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Preparing your business for machine learning and big data

Companies are buried in mountains of big data.

Big data keeps growing bigger every day. Between the IoT (Internet of Things), social media, mobile devices and cloud computing, the mountain grows higher and higher. Machine learning offers companies a way to transform the ever-growing mountains of data surrounding them into actionable insights.

But scaling that mountain is easier said than done.

A recent report from Gartner looks at the tricky situation facing businesses who want to conquer their data using machine learning. The report lays out the challenges of machine learning and the need to take proactive steps to prepare for it.

Let’s start at the beginning.

What is machine learning?

Part of the field of artificial intelligence, machine learning uses statistical techniques that give computer systems the ability to progressively improve on a specific task. Or, said another way, these computers can “learn.”

Although machine learning has technically been around since 1959, its importance has increased as businesses find better ways to understand big data.

What’s driving the adoption of machine learning now?

Big data is a tantalizing ideal, with 74 percent of companies saying they’re trying to be more “data-driven.” But the dream of big data has fallen far short of reality.

Many companies are simply overwhelmed by the structural and staffing challenges of managing big data.

The primary challenge is simple. How do you sift through huge amounts of data to get actionable insights? Currently, only 29 percent of companies able to connect data to action. Machine learning offers a key to unlock the insights buried in data.

It’s all about analyzing data to make accurate predictions.

What are the challenges of using machine learning with big data?

The Gartner report calls attention to a specific issue facing IT analysts. When analytic architectures and end-to-end data aren’t set up to work together, the disconnect creates an underlying problem.

In order to conquer that challenge, it’s ideal to have a data scientist on your team. At the very least, you need access to someone who deeply understands machine learning.

But learning the algorithms is difficult without a specific mathematical background. The way businesses integrate data also becomes more complex. In addition, infrastructure must be addressed.

How should SMBs prepare for machine learning and big data?

The challenges are different for small and midsize business than at the enterprise level, where there are more resources. According to the Gartner report, there are many ways to prepare for machine learning. But the leadership needs to come from the IT department

The people who understand the technical challenges of the issues simply must be involved.

Being proactive is the key to deriving the benefits of machine learning. Here are some of the specific recommended steps:

  • Examine the computer clusters and storage infrastructure needed to support machine learning
  • Select a machine learning platform that works with different machine learning frameworks when planning to work with off-the-shelf algorithms
  • Make sure the data organization is updated in an end-to-end analytics architecture in order to support machine learning algorithms
  • If you’re planning to build custom machine learning algorithms, put a development life cycle in place capable of supporting learning models
  • Take note of new frameworks being packaged with AI and machine learning solutions, which provide seamless solutions
  • Go to the cloud if you don’t have appropriate engineering infrastructure and staff to support machine learning, as it’s flexible enough to scale

The next big thing in big data

Machine learning is what’s next for dealing with big data. Companies should be building for the needs of machine learning by understanding the problems they’re trying to solve.

That means going to the IT support experts who uniquely understand machine learning issues. There’s simply no other way to be prepared for what’s next.

3 ways data capture can make your office more efficient

Data—SMBs have a whole load of it when creating marketing campaigns and customer profiles. In fact, businesses collected more data in 2017 than in the previous 5,000 years combined. But how much of this data is actionable?

Capturing the right data can improve efficiency in the office—the complete prism from high value conversions to clear communication. Here are three ways data collection turbocharges productivity at work.

1. Data capture increases sales.

Data collection provides SMBs with valuable customer intelligence—the type of information that boosts sales across the board. The latest breed of data capture softwares collect data from a wide range of sources—website forms, mobile devices, social media, third parties etc.—and generate accurate insights into consumers’ behavior as they progress through the sales funnel. All of this information is available in one place, so marketers can make quick decisions and solve problems.

48% of sales and marketing teams use data collection for customer value analytics, where information influences market segmentation and predictive analytics. This can have a significant impact on sales. Marketers can discover which customers have the most value, for example, or which products are the most popular.

2. Enhance customer engagement.

Data capture also lets SMBs engage with customers on a more meaningful level. When consumers provide these companies with data—names, addresses, email addresses, etc.—marketers can build customer profiles and send targeted communications via email, social media and SMS. These personalized messages bridge the gap between companies and their customers and foster consumer relationships. SMBs can also forecast future trends by analyzing customers’ behaviors and interests.

Customers who don’t receive good service can take their business elsewhere. Data capture, however, provides SMBs with actionable insights into what consumers really want.

“Today, more and more companies are realizing that true competitive advantage lies in creating an engaging customer experience—one that is personal, fast, easy and useful,” says digital transformation analyst Daniel Newman, writing for Forbes magazine.

3. Improve mobile workforce productivity.

Research suggests that 3.7 million employees now work from home at least half of the time. With data capture, SMBs can monitor their mobile workforce through devices such as laptops, smartphones and tablets. Managers can track time and attendance, manage absences, and assign work to telecommuters with the latest software, which instantly improves productivity in the office.

Many small and medium businesses lack the office space and resources to hire on-premise staff, resulting in an influx of mobile workers. The latest analytics provides these companies with useful information for decision-making and problem solving, such as staff members who have achieved the most sales or workers who have put in the most hours in a given time period.

Data capture also improves digital transformation, which allows managers to provide mobile workers with the right technology.

Data capture is a must-have for any SMB that wants to increase sales, enhance customer engagement and improve mobile workforce productivity. The amount of information that’s available to these companies is vast and far-reaching. As a result, companies can optimize efficiency and productivity in the office.

The Role of Machine Learning in Accelerating Digital Transformation

In 2016, the world champion of an ancient Chinese board game, Go, lost to the AlphaGo algorithm, designed by Google. For many, that was the moment it became obvious that machine learning had successfully emerged and would contribute majorly in shaping the future.

Machine learning will continue to serve a new class of software that is able to learn without being programmed, as well as access and analyze structured and unstructured data. This data will be at a level of complexity that human minds will fail to comprehend.

The Advancements in Machine Learning

Reflecting on the quality of today’s image and voice recognition software and the capabilities of self-driving cars, it can be safely assumed that self-learning algorithms will continue to influence our lives. Computer scientists have been working on artificial intelligence since the 1950s. However, they have made advancements only recently, such as Big Data and Analytics, augmented computer efficiencies, and more sophisticated algorithms.

Not only that, but computers are now capable of competing with or surpassing the human brain – something that was previously considered impossible.

Machines can now learn to speak, write and interpret meanings in videos and images. Intelligent machines will help support humans as we enter the age of the intelligent enterprise. This progress in machine learning means endless acceleration in digital transformation. Businesses can now extend and optimize their processes. Business leaders would have the opportunity to gain a powerful ability to devise customer interaction patterns, as well as operations so they can obtain profound insights.

Machine learning will make it possible for automated systems to out-think the human brain by integrating broad information sets and finding correlations. Machine learning will also help predict future outcomes.

Machine learning has already made its way into several industries, from factories to boardrooms. Machine learning can now be incorporated into tools that help with data classification, object detection, and speech/facial recognition. A large number of tasks that previously involved manual labor are now automated with the help of machine learning.

Therefore, companies all over the world have benefited from machine learning to create smart solutions and powerful customer experiences for all niches, including retail, banking, hospitality, medicine and the corporate world.

What Does the Future Hold?

Machine learning aims to reduce human intervention and simplify user interaction with devices. It will also facilitate automating repetitive tasks and enable people to focus more on their creativity and problem-solving skills. The current machine-learning scenario presents an explosion of data, connections and innovations. Customers are already demanding seamless and personalized experiences at all touch-points, so it’s only logical to benefit from machine learning to accelerate digital experiences to deliver nothing but the best.

Modern advances in machine-learning signify a future where devices run on self-learning algorithms and operate independently. They may deduce their own conclusion within certain parameters and develop a context-based behavior to interact with humans more directly than before.

What Does This Mean for Digital Transformation?

This means that digital transformation could mean automating routine tasks, such as data entry, scheduling meetings, analyzing data and translating documents. Millions of companies are embracing social and digital tools to automate services, share insights and increase revenue. Innovative channels for maintaining smooth customer interactions are being created and new ways for resource utilization are also emerging.

This transformation will help businesses align efforts across the business network to optimize value potential. Organizations must begin to identify the sweet spots for machine-induced improvements, particularly repetitive work.

Final Thoughts

In the future, machine learning will be as essential as electricity. We’ll find it hard to imagine the world without it.

The intelligence machine learning promises to bring to digital transformation will uncover new potentials and enable humans to make the best of their talents. We’ll no longer need to be worried about focusing on repetitive and tedious tasks, which consume most of our time at present.

Credit:  HUGHES SYSTIQUE CORPORATION (HSC)

Machine Learning and the Legal Industry

In 1970, a Japanese roboticist named Masahiro Mori coined the phrase “the uncanny valley.” Even if you’ve never heard the phrase before, you’ve likely experienced the phenomenon.

The uncanny valley describes the unease we feel when we look at something that’s almost human, but not quite. Mori observed that as robots get closer to looking like people, the human emotional response becomes increasingly anxious. When something almost looks right but still seems “off,” it makes us uncomfortable.

That same sense of unease can extend beyond the visible. For many, the idea of Artificial Intelligence (AI) is fundamentally unsettling. Maybe it’s all the sci-fi movies we’ve seen that suggest computers will take over the world. Or perhaps it’s an inherent distrust of machines. They aren’t perfect, after all.

Whatever the case, an early form of AI is starting to gain some real traction. Machine learning has the potential to dramatically change multiple industries. In our last post, we explored how machine learning is reshaping the medical field. This time around, we’ll examine how machine learning could impact the legal industry.

Automated Research Assistance

Research is one of the core tasks for any law firm. Every case includes an extensive period of research that takes into account previous cases, relevant laws and legal precedents. Attorneys spend tremendous amounts of time pouring over huge amounts of information. Anything that can accurately speed up that process would be beneficial.

That’s where machine learning comes into the picture.

As Innovation Enterprise observed in a recent article, machine learning is well suited for processing complex data, capable of identifying patterns and even inferring rules. The technology has already proven this capacity in other industries. It only makes sense to apply the same kind of functionality to the legal industry. This would give attorneys a budget-friendly way to overcome a major slow-down in their process.

Boosting Efficiency

There is, however, a caveat to the idea of automating legal research with machine learning. The technology looks promising, but not perfect. By that we mean we’re a long way off from being able to turn legal research completely over to machine learning.

In this sense, machine learning can be thought of as a significant boost to the efficiency of the research process, but not a solution or replacement for human interaction. Machine learning can do a preliminary sweep of potentially relevant data. However, an attorney will still need to make final strategic decisions about what relates to the case at hand and what does not.

Avoiding Overfitting

A possible pitfall of machine learning as it applies to research is what statisticians call “overfitting.”

In his paper for The Washington Law Review, Associate Professor of Law Harry Surden said this about overfitting: “The general idea is that it is undesirable for a machine learning algorithm to detect patterns in the [data] that are so finely tuned to the idiosyncrasies or biases . . . that they are not predictive of future, novel scenarios.”

In other words, machine learning is limited to the mathematics of algorithms. A human reviewing the same data set may make inferences, observations, an intuition-based reflections that are beyond the capabilities of machine learning. This is especially true in situations that might be thought of as outliers.

Even with the advantage of improved efficiency, care needs to be taken to avoid overfitting.

Continuing Development

“. . . while we don’t see AI eliminating lawyer jobs, AI will fundamentally change what lawyers do and how they do it, in much the same way Westlaw and Lexis changed the way (and speed at which) traditional legal research was conducted.” – Above the Law

That’s an apt conclusion. Machine learning represents a powerful mode of AI within the legal industry. It will almost certain change how law is practiced. But it’s no replacement for human interaction, experience or intuition.

Part of our commitment to our clients includes following new developments, like the emerging field of machine learning and how best to apply it. That’s what sets Process Fusion apart from our competitors. Organizations that partner with us get top-tier support and the benefit of the latest available tools.

If you have process automation needs, we can help. Get in touch with us today to explore what we can do for you.

The State of Machine Learning and AI

At Process Fusion, we’re committed to passing along relevant industry information whenever we find it. In that vein, we’d like to make you aware of a significant study recently published by the McKinsey Global Institute.

The study examines the state of AI research and development. Forbes published an excellent article summarizing some of the more pertinent stats included in the study. The article also include links to both the original study and McKinsey’s own summarizing article.

We encourage you to read Forbes’ summary by clicking here.

Mobile Data Collection for Your Business

For small and medium-sized businesses that rely on properly utilizing data to grow and thrive, data collection is an essential but underappreciated task. When important information is collected on paper, via survey for example, there is usually a long and tedious process to transform the information into usable data – and every step of that process is vulnerable to human error.

Finding a way around that tedious process of data input and analysis has been a goal of efficient businesses for a very long time. But mobile data collection always presented problems with the interface being clunky or the input taking too long.

Thanks to perseverance and emerging technology, some companies like Process Fusion’s partner Mi-Corporation have been able to build a workable platform for mobile data collection that hits all the right notes.

Mobile Data Collection Saves Time for Employees in the Field

Collecting data in the field used to be a lot like herding cats. Getting large groups of people to complete and submit forms and surveys in an orderly manner was nearly impossible; more complex tasks were even worse.

Mi-Forms, our main mobile data collection offering, works to solve that problem by making the process of entering data as intuitive, familiar and paper-like as possible. In a world that’s more paperless by the day, it seems somehow fitting that a screen imitating a traditional paper form is actually one of the most efficient and effective means of data collection.

Mobile Data Collection Helps Your Business Utilize Data

Perhaps the greatest downside of traditional data entry was how long it took. Mobile data collection completely cuts out the time between the end user filling out a form and entering that form data into the system so your business can use it.

This means you can immediately take customer feedback into account in your business practices, and your employees will always be updated with the latest data. Now your business can provide the best, most informed customer service possible.

Our mobile data collection offerings also integrate seamlessly with most pre-existing backend databases and function on any mobile device, laptop or desktop. This versatility is key to helping your business utilize the data you collect – and it also makes the adoption process much simpler.

Mobile Data Collection is Intuitive and Easy to Adopt

One of the largest barriers to the adoption of new workplace technology is the risk of causing a major disruption in business workflows or a negative impact on the end user experience. We keep this in mind with our mobile data collection services, taking special care to ensure that the transition to a new data collection method is smooth for your employees as well as intuitive and engaging for the potential customers that will be entering the data.

Mobile data collection is a powerful IT solution that can change the way your business operates from top to bottom, while the actual technology will directly impact only one of your business’s day-to-day functions. Combining mobile data collection with other services like workflow automation and cloud computing can turn your business from functional but traditional to cutting-edge and highly efficient.

At Process Fusion, we work alongside our partners to provide your business with solutions that empower your people with technology and improve both your business and your life. We’d love to have a conversation about how we can work for you – just get in touch, no strings attached.