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Want to get more from your data? Stop focusing on efficiency

February 23, 2020 Big Data

Ever since Henry Ford came up with the idea of using moving assembly lines to build automobiles faster at lower cost and at higher quality, efficiency has been the driving force in industry.

Unfortunately, it’s the wrong approach for modern businesses. We’re no longer in an industrial economy. Today, information powers our world. Efficiency drives profit in the short term, but making it your primary focus doesn’t drive innovation; it opens the door for competitors to provide something better.

Despite this, we’ve applied the efficiency mantra to almost every aspect of business. The primary use of data in many organizations is to cut costs and increase profitabilty for existing products. Dashboards and reports are really tools to answer questions like: Where is our process breaking? Which parts of our business are the least efficient? Where can we reduce cost? Data is the language of that conversation.

But the focus on efficiency is limiting. Businesses have more opportunities in environments of plenty, fewer in environments of scarcity. Most businesses today exist in a world of plenty: We have new ways to reach customers, like smartphones, chatbots and global logistics networks, and more ways to innovate, like machine learning, 5G, and an abundance of data and cheap processing power.

Using fewer resources, which is the focus of an efficiency program, makes us lean and mean, but it doesn’t provide fertile ground for growth. It can even be counterproductive. Call times can only be so low while still maintaining acceptable customer service. After a certain point, productivity gains are offset by quality issues.

Does this mean we should throw efficiency off a cliff? Obviously not. But using data smartly can provide the best of both worlds. Efficiency is appropriate for non-differentiating, mature cost centers, but it shouldn’t be the prevailing corporate philosophy. To succeed in today’s fast-moving tech landscape, the focus has to be innovation.

That’s all well and good, but how do we apply data to drive innovation instead of efficiency? I have three recommendations.

Think outside your business

Data isn’t just for internal stakeholders. Increasingly, companies realize that providing data externally is a vital part of their business. Whether it’s providing billing detail to business customers, transaction detail to consumers, or performance data to vendors, data is increasingly a differentiator. Making it available externally is a great first step away from an efficiency focus.

Get data to business users, not just technical users

Historically, access to data has been limited to technical people who speak the language of data, but these are rarely the same people who own the business outcome. Business users need wider access to data, which will allow them to think of new uses that only they can identify because they understand the customers and the business.

Opening up access to data is becoming easier. Modern analytics tools allow non-technical users to experiment with data in creative ways. To enable this, make sure the people who can innovate with data have access to it — not just the analysts.

Optimize for maturity, innovate for growth

It’s important to distinguish among different parts of the business. Efficiency should be applied in the mature areas. Use data to identify segments that have slow or declining growth and apply efficiency there. Everything else should be managed through the lens of innovation.

That applies especially to areas of differentiation — and these can vary even within the same industry. For example, a bank may view its customer call center as simply a cost center, so outsourcing those services may be appropriate. Another bank may see customer service as its competitive advantage. It should use data to identify its best customers, predict their future value, and route them to the best agents.

We’re in a remarkably different economy from the world of Henry Ford. His laser focus on speed and cost were appropriate at the time, and still are for some traditional industries. But for most of us, innovating quickly is a prerequisite to survival. That means loosening the chokehold that efficiency has on our thinking and making data-driven innovation the driving force in business.

Doug Bordonaro is Field CTO at ThoughtSpot.

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Big Data – VentureBeat

Under Pressure: 78 Percent of People Feel More Pressure from their Employer than Family When Making Big Decisions

February 6, 2020 NetSuite

Business executives are more anxious about big decisions at work than critical decisions at home that impact their family, according to a new study conducted by Oracle NetSuite. The new study, Unlocking Growth, which provides insights from more than 1,000 business executives in the UK, France, Germany, UAE, Benelux and the Nordics, found that 94 percent are overwhelmed by data when making decisions. Over a quarter are putting risk mitigation ahead of potential success to avoid impacting their career, and 21 percent rely on gut feel and intuition to make critical decisions.

“There’s a lot of talk about a changing economic, technological and political backdrop, but when you step back, organizations across Europe have an increasing number of growth opportunities if they can focus their time and resources in the right places,” said Nicky Tozer, VP EMEA, Oracle NetSuite. “To achieve that focus, organizations need to address the decision-making and planning challenges identified in this study so they can use data to adapt to change faster than their competition and unlock new growth opportunities.”

A Culture of Decision-Making Pressure

Executives across countries and industries are under immense pressure when making critical business decisions and as a result many are putting risk mitigation ahead of potential success.

Most executives (78 percent) said they experience more pressure when making a big decision at work than in their personal life.
Fears about negatively impacting revenue (40 percent), damaging personal reputation (22 percent), losing their job (17 percent), and adversely impacting co-workers (13 percent) are the top four areas executives are concerned about.
Risk aversion is even higher amongst organizations that define themselves as high-performers – 62 percent admit they actively pursue risk-averse decisions, even in the knowledge their choice may not be as successful.

An Unhealthy Relationship with Data

Information overload, time pressure and a lack of trust in senior management is strangling the decision-making process and leading executives to default to ‘gut feel’ to inform their decision-making strategy.

Almost all (94 percent) executives are overwhelmed by data during the decision-making process. Executives in France (99 percent) reported the biggest issues with data, while executives in the UK (92 percent) reported the least.
Time pressure and more complex processes are also making decision-making harder. 27 percent of executives have had less time to focus on critical decisions in the last year and 28 percent note more people have become involved in the process, an issue that was particularly prevalent in the UAE (51 percent).
Only 19 percent – falling to 12 percent in the Nordics – of business executives noted they trust senior management when seeking decision-making guidance. Colleagues (39 percent) and industry peers (21 percent) were the most trusted.
41 percent of respondents expect to turn to a robot as a source of support when making critical decisions in the next year. Executives in France (51 percent) were the most likely, while executives in the UK (33 percent) were the least.
67 percent acknowledge they are not making highly data-driven decisions, with UK executives (73 percent) the most likely to only partially consider data or default to “gut feel”.

A Positive Outlook for Growth and Message to Senior Management

Executives across countries and industries expect their organizations to grow, but highlighted the need to rethink the planning process to ensure data can be used to adjust business plans and that everyone is working towards a clear plan for success.

56 percent of executives expect their business to grow in the next two years. Executives from the UK were the most positive (63 percent) followed by the United Arab Emirates (57 percent), Germany (56 percent), Nordics (54 percent), Benelux (50 percent) and France (49 percent).
Retail industry executives (33 percent) were the most confident that their organizations will exceed growth targets followed by manufacturing (27 percent), distribution (22 percent), and software and technology (29 percent). Executives in professional services (16 percent) and nonprofit organizations (11 percent) had the least confidence.
Almost three quarters (74 percent) of executives say their organization is good at capitalizing on new opportunities, but there are serious concerns about the planning process. Only 31 percent say they are proficient at adjusting business plans based on data analysis and almost one quarter (24 percent) do not think senior management provides a clear plan for success, dropping to just 16 percent in the Nordics.

Methodology
For this study, NetSuite partnered with Raconteur to survey 1,050 manager level and above employees. Respondents originated from the UK (300 respondents), France (150 respondents), Germany (150 respondents), United Arab Emirates (150 respondents), Benelux (150 respondents) and Nordics (150 respondents) and represented small and mid-sized organisations from across a range of industries. Participants took part in an online questionnaire and were surveyed in October and November 2019.

About Oracle NetSuite
For more than 20 years, Oracle NetSuite has helped organizations grow, scale and adapt to change. NetSuite provides a suite of cloud-based applications, which includes financials / Enterprise Resource Planning (ERP), HR, professional services automation and omnichannel commerce, used by more than 19,000 customers in 203 countries and dependent territories.

For more information, please visit https://www.netsuite.com.

Follow NetSuite’s Cloud blog, Facebook page and @NetSuite Twitter handle for real-time updates.

About Oracle
The Oracle Cloud offers complete SaaS application suites for ERP, HCM and CX, plus best-in-class database Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) from data centers throughout the Americas, Europe and Asia. For more information about Oracle (NYSE:ORCL), please visit us at oracle.com.

Trademarks
Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners.

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Will AI Force Humans To Become More Human? (Part 1)

January 21, 2020 BI News and Info

Part 1 of a 2-part series exploring the intersection between AI and humanity

Will artificial intelligence (AI) create an environment where design thinking skills are more valuable than data science skills? Will AI alter how we define human intelligence?

That sounds like questions one might expect from an episode of Rod Serling’s TV series Twilight Zone. Instead of AI replacing humans, will AI actually make humans more human? Will characteristics such as empathy, compassion, and collaboration actually become the future high-value skills that are cherished by leading organizations?

Let’s explore, but we need to start with some definitions.

AI, AI rational agents, and the AI utility function, oh my!

AI is defined as the simulation of human intelligence. AI relies upon the creation of “AI rational agents” that interact with the environment to learn, where learning or intelligence is guided by the definition of the rewards associated with actions. AI leverages deep learning, machine learning, and/or reinforcement learning to guide the “AI rational agent” to learn from continuous engagement with its environment to create the intelligence necessary to maximize current and future rewards (see Figure 1).

Jan 21 blog fig 1 Will AI Force Humans To Become More Human? (Part 1)

Figure 1: AI Rational Agent

The rewards the AI rational agents seek to maximize are framed by the definition of “value” as defined in the AI utility function – the objective criterion that measures the progress and success of an AI rational agent’s behaviors. To ensure the creation of an “AI rational agent” that exhibits the necessary intelligence to make the “right” decision, the AI utility function must cover a holistic definition of “value” that includes financial, operational, customer, societal, environmental, and spiritual (see Figure 2).

Jan 21 blog fig 2 Will AI Force Humans To Become More Human? (Part 1)

Figure 2: “Why Utility Determination Is Critical to Defining AI Success”

To summarize, AI is driven by AI rational agents that seek to drive “intelligent” actions based upon “value” as defined by the AI utility function. To design a holistic AI utility function that drives “intelligence” (whether artificial intelligence or human intelligence), we need to start by defining, or redefining, what we mean by “intelligence.”

Redefining intelligence

Intelligence is defined as the ability to acquire and apply knowledge and skills.

Our U.S. educational institutions have created numerous tests (Iowa Basic Skills, ACT, SAT, GMAT) to measure one’s “intelligence.” Yet there are many stories demonstrating that the education system’s need to put people into “intelligence boxes” has actually stifled creativity. (For two such stories, see the famous podcast by Sir Ken Robinson, “How Do Schools Kill Creativity?” and the story of Gillian Lynne, famous for changing the world of dance and choreography through musicals such as Cats and Phantom of the Opera.) Anyone with children knows the horror of this dilemma as they panic to prepare for ACT and SAT tests that play an outsize role in deciding their future.

This archaic definition of “intelligence” actually has the exact opposite impact, in that it reduces students (our children) to rote learning machines, driving out the creativity and innovation skills that differentiate us from machines.

We already have experienced machines taking over some of the original components of intelligence. How many people use long division, or manually calculate the square root, or multiply numbers with more than two digits in their heads? Traditional measures of intelligence are already under assault by machines.

And AI is going to make further inroads into what we have traditionally defined as intelligence. Human intelligence will no longer be defined by one’s ability to reduce inventory costs, or improve operational uptime, or detect cancer, or prevent unplanned maintenance, or flag at-risk patients and students. Those are all tasks at which AI models will excel. There’s no human competitive advantage there anymore.

We must focus on nurturing the creativity and innovation skills that distinctly make us humans and differentiate us from analytical. We need a new definition of intelligence that nurtures uniquely human creativity and innovation capabilities (said by the new chief innovation officer at Hitachi Vantara, wink, wink).

Part 2 of this series will explore further the human skills that make us unique, the concept of design thinking as it relates to innovation, and answer the question, “Will AI actually make humans more human?”

This article originally appeared on LinkedIn and is republished by permission.

As technology embeds deeper into our lives, companies need to elevate the role of people and culture.

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Digitalist Magazine

Dan Bongino: More Troubling Deep State Connections Emerge

January 17, 2020 Humor

The Dan Bongino Show
The Bongino Report

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ANTZ-IN-PANTZ ……

2020 Predictions For Procurement, Supply Chain, Business, And More

January 16, 2020 SAP

As we turn the corner to a new decade, businesses are looking for ways to address ever-increasing customer expectations: high-quality products and services delivered on demand, without negative impact on the environment or society — and at a reasonable cost.

We asked our global leaders for their perspectives on what to watch for in 2020. Here is what they had to say.

Data moves from an analytical to a decision-making tool

Mike Eberhard, president of SAP Intelligent Spend Group: “In 2020, the shift to leveraging data for real-time decision-making will accelerate for a growing number of business functions.

“For example, through intelligent platforms and network ecosystems, companies will access more and more data sets to shed light on such things as a potential supplier’s financial capability to fulfill a contract. Data can also be used to make budgetary decisions informed by the current status of each and every area of a company’s spending. Organizations possess, and can access, much or all of this data today – with the right tools.

“With an increasing number of data-points uncovered by Internet of Things (IoT) and machine learning, there is more information than ever before to help drive results. This allows organizations to make better sense of what’s happening, what’s coming, and, ultimately, make more intelligent decisions. In the coming year, many more organizations will start to realize the potential of their data to intelligently guide business decisions and leverage it to reach even greater levels of success.

“Looking even further into the future, they will eventually be able to determine whether potential suppliers’ policies adhere to international laws and social ethics, and be able to use data to identify, in advance, a host of potential supply chain disruptions, such as a small number of suppliers concentrated in a region vulnerable to weather or man-made disasters.”

Businesses embrace sustainable procurement

Pat McCarthy, senior vice president and general manager of SAP Ariba and SAP Fieldglass: “We’re entering a ‘greener’ decade spurred by people worldwide demanding innovative solutions to a changing climate, and a year that will feature a resolution to Brexit in Europe and ongoing trade negotiations between East and West. Addressing these and other challenges, including the global movement to end forced labor in supply chains, enterprises are seeking to align their brand values with those of like-minded trading partners.

“As we enter 2020, we can expect to see an uptick in adoption of intelligent spend management solutions as enterprises increasingly reap their benefits – operationally, reputationally, and sustainably. The best solutions, both for the procurement of direct and indirect goods and for external workforce management, guide users through an intuitive process, and leveraging third-party content, prompt them about any upcoming issues or challenges so they can make better-informed decisions. Meanwhile, these insights also enable enterprises to procure with purpose.

“These opportunities, made possible by cloud-based digital networks, are extending competitive advantages and fueling sustainable procurement at a scale never previously imagined – a phenomenon that is only gathering pace as we head into the new year.”

Adaptability and flexibility key

James Lee, chief operating officer of SAP Ariba and SAP Fieldglass: “In times of economic uncertainty, it becomes even more important for businesses to focus on cost-efficiency, compliance, and working capital management.

“To prepare for this uncertainty, in 2020, more organizations will think more strategically about how they spend business cash – investing in better spend management strategies that not only cut costs but also create new revenue-driving opportunities to grow and quickly scale. Adaptability and flexibility will be key to recession-proofing, so procurement leaders will need to embrace technologies that can integrate with intelligent technologies and analytics to improve decision-making, speed, and efficiency.”

A new normal for how work gets done

Arun Srinivasan, general manager of SAP Fieldglass: “With digital disruption reshaping industries and unemployment at record lows, talent is, and will continue to be in the coming decade, in short supply. Many of the skills companies need to compete today aren’t available in the traditional workforce market. The composition of the workforce has forever changed. There is an increased reliance on the external workforce, which comprises contingent workers – think independent contractors, freelancers, and temporary labor – and services providers such as consulting firms, IT outsourcers, and marketing agencies.

“To get work done, organizations will increasingly look beyond their traditional employee populations and turn to the external workforce for the skills needed to thrive in the digital economy. These are skills needed not just to keep up with the pace of change today but also to drive innovation around artificial intelligence, machine learning, data science, process automation, robotics, and more.

“Looking ahead to 2020, expect to see greater engagement of the external workforce as a significant source of competitive advantage. The winners of the war for talent will be the companies paying attention to the demands and desires of all workers – internal and external.”

Profound shift for procurement

Darren Koch, chief product officer at SAP Ariba: “As a new decade approaches, businesses large and small are seeing an increasingly clear connection between embracing intelligent spend management – an approach that brings together every source of spend and buying channels and draws upon vast reservoirs of operational data to manage risk, yield, contextual insights, and fund innovation through savings – extending competitive advantage.

“Where is intelligent spend management headed in 2020 and beyond? Toward systems that can carry out end-to-end procurement processes for all types of spend within parameters strategically set and overseen by humans. In the coming years, we can expect to see intelligent technologies give rise to autonomous functionality, with cloud-based, artificial intelligence (AI)-enabled applications revealing detailed insights, providing prescriptive recommendations, and unshackling talented procurement professionals from the tedium of low-value administrivia. I believe that, in time, autonomous functionality will represent a profound shift for procurement in much the same sense that autonomous vehicles do for transportation.”

Purpose in procurement drives environmental, social, business impacts

Padmini Ranganathan, global vice president of Risk and Sustainability at SAP Ariba: “There has been a general awareness of environmental and societal issues for decades, but now we are seeing a sense of urgency and demand for real change – propelled by easy-to-share information, compliance requirements, and growing demand for transparency. Consumers will vote with their dollars, and because of this, companies are leveraging their impact on issues such as climate impact, forced labor, and impact on livelihood and job opportunities as a part of their brand promises.

“To be credible, the smart ones will shift from focusing just on cost savings and compliance to true value creation and avoid greenwashing. Squeezing suppliers on cost just doesn’t cut it anymore. Buyers and suppliers are coming together to create value for their mutual customer – and where else can this happen but from the beginning of the procurement process when sellers come calling with their business cards or buyers scan the globe for the best suppliers? In 2020, watch for more and more businesses to focus on developing transparent, sustainable relationships that have positive impacts on both the environment and society.”

Partnerships bring more value in 2020

Sean Thompson, senior vice president of Business Network and Ecosystems at SAP Ariba and SAP Fieldglass: “With the rapid rate of technological change and disruption in the marketplace, 2020 will see partners moving beyond the traditional role of strategic advisor to that of business collaborator and innovator. This collaboration will increase the diversity of business networks and allow companies to take advantage of open APIs to solve critical business challenges. Ultimately, the name of the game is co-opetition, where partnerships allow for doing more for customers together than alone.

“We’re also going to see greater efficiency and ease across networks and network-to-network collaboration. All business is global, and companies with varied and diverse supply chains need to rely on clear and concise information to make real-time business decisions across borders and geographies. Data-driven insights will allow buyers and suppliers to think differently about how they source or sell. A continued focus on compliance and reduction in supply chain risk will demand simple, clean, and transparent procurement solutions. The impact will be significant, driving innovation as never seen before.”

Artificial intelligence fully integrated into supplier management

Drew Hofler, vice president of Portfolio Marketing at SAP Ariba and SAP Fieldglass: “In 2020, businesses will extend and accelerate output of existing intelligent technologies, such as AI, by applying them to different business operations and integrating them with one another. The supply chain is a great example of this. For instance, AI will disrupt the manual, error-prone process of managing vendor contracts by automating the process and applying intelligent algorithms. And, AI will be integrated fully into the supplier management process, helping identify high-risk suppliers before purchases are made to ensure suppliers are financially, environmentally, and ethically sustainable.”

Businesses adopt a value-driven approach to procurement

James Marland, global vice president for the SAP Center of Excellence for Spend Management at SAP Ariba and SAP Fieldglass: “In 2020, businesses will start to seriously take account of the ethics and values of their customers to drive procurement decisions. As issues such as sustainability, diversity, and working conditions increasingly displace price in the buying decision, businesses will need to adopt a values-driven approach to spend management to ultimately address them. It’s an exciting time to be in procurement – breaking out of the back office and getting to the front lines.

“One example of where businesses will look to make a tangible difference is with plastics. Redirecting even a small portion of global spend on packaging to precertified suppliers of recycled plastics and alternatives can have a tremendous impact. These suppliers can then grow, reaping benefits from economies of scale, and then start to lower the costs of their products. Ultimately, this practice will make sustainable materials more widely available and economical for buyers. In the next year, inspired by their customers, businesses will leverage spend management and business networks to make a dent in these intractable issues.”

This article originally appeared on SAP News Center and is republished by permission.

Follow SAP Finance online: @SAPFinance (Twitter) | LinkedIn | Facebook | YouTube

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How NetSuite Helps MANA Nutrition Feed More Starving Children

January 9, 2020 NetSuite

How NetSuite Helps MANA Nutrition Feed More Starving Children

Posted by Kendall Fisher, Executive Producer and Host of The NetSuite Podcast

As we head into the first full week of 2020, many of us are diving right into our New Year’s resolutions. While health, self-care and rejuvenation are likely at the top of many lists, this time of year always tends to re-open our drive to give back, too.

NetSuite’s year-round goal is to do good, better, and working with a myriad of incredible social impact organizations only opens our eyes to the countless opportunities in which that’s possible.

For example, MANA Nutrition is one of the nonprofits we’ve had the honor of partnering with since 2011. The organization produces ready-to-use therapeutic food (RUTF) for children suffering from severe acute malnutrition all over the globe. To put that in perspective, nearly three million children are impacted by this disease every year.

MANA Nutrition is changing that.

On this episode of “The NetSuite Podcast,” we sat down with the MANA’s CFO, Chris Whitfield, to find out how NetSuite helps the organization fuel that important mission with the help of better, more efficient processes, seamless financial and operational communication, and visibility and control over quality and traceability.

[embedded content]

To hear MANA Nutrition’s full success story, listen to the episode on Apple Podcasts, Soundcloud or YouTube.

Posted on Mon, January 6, 2020
by NetSuite filed under

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Amazon’s Alexa Knowledge Skills enables voice-guided FAQs, glossaries, and more

December 16, 2019 Big Data

In yet another step toward the enterprise side of the burgeoning conversational intelligence segment, Amazon today launched Alexa Knowledge Skills, a new skill type for its Alexa platform that allows employees and customers to ask questions about an organization’s data without invoking a skill name. It’s available in preview starting today, ahead of general availability in the months to come.

As Alexa senior product marketing manager Ben Grossman explains in a blog post, Alexa Knowledge Skills enable folks to ask Alexa about different types of data, including (but not limited to) organization charts, building information, events, FAQs, glossaries, product catalogs, and more. Developing such a skill requires a spreadsheet of data but no extensive coding or cloud infrastructure, and the skills are privately distributed through Amazon’s Alexa for Business or Alexa for Hospitality services, meaning that they remain accessible only on devices within an organization.

Knowledge skills enable Q&A on spreadsheet data that developers upload and map to templates. These templates correspond to different use cases, like How To (“Alexa, how do I log on to the Wi-Fi?“), Events (“Alexa, when is the next keynote?“), In-Store Assistant (“Alexa, where can I find the pasta?“), and others.

Developers who opt to use the People Directory template for their Knowledge skill can answer questions like these, as well as many others:

Single fact — “Alexa, when did Mary Major start?”
Multiple facts — “Alexa, what’s John Public’s start date and phone number?”
Counts — “Alexa, how many software developers are in New York?”
Lists — “Alexa, what employees are located in New York?”
Statistics — “Alexa, what’s the average tenure for software developers?”
Superlatives — “Alexa, who are the newest employees in New York?”
Reasoning –- “Alexa, is Jane Doe a software developer?”

Amazon says that already, organizations including BayCare Health System and Saint Louis University are developing Knowledge skills to help students and hospital visitors find campus places and resources. “Alexa Knowledge skills bring BayCare the opportunity to customize the Alexa experience across the health system without the need for complex coding and lengthy development processes,” said BayCare Health System director of innovation Craig Anderson. “We see massive potential at our 15 hospitals, BayCare’s many outpatient centers, and our HealthHubs to deliver answers in real time directly to our customers and Team Members via Alexa.”

To start creating a Knowledge skill, head to the Alexa developer portal and log in with an Alexa developer account. Then, choose the “Knowledge” skill type, and within the Knowledge skill interface, add templates to the skill before downloading a preformatted spreadsheet for each template. Add data to the spreadsheet and upload it to the skill, and then review and test the example queries Alexa automatically generates based on the data.

Alexa Knowledge Skills build on Amazon’s previous enterprise forays. Alexa for Business and Alexa for Business Blueprints allow employees to schedule meeting rooms (with third-party services such as Cisco, Polycom, Zoom), share itinerary information (through Concur), check voicemail messages, and quickly see if meeting rooms are available, and they enable admins to control users’ capabilities through a web dashboard and publish private skills from preconfigured templates. As for Alexa for Hospitality, it lets guests at hotels and resorts ask Alexa for specific information, such as pool hours, as well as order room service, control smart devices and appliances, access their personal music and audiobook libraries, and ask the concierge for dinner recommendations using just their voice.

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Big Data – VentureBeat

IBM’s ‘elite’ data science squad has kickstarted AI for more than 100 companies

December 16, 2019 Big Data

Last year, IBM announced a Data Science Elite team whose only job is to help big enterprise companies push their first AI models into production.

Now, more than a year after the program’s launch, Rob Thomas, the IBM executive overseeing the AI SWAT team, reports that it has been a “huge success.” The team has increased from 30 data scientists to 100, and there are plans to grow significantly next year. “We hire them wherever we can, actually,” Thomas said, noting that these data scientists operate all over the world. (Thomas told VentureBeat about this elite team as part of a wider-ranging interview.)

Companies as diverse as Harley Davidson, Lufthansa, Experian, Sprint, Carrefour, and Siemens used the team for a necessary kickstart on AI projects. And the best part: It’s all for free — or at least there are no contractual obligations to pay. IBM is betting the companies will like the products enough, and get so excited by the opportunities, that they’ll retain IBM to build out these projects — paying real money for IBM’s cloud, mainframe, or other services.

In all, the team has done 130 of these engagements, at 115 companies. The team completes these projects rapidly — in four to six weeks. That’s considerably faster than the “up to 12 weeks” timeline the group initially envisioned, partly in order to reduce cost, but also because IBM realized it could get results sooner. And those results are in the form of an AI model deployed as an API — allowing a company to integrate the AI into any of its applications.

“Within that time frame, you’re going to have a great AI success story and a model in production,” says Thomas, who is general manager of data and Watson AI at IBM. “These are some of the best data scientists in the world.”

How it works

Here’s the step-by-step recipe, according to Seth Dobrin, VP of IBM Data and AI, chief data officer of IBM Cloud, and a key architect of the team.

CTO conversation: First, IBM’s data team strikes up a conversation with a company’s chief technology officer (CTO) to identify a real problem they’re trying to solve and agree on a scope for application.
Design workshop: The elite team then starts a design-thinking workshop around AI. This is preferably for two days but could be just one. This is where the team meets with the company’s subject-matter experts to discuss the value of the business decisions the AI project will drive. It also ensures there is adequate available and quality data. A plan is drawn up, usually including three sprints.
Sprints. This is where the heads-down coding happens. In a first sprint, engineers pull data and engineer features. In a second sprint, they develop the features and do a first pass at a model. The third sprint tunes that model and makes sure it delivers what the client needs. The group then deploys the model as an API. In most cases, IBM stops short of integrating these APIs with a company’s applications, though it did in a few cases integrate the API in Watson Assistant, IBM’s white-label virtual assistant.
Visualization: Throughout this process, IBM also invites in a different team — data visualization experts — to design what the output of the project needs to look like so that it’s not just an API, but provides context for business users for how the AI can be used.
Extension: Finally, after the previous steps kickstart the project, a client can take over or engage one of IBM’s services to take over, or there’s a third option, called a Build. This is a co-investment, where IBM agrees to cover half the cost for a 12-week extension of the project.

Examples of success

The team has helped companies in just about every industry, says Dobrin, from sports scheduling to call center optimization, fraud detection, and image recognition.

Dobrin says that almost all of these were considered successes — at least from the client perspective — because of the extensive up-front definition of the problem and the resulting recipe for deploying AI that customers walk away with.

Banking: A good example is Nedbank, a South African bank that wanted a way to predict which ATMs were about to fail and which ones to repair first. When IBM was finished, Dobrin said, Nedbank executives “felt like for the first time ever, they really knew how to implement machine learning and AI in the enterprise in a meaningful and valuable way.” The bank could then take the blueprint of what IBM showed it and replicate it in other areas of the business, he said.

Trading: Another example is JPMorgan Chase, which has advanced knowledge of AI and needed help using tools the bank already had, but in a different way: leveraging GPUs for a scoring engine. IBM helped with that project, which aimed to prevent the bank’s traders from making trades not recommended by their sophisticated models, said Dobrin.

Creative agency: Wunderman Thompson, the WPP-owned creative agency, had a challenge leveraging its data and 17,000 features to customize marketing campaigns for customers. It had been forced to use the same process template over and over. The agency finished an eight-week engagement with IBM’s team and “literally the next day turned it into a product they were selling to their customers,” said Dobrin. Now each client project is customized with data science, he said.

Lessons learned

IBM and its clients have learned some valuable lessons along the way, Dobrin says:

Importance of subject matter experts: In the Nedbank case, IBM dutifully delivered a model that would schedule ATM repairs based on which ATMs were most valuable. That direction came from the three Nedbank managers working with IBM. But then other bank groups argued that another metric was far more important to optimize. Traffic in Johannesburg was so bad that paying truck drivers cost the bank far more than anything that could be gained from servicing the most valuable ATMs first. Concludes Dobrin: “You really need the people who are doing the work in the room, and not, you know, second or third line managers.”

The simplest solution is best: In the Nedbank case, the bank’s rules-based fraud detection system was creating huge volumes of false positives. The bank consequently halted transactions that were otherwise legitimate, upsetting customers. When Nedbank executives asked IBM to replace the bank’s fraud-detection engine with AI, Dobrin said his team wondered aloud why they would replace something that was working. “Why wouldn’t we just focus on reducing your false positives?,” he said. IBM’s project reduced false positives by orders of magnitude, Dobrin says. “If there’s something that’s working and just needs to be improved, just start where you have it.”

Bring in visualization early: IBM learned to bring in data visualization experts earlier to build out an interface that end users could understand. Data science projects sometimes don’t do a good job of explaining why they’re useful, Dobrin says, and so it’s important to integrate them into an application or dashboard. When the elite team started, it hacked these dashboards together at the end of a project, since IBM had few data visualization experts on hand. But IBM learned to bring them in earlier. In one case, James Fisher, an oil and gas company, found the “beautiful” visualization tools the IBM team created to predict failures in transmission lines one of the more valuable parts of the eight-week engagement, according to Dobrin.

Need for a design-thinking workshop: IBM identified the need for an overall sync on a project’s design, beyond visualization, including what data is needed and where it resides. Before introducing the workshops, IBM would go into projects with clients, trusting them when they said they had their data ready. But often they didn’t. Worse, clients couldn’t ensure the quality of their data, and sometimes it wasn’t properly labeled. Twelve-week engagements would extend into 24-week engagements. “We had to put the data in order first, when really the client should have been doing that.”

AI essentials: Python, Spark, Tensorflow, and Kubernetes

Through the elite team deployments, Dobrin has come to see that companies working with AI need to master some essential tools and frameworks.

First, clients working with IBM’s team use three key tools: Watson Studio, which allows companies to build and train AI and machine learning models and prepare and analyze data in a hybrid cloud; Watson Machine Learning, the deployment engine for Watson Studio; and OpenScale, which adapts and governs the AI models over time.

But beyond that, successful teams need to be Python experts and know how to leverage market-leading tools like Spark and Kubernetes to accelerate and distribute processing of data with containers, Dobrin said. Distribution is important because successful companies need to be able to deploy on every cloud. “They’re on IBM, Google, Azure, Amazon, Salesforce, Workday, Concur,” said Dobrin. “And if it’s not containerized in some way, it’s just going to be untenable.” Clients also need a framework like Tensorflow if they want to do deep learning. Over time, IBM’s elite team has expanded projects to include Watson Discovery, a tool for AI feature identification. Moreover, several companies working with the team inserted their model outputs into Watson Assistant, Dobrin said.

Expansion

The Data Science Elite group is planning to grow next year, and is expanding the model into other areas, including data ops. “We’re proposing to grow the team fairly significantly. I don’t know the exact number just yet. But there is a plan to grow,” said Dobrin.

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4 Ways to Leverage Your Current Customer Base to Drive More Business

December 12, 2019 CRM News and Info

by Helen Veyna

Last updated December 12, 2019

As marketers, we’re always chasing the next big trend and trying to come up with new and innovative campaigns that will enable us to stand out and resonate with our target audience. What many of us fail to realize, however, is that our own customers can often speak better to our value than a quirky ad or paid campaign, meaning that developing solid and lasting relationships with them can pay off big in the long run.

shutterstock 1176073267 4 Ways to Leverage Your Current Customer Base to Drive More Business

Leveraging your current customers to secure new leads is one of the most effective marketing techniques at your disposal. Your current customers understand your lead’s pain points, speak their language, and have amazing results to share that show how your solution works. They can make the case for your product in less time than it takes for leads to go through an entire nurture campaign. That’s not saying you should abandon those efforts (because they are extremely important to creating an enjoyable customer journey), but it’s essential that you have several customers who are willing to speak to your organization’s excellence.

Your current customers aren’t just valuable in helping you attract new leads. When you fully leverage these relationships, you’ll find that there’s immense room for growth within your current customers. If you do your due diligence in collecting customer insights and checking in with clients to make sure they’re getting the most out of your products and services, you’ll discover that many of them are great candidates for upsell or cross-sell opportunities.

Still not convinced? Keep reading to learn 4 ways to leverage your current customer base to drive more business.

1. Collaborate With Customers to Develop Success Stories

As I previously mentioned, your customers likely have great results to share that prove the value of your product or solution. It is also very likely that they are already sharing their success among their marketing friends and putting in a good word for your brand. And while that might help you secure one or two new customers, you’re really shooting yourself in the foot if you don’t find a way to share these stories with a wider audience.

Developing customer success stories is simple (the hardest part is just scheduling a brief 30-45 minute interview) and can result in significant ROI. For example, a good success story can help you close a deal if a customer is looking for a particular use case that resonates with the challenges they are currently facing.

To make sure you have all your bases covered, a good place to start building your success story library is by identifying a few happy customers in various industries. Reach out to them and ask if they’d like to schedule a time to talk and collaborate with them to create content pieces you can share through your website, automated nurture campaigns, and social media.

Don’t worry if you’re new to reaching out to customers to secure interviews — the process is much simpler than you probably think. You can identify happy customers who might be willing to talk to you by speaking with your account managers, reviewing customer surveys and net promoter scores (NPS), and monitoring social media for any positive mentions. You can even place a prominent callout in your customer communications to see if your contacts are interested. Chances are that many of them will jump at the opportunity. After all, who doesn’t love some free publicity?

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2. Invite Your Customer Advocates to Spread the Word on the Web

In a world where customers are accustomed to conducting thorough research before making a purchasing decision, establishing a positive web presence is invaluable. To do that, however, you need to customers who are willing to leave a good review on an industry website or rave about you on social media. This sort of advocacy is critical to your success.

Your target audience likely references popular and trustworthy websites when weighing their options. Inviting your customer advocates to leave a good review for you on these sites can help you put your best foot forward. Although many customers won’t need an incentive to share their feedback, sending a small gift (such as a Starbucks gift card) is a great way to show your appreciation and increase your chances of securing a review.

You should also make it a priority to have a positive presence on social media, especially since many of your current customers and leads are likely active on these channels. A great way to do that is by encouraging your advocates to share positive results they’ve experienced on their personal profiles or company accounts, making sure to tag your company and use relevant hashtags. This will improve your chances of appearing in relevant searches and help you establish positive brand recognition among your target audience.

Act-On’s Advanced Social Media Module can help you easily monitor mentions and engage with your customer advocates. Continuing the conversation by commenting on their posts will allow you to keep the momentum going on their own profiles, improving your chances of showing up on your prospect’s feeds. You can also repost any posts that really stand out on your own social media profiles to ensure your followers see them as well.

4 Ways to Leverage Your Current Customer Base to Drive More Business

3. Ask Your Customers to Serve as References

Your high-intent leads are probably evaluating several options when the time comes to talk to Sales. They’ve probably already read up on everything you have to offer and maybe even sat through a demo, so what they’re looking for at this stage is additional proof that you can truly do what you say you do.

This is a great time to pull out some of those success stories you’ve developed. Or you can go the extra mile by scheduling some time for your leads to chat with current customers. This allows your prospects to ask questions they wouldn’t be able to ask one of your salespeople and learn more about how their peers are benefiting from your solution.

To increase your chance of success, you should aim to set up leads with customer advocates who work in similar industries or have similar use cases. Getting as targeted as possible with your customer reference will help ensure that they can provide helpful answers to all your lead’s questions and highlight your organization’s best features.

4. Nurture Your Customers to Improve Retention and Match Them With New Products and Services

We’ve talked a lot about how to leverage your current customer base to charm new leads. A mistake that many companies make, however, is failing to acknowledge the opportunity for growth that exists within their current customer base.

Your current customers have already demonstrated that they believe in the value of your brand, so half your work is already done. Your task now is to keep them happy and offer them opportunities to grow with your company.

An easy and effective way to do that is by entering your customers into automated nurture campaigns that share best practices about your product and offer the opportunity to learn about new products and/or services. You can then track engagement to automatically segment customers that are prime upsell or cross-sell opportunities and enter them into relevant campaigns to help them learn more about these new options.

Last but not least, you should also be tracking customer engagement to identify which customers need a little bit more love. Integrating your CRM with a marketing automation platform can make it easy to flag these contacts so that your account managers can reach out to them directly to offer extra support. Studies show that attracting new customers costs 5X more than retaining current ones, so it’s in your best interest to show them that you’re committed to their success.

Marketing Automation Can Help You Build Long-Lasting Customer Relationships

Whether your focus is customer retention or converting more leads, the right marketing automation tool allows you to leverage your current customer base with ease. Act-On can help you easily distribute content, develop automated nurture campaigns, collect valuable customer insights, and even manage your social media efforts so you can drive more ROI from your current customers.

If you want to learn how to leverage marketing automation to strengthen your customer relationships and drive more business, check out our eBook “Optimize the Customer Journey” (linked below).

And, if you’d like to learn more about Act-On’s capabilities and how our marketing automation platform can help you build a more comprehensive and effective digital marketing strategy, feel free to fill out this brief form to book a demo with one of our digital marketing experts.

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How to Generate Demand and Win More Deals

December 6, 2019 CRM News and Info

Nurture, Engage, and Delight Your Customers Through Personalized Messaging and Content Recommendations

Most sales leaders would like to get a hold of new contacts as soon as they enter the pipeline, but that tactic seldom leads to more closed deals. Today’s consumers like to take their time doing their research and evaluating their options before making a final purchasing decision, and they also expect organizations to deliver the information and resources they need to get them there.

This consumer expectation requires modern marketers to move beyond batch-and-blast email to develop and deliver more targeted campaigns that speak to their consumers’ specific pain points, interests, and stages in the sales cycle. The best way to deliver the right message at the right time is to adopt the following strategies:

Segment Your Audience

If you take a close look at your contact lists, you’ll find that your leads all have different interests, pain points, work in different industries, and are probably at different stages of the sales cycle. Grouping your contacts into segments that reflect those commonalities can help you deliver more targeted and effective communication that will inspire them to engage.

Nurture Leads Through Automated Campaigns

Regardless of how long the sales cycle is, you have to stay on your leads’ radar if you want to remain an option when they’re ready to make a purchase. Automated nurturing campaigns allow you to continue to build a relationship with your prospects while directing them to valuable content and information.

If you’re used to using a basic email service provider (ESP), launching multiple campaigns might seem like a huge undertaking. A marketing automation platform, however, can eliminate most of the manual labor traditionally involved by allowing you to automate many of these processes. All you have to do is develop a campaign, choose a target segment, and your contacts will automatically get entered as they meet the specified criteria.

Deliver Personalized Content Recommendations

Content is the best way to provide your leads the information they need to learn more about your product and how you can solve their pain points. Your team should develop a variety of web pages, videos, eBooks, infographics, and other informative content pieces that your target audience can refer to as they progress through the buyer journey.

How you deliver this information depends on your target customer’s preferences and engagement history, so keep that in mind when developing your distribution strategy. A tool such as Act-On’s Website Prospector allows you to gain a sense of how specific customers interact with your website so you can identify trends in what they’re searching for and distribute your content accordingly.

Although many marketers are already familiar with the strategies listed above, the challenge in implementing them usually exists in the execution. Many of the tasks involved in delivering a personalized experience require a great amount of time and resources, especially for organizations using an ESP. Marketing automation platforms allow marketers to automate many of the processes mentioned above so they can focus their time and attention toward creating more creative and effective campaigns.

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1. Collaborate With Customers to Develop Success Stories

2. Invite Your Customer Advocates to Spread the Word on the Web

3. Ask Your Customers to Serve as References

4. Nurture Your Customers to Improve Retention and Match Them With New Products and Services

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