By Greg Van den Heuvel, Head of Pitney Bowes Software's Operations Department
Machine learning, or Machine Learning (ML), is innovating companies around the world, creating waves within the IT community and totally transforming organizations. There is no doubt that companies everywhere are moving to deploy ML capabilities. But the question is "Are organizations taking full advantage of the conclusions generated by the machines?"
My prediction is that these conclusions are found, but are not applied, because business leaders do not understand and do not trust the results, or they may not have the budget and even the competence necessary to make real changes. For others, ML has just become an item on the list - you buy it, delete it from your list and never think about it again.
If your organization has already considered or is considering buying machines with learning capabilities, you first need to understand what Machine Learning really is, the role that big data plays in drawing conclusions and what actions you should be taking now to take. really transform with ML.
Machine Learning is part of Artificial Intelligence (AI), but uses algorithms to generate learnings from structured and unstructured data. It examines large sets of data that can include images, text, voice, video, location and even facial recognition data. When analyzing these data sets, ML identifies correlations, patterns and trends that can be used to make predictions.
The role of big data
Machine learning is unique, as it works in a very similar way to the human brain. The more information that comes in, the smarter machines with learning capabilities become.
Globally, companies and consumers together produce 2.5 quintillion bytes of data each day, which would be enough to fill 100 million blu-ray discs! For ML, that amount of data would be considered a banquet, as it thrives thanks to large unstructured and structured data sets, which can reveal hidden predictions and conclusions using algorithms.
You were probably somehow impacted by this analytical process without realizing it. If you like watching Netflix you have already seen the category “Why did you watch…”, which makes recommendations based on your past behaviors. Or if you post photos on Facebook, you may have already used facial recognition to tag a photo instead of having to type the person's name.
Think of it this way - you can buy a package for a gym, but if you're not, you'll never get the results you want. Unfortunately, this is the mentality of most organizations, as many buy ML software, but do not make that extra effort to promote any real business value.
Barriers such as culture, budget constraints, internal talent or a lack of desire to change the status quo have plagued organizations and prevented the transition from “first adopters” to “innovators”.
Face your fears
Despite these organizational challenges, business leaders have an opportunity to face their fears and take immediate action to help remove barriers and move forward with ML strategies:
1. Integrate ML into your digital transformation journey. By doing this during the planning stage, you can prevent ML from falling behind. Employees will include ML from the beginning and treat it with due importance.
2. Make a commitment. And make it public. Letting others know that you are committed to understanding, embracing, adopting and integrating ML counts a lot.
3. Start at the top. Encourage leaders to incorporate analysis into the strategic vision to foster a culture of analysis.
4. Assess internal talent. Identify people in the organization who can be ambassadors for ML. And don't be afraid to point out flaws. Investing in outside talent can bring these skills to your organization.
5. Implement. Machine learning capabilities should not only benefit the customer. Deploy these technologies within your own organization so that employees can see the value they generate first hand.
6. Stay focused. Don't buy ML capabilities just to say you have them. Know the desired result and design your strategy to meet those specific needs. Through this process, you will gradually discover other areas that technologies can benefit from, ensuring that you always place resources where they make a difference.
7. Ask for help. Machine learning is not a simple concept. Ask tough questions and make sure that everything you do ends up benefiting someone, be it a customer or an employee.
8. Start small. Start with small-scale projects so that you can run tests with low risk. This will give you the chance to become more familiar with the technology so that you will be successful with larger projects.
Probably everyone can agree that ML is one of the biggest agents in existence for technological change. But reaping its benefits cannot come from technology alone. Business leaders have an immediate opportunity to take advantage of these capabilities and foster the transformation of the market through innovation. Don't just cross that off your list. Be an agent of change.
My prediction is that these conclusions are found, but are not applied, because business leaders do not understand and do not trust the results, or they may not have the budget and even the competence necessary to make real changes. For others, ML has just become an item on the list - you buy it, delete it from your list and never think about it again.
If your organization has already considered or is considering buying machines with learning capabilities, you first need to understand what Machine Learning really is, the role that big data plays in drawing conclusions and what actions you should be taking now to take. really transform with ML.
Machine Learning is part of Artificial Intelligence (AI), but uses algorithms to generate learnings from structured and unstructured data. It examines large sets of data that can include images, text, voice, video, location and even facial recognition data. When analyzing these data sets, ML identifies correlations, patterns and trends that can be used to make predictions.
The role of big data
Machine learning is unique, as it works in a very similar way to the human brain. The more information that comes in, the smarter machines with learning capabilities become.
Globally, companies and consumers together produce 2.5 quintillion bytes of data each day, which would be enough to fill 100 million blu-ray discs! For ML, that amount of data would be considered a banquet, as it thrives thanks to large unstructured and structured data sets, which can reveal hidden predictions and conclusions using algorithms.
You were probably somehow impacted by this analytical process without realizing it. If you like watching Netflix you have already seen the category “Why did you watch…”, which makes recommendations based on your past behaviors. Or if you post photos on Facebook, you may have already used facial recognition to tag a photo instead of having to type the person's name.
Think of it this way - you can buy a package for a gym, but if you're not, you'll never get the results you want. Unfortunately, this is the mentality of most organizations, as many buy ML software, but do not make that extra effort to promote any real business value.
Barriers such as culture, budget constraints, internal talent or a lack of desire to change the status quo have plagued organizations and prevented the transition from “first adopters” to “innovators”.
Face your fears
Despite these organizational challenges, business leaders have an opportunity to face their fears and take immediate action to help remove barriers and move forward with ML strategies:
1. Integrate ML into your digital transformation journey. By doing this during the planning stage, you can prevent ML from falling behind. Employees will include ML from the beginning and treat it with due importance.
2. Make a commitment. And make it public. Letting others know that you are committed to understanding, embracing, adopting and integrating ML counts a lot.
3. Start at the top. Encourage leaders to incorporate analysis into the strategic vision to foster a culture of analysis.
4. Assess internal talent. Identify people in the organization who can be ambassadors for ML. And don't be afraid to point out flaws. Investing in outside talent can bring these skills to your organization.
5. Implement. Machine learning capabilities should not only benefit the customer. Deploy these technologies within your own organization so that employees can see the value they generate first hand.
6. Stay focused. Don't buy ML capabilities just to say you have them. Know the desired result and design your strategy to meet those specific needs. Through this process, you will gradually discover other areas that technologies can benefit from, ensuring that you always place resources where they make a difference.
7. Ask for help. Machine learning is not a simple concept. Ask tough questions and make sure that everything you do ends up benefiting someone, be it a customer or an employee.
8. Start small. Start with small-scale projects so that you can run tests with low risk. This will give you the chance to become more familiar with the technology so that you will be successful with larger projects.
Probably everyone can agree that ML is one of the biggest agents in existence for technological change. But reaping its benefits cannot come from technology alone. Business leaders have an immediate opportunity to take advantage of these capabilities and foster the transformation of the market through innovation. Don't just cross that off your list. Be an agent of change.
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