Read on the extract of the guide “6 steps to jumpstart machine learning using the resources you already have” written by Explorium – it explains how senior data professionals can enable data science in their organizations with the resources they have available, how to use cutting-edge technology to make the most of your budget and expertise, enabling a productive, machine-learning- backed strategy:

It’s simply no longer enough to know how well your organization performed in the past. You need to know what’s coming so that you can figure out how to respond to market pressures and volatility, new trends, tastes, and technologies.

You need datasets drawn from inside and outside your organization, and you need data science to make sense of it. This is great if you have your own in-house team of data scientists.

What if you don’t have any machine learning experts in your organization?

Even if you’ve never incorporated sophisticated forms of data science into your operations before, you no doubt already have systems for data collection, management, and analytics in place.

In the best of times, no business leader wants to risk wasting resources on big projects that won’t deliver results. It’s up to you to demonstrate, first, how data analytics have already helped to improve visibility and drive best practices in your organization.

When highlighting the benefits of moving towards machine learning, explain how this approach will:

  • Protect the business by giving your colleagues time and knowledge to prepare and strategize for fast-emerging threats.
  • Help you identify new and alternative revenue opportunities at a time when core business operations may be hindered.
  • Give your organization the gift of certainty at a volatile time, driving better decisions and avoiding costly mistakes.

Step 1: Facilitate collaboration across the organization

The key here is multidisciplinary collaboration. You need to engage heads of departments to figure out what is required and how this would fit in with their existing processes. You need to engage IT professionals to establish what is possible and how this will work with source systems and target systems, ensuring smooth access to the data you need. You also need to get the blessing of your CEO and CTO.

Step 2: Build your team

Your machine learning project won’t get off the ground unless there are specific people in the organization taking ownership of it. That means you need to put together a dedicated team. This includes people on the business and domain knowledge side to keep things on track, as well as those with the practical roles and skills needed to deal with data and deploying models.

Step 3: Set up the right data pipelines

To make this project work, you need to figure out how to bring data from all over the organization together into a single source of truth, treating it as a coherent resource.

That means moving from a silo mentality to a platform one, which will permit you to collaborate across the organization and scale your machine learning efforts.

To get information about steps 4, 5 & 6, and detailed guidelines, download the guide for free here.

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