Data Analysis Methods

What are the methods for performing data analysis?

There are a number of techniques for conducting data analysis, and the methods you select depend on the nature and needs of your business. These methods run the gamut from the basic to the complex and include:

• fundamental business intelligence (BI)
• reporting
• online analytical processing (OLAP)
• data mining
• predictive analytics
• machine learning

BI

BI is an organisation’s technologies and strategies for extracting and analysing news, data and business information. As a whole, BI uses software and other services to transform raw data into useful, actionable insights that companies can use to drive strategic decision-making. BI can provide:

• reporting functionality
• tools for identifying clusters
• support for data mining techniques
• business performance management
• predictive analysis.

Reporting

Reporting organises and summarises raw data into useful information so organisations can see how areas within the business are performing. It can alert a company to changes, highlight patterns and trends, and raise questions about the company’s direction. Robust reporting can strongly influence organisations, prompting them to take action with the goal of business gain.

OLAP

With this powerful computing process, users can quickly and easily extract multidimensional data so they can analyse it from different perspectives. OLAP enables complex calculations, trend analysis and advanced data modelling. It’s useful for budgeting, financial reporting, analysing trends, sales forecasting and more. Again, it provides organisations with more of the knowledge and insight they need for smart decision-making.

Data mining

Data mining involves sorting through, examining and categorising vast amounts of data to identify anomalies, correlations and patterns. It extracts and structures information into an understandable and useful form, and calculates future trends and outcomes. Data mining should move beyond internal data and look at additional data sets, such as filtered news datasets and company, regulatory and legal datasets information. Such robust data mining helps organisations solve problems and further drives decision-making.

Predictive analytics

Predictive analytics is an advanced form of analytics that allows organisations to look at current and historical facts and figures to make predictions about unknown future events. It draws on many techniques from data mining, statistics, machine learning, predictive modelling and artificial intelligence to make these predictions, identify and assess risks, pinpoint opportunities and ultimately guide decision-making.

Machine learning

Machine learning studies the algorithms and mathematical models that computer systems use to progressively improve their performance of a specific task. It’s based on the idea that systems can learn from data and information, find patterns and autonomously make decisions with little human intervention.

LexisNexis

Among the organisations that want to extract actionable insights and benefit from our data-analysis methods are:

• financial services businesses
• corporate data scientists looking to take advantage of different data types so they can analyse product and market trends and fuel machine-learning applications
• risk and supply management departments that want to track sanctions data and analyse news to support due diligence and risk monitoring
• PR, marketing and sales teams, who identify signals in news and social commentary related to their competitors, brand portfolios and customer insights
• academic researchers, who integrate archival and current data to support R&D, trend analysis and innovation.

Nexis Data as a Service

Nexis® Data as a Service (DaaS) from LexisNexis® can help with your data-analysis needs. Implementing the right data-analysis methods for your organisation into your existing workflows can translate to increased revenue, reduced costs, better efficiencies, more effective marketing efforts, stronger customer-centricity, reduced risk and a competitive advantage.

What’s more, the Nexis DaaS offering provides access to highly relevant content that suits a variety of data-analysis methods and uses. You’ll have news, company and industry data; legal information; magazines and trade publications; intellectual property (IP) and patents data; and newswires and press releases all at your fingertips.

The Nexis DaaS CORE advantage

Get to the core of what your data means – and how it can benefit your organisation – with the Nexis DaaS CORE advantage:

• Comprehensive: For more than four decades, LexisNexis has built strong relationships with global news and data providers to support data aggregation spanning a wide range of sources and topics. Nexis DaaS application programming interfaces (APIs) empower organisations with a curated content collection of normalised Big Data. These voluminous datasets link businesses to the information they need for qualitative and quantitative data analysis and, ultimately, insights to drive business gain.
• Optimal: Nexis DaaS delivers relevant, high-quality, normalised Big Data, enriched with metadata using an easy-to-integrate API.
• Robust: Big Data’s exponential growth means it’s even more challenging for organisations to uncover the information essential for driving business insights. Nexis DaaS provides access to smarter data through its API, which leverages predictive analytics, builds quantitative financial models and fuels machine-learning applications.
• Experienced: At the heart of LexisNexis is our vast experience as a content aggregator. Our global team of content experts is dedicated to building an unmatched collection of text-based data that encompasses current and archival news data, company information, legal and regulatory data, and more.

Find out more about how Nexis DaaS can assist you gain relevant, reliable business insights that you can translate into intelligent, well-informed, strategically driven decisions.