Data Analyst

A data analyst garners insights, draws conclusions and solves problems.

To do this, data analysts:

• collect information from various sources
• process this data
• perform statistical analyses on the data to identify trends and patterns
• summarise and package the data, usually in the form of reports.

Essentially, data analysts take data and use it to help companies make more informed, more impactful business decisions.

Additionally, data analysts often work closely with business, engineering and management teams to assess company needs. And, because of their familiarity with company data and data-analysis tools, data analysts also frequently make recommendations to their business counterparts around how the company gathers, processes and analyses data. From these suggestions, the organisation can then continually take steps to improve both the quality and efficiency of its data systems.

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

Data analysts have much in common with data scientists, but the roles are different.

Both data analysts and data scientists collect, process and summarise data to uncover insights and solve problems. And both need programming, mathematical, statistical, data-wrangling and data-visualisation skills to do their jobs. Both write queries and reports and work alongside the engineering and business teams to gather the correct data. And both must organise the data in such a way that they and others can analyse and interpret it.

However, building statistical models and dealing directly with machine learning and advanced programming generally falls under the realm of data scientists. Data analysts, on the other hand, typically work with simpler databases or with other business-intelligence tools and packages.

Also, data analysts usually look for answers to questions or problems based on guidance they receive from their business and management counterparts, whereas data scientists often spearhead business-critical Big Data projects. In other words, data scientists will formulate questions impacting the company, devise the means for addressing those questions and carry out those processes. Data analysts are part of this carrying-out phase.

Data-analysis tools

DataThese days, robust data analysis is critical to successful business. And given the endless stream of data available today, data analysts need the right data-analysis tools and technology to effectively do their jobs. These tools enable analysts to quickly and conveniently extract, merge, study and generally mine this vast data from any number of sources to pinpoint trends, patterns, relationships, connections and inconsistencies.

With the help of these tools, data analysts can gain invaluable insight into their company, their competitors, their market and their customers. In turn, organisations can then make more informed, effective decisions and improve business performance.

Thousands of Big Data analysis tools and innovative technologies are available today, from data-extraction and data-mining tools to databases (data storage), data-visualisation tools and more. But again, it’s also about finding the right tools to suit your business.

Nexis® for data analysts

LexisNexis Data as a Service allows data analysts to easily integrate near-real-time news and public data streams into applications to support business-critical analytics projects. The Nexis® news application programming interface (API) can deliver billions of relevant documents and data points, enabling data analysts 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.

LexisNexis Data as a Service includes:

• Metabase, a single, unified API that allows data analysts to integrate targeted near-real-time news and social media into your analytics.
• 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, LexisNexis Data as a Service enhances your data analysis by complementing your existing data sources with a targeted news data stream. As a result, you can deliver valuable intelligence to increase decision-making accuracy and business gain.