80% of time is spent preparing data, the other 20% is spent complaining about preparing the data.
Ask a data scientist if they’ve heard that joke and the answer will probably be yes. Thankfully, implementing Machine Learning (ML) in your organization is hard but in no way impossible. The key is to approach ML adoption the right way.
Before we get to that approach, though, let’s look at why organizations often fail when trying to adopt ML, along with the resources you will need to successfully get started.
Common barriers to ML success
The availability of the public cloud has changed the ML landscape in many ways. However, the most common roadblocks organizations have in adopting ML are still:
- High set-up costs, including tools, expertise, and storage
- Siloed data that limits access to those who need it
- Complex and fragmented tools that get in the way of exploring data
- Deployment complexity that leads to difficulties in putting ML models into production
Resources you need to get started
Outside of the data necessary to run ML models effectively, your organization needs to have these resources and skills in-house—or an outside partner with the expertise—to successfully adopt ML.
This includes, at the very least:
- A business stakeholder to drive the ML adoption process
- A data analyst to identify and make actionable insights from the ML model
- A data engineer to store, clean, and manipulate data
- A data platform architect to build the data environment
- A data scientist to experiment with and construct ML models
Putting machine learning to work
Now that we’ve talked about the common causes of ML adoption failure and the resources you’ll need, let’s break down the four stages we recommend for successfully putting ML to work in your organization.
Stage 1: Business assessment
Every ML initiative needs a goal. Before you start looking for ML solutions, you need to understand what your business objectives are.
Are you …
- Looking to find deeper insight from your customer data?
- Trying to locate greater efficiencies within your organization?
- Interested in forecast trends and budgets?
Knowing where you’re going and what you want to accomplish will help you narrow down the pools of data your ML analysis swim in, which can go a long way toward keeping your ML projects and initiatives from failing.
Stage 2: Develop & proof
ML is only as good as the data it utilizes.
In this stage, you need to collect and catalogue your data from its various resources and pool it into an accessible environment, such as a data warehouse platform. During this stage you also:
- Clean the data you have so it’s of a high enough quality to be useful
- Identify gaps in your data
- Enhance your data in order to fill in gaps
Stage 2 is also when you develop a POC ML model utilizing a small amount of data, then verify the results.
Stage 3: Pilot
Once you’ve tested your POC model, it’s time to integrate that model into your processes and tools.
This involves running a side-by-side pilot with your existing analytics process and your new ML model, then comparing their effectiveness. If your ML model delivers better results, you’re ready to move on to the final stage.
Stage 4: Production
With your pilot tests complete, it’s time to put your ML model into production. That means full integration, deployment, and then continuous improvement and refinement.
To learn more about ML and data management, download our free whitepaper, Navigating the Flood: Building Value by Reducing Data Complexity and Properly Managing Your Data.
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