---
title: "Is Your Company’s Data Ready for Machine Learning?"
id: "16647"
type: "post"
slug: "is-your-companys-data-ready-for-machine-learning"
published_at: "2018-10-16T20:41:10+00:00"
modified_at: "2026-05-29T18:07:42+00:00"
url: "https://tealium.com/blog/artificial-intelligence/is-your-companys-data-ready-for-machine-learning/"
markdown_url: "https://tealium.com/blog/artificial-intelligence/is-your-companys-data-ready-for-machine-learning.md"
excerpt: "Machine Learning (ML) is a new field and there is a competitive advantage to any first adopters. Yet, many companies are buying ML technologies and developing strategies before they have taken the appropriate step to evaluate whether their data is..."
taxonomy_category:
  - "Artificial Intelligence"
taxonomy_post_tag:
  - "Artificial Intelligence"
---

Artificial Intelligence

# Is Your Company’s Data Ready for Machine Learning?

Julie GrahamOctober 16, 2018

[rt_reading_time label="Reading Time:" postfix="minutes" postfix_singular="minute" padding_vertical="4"]

Machine Learning (ML) is a new field and there is a competitive advantage to any first adopters. Yet, many companies are buying ML technologies and developing strategies *before* they have taken the appropriate step to evaluate whether their data is ready or not.

***Ted Sfikas, Director for Solutions Consulting, North America and LATAM, recently did a webinar on “Is Your Company's Data Ready for Machine Learning****.”*

[http://videos.tealium.com/watch/jPnoKiEeLd8fKMh4zA3x8u](http://videos.tealium.com/watch/jPnoKiEeLd8fKMh4zA3x8u)

[In this webinar](http://videos.tealium.com/watch/jPnoKiEeLd8fKMh4zA3x8u)
, Ted takes us through the history and background of data science, what ML is, what a data supply chain’s role in an ML environment is and ML use cases. The webinar includes key takeaways such as:

**What is Data Science?**

- A concept to unify statistics, data analysis, ML and their related methods in order to understand and analyze actual phenomena with data
- It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science and computer science
- Data science brings these things together to produce new insights and assist in decision making - resulting in a product or service

**Key Common Terms**

- Data Mining and Knowledge in Databases (KDD) is the unifying practice in data science, it essentially explains patterns
- Statistics - quantifies numbers
- Machine Learning - predicts with models
- Artificial Intelligence - behaves and reasons

[Statistics quantifies numbers, Machine Learning predicts with models and Artificial Intelligence behaves and reasons.](https://twitter.com/intent/tweet?url=https%3A%2F%2Ftealium.com%2Fblog%2Fartificial-intelligence%2Fis-your-companys-data-ready-for-machine-learning%2F&text=Statistics%20quantifies%20numbers%2C%20Machine%20Learning%20predicts%20with%20models%20and%20Artificial%20Intelligence%20behaves%20and%20reasons.&via=tealium)
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**What is Machine Learning (ML)?**

- Mathematical computations that “learn” from input datasets without relying on a predetermined equation input by humans
- The model behind ML is provided by a human, and it defines the relationship between the input data (features) and the thing to predict (label)

[ML models are cyclically trained with labelled data, then applied for inference to unlabelled data to improve the model. So predictions can improve throughout as the cycle continues.](https://twitter.com/intent/tweet?url=https%3A%2F%2Ftealium.com%2Fblog%2Fartificial-intelligence%2Fis-your-companys-data-ready-for-machine-learning%2F&text=ML%20models%20are%20cyclically%20trained%20with%20labelled%20data%2C%20then%20applied%20for%20inference%20to%20unlabelled%20data%20to%20improve%20the%20model.%20So%20predictions%20can%20improve%20throughout%20as%20the%20cycle%20continues.&via=tealium)
[Share on X](https://twitter.com/intent/tweet?url=https%3A%2F%2Ftealium.com%2Fblog%2Fartificial-intelligence%2Fis-your-companys-data-ready-for-machine-learning%2F&text=ML%20models%20are%20cyclically%20trained%20with%20labelled%20data%2C%20then%20applied%20for%20inference%20to%20unlabelled%20data%20to%20improve%20the%20model.%20So%20predictions%20can%20improve%20throughout%20as%20the%20cycle%20continues.&via=tealium)
**2 Types of Machine Learning**

1. Unsupervised Learning - group and interpret data based only on input data
2. Supervised Learning - develop predictive models based on both input and output data

To get more key takeaways and knowledge around all things ML [watch the on-demand webinar](http://videos.tealium.com/watch/jPnoKiEeLd8fKMh4zA3x8u)
and learn:

- Why the fundamental need for data readiness is so crucial when it comes to ML
- Key steps to evaluate the readiness of your data
- How a governed data supply chain using automated data orchestration can deliver a reliable engine for ML platforms
- And so much more!

[Watch the on-demand webinar](http://videos.tealium.com/watch/jPnoKiEeLd8fKMh4zA3x8u)
 and determine if your company’s data is ready for Machine Learning today!