Friday, July 12, 2024

Data Analytics vs. Data Science

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Data analytics and data science are closely related technologies, yet significant differences exist between them.

  • Data analytics mines big data sets to uncover specific insights and trends, usually with the goal of competitive business advantage.
  • Data science, in contrast, focuses on the larger picture of data, and involves creating new models and systems to build an overall portrait of a given data universe.

In essence, data science takes a “larger view” than data analytics. But both data methodologies involve interacting with big data repositories to gain important insights.

For more information, also see: What is Big Data Analysis

Key Differences Between Data Analytics and Data Science

Data Science Data Analytics
Scope Macro Micro
  • ML software development
  • Predictive analytics
  • Engineering and programming
  • BI tools
  • Statistical analysis
  • Data mining
  • Data modeling
Goal To extract knowledge insights from data To gain insights and make decisions based on data
Popular Tools Python, ML, Tableau, SQL SQL, Excel, Tableau

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Data Analytic vs. Data Science: Micro and Macro 

As noted, while data analytics and data science and are closely related, they both perform separate tasks. Some more detail:

Data Analytics

Data analytics analyzes defined data sets to give actionable insights for a company’s business decisions. The process extracts, organizes, and analyzes data to transform raw data into actionable information. Once the data is analyzed, professionals can find suggestions and recommendations for a company’s next steps.

Data analytics is a form of business intelligence that helps companies remain competitive in today’s data-driven market sectors.

For more on data analytics: Best Data Analysis Methods

Data Science

Data science is the process of assembling data stores, conceptualizing data frameworks, and building all-encompassing models to drive the deep analysis of data.

Data science uses technologies that include statistics, machine learning, and artificial intelligence to build models from huge data sets. It helps businesses answer deeper questions about trends and data flow, often allowing a company to make business forecasts with the results.

Given the complexity of data science, it’s no surprise that the technology and tools that drive this process are constantly – and rapidly – evolving, as they are with data analytics.

For more on data science: Data Science Market Trends

Data Analytics vs. Data Science: Benefits

Both data analytics and data science are essential disciplines for companies seeking to find maximum benefit from their data repositories. Among the benefits:

Data Analytics

  • Improve decision-making: Data analytics can help guide business decisions by offering specific suggestions about what might happen if there are changes within the business. Data analytics also offers advice on how a business might react to changes.
  • Streamline operations: Data analytics has the potential to gather and analyze a company’s data to find where current production is slowing and improve efficiency by helping a company predict future delays.
  • Mitigate risks: Data analytics can help companies see and understand their risks. Data analytics can help take preventative measures as well.

Data Science

  • Discover unknown patterns: Data science can find overall patterns within a company’s collection of data that can potentially benefit them. Analyzing these larger, systemic models can help a business understand their workflow better, which can support major business changes.
  • Company innovation: With data science, a company can find foundational problems that they previously did not fully realize. This deep insight benefits may benefit the company at several different levels of operation.
  • Real-time optimization: The larger vision offered by data science enables businesses to react to change quickly –  an overall systemic view offers great guidance.

For more information: Data Science & Analytics Predictions, Trends, & Forecasts

Data Analytics vs. Data Science: Disadvantages

While both data analytics and data science have great benefits for any business, they have disadvantages as well:

Data Analytics

  • Lack of communication within teams: Team members and executives may not have the expertise to provide much granular insight into their data, despite their control over it. Without a data analyst, a company could miss information from different teams.
  • Low quality of data: Decisions for a company can be negatively affected if low-quality data or data that has not been fully prepped is involved in the process.
  • Privacy concerns: Similar to data science, there are problems with privacy while using data analytics. If a company or professional does not govern sensitive information in a compliant manner, the data can be compromised.

Data Science

  • Domain knowledge required: Using data science requires a company or staffer to have significant knowledge about data science as it grows and changes, which means that companies must allot budget for hiring and training qualified professionals.
  • Unexpected results: Occasionally, data science processes cannot incorporate or mine data that is considered “arbitrary” data, meaning data this is not recognized by the system for any reason. Because a data scientist may not know which data is recognized, data problems could go under the radar.
  • Data privacy: As with data analytics, if data is treated without careful standards, the large datasets are more susceptible to cybersecurity privacy problems.

Data Analytics vs. Data Science: Tools

Companies need to select the optimum tools to use data analytics and data science most  effectively. See below for examples of some leading tools:

Data Analytics

Here are the top six data analytics tools and what they can do for a business:

  • Tableau: Collects and combines multiple data inputs and offers a dashboard display with visual data mining.
  • Microsoft Power BI: AI and ML functionality, powering the augmented analytics, and image analytics.
  • Qlik: AI and ML, easy deep data skills, and data mining.
  • ThoughtSpot: Search-based query interface, augmented analytics, and comparative analysis to anomaly detection.
  • Sisense: Cloud-native infrastructure, great scalability, container technology, caching engine, and augmented data prep features.
  • TIBCO: Streaming analytics, data mining, augmented analytics, and natural language user interface.

Data Science

Here are the top six data science tools and what they can do for a business:

Which Data Tool is Best For Your Business?

When researching which data analytics and data sciences tools to buy, it is important to understand that data analytics and data science work in combination with one another – meaning that more than one software tool may be needed to create the optimum data strategy.

Given that data science and data analytics are unique fields that have major differences, the tools that best serve these different technologies will be different – yet they ideally will interoperate with one another. This is a crucial point: each business should select the best tool for both disciplines, but as they research, they must seek for a commonality between the two advanced data tools.

In some cases this means buying both data solutions from one vendor, but this isn’t necessary. It also works to buy “best of breed” from two different – competing – vendors. Just make sure to do an extensive trial run with both applications working in concert, to ensure that the combination creates the ideal result.

Bottom Line: Data Analytics vs. Data Science

Data science and data analytics are separate disciplines but are both are crucially important to businesses.

For businesses looking to increase their understanding of data and how it can help their organizations, data analytics and data science play a contrasting and complimentary role. They are different – but they are both essential.

Therefore, business must understand the differing roles of data analytics and data science, and be prepared to select tools for each discipline that work well in combination.

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