Data Analytics 

What is data analytics?  

Data analytics, or data analysis, is a science that comprises the tools, technologies, techniques, and processes by which an organization uses data to improve productivity and enhance business gain. Data scientists and researchers also rely on data analytics to prove or disprove scientific models, theories, and hypotheses.  

Data analytics encompasses everything from fundamental business intelligence, reporting, and online analytical processing (OLAP) to more advanced analytics. These advanced forms of analytics can include: 

• Data mining, which involves sorting through and examining large data sets to identify anomalies, correlations, and patterns to calculate outcomes 
• Predictive analytics, which enables organizations to draw on current and historical facts and figures to make predictions about unknown future events, such as customer behavior 
• Machine learning, an area of artificial intelligence that studies the algorithms and mathematical models that computer systems use to incrementally improve their performance of a particular task; it’s based on the notion that systems can learn from data and information (including observations and real-world interactions), find patterns and make decisions autonomously, with little help from humans 

By extracting and cataloguing data, organizations can pinpoint and evaluate relationships, patterns, and trends so they can glean insights and draw conclusions based on the data and use these to make informed decisions. Such data sets can include information on: 

• Customers 
• Competitor practices 
• Business processes 
• The industry 
• Market conditions 

Why is data analytics important? 

Today, we’re living in a data-driven world, and it’s only going to become more so tomorrow and into the future. To make the right decisions for your business to succeed, you need the right data. So, it’s important to have a data analytics strategy in place. Such plans can help organizations: 

• Boost revenue 
• Cut costs 
• Improve efficiencies 
• Enhance marketing efforts 
• Strengthen customer focus and customer service
• Respond quickly and effectively to market events and industry trends
• Reduce risk
• Gain a competitive edge 

Types of data analytics 

Data analytics can be categorized into various types: 

• Descriptive analytics: Describes what has happened based on historical data, offering insights into past events and trends. 
• Diagnostic analytics: Examines data to understand why certain events occurred, providing insights into the root causes of phenomena. 
• Predictive analytics: Utilizes data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes. 
• Prescriptive analytics: Recommends actions to optimize a given outcome based on predictive analytics. 

Benefits of data analytics 

Data analytics offers numerous benefits, including: 

• Improved decision making: Informed decision-making based on data-driven insights. 
• Competitive advantage: Gaining a competitive edge by understanding market trends and customer behavior. 
• Efficiency: Optimizing operations and resource utilization for increased efficiency. 
• Innovation: Driving innovation through a deeper understanding of data patterns. 

Obstacles in data analytics 

Challenges associated with data analytics include: 

• Data quality: Ensuring the accuracy and reliability of the data being analyzed. 
• Interpretability: Making complex analytics results understandable to non-experts. 
• Security: Safeguarding data against breaches and unauthorized access. 

How LexisNexis supports data analytics 

LexisNexis provides robust research solutions that can support data analytics activities. With Nexis^® Data+, you gain access to a vast array of reliable and up-to-date information from diverse sources. This extensive database facilitates comprehensive research, empowering users to extract valuable insights for data mining processes. 

Learn more about Nexis^® Data+