Are Your Machine Learning Projects Not Delivering The ROI?
For organizations focused on driving enterprise-wide innovation, machine learning opens up a world of opportunities: better insights that lead to better decisions and better business results.
Whilst the technology has so much to offer, most companies still don’t know the right way to approach machine learning. And those who do get started on the right foot, often end up not achieving the ROI they expected in the first place.
A failed machine learning project is enough reason to keep business leaders awake at night, but do you know what’s pulling your project down? Is it because you’re not using the technology in the right manner? Or is it because of ineffective strategies?
Here are some questions to ponder over, to really understand why your machine learning project might not be delivering the ROI you expect:
Is your organization clear on its machine learning objectives?
It’s a known fact that machine learning brings a host of benefits to organizations. But is your organization clear on its machine learning objectives? Are you looking to leverage machine learning to drive cost-effectiveness? Or are you looking to enhance your profits? Is increasing the market share your goal? Or is empowering your workforce through better insights is your main objective?
Every machine learning project has the capability to nourish different objectives. However, it’s important to contextualize the objectives, so you can avoid conflicts and get maximum value from your machine learning investment.
For example: if machine learning is being used to accelerate the speed of software development, you might also want to pay attention to the impact the speed-quality trade-off will have on your end product – and eventually your customer experience.
Do you have sufficient support to run your machine learning project?
A project as massive as machine learning requires immense support from business and tech leaders (and employees). In the absence of the right support, businesses do not get the investment they need while spending a lot of time gathering and preparing data and lose out on leveraging the insights to drive their business forward.
Since the cost of developing and implementing a machine learning strategy is substantially high, you need the right amount of investment from your leaders and support from your employees to validate (and sustain) the value proposition. While the value might not always be visible at the beginning of your project, document the improvements in business process efficiency, employee and customer experience, profits, and revenue to get the backing you need to ensure continued project success.
Are your machine learning algorithms being fed with the right data?
Any machine learning project is only as good as the data that is fed into it. Data is the lifeblood of machine learning, so unless you train the models with the right data sets, you will not achieve accurate and precise results. Are you feeding your models with the right data?
Make sure the resources you use and process with machine learning are on-point. Always only use updated and relevant quality data. As far as possible, use bigger data sets and try not to restrict data to achieve fair and unbiased outcomes. Also, make data privacy an integral aspect of your machine learning undertaking, so you can ensure compliance with international rules and regulations.
Do your employees understand how to leverage machine learning?
There are a host of problems machine learning can solve. But do your employees understand how to leverage the features and capabilities of the technology? If employees do not know how to use the insights of machine learning in their everyday jobs or if they use it the wrong way, you are not going to achieve the results you expect from your machine learning project.
Since employees need to apply the output of machine learning tools directly into their jobs, it is important they are trained on how to make the most of the technology. Make sure to take concrete steps to help users make the right use of insights that are generated.
Do you constantly evaluate and monitor your machine learning strategy?
Like with any technology implementation, measurement and monitoring of the success (and bottlenecks) of your machine learning program is important to ensure that you’re on track. Do you have processes in place to constantly evaluate and monitor your machine learning strategy?
Understanding critical success factors that guarantee the health of the machine learning program is important to achieve the intended ROI. Make sure to constantly review and revise your algorithms and check for inefficient models, corrupted data sources, biases, and unstable data relationships. Devise different metrics for different segments and focus on constant optimization of the machine learning undertaking.
With investments in data at an all-time high, machine learning projects are enabling organizations to unearth critical insights about their business, customers, and market. However, the results and ROI can only be achieved when you are clear on your machine learning objectives, have support from your leaders (and employees), feed your models with the right data, use the insights in the right way, and constantly monitor your strategy. Focusing on these efforts will allow you to drive the value and ROI you deserve from your machine learning project.