5 Lessons What Enterprises Need to Learn about Machine learning

by August 6, 2020

Hard Truth- Too many hopes pinned on machine learning may lead to unrealistic expectations!

What many IT leaders forget to convey-Huge expectations pinned about technology may cause unfulfilled and downright unrealistic expectations. ML and Data Science experts say that the tough truths that enterprises learn are realized when they turbocharge Machine Learning and Disruptive technologies into implementation and adoption.

 

Here are the five lessons what enterprises need to learn about ML –

 

1. It is all about building the right Data Science Teams

Yes, you heard it right! No blueprints or strategies, first an enterprise needs to chalk the right team. The perfect blend of data science experts, data analysts, ML experts, IT support and data warehouse professionals.

Making a Strategic blueprint would be fruitless if there is no expert team to support it!

 

2. Create a bridge between Technical realities and Business Objectives

Most of the IT pros can empathize on this lesson, while an enterprise needs to weigh its technical capabilities, equally important is taking an account of the business objectives and how does its technical acumen support it. Enterprises may consider strategic partnerships with 3rd. party vendors for technical backup to meet its business goals and stakeholder returns.

 

3. Not a One Size Fits All approach works in Disruptive Technologies

Yea, that’s right. Every enterprise is different and so is its objectives, case studies and work schedules. The decision-makers both on the business and the technical side must sit together, discuss and debate and reach a conclusion as to which business use cases will suit the enterprise functionaries and what work process should be chosen for an ML model pilot.

 

4. Getting the Data in Order

Data is a goldmine, but the question is which data is important and which one to discard? An enterprise must bring together data teams who channelize big data and build viable data pipelines or create and store business-ready data in each to access data warehouses and data lakes that even citizen data scientists can break these data silos.

 

5. Encourage Data Literacy

Perhaps the most important, enterprises must work together to instil data awareness to each of the team member. This includes the C-Suite and the lowest-ranked IT professional in the hierarchical ladder. Only then all would understand the importance of data and the ML models that are built to get the most value from them.

 

Though these are generalised lessons, each enterprise will have its own data stories, so, it’s on the C-Suite to decide and adopt which lesson and approach suit them the best to steer their Digital Transformation journey course!