While many industries are struggling amid the coronavirus pandemic, both the IT industry and the broader trend of transition to remote work have revealed many areas where traditional approaches to managing businesses create unnecessary waste. Still, data science and its subdivision – machine learning – reveal that such expansion is nearly limitless.

Machine learning uses powerful algorithms to discover insights based on real-world data that can then be used to make predictions about future outcomes. As new data comes available, machine learning programs can automatically adapt and produce updated predictions. As with any tool, machine learning is not a silver bullet. However, there are many situations in which the technology can outperform linear and statistical algorithms.

Here are five of the most common use cases where machine learning can make a big difference:

When engineers can’t code rules for certain problems

Many human-oriented tasks (such as recognising whether an email is spam) aren’t solvable using simple (deterministic), rule-based solutions. Because so many factors may influence an answer, engineers would have to write and frequently update billions of lines of code. In addition, when rules depend on too many factors, and when those rules overlap or need fine-tuning, it becomes difficult for humans to code precise rules. Fortunately, machine learning programs don’t require users to encode actual patterns. These programs only need proper algorithms to extract patterns automatically.

When you need to scale a solution to millions of cases

You might be able to manually categorise a few hundred payments as either fraudulent or not. However, this becomes tedious or impossible when dealing with millions of transactions. As user bases grow, it’s no longer feasible for organisations to process payments by hand – end-users today want answers about their money in milliseconds, not minutes or hours. Machine learning solutions are effective at handling these types of large-scale problems with little or no human intervention.

When you can do it manually, but it’s not cost-efficient

There are situations in which in-house experts could process many requests quickly and accurately but at a high cost. For instance, imagine you assess DMV forms for in-state and cross-state car purchases to determine their validity before passing them on. In this situation, the business processes are well-defined, optimised, and serialised. It may take only a few minutes to check each form thoroughly. But allocating so much manual labor to this work is likely not the best use for your budget. Machine learning, on the other hand, offers predictable, pay-as-you-go pricing for fully scaled operations.

When you have a massive dataset without obvious patterns

Consider this – you’ve successfully prepared a well-curated dataset and know the underlying problem. However, you don’t see any explicit patterns in the data, preventing you from encoding those validations. Plus, there are many typos, missing fields, and other human-caused errors with no validation in place. You may even know the data is poor quality and can manually determine every affected row. But you can’t see any actual connections between valid and invalid records. Machine Learning algorithms can solve this problem. They can find hidden connections between data points that aren’t clear to humans. Tools like Interpreting Tracers can even describe how machine learning models arrive at their conclusion.

When you live in an ever-changing universe (adaptive)

The world, and its problems, are always changing. A problem you solved yesterday can easily mutate into something else entirely, rendering your previous solution inefficient or even useless. For example, if your organisation processed medical appointment recordings to extract diagnoses, procedure information, and billing codes, your rules might have to evolve constantly. However, you can’t make updates in real-time 24/7. Meanwhile, incorrectly labelled items could lead to insurance rejections, huge fines, and legal penalties. One major advantage of machine learning methods is that they can learn from data across the entire lifecycle of your application – from the first line of code written to the moment when the model is finally shut down. Moreover, it’s important for production-grade systems to have feedback loops so that you can catch the moment when your model no longer solves problems correctly.

It’s important to remember that machine learning is a tool – it’s not magic. Machine learning models are essentially advanced math-based algorithms, which identify patterns in data and learn from them. However, when properly applied to the right use cases, machine learning can reduce the amount of time spent error-prone manual IT operations, adding significant business value and greatly reducing IT costs.

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It’s all about the data — or, so they say. If you’ve kept your ear to the ground on business technology, you’ve certainly heard the term Big Data. It’s commonly used to describe massive amounts of unstructured information coming from various channels of our business and personal lives. But what’s so special about Big Data? Nothing. In fact, Big Data alone is about as useful as the internet was in 1984.

Our expectation of instant information gratification in 2021 is high. Our world is so hyperconnected, we are conditioned to needing our data now. Most people don’t realize the enormous network and computer infrastructure required to make data fast. When we think about this in our personal lives, there are many examples: Facebook, LinkedIn, Google and Netflix all have massive amounts of data that need to be delivered to their end users. Search, video, messages — you name it, it’s almost instantaneous.

In the business world, the power behind the curtains of massive data companies is usually not attainable. Thus, for many companies, data may seem slower to reach users at work than it does in daily life. However, businesses are quickly learning the scalable and affordable options they have to make their data fast. Leveraging networking techniques that minimize latency between users and data is key. This includes everything from increasing bandwidth to user locations, keeping users on the same network or keeping frequently used data/content cached for quick access. Modern data storage and computer systems provide much higher levels of performance than conventional systems. Both cloud-based systems and local infrastructure have several options to consider that are flexible and economical for a dynamic business.

Reliable Data

At the end of the day, the data must be available when and where we need it. If we cannot rely on it consistently, then it becomes a risk to the business. Organizations that need reliable data build reliable infrastructure, deploy reliable applications and create a reliable network. Reliability requires an investment in the right resources, but that doesn’t mean it can’t be an affordable endeavor.

Over the past decade, IT costs may have grown but value is on the rise as well. As Digital Transformation shapes the strategy of many organizations, companies are realizing smart technology investments can have a significant ROI. The cost of “downtime” calculation is key to estimating true cost and risk in today’s business. Gartner estimates the average enterprise impact of unplanned downtime to cost $5,600 per minute. Compare that to the falling costs of network, infrastructure and cloud services, and you’ll discover building reliability is worth it.

Usable Data

59 zettabytes. That’s what experts at the IDC claim the global data footprint looks like in 2020. That’s 59 billion terabytes! The IDC also cites that our global consumption of data over the next three years will surpass all of what we’ve produced and consumed over the last 30. That is a staggering statistic that most people cannot comprehend. 

Interestingly, most of this massive data is just not usable to business, science and government. Our ability to mine this data into meaningful information has not kept pace with the data explosion of the last decade. 

One reason for this is that many companies have not invested the time and resources to fully understand the data available to them. The shift to a culture centered around data-first decisions is not easy. Through Digital Transformation initiatives, companies are just starting to tap into the “gold” available to them under the mounds of data that exist. The ability to better understand our business with solid insights is within reach. It will take time, but it will be worth the wait and open doors we can’t even predict today.

Secure Data

For many businesses, the security of data may be the most important on this list, and for good reason. Data is power. For that reason, it is sought after by cybercriminals and leveraged to cripple an organization’s operation by holding data hostage. Make no mistake, we all suffer each time a breach occurs. 

The constant need to be vigilant in security efforts makes it harder than ever to run a successful business. But, with such a complex security arena, many businesses of all sizes struggle to find the right path. If your company wants to improve its security posture, it’s good to consider two concepts.

First, start with a foundation. There are numerous programs and frameworks to help here. The NIST Cybersecurity Framework is an excellent resource to get started. Get educated and identify your baseline so that you can begin to fill company gaps.

Secondly, work in layers. Cybersecurity is best addressed with a layered approach that includes the network, endpoints, email and employee training. Developing a solid plan to minimally cover these four critical areas is an excellent start to securing your data.

Accurate Data

Admit it: You’ve looked at your weekly business reports and said, “That can’t be right.” There is always skepticism in data since we all know there are many ways its accuracy can fall short. Input methods, system processes, bad code and many other issues can invalidate data and leave a business shortsighted on decisions.

To avoid the bad data conundrum, ensure your sources are clean and offer options to keep things in check. Data entry validation, data scrubbing algorithms and enterprise system integration points are excellent approaches to data hygiene. If your business shares data with third parties, do your homework and be sure it’s from a trusted source. That will help you avoid regrettable business decisions based on bad data.  

Big to Better to Best data is what we should expect going forward. In our not-so-distant future, if the data isn’t “best,” it’s just “big.” Be sure to keep pace by addressing some of the techniques mentioned here and demand the most of your data.

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