5 Ways to Improve Your Next Data Science or Analytics Project Using Data as a Service

06 Aug 2021 10:30 am by Sam Hemmant


If there was one stat that data scientists wish they could overlook, it would probably be this one: Data scientists spend up to 80% of their time finding, cleaning, and organising data. And that means there’s very little time (not to mention energy) left over to focus on the actual data analysis and interpretation that’s crucial to data science.

Luckily, Nexis Data as a Service (DaaS) takes care of the data wrangling tasks for you. Data scientists can use Nexis DaaS to gain immediate access to clean, enriched datasets that are ready for consumption straight out of the box. So, no more searching the web for raw data that has to then be cleaned and prepared for your analytics and AI applications. Translation: You can hit the ground running.

The benefits of having ready-to-use datasets are plentiful, but let’s take a look in the data science microscope at five of the ways you can use Nexis DaaS to improve your next data science or analytics project.

Improvement #1: Reduce the time and expenses of data wrangling—while improving data discovery

We’re going to tackle the most obvious one first. Nexis DaaS continuously normalizes and enriches data, which is what makes rapidly accessing clean, semi-structured data possible. You not only work more efficiently, but you gain insights much faster than if you otherwise had to invest your energy and resources into data wrangling.

Improvement #2: Get more relevant results from enriched data

What do we mean when we say “enriching” data? It means that millions of documents are analysed and tagged for you, so you don’t have to scour the web or scrape data to find what you need. Nexis DaaS does all the heavy lifting, so you can go straight to the data you need to make more confident decisions and quickly prove or disprove your hypotheses.

Improvement #3: Quickly integrate data into your databases and applications

Get your projects up and running faster—and make sure your databases and applications are using nothing short of clean, semi-structured XML data. Doing so is possible because Nexis DaaS does more than use an industry-leading data fabrication, classification, and enrichment process; it provides data scientists with flexible data APIs and on-premises data environment options, enabling them to seamlessly integrate data wherever needed.

Improvement #4: Easily identify and refine results from searches against large data volumes

Whether you want to use social commentary to enrich your natural language processing or integrate historical data to conduct quant modeling, the more data you have the better. The catch is, however, that more data usually means more time spent making sure the data is clean and consumable. You never have to worry about that with Nexis DaaS. You can take advantage of an unmatched volume of historical and current data that’s already been cleaned using metadata and enrichments. You can search for data and refine your results to quickly find the exact semi-structured data you need—and then integrate that data directly into your data science and analysis tools.

Improvement #5: Take advantage of a powerful taxonomy

Nexis DaaS is backed by a taxonomy with deep family entries that simply aren’t achievable with old-fashioned web scraping. We also grow this taxonomy continuously, so historical references and current iterations are also included. So, while you save time, your data science projects benefit from a rich, in-depth data taxonomy.

When we take a closer look at Nexis DaaS, its benefits come into focus pretty clearly. Improve your data science initiatives, analytics models, and AI projects with the relevant and reliable datasets found in Nexis DaaS. You can learn more about how to improve your data science and analytics projects here.