6 Data Science Buzzwords & What They Mean

There’s a lot of buzz when it comes to data. And with buzz comes buzzwords. Below, we’re exploring six of the most popular buzzwords and how they affect your business. Let’s dig in.

Data Mesh

Data mesh is an approach that enables organizations to deliver a decentralized architecture that groups and curates data by a specific business domain. The goal of data mesh is to provide a more consistent view of enterprise data resources by solving the challenge that organizations face when it comes to standing up a single point of access that can query data wherever it lives.

Data mesh is certainly experiencing a lot of hype, particularly for large firms. But for data mesh to work, organizations need to clean and standardize their enterprise data. That’s why data mastering is so critical. It both complements and augments their distributed data initiatives by providing standardized keys for data that can be understood across systems and domains. And, it creates useful mappings between data identifiers across the organization, both of which are often critical bottlenecks in a data mesh strategy.

Data product

Data products are a consumption-ready set of high-quality, trustworthy, and accessible data that people across an organization can use to solve business challenges such as increasing competitiveness by improving the customer experience or creating product differentiation, and deliver value by helping companies to drive growth, save money, and reduce risks.

Data products make data tangible for the organization. A product is a familiar concept. It’s something that the consumer - or data consumer, in this case - needs. It serves a purpose that they’ve defined and aligned it to an outcome they want to attain, and therefore they deem it useful. And because it’s concrete, recognizable, easy to find, and easy to use, they are more likely to realize value from it.

Implementing a data product strategy through the design and use of data products is a surefire way for organizations to treat data as an asset and drive greater value from it. Data products elevate the value of data as an asset by making it discoverable and consumable for everyone across the organization.

Tamr’s Chief Product Officer, Anthony Deighton, said it best:

“At its core, every business is a data business. Which is another way of saying every business should have data products and think about managing their product – which they might think is software or retail or healthcare. But it’s not. It is, in fact, the data. And they should manage that asset like a product.”

Machine learning (ML)

Machine learning is a buzzword that’s been around for a long time. But ML means different things to different people.

According to Wikipedia, machine learning is “is a computer science field devoted to understanding and building methods that let machines "learn" – that is, methods that leverage data to improve computer performance on some set of tasks.”

Companies and technologies employ machine learning algorithms to replace manual, human-driven processes with automated ones that provide greater speed and scalability. Take data mastering, for example. Tamr uses a machine learning-driven approach to clean, curate, and continuously-update data at scale, enabling organizations to realize greater value from it.

But we also know that when it comes to data, machines alone are not enough. Why? Because while machine learning can automate and improve parts of the data cleaning process, it can't account for all types of data inaccuracies because it's inherently based on patterns and probability. It can struggle to handle unique, unexpected, or one-off scenarios that a human intelligence might catch.

That’s why our approach to data mastering keeps humans in the loop. Humans provide the feedback needed to refine the model and ensure it’s delivering the highest quality, most trusted version of your data to power analytic insight and accelerate business outcomes

If you’re wondering how to strike the right balance between human and ML, here is a formula that illustrates how the best modern data mastering solutions work:

Modern Data Mastering = 80% machine + 10% humans + 10% rules

Large language models

Tools like ChatGPT have been taking the world by storm recently, bringing a lot of buzz and even more hype to large language models.

Here’s how it works. These models essentially take a bunch of text, create slight variations that are consistent with the model (but slightly different), and play the text back through the model for training purposes.

In the case of ChatGPT, the outcome is text that is human-like. ChatGPT learned how to make decisions about whether to use the word “a” or “an” using Internet data. And while often very accurate, it cannot recreate the unique human creation of content. That remains distinct to humans.

So just like data mastering requires humans to stay in the loop, the outputs from large language models such as ChatGPT also require some level of human review to capture the thoughtful, more subtle human creation of language that a machine could never recreate.

Data Quality

Our last (but not least) buzzword is data quality. And you may be wondering why we’ve included it on our list. After all, isn’t high data quality something every business wants?

Data quality is the outcome that comes from having clean, curated, continuously-updated data. And while many companies claim to deliver “data quality solutions,” we believe that data quality is an outcome, not a product. And that how you manage and master your data is key to achieving high data quality.

Organizations implement a data product strategy through the use of data products are the ones who will reap the benefits of quality data. Why? Because data products are the best version of data.

Organized by business entities and governed by domain, data products are comprehensive, clean, curated, continuously-updated data sets, aligned to key business entities, that both humans and machines can consume broadly and securely across an enterprise.

To sum it up, data buzzwords come with a lot of hype. But when you do your research and understand what’s behind them, you can decide for your organization which ones are worth embracing and which ones are overhyped.

‍