Who is a Data Scientist?

A data scientist sifts through, examines and interprets large amounts of both structured and unstructured data to identify patterns. Based on these patterns, data scientists can then make predictions about the future, as well as develop new methods for analysis and machine-learning models.

With the onslaught of Big Data in recent years, data scientists are currently in high demand. Companies need professionals who can not only make sense of all the data they collect, but also weave together the story of that data in a compelling, useful way.

The role of a data scientist requires a diverse range of skills, including:

  • programming
  • mathematics and statistics
  • an understanding of machine learning and algorithms
  • data handling and data visualisation skills
  • an aptitude for bringing data to life through their analysis

The job of data scientists essentially involves creating order from chaos - combing through large amounts of data, organising it in a meaningful way, analysing trends, building predictive models, showing cause and effect, and gleaning important insights from which crucial business decisions can be made.

Beyond dealing with the data, technology and analyses, data scientists must also possess a degree of business acumen. They have to be able to demonstrate how the available data can ultimately generate better outcomes for their business - and they’re often responsible for these improved results.

What’s the difference between a data scientist and a data analyst?

Although data analysts do share a lot in common with data scientists, the roles are different. Like data scientists, data analysts collect, process and summarise data to uncover insights and solve problems. And like data scientists, they need strong programming, mathematical, statistical, data-wrangling and data-visualisation skills to do their jobs.

However, data scientists typically spearhead business-critical Big Data projects - devising the means for addressing the questions that arise and carrying them out. On the other hand, data analysts generally look for answers to questions or problems based on guidance they receive from their business counterparts. What’s more, data analysts generally don’t build statistical models, nor are they involved in advanced programming and machine learning, working instead with simpler databases and other business-intelligence tools and services.

Finally, data scientists also draw on their storytelling skills to convert data into meaningful (and visual) insights, creating a business plan with these learnings, whereas data analysts normally don’t have to perform these tasks.

Why is data analysis important?

In this day and age, robust data analysis is critical to business success, as it can provide invaluable industry, competitor and customer information.

With the help of a data scientist and the right data, companies can:

  • analyse the industry and competitive landscape to support strategic planning
  • gain business intelligence to strengthen brands, increase productivity and drive greater revenues
  • accurately assess trends and identify risks
  • respond proactively and quickly to market opportunities or disruption
  • enhance predictive modelling, machine learning and other Big Data initiatives

Of course, for data scientists to do their jobs well, they need two essentials:

  • relevant data
  • the right tools and technology to process the vast amount of data available

Companies need to make sure they have the appropriate data streams for generating reliable business modelling and predictive analysis. And fortunately, innovative technologies are available that enable data scientists to analyse and evaluate the never-ending stream of data in today’s complex world. This empowers companies to make better informed, more effective business decisions.

Nexis® Data as a Service for data scientists


LexisNexis Data as a Service allows data scientists to easily integrate near-real-time news and public data streams into applications to support business-critical analytics projects. The application programming interface (API) can deliver billions of relevant documents and data points, enabling data scientists to:
  • Integrate into your platforms and applications unstructured data from the most comprehensive, global content collection in the industry - including both open web and licensed content, with news archives going back more than 40 years.
  • Leverage our metadata and powerful content enrichment based on a combination of human curation, smart indexing, tagging and text normalisation to refine data feed results for greater relevance.

Nexis® Data as a Service solutions include:

  • Metabase, which makes it easy to extract business intelligence from the most comprehensive, global content collection of print, online news and social media sources in the industry. New sources are added every week, including the ability to integrate custom sources.
  • WSK (Web Services Kit), which performs dynamic search retrieval across LexisNexis content to integrate highly relevant data - filtered by topic - into Big Data projects and business-critical platforms.

Ultimately, Nexis® Data as a Service solutions enhance your data analysis by complementing your existing data sources with a stream of open web and licensed content. As a result, you can deliver valuable intelligence to increase decision-making accuracy and business gains.

Expand your data science capacity with normalised big data

Whether you’re looking to identify movements with trend analysis, conduct quant modeling to inform investment strategies or integrate big data into machine learning, we have the data you need. We convert disparate types of data into clean, semi-structured datasets, making it easier for machine reading and processing. This process allows for standard and flexible integration of a semi-structured XML data feed into any database or application.

What types of data can LexisNexis offer?

  • Online news, blogs, and social commentary: Access both licensed and open source data and news from a single source. Also includes magazines, trade journals, newswires and press releases
  • Licensed print, broadcast and web news: Leading daily newspapers plus transcripts of radio and television broadcast reporting. Access 80,000+ news sources from 100+ countries in 75 languages
  • Legal: Vital regulatory and legal information, including data on civil and criminal cases covering multiple jurisdictions, and cases from the International Court of Justice
  • PEPs, sanctions and watchlists: 1.5 million PEPs, international sanctions, and 1,400+ watch lists and blacklists originating from enforcement agencies in 240 countries and territories
  • Company information: 280 million global public and private companies data includes key financial stability indicators and corporate hierarchies, plus legal entity data for DBA names

What value does LexisNexis add to its data?


Our industry leading data fabrication, classification, and enrichment process:

  • Creates clean, semi-structured XML data for integration into a database or application
  • Applies algorithmic semantic analysis to enable data discovery—even when content is in different languages
  • Adds metadata tags, topical classification, entity extraction, and other enrichments for more targeted data calls.

Why choose LexisNexis Data as a Service?


LexisNexis has decades of experience as a trusted content aggregator, enabling us to develop a world-leading source universe. Nexis DaaS enables you to:

  • Analyse the competitive landscape with data gathered from licensed and web news sources,
    company and industry information, and patents data to support strategic planning
  • Identify local to global movements with trend analysis tools quickly so your company can respond proactively to market opportunities or disruption
  • Conduct quant modeling to inform investment strategies by mutual fund, hedge fund and
    institutional investors
  • Integrate big data into machine learning to increase the efficiency and effectiveness of critical business processes like risk management.

How does LexisNexis deliver the data?

LexisNexis offers flexible data delivery via easy to integrate API feeds, or on-premises applications, with a redundant data infrastructure that boasts 99.9% reliability and availability.

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Bulk APIs

Access high-volume feeds of news, company, or patents
data for historical analysis and predictive analytics.

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Search & Retrieve APIs

Retrieve data on demand with enhanced functions - search, retrieve and saved
searches - that work within your own user interface.

Nexis Solutions offers an extensive, well-established ecosystem of trusted third-party technology providers to help our customers integrate data quickly and efficiently to power digital transformation initiatives. These technology collaborations range from System Integrators to Software Integrators of content to deliver intelligent automation.

Frequently Asked Questions

Answers to some popular questions

What is data science?

Data science is the systematic study of data. It draws on a combination of scientific methods, processes, statistics, algorithms and technology to extract, evaluate, visualise, manage and store both structured and unstructured data. Read more

Who is a Data Scientist?

A data scientist sifts through, examines and interprets large amounts of both structured and unstructured data to identify patterns. Read more

What’s the difference between a data scientist and a data analyst?

Although data analysts do share a lot in common with data scientists, the roles are different. Like data scientists, data analysts collect, process and summarise data to uncover insights and solve problems. Read more

Why is data analysis critical to business success?

In this day and age, robust data analysis is critical to business success, as it can provide invaluable industry, competitor and customer information.

With the help of a data scientist and the right data, companies can:

  • analyse the industry and competitive landscape to support strategic planning
  • gain business intelligence to strengthen brands, increase productivity and drive greater revenues
  • accurately assess trends and identify risks
  • respond proactively and quickly to market opportunities or disruption
  • enhance predictive modelling, machine learning and other Big Data initiatives

Read more

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