A data science team is only as good as the data it has to work with. As data science leader Scott Ernst writes:

“Improper data collection produces garbage results.”

Not only is the quality of data important, but so is the quantity. Without enough insightful data, a data science team will be limited in what it can produce.

Largely due to the lack of large, high-quality data available to data science teams, VentureBeat reports that over 87% of data science projects fail. 

The solution is almost staring us in the face: Provide data science teams with data. A lot of it. However, that’s easier said than done, especially considering that the vast majority of data on the Internet is unstructured, making it harder to mine for insights.

One solution is “data engines,” such as Commerce.AI’s product data engine, which has analyzed a trillion data points on products, services, and reviews.

If you’re a company with a limited budget or constrained resources, then it’s critical not to waste time or money on low-quality data.

Empowering data teams with the right data

Data science teams need data to get anything done, but they also need the right kind of data from the right sources in order for any insights to be generated at all.

In the past, this often meant having to build an entire dataset from scratch or spend time and money on datasets that might not meet their needs. But data engines offer flexibility in how you can use large, existing databases.

With large data engines, companies can build their own datasets on a specific subset of the database without having access to any other information.

Leveraging data engines

No company should have an inefficient data science team because that team is supposed to help make decisions for product innovation—which should ultimately help improve your company’s bottom line.

Good data engines are a unified source of truth that empowers data science teams to make smarter decisions faster. This lets team integrate with the current world of data science by providing insightful dashboards for everyone and translating the broader business value of AI into insights tailored to the company’s needs.

Integrating siloed data teams

Data engines solve a common problem: Data science teams are often siloed and lack visibility into each other’s work, making it difficult for them to collaborate effectively and produce insights quickly enough to make a difference. 

As a unified source of truth, data engines give everyone access to an integrated hub for all their data and AI needs, completely removing any barriers to collaboration or information sharing.

The problems with low-quality data

Data is the new oil. As a relatively new, mostly digital construct, big data possesses traits that make it ripe for abuse: It’s abundant, easy to access, and hard to verify. Data is everywhere, from the latest nationwide unemployment numbers reported every month by the Bureau of Labor Statistics and the Presidential approval ratings published by Gallup to big data initiatives at major companies like Amazon and Netflix.

When mishandled or used without sufficient context, data can corrupt initiatives big and small. 

Take Enron, which provided false financial data for years before collapsing in 2001 after widely touted analysis predicted its stock would rise. The company’s executives manipulated accounting numbers that for years were accepted as factual by analysts. 

In 1999, NASA lost a Mars Orbiter probe because it used English measurements, while NASA used metric measurements – an error that was not discovered until after the spacecraft had entered orbit around Mars and was deemed lost.

The problems with low-quality data extend to product strategy teams, which rely on data for decision-making. Low-quality data will inherently result in a low-quality product strategy, which translates to failed launches. Indeed, around 95% of new products fail, as quality data and insights are hard to come by.

By using large, high-quality data engines, data science teams have a unified source of truth, enabling high-quality insights for product teams to create successful launches.

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Source: https://hackernoon.com/the-simple-way-to-empower-data-science-teams-with-data-z81735wa?source=rss