6 Questions CXOs Should ask before a Machine Learning Implementation

Is your company ready for a Machine Learning implementation? Here's a checklist to help you decide.

Machine Learning, being the technology of the decade, has surely emerged as the game-changer. From automated bots to the robotic figures, Machine Learning is on the roll. Nearly every sector plans to deploy ML models within their line of business operations. This not only promotes the end to end functioning but also enhances overall productivity.

However, the pathway of embedding machine learning models isn't easy. In fact, as much as the idea of automating the organizational operations seems fascinating, the implementation process would scare you off. Dealing with the technology and applying the same to the day-to-day business activities is largely complex and mandates extra attention. To remain on the safer side, we suggest organizations to have a clear roadmap of the ML integration journey. Here is the readiness checklist, to begin with, which should be on every CIO’s desk.

Data Deluge

• Do you have the required level of data, which is in a structured format?

  Machine Learning algorithms work on your existing and historical data. So, before you start a data analytics project, you need to have significant data in hand. ML models aren't just about data. It is desirable that the data to be fed into the system is of a standard format. Before, starting up with the project, it is expected that the data structure justifies the need.

ML Strategy

• Does the model render the needed value, and do you have an alternative in place?

  The first to consider is the value rendered by the ML model. Whether or not, the implications of the ML algorithm would help you attain your business goals, let alone add value to it. Also, what is your plan B? What if the model fails or experiences risks along the journey, how will the organization deal with it? There are times when things fail to unfold the way you want, or they could be risks that would appear insolvable. In such a case, having an alternative to keep the solution going is important.

Cost-Benefit

• Have you assessed the monetary value of the model?

  Surely, adding and integrating models isn't that every organization can do or afford to do. It not only requires enormous efforts but also a great deal of money. So, before moving ahead with the first phase of the project, you need to be pretty sure about whether the model would simply drain your resources or generate revenue in real-time.

Scaling ROI

• Do you have the tools needed to measure the success rate of the model?

  Just implementing the model won't help, it is important to have certain metrics that would assess and identify the success rate of the models. It could be anything from the customer acquisition rate to the conversion rate.

Flexibility

• Is your organization flexible enough to collaborate and share data with third party developers?

  Collaborating with team overseas requires constant communication and frequent data flows. To ensure seamless interaction, your organization must allow data scientists or developers to access in-house data and information.

Ideal Workforce

• Do you have the right talent to host the model?

  To unlock the ideal benefits of the implementation of machine learning models, you need to have both skills and expertise in line. Perform a check on your in-house team for the required skills and filter the ones that map the requirements. In case, your organization doesn't possess skilled employees, either train them or look for Machine Learning consultants to do the job.