Forrester expects investment in AI to triple this year. By 2020, 85% of customer interactions will be managed by AI according to research by Gartner. It’s clearly becoming big business across industries, and not just in retail. The value of AI is estimated to be worth $36.8bn globally by 2025 predicts US market intelligence firm Tractica.

With the proliferation and accumulation of so much data as people shop anytime, anywhere – whether online, in physical stores or increasingly via their mobile phones – the conundrum for many remains: there’s just too much information to be able to make any meaningful sense out of it.

And that’s where artificial intelligence comes in. AI relies on a continual process of technological learning from experience and getting better and better at answering complex questions. Algorithms powered by AI can rapidly come up with alternative options which are otherwise much more time-consuming and laborious using conventional computer-powered A/B testing. Like the human brain, AI adapts to the environment and gets better the more you use it. But unlike humans, the capacity for improvement is unlimited. What’s more, boring, repetitive tasks are never a problem.

AI is not necessarily a concept that’s all that new. And with the tech industry’s love of jargon, various different names refer to more or less the same thing. Machine learning is used to steer self-driving cars. AI is proving instrumental in healthcare for identifying and diagnosing complicated ailments. In fintech, all stock markets are now dominated by computer decision-making systems. And even everyday search engines like Google use AI to refine and improve the information it comes up with the moment you tap in a few keywords.

Plenty of examples in retail already fall under the hat of AI: for instance, online “chatbots” being used by the likes of eBay or North Face to help with customer service; personal shopping assistants like Amazon’s Alexa that respond to voice prompts; or robots replacing information kiosks in stores at Lowe’s in the US. “Live Chat” functions on retailer’s websites are also proving popular for replacing staff with always-on robots and providing a continuous machine-learning customer service experience.

Personalised service

The recently launched eBay ShopBot, an AI-powered personal shopping assistant on Facebook Messenger, helps users find the best deals and sift through over a billion listings. These chatbots have question and answer recommendation capabilities that are much more personalised than previous systems. Nowadays, it’s difficult to know whether your questions about a particular delivery are being answered by a real human, or a machine.

They’re all examples of retailers trying to create a near human interaction. An IBM study in retail deduced that traditional retailing is too constrained to cope with recent technological advances and that the technology to date is just not human enough.

Retailers have long since struggled with maintaining ever increasing standards of customer service as consumer expectations continue to rise. As people continue to shop more via the internet, retailers have to provide a faster, more effective, personalised service specifically aimed at the needs and wants of individual customers.

The trouble with traditional retail IT systems is not only the breadth of fragmented data that’s so often meaningless and out-of-date; but moreover, it’s the over-reliance on linear computing techniques that are too simplistic when it comes to the complicated task of forecasting exactly what style, colour or size combinations are most likely to sell in any given area.

Machine learning

AI learns from past behaviour, as well as trial and error, to come up with more intelligent solutions. It’s not just science. There’s an art to selling too. Old fashioned rules-based analytics will soon become a thing of the past.

At Detego, this means making more informed recommendations to retailers using predictive analytics. So, much like the practice of online retailers flagging up similar items you might like as you browse the web, some retailers are now taking this to the next level using AI – and not just online, but in their physical stores as well (where still over eighty percent of sales are driven).

For example, whereas a sales assistant might, if you’re lucky, recommend something that’s evidently there on the shelves, an AI system would be better at identifying what would be the best items to offer based on many more criteria. These would include fundamental credentials like real-time product availability and the resulting profitability for the retailer, as well as other important considerations, like the consumer’s browsing history, or even what they’ve tried on before in the fitting room (thanks to “smart” RFID tags imbedded into garments).

Informed recommendations can be made by tapping into social media and other factors that might influence product choices, like current fashion trends or weather forecasts in different regions.

Effective AI systems are looking for re-occuring patterns to help avoid out-of-stocks and unnecessary markdowns: for instance, by promoting underselling lines held in reserve that otherwise would later have to be discounted. Not only will such advanced technology know when shelves are empty, but more importantly, it will predict what will happen next.

One of the biggest growth areas where AI can make a significant difference to a retailer’s bottom line – for mobile, online and bricks-and-mortar retailing – is in intelligent forecasting systems. Previously, retailers were only able to predict roughly the quantities of products to order to keep shelves fully stocked using (often out-of-date) inventory levels and historical sales data (usually going back a few years, at best). These days, AI can develop a much more accurate picture of exactly what types of products, sizes and colours are likely to sell, by looking at multiple scenarios in real time (fashion trends, consumer behaviour, the weather etc.) and drawing on data from the internet. This means forecasting is no longer so much “stab in the dark” guess work.

Using AI, German online retailer, Otto, predicts with 90% accuracy what will be sold within the next thirty days and has reduced the amount of surplus stock it holds by a fifth . It has also reduced the number of returns by over two million products a year. It claims to be so reliable, in fact, that it now uses an automated AI system to purchase 200,000 items a month from third party suppliers with no human intervention. Humans simply wouldn’t be able to keep up with the volume of colour and style choices to be made.

Artificial Intelligence offers the potential for a considerable reduction in labour costs for retailers. For consumers, it means getting more reliable information and personalised offers, not to mention considerable time-savings for both. Human machines

A report by PwC says that around 44% of jobs in the retail sector are at risk of automation by 2030. Some of the mid-level employee positions will disappear – particularly warehouse staff and employees in the back-office. AI technology is extremely good at repeated tasks and number crunching, so lots of manual processes will undoubtedly be done by machines in future. For instance, we’re already seeing some retailers wanting to close off stock rooms and using robots to make automatic decisions about what needs replacing on the shelves, or managing the flow of goods for deliveries and onto the shopfloor.

In the not too distant future, it will be common practice to pull out your phone and ask it a question as you enter a store, rather than seeking out a sales assistant or searching through the rails yourself. For instance, your smartphone can immediately respond that a desired article is available in your size and that sales staff can bring it. Voice recognition systems and speaking to a computer or smartphone (like Apple’s Siri) for answers is clearly the way forward. Talking interactive screens and self-checkouts in fitting rooms is something we’re already engaged with.

While some fashion retailers are working with Detego to exploit many of the latest technologies to help encourage more people into their stores and improve levels of customer service – including smart fitting rooms with interactive displays showing more buying options that digitally connect with sales staff – forecasting in fashion is generally quite poor. Despite more than 1,500 stores already equipped with Detego’s software and over a billion garments digitally connected, the wider industry average for forecasting accuracy in fashion still lags at a paltry sixty or seventy percent. Although RFID tagging and real-time stock monitoring offers near hundred percent inventory accuracy, relatively few fashion retailers have rolled-out digitally connected technology on a wider scale. It’s still only the early stages of AI. But with the promise of AI making forecasting and product selections even more accurate, it’s sure to become a reality.

Uwe Hennig is chief executive of retail tech specialist Detego

The post GUEST COMMENT The art of selling: AI in retail appeared first on InternetRetailing.

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[beatrixk1ddo-blog]

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Predictive analytics: What are the challenges and opportunities? – Econsultancy (blog)

In August 2016, Econsultancy published a report in association with IBM called The Secrets of Elite Analytics Practices.

Part of this wide ranging report seeks to discover just how automation and AI have changed analytics in marketing.

Let’s look at some of the talking points…

From automation to automaton?

Time was identified as a business’s most precious resource. Being able to streamline the marketing function through automation and, in particular, the analytics portion, was something executives deemed hugely valuable.

But is automation driving out innovation and originality? With so much potentially determined by machines and algorithms, do brands risk losing the essence that made them unique and the innovation that could keep them alive?

Understanding where automation delivers real results

Nearly 60% of respondents stated that their analytics solutions produced data-based insights without analyst involvement.

A further 80% of those stated it saved them significant time as a result. Either the analysts themselves could be redeployed to focus on trickier tasks or the insights generated pointed to opportunities elsewhere.

Hotel group IHG’s head of CRM, Jim Sprigg, explains his position on automation thusly: “Automation and machine learning will be critical for the sort of thinking that requires many calculations done in a somewhat predictable way.”

“It is definitely in our roadmap for broad use in predictive modeling which can drive, say, the assignment of offers and content in digital media based on individual customers’ attributes, behaviors and transaction histories.”

Dealing with the routine but complex

The idea of automation means different things to different people. For some organizations, particularly those that operate well on defined processes and rules-driven decision making, automation saves a great deal of time.

This is either because it can create a trickle down effect of automation allowing other systems to take appropriate action without human intervention, or alert business users when intervention is needed.

In complex, real time environments such as programmatic advertising, the automated processing of information into insights, and insights into action is viewed as essential to realizing opportunities before they pass brands by.

Danish AI-based media buying agency, Blackwood Seven, is expanding across Europe based on the success of its model that claims clients get a 25-50% improvement in the effect of their media (according to Campaign magazine) through using the company’s AI technology.

The software analyzes 82 different data inputs (such as sales, YouGov data and weather info) to determine a media plan’s likely outcome and optimizes in real time accordingly.

Can we automate creativity?

The advertising community is already looking at the question of whether or not automation and machine learning can actually create ad executions, not just supply humans with the insight with which to build their own creations.

However, the idea that AI would be integral to developing creative is still a pipedream. This begs the question, beyond a degree of grunt work or speedy number crunching to get the the right ads to the right audiences in real time, does automation have anything to contribute to the creative, innovative side of marketing.

In a discipline that has always been described as the marriage of art and science, can science begin to replicate (and replace) art?

The limits of automation

There is a sense, however, that analytics will never fully be automated. The feeling persists that strong marketing is an intelligent marriage of art and science, even in today’s data obsessed environment.

“Humans still have an advantage over computers,” Sprigg insists. “We used to call these the big ‘ah-ha’ insights. The sort that come from intuition and highly synthesized recognition.”

Sprigg gives the example of a time he showed the output from an automated learning process that suggested some offers landed differently with customers who came to the company via customer service than for those who used the web.

The group to whom Sprigg presented this data made the connection between the streams of information the computers already had access to – that there was a gap in the merchandising. “Humans were synthesizing information along with practical human experience in ways that we would have never known to code into the computer’s consideration set,” he adds.

Sprigg identifies that the biggest problem with automated analytics may yet be human in origin – it is a case of scenario planning.

Programmed with the information around any given scenario, a computer could undoubtedly come up with the relevant insight. It’s just that humans cannot prepare the machines to anticipate every possible nuance or scenario.

“Marketing functions can’t build automation for out-of-the-box thinking, but they can recruit for it,” Sprigg concludes.

Humans, while lacking in the number crunching abilities of their automated colleagues, benefit from years of emphatic “programming” that contributes hugely to strategic success.

The dangers of machine-based innovation

While marketers may be in danger of forgetting the worth of their human resources in favor of the speed and efficiency of automation, there is also a danger in relying 100% on data outputs to inform future direction.

Some executives interviewed for this report warned that an over-reliance on data to substantiate decision-making was hampering innovation.

The Hard Rock Cafe’s Claudia Infante complains that “the ideas that get shelved are the victims of a hybrid data-driven culture that we’re creating around ourselves.”

“We’re no longer as nimble and willing to go looking for the new shiny thing because we have to look at the data. There is no data to back those ideas up and you can’t get data unless you activate the idea.”

Paralysis by analysis

Automation builds a data-driven culture because it allows for faster reporting, analysis and optimization of existing channels, building on what is known. This use of data as a comfort blanket, however, can slowly suffocate innovative organizations.

On that note, Infante adds that “a company may have been innovative and forward thinking but of course it grows on the back of that success. Then, when you’re a big player, you have to take care of the day-to-day: make sure the lights are on, guarantee growth. As a result, we end up leaving behind a bit of that ideation process.”

It’s clear from Infante’s illustration that companies need to make sure they continue to use automation as a solution to an existing problem rather than a panacea for everything.

It may seem faster, and the results from it more tangible, but automation is not going to deliver on every aspect. It’s all about finding its place.

“Companies have many people analyzing reports trying to identify tactical performace gaps and opportunities so they make predictable adjustments. As a result, companies hire a lot of people who like trying to think like a computer. If that can all be automated, the goal should be to recruit different types of thinkers,” Sprigg explains. 

Automation must be omni-channel

The immediate challenge for developers of analytics automation is in creating a solution that moves beyond the point and into an omni-channel environment. IHG’s Sprigg explains:

“Out-of-the-box solutions tend to be limited in their scope of operations. They are designed to optimize one channel or only one page at a time. This is multichannel optimization. We want to optimize in a way that allows us to maintain a consistent omni-channel experience across all channels and even extends to customer service and in-hotel interactions.”

Understand the question before anticipating the answer

Over and over again however, executives have reinforced the old computing adage of “garbage in, garbage out.” Automation in analytics is only ever going to be as good as the premise it is set up to work toward. Marketers must understand its power and its limitations to fully benefit from its potential.

For some, it is a simple question of plug and play to speed up number crunching and use the efficiencies to divert resources elsewhere.

Other organizations may find that embedding automation in their analytics process requires a wholesale change of departmental and HR organization. In some cases the whole culture of the company could change.

This post was co-written by Morag Cuddeford-Jones.

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The post Predictive analytics: What are the challenges and opportunities? – Econsultancy (blog) appeared first on Big Data News Magazine.

from Predictive analytics: What are the challenges and opportunities? – Econsultancy (blog)

[bigdatanewsmagazine]

Predictive analytics: What are the challenges and opportunities? – Econsultancy (blog)

In August 2016, Econsultancy published a report in association with IBM called The Secrets of Elite Analytics Practices.

Part of this wide ranging report seeks to discover just how automation and AI have changed analytics in marketing.

Let’s look at some of the talking points…

From automation to automaton?

Time was identified as a business’s most precious resource. Being able to streamline the marketing function through automation and, in particular, the analytics portion, was something executives deemed hugely valuable.

But is automation driving out innovation and originality? With so much potentially determined by machines and algorithms, do brands risk losing the essence that made them unique and the innovation that could keep them alive?

Understanding where automation delivers real results

Nearly 60% of respondents stated that their analytics solutions produced data-based insights without analyst involvement.

A further 80% of those stated it saved them significant time as a result. Either the analysts themselves could be redeployed to focus on trickier tasks or the insights generated pointed to opportunities elsewhere.

Hotel group IHG’s head of CRM, Jim Sprigg, explains his position on automation thusly: “Automation and machine learning will be critical for the sort of thinking that requires many calculations done in a somewhat predictable way.”

“It is definitely in our roadmap for broad use in predictive modeling which can drive, say, the assignment of offers and content in digital media based on individual customers’ attributes, behaviors and transaction histories.”

Dealing with the routine but complex

The idea of automation means different things to different people. For some organizations, particularly those that operate well on defined processes and rules-driven decision making, automation saves a great deal of time.

This is either because it can create a trickle down effect of automation allowing other systems to take appropriate action without human intervention, or alert business users when intervention is needed.

In complex, real time environments such as programmatic advertising, the automated processing of information into insights, and insights into action is viewed as essential to realizing opportunities before they pass brands by.

Danish AI-based media buying agency, Blackwood Seven, is expanding across Europe based on the success of its model that claims clients get a 25-50% improvement in the effect of their media (according to Campaign magazine) through using the company’s AI technology.

The software analyzes 82 different data inputs (such as sales, YouGov data and weather info) to determine a media plan’s likely outcome and optimizes in real time accordingly.

Can we automate creativity?

The advertising community is already looking at the question of whether or not automation and machine learning can actually create ad executions, not just supply humans with the insight with which to build their own creations.

However, the idea that AI would be integral to developing creative is still a pipedream. This begs the question, beyond a degree of grunt work or speedy number crunching to get the the right ads to the right audiences in real time, does automation have anything to contribute to the creative, innovative side of marketing.

In a discipline that has always been described as the marriage of art and science, can science begin to replicate (and replace) art?

The limits of automation

There is a sense, however, that analytics will never fully be automated. The feeling persists that strong marketing is an intelligent marriage of art and science, even in today’s data obsessed environment.

“Humans still have an advantage over computers,” Sprigg insists. “We used to call these the big ‘ah-ha’ insights. The sort that come from intuition and highly synthesized recognition.”

Sprigg gives the example of a time he showed the output from an automated learning process that suggested some offers landed differently with customers who came to the company via customer service than for those who used the web.

The group to whom Sprigg presented this data made the connection between the streams of information the computers already had access to – that there was a gap in the merchandising. “Humans were synthesizing information along with practical human experience in ways that we would have never known to code into the computer’s consideration set,” he adds.

Sprigg identifies that the biggest problem with automated analytics may yet be human in origin – it is a case of scenario planning.

Programmed with the information around any given scenario, a computer could undoubtedly come up with the relevant insight. It’s just that humans cannot prepare the machines to anticipate every possible nuance or scenario.

“Marketing functions can’t build automation for out-of-the-box thinking, but they can recruit for it,” Sprigg concludes.

Humans, while lacking in the number crunching abilities of their automated colleagues, benefit from years of emphatic “programming” that contributes hugely to strategic success.

The dangers of machine-based innovation

While marketers may be in danger of forgetting the worth of their human resources in favor of the speed and efficiency of automation, there is also a danger in relying 100% on data outputs to inform future direction.

Some executives interviewed for this report warned that an over-reliance on data to substantiate decision-making was hampering innovation.

The Hard Rock Cafe’s Claudia Infante complains that “the ideas that get shelved are the victims of a hybrid data-driven culture that we’re creating around ourselves.”

“We’re no longer as nimble and willing to go looking for the new shiny thing because we have to look at the data. There is no data to back those ideas up and you can’t get data unless you activate the idea.”

Paralysis by analysis

Automation builds a data-driven culture because it allows for faster reporting, analysis and optimization of existing channels, building on what is known. This use of data as a comfort blanket, however, can slowly suffocate innovative organizations.

On that note, Infante adds that “a company may have been innovative and forward thinking but of course it grows on the back of that success. Then, when you’re a big player, you have to take care of the day-to-day: make sure the lights are on, guarantee growth. As a result, we end up leaving behind a bit of that ideation process.”

It’s clear from Infante’s illustration that companies need to make sure they continue to use automation as a solution to an existing problem rather than a panacea for everything.

It may seem faster, and the results from it more tangible, but automation is not going to deliver on every aspect. It’s all about finding its place.

“Companies have many people analyzing reports trying to identify tactical performace gaps and opportunities so they make predictable adjustments. As a result, companies hire a lot of people who like trying to think like a computer. If that can all be automated, the goal should be to recruit different types of thinkers,” Sprigg explains. 

Automation must be omni-channel

The immediate challenge for developers of analytics automation is in creating a solution that moves beyond the point and into an omni-channel environment. IHG’s Sprigg explains:

“Out-of-the-box solutions tend to be limited in their scope of operations. They are designed to optimize one channel or only one page at a time. This is multichannel optimization. We want to optimize in a way that allows us to maintain a consistent omni-channel experience across all channels and even extends to customer service and in-hotel interactions.”

Understand the question before anticipating the answer

Over and over again however, executives have reinforced the old computing adage of “garbage in, garbage out.” Automation in analytics is only ever going to be as good as the premise it is set up to work toward. Marketers must understand its power and its limitations to fully benefit from its potential.

For some, it is a simple question of plug and play to speed up number crunching and use the efficiencies to divert resources elsewhere.

Other organizations may find that embedding automation in their analytics process requires a wholesale change of departmental and HR organization. In some cases the whole culture of the company could change.

This post was co-written by Morag Cuddeford-Jones.

Let’s block ads! (Why?)

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The post Predictive analytics: What are the challenges and opportunities? – Econsultancy (blog) appeared first on Big Data News Magazine.

from Predictive analytics: What are the challenges and opportunities? – Econsultancy (blog)

[bigdatanewsmagazine]

Predictive analytics: What are the challenges and opportunities? – Econsultancy (blog)

In August 2016, Econsultancy published a report in association with IBM called The Secrets of Elite Analytics Practices.

Part of this wide ranging report seeks to discover just how automation and AI have changed analytics in marketing.

Let’s look at some of the talking points…

From automation to automaton?

Time was identified as a business’s most precious resource. Being able to streamline the marketing function through automation and, in particular, the analytics portion, was something executives deemed hugely valuable.

But is automation driving out innovation and originality? With so much potentially determined by machines and algorithms, do brands risk losing the essence that made them unique and the innovation that could keep them alive?

Understanding where automation delivers real results

Nearly 60% of respondents stated that their analytics solutions produced data-based insights without analyst involvement.

A further 80% of those stated it saved them significant time as a result. Either the analysts themselves could be redeployed to focus on trickier tasks or the insights generated pointed to opportunities elsewhere.

Hotel group IHG’s head of CRM, Jim Sprigg, explains his position on automation thusly: “Automation and machine learning will be critical for the sort of thinking that requires many calculations done in a somewhat predictable way.”

“It is definitely in our roadmap for broad use in predictive modeling which can drive, say, the assignment of offers and content in digital media based on individual customers’ attributes, behaviors and transaction histories.”

Dealing with the routine but complex

The idea of automation means different things to different people. For some organizations, particularly those that operate well on defined processes and rules-driven decision making, automation saves a great deal of time.

This is either because it can create a trickle down effect of automation allowing other systems to take appropriate action without human intervention, or alert business users when intervention is needed.

In complex, real time environments such as programmatic advertising, the automated processing of information into insights, and insights into action is viewed as essential to realizing opportunities before they pass brands by.

Danish AI-based media buying agency, Blackwood Seven, is expanding across Europe based on the success of its model that claims clients get a 25-50% improvement in the effect of their media (according to Campaign magazine) through using the company’s AI technology.

The software analyzes 82 different data inputs (such as sales, YouGov data and weather info) to determine a media plan’s likely outcome and optimizes in real time accordingly.

Can we automate creativity?

The advertising community is already looking at the question of whether or not automation and machine learning can actually create ad executions, not just supply humans with the insight with which to build their own creations.

However, the idea that AI would be integral to developing creative is still a pipedream. This begs the question, beyond a degree of grunt work or speedy number crunching to get the the right ads to the right audiences in real time, does automation have anything to contribute to the creative, innovative side of marketing.

In a discipline that has always been described as the marriage of art and science, can science begin to replicate (and replace) art?

The limits of automation

There is a sense, however, that analytics will never fully be automated. The feeling persists that strong marketing is an intelligent marriage of art and science, even in today’s data obsessed environment.

“Humans still have an advantage over computers,” Sprigg insists. “We used to call these the big ‘ah-ha’ insights. The sort that come from intuition and highly synthesized recognition.”

Sprigg gives the example of a time he showed the output from an automated learning process that suggested some offers landed differently with customers who came to the company via customer service than for those who used the web.

The group to whom Sprigg presented this data made the connection between the streams of information the computers already had access to – that there was a gap in the merchandising. “Humans were synthesizing information along with practical human experience in ways that we would have never known to code into the computer’s consideration set,” he adds.

Sprigg identifies that the biggest problem with automated analytics may yet be human in origin – it is a case of scenario planning.

Programmed with the information around any given scenario, a computer could undoubtedly come up with the relevant insight. It’s just that humans cannot prepare the machines to anticipate every possible nuance or scenario.

“Marketing functions can’t build automation for out-of-the-box thinking, but they can recruit for it,” Sprigg concludes.

Humans, while lacking in the number crunching abilities of their automated colleagues, benefit from years of emphatic “programming” that contributes hugely to strategic success.

The dangers of machine-based innovation

While marketers may be in danger of forgetting the worth of their human resources in favor of the speed and efficiency of automation, there is also a danger in relying 100% on data outputs to inform future direction.

Some executives interviewed for this report warned that an over-reliance on data to substantiate decision-making was hampering innovation.

The Hard Rock Cafe’s Claudia Infante complains that “the ideas that get shelved are the victims of a hybrid data-driven culture that we’re creating around ourselves.”

“We’re no longer as nimble and willing to go looking for the new shiny thing because we have to look at the data. There is no data to back those ideas up and you can’t get data unless you activate the idea.”

Paralysis by analysis

Automation builds a data-driven culture because it allows for faster reporting, analysis and optimization of existing channels, building on what is known. This use of data as a comfort blanket, however, can slowly suffocate innovative organizations.

On that note, Infante adds that “a company may have been innovative and forward thinking but of course it grows on the back of that success. Then, when you’re a big player, you have to take care of the day-to-day: make sure the lights are on, guarantee growth. As a result, we end up leaving behind a bit of that ideation process.”

It’s clear from Infante’s illustration that companies need to make sure they continue to use automation as a solution to an existing problem rather than a panacea for everything.

It may seem faster, and the results from it more tangible, but automation is not going to deliver on every aspect. It’s all about finding its place.

“Companies have many people analyzing reports trying to identify tactical performace gaps and opportunities so they make predictable adjustments. As a result, companies hire a lot of people who like trying to think like a computer. If that can all be automated, the goal should be to recruit different types of thinkers,” Sprigg explains. 

Automation must be omni-channel

The immediate challenge for developers of analytics automation is in creating a solution that moves beyond the point and into an omni-channel environment. IHG’s Sprigg explains:

“Out-of-the-box solutions tend to be limited in their scope of operations. They are designed to optimize one channel or only one page at a time. This is multichannel optimization. We want to optimize in a way that allows us to maintain a consistent omni-channel experience across all channels and even extends to customer service and in-hotel interactions.”

Understand the question before anticipating the answer

Over and over again however, executives have reinforced the old computing adage of “garbage in, garbage out.” Automation in analytics is only ever going to be as good as the premise it is set up to work toward. Marketers must understand its power and its limitations to fully benefit from its potential.

For some, it is a simple question of plug and play to speed up number crunching and use the efficiencies to divert resources elsewhere.

Other organizations may find that embedding automation in their analytics process requires a wholesale change of departmental and HR organization. In some cases the whole culture of the company could change.

This post was co-written by Morag Cuddeford-Jones.

This article passed through the Full-Text RSS service – if this is your content and you’re reading it on someone else’s site, please read the FAQ at http://ift.tt/jcXqJW. Recommended article: The Guardian’s Summary of Julian Assange’s Interview Went Viral and Was Completely False.

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The post Predictive analytics: What are the challenges and opportunities? – Econsultancy (blog) appeared first on Big Data News Magazine.

from Predictive analytics: What are the challenges and opportunities? – Econsultancy (blog)

[bigdatanewsmagazine]

Predictive analytics: What are the challenges and opportunities? – Econsultancy (blog)

In August 2016, Econsultancy published a report in association with IBM called The Secrets of Elite Analytics Practices.

Part of this wide ranging report seeks to discover just how automation and AI have changed analytics in marketing.

Let’s look at some of the talking points…

From automation to automaton?

Time was identified as a business’s most precious resource. Being able to streamline the marketing function through automation and, in particular, the analytics portion, was something executives deemed hugely valuable.

But is automation driving out innovation and originality? With so much potentially determined by machines and algorithms, do brands risk losing the essence that made them unique and the innovation that could keep them alive?

Understanding where automation delivers real results

Nearly 60% of respondents stated that their analytics solutions produced data-based insights without analyst involvement.

A further 80% of those stated it saved them significant time as a result. Either the analysts themselves could be redeployed to focus on trickier tasks or the insights generated pointed to opportunities elsewhere.

Hotel group IHG’s head of CRM, Jim Sprigg, explains his position on automation thusly: “Automation and machine learning will be critical for the sort of thinking that requires many calculations done in a somewhat predictable way.”

“It is definitely in our roadmap for broad use in predictive modeling which can drive, say, the assignment of offers and content in digital media based on individual customers’ attributes, behaviors and transaction histories.”

Dealing with the routine but complex

The idea of automation means different things to different people. For some organizations, particularly those that operate well on defined processes and rules-driven decision making, automation saves a great deal of time.

This is either because it can create a trickle down effect of automation allowing other systems to take appropriate action without human intervention, or alert business users when intervention is needed.

In complex, real time environments such as programmatic advertising, the automated processing of information into insights, and insights into action is viewed as essential to realizing opportunities before they pass brands by.

Danish AI-based media buying agency, Blackwood Seven, is expanding across Europe based on the success of its model that claims clients get a 25-50% improvement in the effect of their media (according to Campaign magazine) through using the company’s AI technology.

The software analyzes 82 different data inputs (such as sales, YouGov data and weather info) to determine a media plan’s likely outcome and optimizes in real time accordingly.

Can we automate creativity?

The advertising community is already looking at the question of whether or not automation and machine learning can actually create ad executions, not just supply humans with the insight with which to build their own creations.

However, the idea that AI would be integral to developing creative is still a pipedream. This begs the question, beyond a degree of grunt work or speedy number crunching to get the the right ads to the right audiences in real time, does automation have anything to contribute to the creative, innovative side of marketing.

In a discipline that has always been described as the marriage of art and science, can science begin to replicate (and replace) art?

The limits of automation

There is a sense, however, that analytics will never fully be automated. The feeling persists that strong marketing is an intelligent marriage of art and science, even in today’s data obsessed environment.

“Humans still have an advantage over computers,” Sprigg insists. “We used to call these the big ‘ah-ha’ insights. The sort that come from intuition and highly synthesized recognition.”

Sprigg gives the example of a time he showed the output from an automated learning process that suggested some offers landed differently with customers who came to the company via customer service than for those who used the web.

The group to whom Sprigg presented this data made the connection between the streams of information the computers already had access to – that there was a gap in the merchandising. “Humans were synthesizing information along with practical human experience in ways that we would have never known to code into the computer’s consideration set,” he adds.

Sprigg identifies that the biggest problem with automated analytics may yet be human in origin – it is a case of scenario planning.

Programmed with the information around any given scenario, a computer could undoubtedly come up with the relevant insight. It’s just that humans cannot prepare the machines to anticipate every possible nuance or scenario.

“Marketing functions can’t build automation for out-of-the-box thinking, but they can recruit for it,” Sprigg concludes.

Humans, while lacking in the number crunching abilities of their automated colleagues, benefit from years of emphatic “programming” that contributes hugely to strategic success.

The dangers of machine-based innovation

While marketers may be in danger of forgetting the worth of their human resources in favor of the speed and efficiency of automation, there is also a danger in relying 100% on data outputs to inform future direction.

Some executives interviewed for this report warned that an over-reliance on data to substantiate decision-making was hampering innovation.

The Hard Rock Cafe’s Claudia Infante complains that “the ideas that get shelved are the victims of a hybrid data-driven culture that we’re creating around ourselves.”

“We’re no longer as nimble and willing to go looking for the new shiny thing because we have to look at the data. There is no data to back those ideas up and you can’t get data unless you activate the idea.”

Paralysis by analysis

Automation builds a data-driven culture because it allows for faster reporting, analysis and optimization of existing channels, building on what is known. This use of data as a comfort blanket, however, can slowly suffocate innovative organizations.

On that note, Infante adds that “a company may have been innovative and forward thinking but of course it grows on the back of that success. Then, when you’re a big player, you have to take care of the day-to-day: make sure the lights are on, guarantee growth. As a result, we end up leaving behind a bit of that ideation process.”

It’s clear from Infante’s illustration that companies need to make sure they continue to use automation as a solution to an existing problem rather than a panacea for everything.

It may seem faster, and the results from it more tangible, but automation is not going to deliver on every aspect. It’s all about finding its place.

“Companies have many people analyzing reports trying to identify tactical performace gaps and opportunities so they make predictable adjustments. As a result, companies hire a lot of people who like trying to think like a computer. If that can all be automated, the goal should be to recruit different types of thinkers,” Sprigg explains. 

Automation must be omni-channel

The immediate challenge for developers of analytics automation is in creating a solution that moves beyond the point and into an omni-channel environment. IHG’s Sprigg explains:

“Out-of-the-box solutions tend to be limited in their scope of operations. They are designed to optimize one channel or only one page at a time. This is multichannel optimization. We want to optimize in a way that allows us to maintain a consistent omni-channel experience across all channels and even extends to customer service and in-hotel interactions.”

Understand the question before anticipating the answer

Over and over again however, executives have reinforced the old computing adage of “garbage in, garbage out.” Automation in analytics is only ever going to be as good as the premise it is set up to work toward. Marketers must understand its power and its limitations to fully benefit from its potential.

For some, it is a simple question of plug and play to speed up number crunching and use the efficiencies to divert resources elsewhere.

Other organizations may find that embedding automation in their analytics process requires a wholesale change of departmental and HR organization. In some cases the whole culture of the company could change.

This post was co-written by Morag Cuddeford-Jones.

This article passed through the Full-Text RSS service – if this is your content and you’re reading it on someone else’s site, please read the FAQ at http://ift.tt/jcXqJW. Recommended article: The Guardian’s Summary of Julian Assange’s Interview Went Viral and Was Completely False.

Originally posted on http://ift.tt/2hR5iji

The post Predictive analytics: What are the challenges and opportunities? – Econsultancy (blog) appeared first on Big Data News Magazine.

from Predictive analytics: What are the challenges and opportunities? – Econsultancy (blog)