Data Analysis vs. Data Analytics: Top 12 Differences

You’ve probably heard the terms “data analysis” and “data analytics” used in the media or a professional capacity. While the terms appear similar, there are some key differences between them. Data analytics is a field of data science in which experts use advanced tools and methods to analyze and interpret data. The purpose of data analytics is to help organizations make informed decisions. Data analysis is the actual process of evaluating data.¹

Read on to learn 12 key differences between data analysis versus analytics and how they impact business success. 

 The first distinction between data analytics vs data analysis lies in each term’s definition. Data analytics is an umbrella term that refers to a wide array of techniques used to make decisions and predict trends. It includes data analysis and more advanced predictive and prescriptive analytics methods.¹ Some of the techniques used in data analytics include regression analysis, Monte Carlo simulations, time series analysis, and cohort analysis.²

Data analysis involves examining specific data sets to uncover patterns and relationships. Data trends gleaned from data analysis are showcased using descriptive statistics and data visualization methods such as charts and graphs.¹ 

Another difference between data analytics vs analysis lies in their purpose. Data analytics takes a predictive approach to data, aiming to forecast an outcome based on historical data and other relevant information.¹ For instance, a marketing analyst might use data analytics to forecast sales or personalize marketing campaigns.

In data analysis, the objective is to provide a summary of historical data. Rather than using the data to make predictions, the data scientist focuses on generating useful and informative reports of past performance or other existing information subsets.¹

The scope of data analytics vs data analysis is quite different. Data analytics takes a broad approach to data, encompassing its entire lifecycle, from data collection and cleaning to organization.³

Data analysis follows a much more restricted approach. The data is usually already available, and the analyst reviews and interprets it using various data visualization tools.³ 

Data analytics requires complex mathematical and statistical analysis and modeling. Some models used in data analytics include:²

• Regression analysis, which examines relationships between multiple independent variables and a single dependent variable
• Time series analysis, which defines relationships between data points over time
• Monte Carlo simulations, which provide forecasts by analyzing the probability of various outcomes 

In data analysis, there’s no need to scrape data or create complex models. The information is already available, and the analyst uses different methods, such as diagnostic analysis or descriptive analysis, to interpret the data.⁴

• Diagnostic analysis explains why something happened; it drills down into the reasons behind a data trend
• Descriptive analysis aims to summarize and explain data trends in order to illustrate what happened; An analyst might, for instance, use the data to determine average sales for prior periods of time 

Data analytics uses advanced techniques to harness real-time big data in structured and unstructured forms. It may pull data from various sources, including social media, streaming and IoT sensors. As data is retrieved, it may be stored in databases or data warehouses for future use.¹

Data analysis retrieves data from existing resources, including company records, publicly available data sets and transaction logs. It is systematically processed to assess specific metrics related to historical performance.¹

By nature, data analytics is much more complex than data analysis. In data analytics, professionals must collect and clean data before applying statistical models to analyze data and arrive at an insight. Analysts may build predictive models to evaluate data and derive potential outcomes once enough data is available.¹

Data analysis is far less complex. Since the data is already available, collecting or cleaning it is unnecessary. Instead, analysts review and interpret data using specific, pre-defined metrics.¹ For instance, a marketing analyst might use data analysis to calculate average sales for a specified period of time or classify customer segments. 

In data analytics, professionals use business intelligence tools to collect, filter, process and interpret data. Common data analytics software examples include R, Python, Tableau, Apache Spark and SAS.⁵

Data analysis doesn’t require collecting data. Instead, the focus is on refining and transforming data into actionable information. Some software used for data analysis include KNIME, Tableau, OpenRefine and RapidMiner.⁵  

Data analytics output may include actionable insights and recommendations, forecasts, reports and dashboards.³ For instance, an analyst may use data analytics to create a quarterly sales forecast or establish personalized marketing recommendations.

Data analysis provides visualizations of historical data, which may include charts, graphs and descriptive reports.³ Businesses may use data analysis to examine sales trends or present prior operational and financial results. 

Interpreting data analytics requires using advanced statistical models, simulations, and predictive tools. Results may be presented through data visualization tools such as charts, graphs or interactive dashboards, or in predictive algorithms for marketing and online apps.⁶

Data analysts can also present their insights in storytelling form. For instance, a data analyst might review quarterly sales information to provide a deep dive into product sales by type, customer, and region. The data analyst could compare sales data over different periods, providing additional insights to management and other stakeholders.⁷ 

Data analytics is capable of handling extensive amounts of data from multiple sets to arrive at a forecast or conclusion.⁷ It’s useful in numerous industries, including finance, manufacturing, technology, and retail. For instance, a retailer may use data analytics to optimize inventory levels.

Data analysis is also helpful in multiple market sectors. For instance, it may be used in healthcare to track patient outcomes or in accounting to share financial reporting insights. Other use cases for data analysis include evaluating customer survey feedback or assessing product defects.⁶ 

Data analytics and data analysis both support business decisions. In data analytics, predictive insights support future outcomes by enabling businesses to evaluate various potential decision factors. For example, a business may use forecasted outcomes from data analytics to predict product demand or optimize its pricing strategy.⁶

Business users can use the information from data analysis to make decisions based on historical data. As an example, a company might make strategic planning decisions using data analysis reports on historical customer behavior or prior sales trends.⁶ 

The era of artificial intelligence (AI) and machine learning is here, and data analytics stands to benefit from the new technology. With AI, it’s possible to create streamlined, reusable data models that provide real-time data insights. It will be a tremendous time saver for analysts who leverage its capabilities.⁸

Similarly, data analysis can benefit from AI used in augmented analytics. AI may automate many data processing tasks, increasing efficiency and user experience among analysis teams.⁹

All industries need help managing and interpreting data to make reliable and knowledgeable business decisions. The Leavey School of Business at Santa Clara University’s Online Master of Science in Business Analytics program helps you develop the skill set needed for a successful career in the in-demand field of data analytics.

The flexible online program is expertly curated by Silicon Valley leaders from Adobe, Intel, Meta, Atlassian, and more. Expand your network and improve your career potential. Build the foundational business knowledge and principled leadership skills to propel your advancement in Silicon Valley and beyond.

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