Preparing for an AI Driven World

AnalyticsAnywhere

In the late 1960s and early 70s, the first computer-aided design (CAD) software packages began to appear. Initially, they were mostly used for high-end engineering tasks, but as they got cheaper and simpler to use, they became a basic tool to automate the work of engineers and architects.

According to a certain logic, with so much of the heavy work being shifted to machines, a lot of engineers and architects must have been put out of work, but in fact just the opposite happened. There are far more of them today than 20 years ago and employment in the sector is supposed to grow another 7% by 2024.

Still, while the dystopian visions of robots taking our jobs are almost certainly overblown, Josh Sutton, Global Head, Data & Artificial Intelligence at Publicis.Sapient sees significant disruption ahead. Unlike the fairly narrow effect of CAD software, AI will transform every industry and not every organization will be able to make the shift. The time to prepare is now.

Shifting Value to Different Tasks

One of the most important distinctions Sutton makes is between jobs and tasks. Just as CAD software replaced the drudgery of drafting, which allowed architects to spend more time with clients and coming up with creative solutions to their needs, automation from AI is shifting work to more of what humans excel at.

For example, in the financial industry, many of what were once considered core functions, such as trading, portfolio allocation and research, have been automated to a large extent. These were once considered high-level tasks that paid well, but computers do them much better and more cheaply.

However, the resources that are saved by automating those tasks are being shifted to ones that humans excel at, like long-term forecasting. ““Humans are much better at that sort of thing,” Sutton says. He also points out that the time and effort being saved with basic functions frees up a lot of time and has opened up a new market in “mass affluent” wealth management.

Finally, humans need to keep an eye on the machines, which for all of their massive computational prowess, still lack basic common sense. Earlier this year, when Dow Jones erroneously reported that Google was buying Apple for $9 billion — a report no thinking person would take seriously — the algorithms bought it and moved markets until humans stepped in.

Human-Machine Collaboration

Another aspect of the AI-driven world that’s emerging is the opportunity for machine learning to extend the capabilities of humans. For example, when a freestyle chess tournament that included both humans and machines was organized, the winner was not a chess master nor a supercomputer, but two amateurs running three simple programs in parallel.

In a similar way, Google, IBM’s Watson division and many others as well are using machine learning to partner with humans to achieve results that neither could achieve alone. One study cited by a White House report during the Obama Administration found that while machines had a 7.5 percent error rate in reading radiology images and humans had a 3.5% error rate, when humans combined their work with machines the error rate dropped to 0.5%.

There is also evidence that machine learning can vastly improve research. Back in 2005, when The Cancer Genome Atlas first began sequencing thousands of tumors, no one knew what to expect. But using artificial intelligence researchers have been able to identify specific patterns in that huge mountain of data that humans would have never been able to identify alone.

Sutton points out that we will never run out of problems to solve, especially when it comes to health, so increasing efficiency does not reduce the work for humans as much as it increases their potential to make a positive impact.

Making New Jobs Possible

A third aspect of the AI-driven world is that it is making it possible to do work that people couldn’t do without help from machines. Much like earlier machines extended our physical capabilities and allowed us to tunnel through mountains and build enormous skyscrapers, today’s cognitive systems are enabling us to extend our minds.

Sutton points to the work of his own agency as an example. In a campaign for Dove covering sport events, algorithms scoured thousands of articles and highlighted coverage that focused on the appearance of female athletes rather than their performance. It sent a powerful message about the double standard that women are subjected to.

Sutton estimates that it would have taken a staff of hundreds of people reading articles every day to manage the campaign in real time, which wouldn’t have been feasible. However, with the help of sophisticated algorithms his firm designed, the same work was able to be done with just a few staffers.

Increasing efficiency through automation doesn’t necessarily mean jobs disappear. In fact, over the past eight years, as automation has increased, unemployment in the US has fallen from 10% to 4.2%, a rate associated with full employment. In manufacturing, where you would expect machines to replace humans at the fastest rate, there is actually a significant labor shortage.

The Lump of Labor Fallacy

The fear that robots will take our jobs is rooted in what economists call the lump of labor fallacy, the false notion that there is a fixed amount of work to do in an economy. Value rarely, if ever, disappears, it just moves to a new place. Automation, by shifting jobs, increases our effectiveness and creates the capacity to do new work, which increases our capacity for prosperity.

However, while machines will not replace humans, it’s become fairly clear that it can disrupt businesses. For example, one thing we are seeing is a shift from cognitive skills to social skills, in which machines take over rote tasks and value shifts to human centered activity. So it is imperative that every enterprise adapt to a new mode of value creation.

“The first step is understanding how leveraging cognitive capabilities will create changes in your industry,” Sutton says, “and that will help you understand the data and technologies you need to move forward. Then you have to look at how that can not only improve present operations, but open up new opportunities that will become feasible in an AI driven world.”

Today, an architect needs to be far more than a draftsman, a waiter needs to do more than place orders and a travel agent needs to do more than book flights. Automation has commoditized those tasks, but opened up possibilities to do far more. We need to focus less on where value is shifting from and more on where value is shifting to.

Source: Innovation Excellence

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Decoding Machine Learning Methods

Machine Learning, thinking systems, expert systems, knowledge engineering, decision systems, neural networks – all synonymous loosely woven words in the evolving fabric of Artificial Intelligence. Of these Machine Learning (ML) and Artificial Intelligence (AI) are often debated and used interchangeably. broadly speaking AI can be termed as a futuristic state of self aware smart learning machines in true sense, but for all practical purposes we deal more often with ML at present.

In very abstract terms, ML is a structured approach for deriving meaningful predictions/insights from both structured and unstructured data. ML methods employ complex algorithms that enable analytics based on data, history and patterns. The field of data science continues to scale new heights enabled by the exponential growth in computing power over the last decade. Data scientists are continuously exploring new models & methods each day and sometimes it’s scary to even keep pace with the trends. However to keep matters simple, here is a clean starting point.

Below is an attempt to put a simplified visual representation of the popular ML methods leveraged in the data science field along with their classification. Each of these algorithms are encoded through languages such as R, Python, Scala etc to provide a framework to data scientists in solving complex data driven business problems. However there is an underlying maze of statistical and probabilistic abyss that data scientists need to navigate in order to put these methods to meaningful use.

AnalyticsAnywhere

A brief summary of the above ML methods and how they model are presented in the slides below.

Some of the business applications of these ML methods can be classified as shown in below visual.

machinelearningML

As data becomes the new oil that drives virtual machines, I conclude with the below quote,

“Without data you’re just a person with an opinion.” – W. Edwards Deming

Source: datasciencecentral.com

Accuracy and Speed of Customer Service Improved with AI

AnalyticsAnywhere

Artificial Intelligence (AI) and Machine Learning (ML) are becoming more commonplace in the workplace than ever before, and it is making it possible for customer service speed and accuracy to improve significantly. This means that businesses that are already taking advantage of AI and ML are already ahead of the game, and those who are not have already fallen behind. Most businesses already have ML in mind, as 90% of CIOs interviewed said they were either already using ML or planned to incorporate it very soon into their business model. Here are some reasons you should start working with an artificial intelligence company as soon as possible.

Automation creates efficiency

There are so many things about customer service that can be automated in a customer service environment to save time. For example, customers may text and ask your store hours or return policy 20 times a day. But with AI, those questions will be answered immediately allowing your customer service agents to focus on tasks that require human processing instead of wasting time answering mundane and repetitive questions. AI and ML are also very helpful when doing other mundane tasks such as paperwork.

Accurate measurements and report

To determine what methods of communication are effective, your business should be running reports and measuring the effectiveness of what you are doing. With ML in place, you can run these reports and then use that data to create a more effective AI programs that your customer service department can use.

Automate paperwork

Maybe it used to take your customer service representatives half their day to complete all the paperwork associated with each call or text they took, but it was something your business dealt with to ensure you got enough information. Fortunately, the right ML and AI can do that for you making it possible for your customer service to get to customers faster and be more efficient with the time they have.

Use ML for complex decisions

Many people know that AI and ML can be used to answer simple and mundane questions, but it is more than that. ML can actually be very helpful in making complex decisions, as 52% of CIOs said they already are using it for that very purpose.

Get decision automation

Another great benefit from using AI in your customer service is that it can help with decision automation. In the next three years, it is expected that AI in customer service will drastically improve the speed of decisions, accuracy of decisions, and it is expected to drive top line growth.

Improve customer satisfaction

Customers are as annoyed with long wait times and long customer service experiences just as much as businesses are. It can also help improve the speed of the interaction ensuring the customer is given the right information as quickly as possible or directed to the right customer service representative. There is almost nothing worse than waiting on hold for 20 minutes to talk to someone just to then be transferred around and wait on hold again. Rather than having frustrated employees and frustrated customers, use AI and ML technology to improve your customer satisfaction.

Source: becominghuman.ai

Every Business Today Needs To Prepare For An AI-Driven World. Here’s How:

AnalyticsAnywhere

In the late 1960s and early 70s, the first computer-aided design (CAD) software packages began to appear. Initially, they were mostly used for high-end engineering tasks, but as they got cheaper and simpler to use, they became a basic tool to automate the work of engineers and architects.

According to a certain logic, with so much of the heavy work being shifted to machines, a lot of engineers and architects must have been put out of work, but in fact just the opposite happened. There are far more of them today than 20 years ago and employment in the sector is supposed to grow another 7% by 2024.

Still, while the dystopian visions of robots taking our jobs are almost certainly overblown, Josh Sutton, Global Head, Data & Artificial Intelligence at Publicis.Sapient, sees significant disruption ahead. Unlike the fairly narrow effect of CAD software, AI will transform every industry and not every organization will be able to make the shift. The time to prepare is now.

Shifting Value To Different Tasks

One of the most important distinctions Sutton makes is between jobs and tasks. Just as CAD software replaced the drudgery of drafting, which allowed architects to spend more time with clients and coming up with creative solutions to their needs, automation from AI is shifting work to more of what humans excel at.

For example, in the financial industry, many of what were once considered core functions, such as trading, portfolio allocation and research, have been automated to a large extent. These were once considered high-level tasks that paid well, but computers do them much better and more cheaply.

However, the resources that are saved by automating those tasks are being shifted to ones that humans excel at, like long-term forecasting. ““Humans are much better at that sort of thing,” Sutton says. He also points out that the time and effort being saved with basic functions frees up a lot of time and has opened up a new market in “mass affluent” wealth management.

Finally, humans need to keep an eye on the machines, which for all of their massive computational prowess, still lack basic common sense. Earlier this year, when Dow Jones erroneously reported that Google was buying Apple for $9 billion — a report no thinking person would take seriously — the algorithms bought it and moved markets until humans stepped in.

Human-Machine Collaboration

Another aspect of the AI-driven world that’s emerging is the opportunity for machine learning to extend the capabilities of humans. For example, when a freestyle chess tournament that included both humans and machines was organized, the winner was not a chess master nor a supercomputer, but two amateurs running three simple programs in parallel.

In a similar way, Google, IBM’s Watson division and many others as well are using machine learning to partner with humans to achieve results that neither could achieve alone. One study cited by a White House report during the Obama Administration found that while machines had a 7.5 percent error rate in reading radiology images and humans had a 3.5% error rate, when humans combined their work with machines the error rate dropped to 0.5%.

There is also evidence that machine learning can vastly improve research. Back in 2005, when The Cancer Genome Atlas first began sequencing thousands of tumors, no one knew what to expect. But using artificial intelligence researchers have been able to identify specific patterns in that huge mountain of data that humans would have never been able to identify alone.

Sutton points out that we will never run out of problems to solve, especially when it comes to health, so increasing efficiency does not reduce the work for humans as much as it increases their potential to make a positive impact.

Making New Jobs Possible

A third aspect of the AI-driven world is that it is making it possible to do work that people couldn’t do without help from machines. Much like earlier machines extended our physical capabilities and allowed us to tunnel through mountains and build enormous skyscrapers, today’s cognitive systems are enabling us to extend our minds.

Sutton points to the work of his own agency as an example. In a campaign for Dove covering sport events, algorithms scoured thousands of articles and highlighted coverage that focused on the appearance of female athletes rather than their performance. It sent a powerful message about the double standard that women are subjected to.

Sutton estimates that it would have taken a staff of hundreds of people reading articles every day to manage the campaign in real time, which wouldn’t have been feasible. However, with the help of sophisticated algorithms his firm designed, the same work was able to be done with just a few staffers.

Increasing efficiency through automation doesn’t necessarily mean jobs disappear. In fact, over the past eight years, as automation has increased, unemployment in the US has fallen from 10% to 4.2%, a rate associated with full employment. In manufacturing, where you would expect machines to replace humans at the fastest rate, there is actually a significant labor shortage.

The Lump Of Labor Fallacy

The fear that robots will take our jobs is rooted in what economists call the lump of labor fallacy, the false notion that there is a fixed amount of work to do in an economy. Value rarely, if ever, disappears, it just moves to a new place. Automation, by shifting jobs, increases our effectiveness and creates the capacity to do new work, which increases our capacity for prosperity.

However, while machines will not replace humans, it’s become fairly clear that it can disrupt businesses. For example, one thing we are seeing is a shift from cognitive skills to social skills, in which machines take over rote tasks and value shifts to human centered activity. So it is imperative that every enterprise adapt to a new mode of value creation.

“The first step is understanding how leveraging cognitive capabilities will create changes in your industry,” Sutton says, “and that will help you understand the data and technologies you need to move forward. Then you have to look at how that can not only improve present operations, but open up new opportunities that will become feasible in an AI driven world.”

Today, an architect needs to be far more than a draftsman, a waiter needs to do more than place orders and a travel agent needs to do more than book flights. Automation has commoditized those tasks, but opened up possibilities to do far more. We need to focus less on where value is shifting from and more on where value is shifting to.

Source: Digital Tonto

Understanding Data Roles

AnalyticsAnywhereWith the rise of Big Data has come the accompanying explosion in roles that in some way involve data. Most who are in any way involved with enterprise technology are at least familiar with them by name, but sometimes it’s helpful to look at them through a comprehensive lens that shows us how they all fit together. In understanding how data roles mesh, think about them in terms of two pools: one is responsible for making data ready for use, and another one that puts that data to use. The latter function includes the tightly-woven roles of Data Analysts and Data Scientist, and the former includes such roles as Database Administrator, Data Architect and Data Governance Manager.

Ensuring the data is ready for use

Making Sure the Engine Works.

A car is only as good as its engine, and according to PC Magazine the Database Administrator (DBA), is “responsible for the physical design and management of the database and for the evaluation, selection and implementation of the DBMS.” Techopedia defines the position as one that “directs or performs all activities related to maintaining a successful database environment.” A DBA’s responsibilities include security, optimization, monitoring and troubleshooting, and ensuring the needed capacity to support activities. This of course requires a high level of technical expertise–particularly in SQL, and increasingly in NoSQL. But while the role may be technical, TechTarget maintains that it may require managerial functions, including “establishing policies and procedures pertaining to the management, security, maintenance, and use of the database management system.”

Directing the Vision. With the database engines in place, the task becomes one of creating an infrastructure for taking in, moving and accessing the data. If the DBA builds the car, then the Enterprise Data Architect (EDA) builds the freeway system, laying the framework for how data will be stored, shared and accessed by different departments, systems and applications, and aligning it to business strategy. Bob Lambert describes the skills as including an understanding of the system development life cycle; software project management approaches; data modeling, database design, and SQL development. The role is strategic, requiring an understanding of both existing and emerging technologies (NoSQL databases, analytics tools and visualization tools), and how those may support the organization’s objectives. The EDA’s role requires knowledge sufficient to direct the components of enterprise architecture, but not necessarily practical skills of implementation. With that said, Monster.com lists typical responsibilities as: determining database structural requirements, defining physical structure and functional capabilities, security, backup, and recovery specifications, as well as installing, maintaining and optimizing database performance.

Creating and Enforcing the Rules of Data Flow. A well-architected system requires order. A Data Governance Manager organizes and streamlines how data is collected, stored, shared/accessed, secured and put to use. But don’t think of the role as a traffic cop–the rules of the road are there to not only prevent ‘accidents’, but also to ensure efficiency and value. The governance manager’s responsibilities include enforcing compliance, setting policies and standards, managing the lifecycle of data assets, and ensuring that data is secure, organized and able to be accessed by–and only by– appropriate users. By so doing, the data governance manager improves decision-making, eliminates redundancy, reduces risk of fines/lawsuits, ensures security of proprietary and confidential information, so the organization achieves maximum value (and minimum risk). The position implies at least a functional knowledge of databases and associated technologies, and a thorough knowledge of industry regulations (FINRA, HIPAA, etc.).

Making Use of the Data

We create a system in which data is well-organized and governed so that the business can make maximum use of it by informing day-to-day processes, and deriving insight from data analysts/scientists to improve efficiency or innovation.

Understand the past to guide future decisions. A Data Analyst performs statistical analysis and problem solving, taking organizational data and using it to facilitate better decisions on items ranging from product pricing to customer churn. This requires statistical skills, and critical thinking to draw supportable conclusions. An important part of the job is to make data palpable to the C-suite, so an effective analyst is also an effective communicator. MastersinScience.org refers to data analysts as “data scientists in training” and points out that the line between the roles are often blurred.

Data scientist–Modeling the Future. Data scientists combine advanced mathematical/statistical abilities with advanced programming abilities, including a knowledge of machine learning, and the ability to code in SQL, R, Python or Scala. A key differentiator is that where the Data Analyst primarily analyzes batch/historical data to detect past trends, the Data Scientist builds programs that predict future outcomes. Furthermore, data scientists are building machine learning models that continue to learn and refine their predictive ability as more data is collected.

Of course, as data becomes increasingly the currency of business, as it is predicted to, we expect to see more roles develop, and the ones just described evolve significantly. In fact, we haven’t even discussed one of a role that is now mandated by the EU’s GDPR initiative: The Chief Data Officer, or ‘CDO’.

Source: datasciencecentral.com

The Ultimate Data Set

AnalyticsAnywhere

Until recently, using entire populations as data sets was impossible—or at least impractical—given limitations on data collection processes and analytical capabilities. But that is changing.

The emerging field of computational social science takes advantage of the proliferation of data being collected to access extremely large data sets for study. The patterns and trends in individual and group behavior that emerge from these studies provide “first facts,” or universal information derived from comprehensive data rather than samples.

“Computational social science is an irritant that can create new scientific pearls of wisdom, changing how science is done,” says Brian Uzzi, a professor of management and organizations at the Kellogg School. In the past, scientists have relied primarily on lab research and observational research to establish causality and create descriptions of relationships. “People who do lab studies are preoccupied with knowing causality,” Uzzi says. “Computational work says, “I know that when you see X, you see Y, and the reason why that happens may be less important than knowing that virtually every time you see X, you also see Y.”

“Big data goes hand in hand with computational work that allows you to derive those first facts,” Uzzi says. “Instead of trying to figure out how scientists come up with great ideas by looking at 1,000 scientists, you look at 12,000,000 scientists—potentially everyone on the planet. When you find a relationship there, you know it’s universal. That universality is the new fact on which science is being built.”

 

Computation in the Social Sphere

Studying large data sets for first facts about human behavior has led to striking advances in recent years. Uzzi notes how one particular data set—mobile-phone data—“has taught us very distinctively about human mobility and its implications for economical and social stratification in society.” It has also shed light on how people behave during evacuations and emergency situations, including infectious-disease outbreaks. Knowing how behaviors affect the spread of diseases can help public health officials design programs to limit contagion.

The ability to track the social behavior of large groups has also shifted people’s understanding of human agency. “Until recently, we really believed that each of us made our decisions on our own,” Uzzi says. “Our friends may have influenced us here or there but not in a big way.” But troves of social-media data have shown that people are incredibly sensitive and responsive to what other people do. “That’s often the thing that drives our behavior, rather than our own individual interests or desires or preferences.”

This may change how you think about your consumer behavior, your exercise regimen, or what you Tweet about. Researchers like Uzzi are also deeply interested in how this responsiveness influences political behavior on larger issues like global climate change or investments in education systems. Think of it as a shift from envisioning yourself as a ruggedly individual, purely rational, economic person to a sociological person who encounters and engages and decides in concert with others.

One aspect of computational social science—brain science—has already discovered that those decisions are often being made before we even know it. “Brain science has taught us a lot about how the brain reacts to stimuli,” Uzzi says. With the visual part of your brain moving at roughly 8,000 times the speed of the rest of your brain, the visual cortex has already begun processing information—and leaping to certain conclusions—before the rest of your brain ever catches up. And with 40 percent of the brain’s function devoted strictly to visualization, “if you want to get in front of anything that’s going to lead to a decision, an act of persuasion, an in-depth engagement with an idea, it has got to be visual.”

“The really big things are understanding how something diffuses through a population and how opinions change,” Uzzi says. “If you put those two things together, you really have an understanding of mass behavior.”

This increased understanding of both mass and individual behavior presents huge opportunities for businesses, notably in the health sphere. “There is going to be an entirely new ecology of business that goes beyond how we think about health today,” Uzzi says. “For many people, there is no upper threshold on what they will pay for good health and beauty. With health increasingly decentralized to the individual, that’s going to spin off to companies that want to take advantage of this information to help people do things better.”

Scaling from One to Everyone

While gathering data on groups as large as the entire population is beneficial to scientists, marketers, and the like, computational social science has the scalability to allow for practical data generation on an individual level as well. This means that you can be the subject of your own data-rich computational study, without control groups or comparison testing. “You actually generate enough data on yourself, every day, that could be collected, that you can be the subject of a computational study,” Uzzi says.

Developments in the ability to collect and parse data on individuals is one area where computational social science has the potential to transform people’s lives—from providing more information about individuals’ own health to raising their awareness of unconscious biases to showing how their decision-making processes are influenced by others. “It’s going to allow people to personally use data that can help them improve their lives in a way that they never imagined before,” Uzzi says.

For example, using wearable technologies allows for sensor data collection that can include emotional activation and heart-rate monitoring in social interactions, caloric intake, biorhythms, and nervous energy. The crunching of that raw data into actionable information will happen through our machines. If you think you have a close connection to your smartphone and your tablet now, wait until you rely on it to tell you how much that last workout helped—or did not help—you shake off the tension of a long day at the office.

“Our closest partnership in the world is probably going to be our machine that helps us manage all this,” Uzzi says. This can be transformative by making us healthier.

It may make us less discriminatory, too. We all have cognitive biases that lead us to make irrational decisions. These are thought to be hard-wired things we can identify but not necessarily change on our own. Sensor data can provide a feedback loop of how we have acted in the past. This has the potential to improve future decision making. If your sensors pick up signals that show your body acting differently around certain groups, perhaps in ways that you suppress or to which you are oblivious, that may be harder to ignore.

“Our own sense of identity could be greatly shaken by this, or improved, or both.”

Source: Kellogg Insight

What Is Machine Learning???

Machine Learning for Dummies

AnalyticsAnywhere

Amazon uses it. Target uses it. Google uses it. “It” is machine learning, and it’s revolutionizing the way companies do business worldwide.

Machine learning is the ability for computer programs to analyze big data, extract information automatically, and learn from it. With 250 million active customers and tens of millions of products, Amazon’s machine learning makes accurate product recommendations based on the customer’s browsing and purchasing behavior almost instantly. No humans could do that.

Target uses machine learning to predict the offline buying behaviors of shoppers. A memorable case study highlights how Target knew a high school girl was pregnant before her parents did.

Google’s driverless cars are using machine learning to make our roads safer, and IBM’s Watson is making waves in healthcare with its machine learning and cognitive computing power.

Is your business next? Can you think of any deep data analysis or predictions that your company can produce? What impact would it have on your business’s bottom line, or how could it give you a competitive edge?

Why Is Machine Learning Important?

Data is being generated faster than at any other time in history. We are now at a point where data analysis cannot be done manually due to the amount of the data. This has driven the rise of MI — the ability for computer programs to analyze big data and extract information automatically.

The purpose of machine learning is to produce more positive outcomes with increasingly precise predictions. These outcomes are defined by what matters most to you and your company, such as higher sales and increased efficiency.

Every time you search on Google for a local service, you are feeding in valuable data to Google’s machine learning algorithm. This allows for Google to produce increasingly more relevant rankings for local businesses that provide that service.

Big Big Data

It’s important to remember that the data itself will not produce anything. It’s critical to draw accurate insights from that data. The success of machine learning depends upon producing the right learning algorithm and accurate data sets. This will allow a machine to obtain the most efficient insights possible from the information provided. Like human data analysts, one may catch an error another could potentially miss.

Digital Transformation

Machine learning and digital technologies are disrupting every industry. According to Gartner, “Smart machines will enter mainstream adoption by 2021.” Adopting early may provide your organization with a major competitive edge. Personally, I’m extremely excited by the trend and recently spent time at Harvard attending its Competing on Business Analytics and Big Data program along with 60 senior global executives from various industries.

Interested In Bringing The Power Of Machine Learning To Your Company?

Here are my recommendations to get started with the help of the right tools and experts:

  1. Secure all of the past data you have collected (offline and online sales data, accounting, customer information, product inventory, etc.). In case you might think your company doesn’t generate enough data to require machine learning, I can assure you that there is more data out there than you think, starting with general industry data. Next, think about how you can gather even more data points from all silos of your organization and elsewhere, like chatter about your brand on social media.
  2. Identify the business insights that you would benefit from most. For example, some companies are using learning algorithms for sales lead scoring.
  3. Create a strategy with clear executables to produce the desired outcomes such as fraud protection, higher sales, increased profit margin and the ability to predict customer behavior. Evaluate and revisit this strategy regularly.

Source: Forbes