What does machine learning (ML) really mean for marketers? Here’s three key facts you need to know.

1.Machine learning isn’t new.  Machine learning, a method for transforming data into practical knowledge through the application of computer programs that automate the process of statistical modeling and data mining, has been around for a while. A lot of it is old statistical techniques rebranded to sell new software to run these techniques.

For instance, a week or so ago, I got a pitch from a company that noted it was using linear regression. Is linear regression some cool new AI feature? No, it’s a statistical technique that dates back to the 1800s, when the “machine” was your brain. What is new is our ability to combine data, algorithms and processing power. IBM’s Watson is a great example of applying decades-old algorithms to new “datasets” like Wikipedia. Machine learning is also based on two factors, an algorithm and data. IBM Watson’s algorithms were developed in the 1970s, but it didn’t get the data it needed to be effective until the 1990s or the 2000s.

2. Machine learning has practical applications.  Even though ML has been around for a while, there’s a reason why we’re suddenly hearing a lot about it: There’s an abundance of data that didn’t exist even 10 years ago. That data will grow exponentially as IoT takes off.

Think about it: Soon, when a consumer gets up at 10 PM to fetch a beer while watching Game of Thrones, he leaves a data trail. His fitness tracker knows how many steps he took to the get the fridge, his smart refrigerator will know he’s down to his last beer and tell Alexa to order a fresh six pack, and Google will know his search behavior on his mobile phone. ML has the ability to find connections and patterns in that data that could provide new revenue opportunities and increase customer retention, among other benefits.

The other part of the equation is that there is now more computer processing power available than ever before. Using the cloud, companies have a nearly infinite amount of processing power at a price point that has never been lower.

3. All of this is pointless if you don’t have good data.  What marketers don’t see as part of the software demonstration is the effort required to build the dataset that is the input into a ML algorithm. Building these datasets requires time, technical skill and business knowledge. If you want a ML algorithm to have a fighting chance of identifying patterns in your data that led to great insights, you need to assemble the right data sets in the right way. The marketing world needs better data, not better algorithms.

Temper Your  Expectations

Marketers need to be skeptical about vendors’ claims. Ask them about their algorithms and terms like linear regression and pinpoint whether what they’re selling is really new or just repackaged algorithms that they’re calling machine learning.

Despite such sales bluster, the hype over ML is largely justified; you really can make huge strides by running a deep analysis of your data. But that assumes that you’ve already collected and assembled your data correctly and the information is solid.

Since most marketers are still stumbling around this step, their skepticism over ML is justified. But, their optimism about ML is also justified.

Many marketers are sitting atop a mountain of data about their consumers. Even in the face of the hype, marketer’s can still be optimistic about ML, just add a dash of pragmatism to go with the optimism.

Think deeply about your business. Identify the few specific – not general – problems where ML will help you make gains. After this, build a team and a process to build quality datasets, experiment with different ML methodologies, and learn what works for your business. This simple formula will ensure your expenditures on ML pay off.

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As machine learning methods continue to improve, so do the possibilities of their utility throughout all industries. The worlds of healthcare and finance, for example, are particularly vulnerable to breaches in data security. Social security numbers, names, addresses and financial information are the data goldmine hackers are seeking to find and exploit.

While technologies such as blockchain are quickly evolving to make it nearly impossible for data to be hacked, their complexity and limitations aren’t currently functional for the millions of transactions that occur (particularly in the finance sector).

Machine learning and the human factor

Experian recently released an report stating that 66 percent of companies surveyed admitted that employees are “the weakest link in their efforts to create a strong security posture.” As such, not only are there serious threats external to organizations, but also a distinct risk of negligence from employees who are careless or purposeful in enacting a data breach.

The more data breaches that occur, the less confidence consumers have in the organizations that store sensitive data. Capital One is one organization working to scale machine learning methodologies to manage both external and internal user access. Recently, Jon Austin, a machine learning engineer from Capital One, gave a demonstration at the Qubole Data Platforms Conferenceof how the enterprise is using machine learning to manage administrative access on the back end.

“Start with the problem you are trying to solve” Austin said, while swiftly clicking through an array of graphics depicting edges and nodes. “Nodes are your individuals who have access to the data, and the edges are the relationships between the nodes.” The primary information Austin emphasized through his training data was the frequency of each user’s access to areas of the Capital One database. Using the Jaccard Index calculation, Austin and his team could then measure the similarities among users. This would further assist in determining the who, what, where, and when of access.

How machine learning identifies user patterns

But how can this help user access management? As Austin explained, machine learning models can be trained to recognize normal vs. abnormal (or risky) user access patterns. For example, if data scientist Dana usually enacts access and initiates her pull requests from her work computer between 8 a.m. and 5 p.m. Pacific time, the machine learning algorithm recognizes this as a typical pattern. However, if she’s traveling for a conference and tries to access the database from her laptop outside the normal timeframe – even if using a VPN – the machine learning algorithm will flag this as abnormal and prevent Dana from access.

Of course, these edge-case scenarios can be accounted for with further training and test sets. Also, Dana could notify the administrator that she’ll be requesting access from a different location and within a different time zone. As such, access privileges can be modified to accommodate the shift.

The primary takeaway here is to continuously update the machine learning model to adapt to new situations, but also to discern outlier scenarios and alert administrators. From that point, the administrator can immediately act so Dana can continue with her work. Or, if Dana is not traveling and this is a possible data breach threat, then the organization is alerted before any damage is done.

Certainly, machine learning is in the fine-tuning stages. And humans still need to play intermediary for taking decisive action on the information that algorithms provide. Machine learning can, however, lessen the latency gap between unauthorized access and a disastrous data breach.

Human beings are perpetually penetration testing. Machine learning won’t change that fundamental aspect of human nature. But, it’s a handy prevention tool that should be seriously considered in managing the intricacies of user access.

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