CIO’s all over the globe are advancing their Machine Learning plans. The debate over Machine Learning as a boon or bane has long ended. Machine Learning has emerged as a victor with tangible benefits that CIOs want to embrace with all their might.
The ServiceNow Global CIO Point of View has found that 53% of CIOs surveyed have specified Machine Learning as their focal point. The survey also revealed that C-suite is leaning on ML as a catalyst for Digital Transformation.
But, for the CIO who is used to steering a mammoth organization with legacy systems, ML might appear to be quite a slippery ground.
An uncertainty about where to start, how to start and how to integrate the short-term with the long-term strategy is bound to kick in.
That uncertainty is what exactly what we are trying to dispel with this blog.
This is a CIO’s quick guide to getting started with Machine Learning. Here you go.
1.Identify Areas Of Application
Machine Learning implementation is possible only if you have enough data to build data models for predictions. Data from diverse sources like structured databases and unstructured databases must be consolidated and also cleansed for uniformity. It helps identify the exact operations or areas where the data lake can be used. For instance, integrating ML into accounting, marketing, HR, sales, customer service, etc.
2.Start Small, Scale Fast
Machine Learning is not a magic wand that can transform an entire function overnight through automation. Like building a skyscraper, an ML model is built brick by brick through continuous learning. A logical way to start ML would be to run smaller projects as ML experiments to test feasibility. Once the feasibility is proved, the RoI can be measured for scaling the data model across a function.
For instance, what kind of questions is repeated by customers when they ring up your company? If a pattern can be detected, a virtual assistant like a chatbot can be trained to automate the interactions which will reduce the efforts and time spent by personnel on addressing recurring questions with standard replies.
3.Ensure Data Integrity
The predictions you would get out of your ML system is directly proportional to the integrity of your data. You present the system with dirty data, you are bound to get wrong predictions that will make the whole ML system ineffective.
It is no surprises that data-driven enterprises have already invested resources to create new data strategies by harmonizing ERP systems, standardizing data definitions and cleansing data. The unified data strategy would help them look at their business from a 30,000 feet height with the ability to zoom down to 3-inch detailing.
So cleansing your data and making it ready for the ML system to weave through and arrive at accurate decisions is a prerequisite to taste success in machine learning.
4.Set Up A Data Team
Machine learning requires the expertise of a team with diverse skills. A single software engineer with graduation in mathematics and science is not going to help you scale your ML implementation. You will need to assemble a team comprising of data scientists, Big Data Architect, Systems Analyst and maybe a Business Analyst too.
Each team member will handle specific tasks ranging from setting up the data pipeline to teach the ML system to provide accurate predictions. That said, you cannot plan on hiring a single person who can do everything to set up the ML system as well have domain expertise to train the data model.
5.Build Domain Expertise
Although we are building a system that can automate tasks and make accurate predictions, the system first needs to be taught with basic data called test data. A domain expert who knows the in-out of the industry and its way of working must train the system with data models.
For instance, if you are automating the task of taking customer support calls for a software, you have to teach the ML system how the software functions under various scenarios and also the various scenarios when things go wrong. Without domain expertise, the system is bound to run into several wrong predictions.
6.Craft Accurate Data Models
Accurate data models are what enables Artificial Intelligence to reach its maximum potential. The machine learning system has to be fed with testing data from which it can learn to infer information as well make predictions. Such test data must a population of data that represents all possible scenarios that the ML system would have to confront.
7.RoI doesn’t happen overnight
Be informed that while ML is a phenomenal technology, results don’t appear overnight. It takes some time before the system is wholly ready to make predictions with substantial accuracy. The accuracy of predictions improves with time as the system continuously learns from recurring input and responses given to it.
Globally CIO’s are getting serious about Machine Learning and the positive impact it can bring to their businesses. PwC’s Digital IQ Survey 2017 found that 63% of executives are betting big on Artificial Intelligence as disruptive technologies. Machine Learning, being an arm of Artificial Intelligence is a surefire priority for CIOs. But, understanding the technology, its requirements and the immediate agenda is a tricky affair. We have tried to simplify that transition with this blog.