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What is Data Analytics?

It’s almost two decades since British mathematician and entrepreneur Clive Humby declared data the “new oil,” of the 21st century. In the meantime, companies big and small have devoted huge amounts of resources to drilling for this oil (or “mining,” as it goes) and refining and processing it to extract valuable insights that can help them better understand past performance and better prepare for the future. 

Page through the FT, Wall Street Journal, or any leading industry rag, and you’re liable to read about some exciting new way that data analytics is changing the world — but often these articles fail to define what exactly data analytics is. Some readers might already be familiar with the different types and the common skills and techniques required, but others, especially those who might be considering a career in the field, might not be.

If you fall into the latter group and want to learn more, you’ve come to the right place. In this article, we’ll give you everything you need to know about data analytics — illustrated with real-world examples — to decide if it’s the right place for you, plus some ideas for how to get started down a data analytics career path.

What is data analytics, and what are the core types?

Broadly defined, data analytics is a field concerned with collecting, organizing, and analyzing data to gain insights and make informed decisions. Data analytics offers a variety of tools and techniques that can be applied to a wide range of fields and industries, with applications ranging from improving efficiency and productivity to identifying new opportunities for growth and innovation.

There are four basic types of data analytics within the larger field: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.

Descriptive Analytics

Descriptive data analytics involves using data to understand and describe what has happened in the past. One of the most accessible types of data analytics, descriptive analytics requires only rudimentary statistical software like Excel to manipulate and visualize data so that key performance indicators (KPIs) can be assessed and trends can be identified.

In retail, descriptive analytics is used to calculate year-over-year growth in sales, which can help a company better understand its performance and identify any patterns and trends in consumer demand. 

Diagnostic Analytics

Diagnostic data analytics is the process of using data to understand why something happened, which is why this type of analytics is alternatively called root cause analysis. To understand the root causes of trends identified in descriptive analytics, diagnostic analytics will go deeper into internal data sets to gain a more nuanced view of a trend or look to external data sets that might suggest correlations between a business’s performance and environmental factors. In the latter case, data mining might be employed in which machine learning algorithms search big data sets for patterns and associations.

To continue with our example from retail, if descriptive analytics identifies year-over-year growth in sales of certain products, diagnostic analytics might then be employed to better understand whether there are links between the kinds of products that are performing well and broader customer sentiment, perhaps by mining social media data.

Predictive Analytics

Predictive data analytics involves using data to make predictions about what may happen in the future. Predictive analytics makes extensive use of machine learning and other statistical techniques to build models that output forecasts from inputs of historical data. Predictive analytics is especially useful for risk assessment and identifying new areas of opportunity.

After determining which kinds of products perform well given a certain customer sentiment through diagnostic analytics, our example retailer might employ predictive analytics to better understand the chances of similar kinds of customer sentiment recurring in the near-, mid-, and long-term.

Prescriptive Analytics

Prescriptive data analytics involves using data to determine the best course of action for the future. As with predictive analytics, machine learning is crucial for prescriptive analytics. With complex machine learning algorithms, a data analyst or data scientist can turn large amounts of data into concrete recommendations for future action — or even automate actions to occur in response to certain inputs.

In retail, a common place you’ll find prescriptive analytics at work is in online product marketing. While predictive analytics can predict the likelihood of certain kinds of customer sentiment returning in the future, prescriptive analytics can employ data mining to identify current consumer sentiment and automatically send personalized marketing emails to segments of a retailer’s customer base it thinks is best poised to respond given its sentiment analysis.

Now that we’ve covered the four main types of data analytics, we’ll move on to discussing how data analytics compares to two fields it’s frequently confused with: business analytics and data science.

How does data analytics compare to business analytics and data science?

In today’s data-centric climate, a frequent source of confusion is the overlap between data analytics, business analytics, and data science. Here’s how we think they compare:

Data analytics vs. business analytics

A search for "data analytics vs business analytics" will yield numerous articles claiming to provide a clear distinction between the two fields. However, it’s often the case that the work labeled as business analytics or business intelligence is similar to what data analysts do. This is particularly true in smaller companies where employees may have a range of responsibilities.

At larger companies, the work of a business analyst may shift to focus on finding ways to increase revenue, decrease costs, expand the market, or enter a new market, while a data analyst may focus more on the daily operations of the organization. In this situation, a business analyst or business intelligence expert needs to have a strong understanding of business. However, even at large enterprises, these roles may not always be clearly defined.

For more, check out our deep dive into the relationship of data analytics and business analytics or the differences between a data analyst and a business analyst.

Data analytics vs. data science

As with business analytics, there is substantial overlap between data analytics and data science. That said, we believe there is a meaningful difference:

Data analytics involves the use of tools and techniques to analyze data. These professionals may work with existing databases and use existing tools and techniques for analysis.

Data science involves the creation and implementation of new tools, techniques, and methods for collecting and analyzing data. This often involves advanced techniques, such as machine learning, a subdiscipline of artificial intelligence. Data analysts and other data analytics professionals may use methods and tools developed by data scientists. If you are interested in data science, you may want to check out articles on the topic:

If you’re interested in reading more about the difference between data analytics and data science or how a role as a data analyst compares to a data scientist, check out our articles on those topics.

Above, we began looking into the kind of work done by data analysts and other data analytics professionals. In the next section, we’ll dive deeper into the specific data analytics techniques they employ on a daily basis to produce actionable insights for their companies.

What are the core data analytics techniques?

Regression analysis

Regression analysis is a kind of data analysis that uses independent, known variables to predict the outcome of dependent variables. In linear regression, independent variables are used to predict the outcome of a single dependent variable.

Regression is a crucial technique for proving or disproving hypotheses about whether two things are correlated or exist in a causal relationship. Say, for example, a motorcycle insurance company wants to better understand if having more years of experience driving a car might impact the likelihood that a motorcyclist gets into an accident to set competitive rates. Regression analysis could be used to determine if a relationship exists and, if so, the nature of this relationship.

For complex forms of regression analysis, data analysts will often use supervised learning, a form of machine learning that uses labeled, structured data sets — where each piece of data is tagged and classified — to train a machine learning model to give a precise and accurate output when fed an input.

Factor analysis

Factor analysis is a kind of data analysis that reduces complexity in a data set by expressing a large number of variables as a smaller number of “factors” that incorporate commonalities identified across the variables.

Factor analysis is an important tool when searching for an actionable signal within a complicated or noisy data set. This is particularly useful for data sets like those produced by quantitative or qualitative polls. Take, for example, a website that wants to aggregate online product reviews. Here, factor analysis could help to take the reviews and numerical scores available in different places on the internet and reduce them to one customer satisfaction score.

Cohort analysis

Cohort analysis is a kind of behavioral analysis that breaks a larger data set into smaller groups of data (cohorts) before analysis, usually based on commonalities or similarities in experience or situation.

Cohort analysis is particularly important for companies looking to better understand and serve different segments of its user or customer base. A social media company might use cohort analysis to break up the data gathered from its users based on which services they use to optimize improvements to user experience.

Cluster analysis

Cluster analysis is a kind of data analysis that involves identifying similarities and differences in an unlabeled data set and sorting this data set accordingly.

Clustering is often used for tasks like market segmentation, online recommendation, search results, and medical imaging analysis. Online content streamers like Netflix and Youtube use sophisticated cluster analysis to identify viewers with similar viewing habits and recommend new content to them.

Time-series analysis

Time-series analysis involves analyzing data points collected over a period of time at regular intervals. 

As would be expected, time-series analysis is particularly useful for understanding change over time, as well as for predicting future events. In finance, time-series analysis can be used to draw meaningful insight from daily closing values of stocks or other securities.

In addition to these techniques, data analysts and other data analytics professionals are required to have a host of other skills. We’ll dive into these next.

What skills are required to work in data analytics?

To work in data analytics, you must possess a diverse skill set. Though exact requirements will vary depending on the industry, seniority, and responsibilities, key skills generally include:

  • Programming: The ability to use Structured Query Language (SQL) to store, manipulate, and retrieve data from databases is essential for data analytics professionals. Increasingly, data analytics professionals are also proficient in R, a statistics-specific programming language, and/or Python, a general-use programming language.

  • Data analysis techniques: As detailed above, there are a host of statistical analysis techniques that data analysts must master, including regression analysis, factor analysis, cohort analysis, cluster analysis, and time-series analysis.

  • Data analytics process: A data analytics professional must be skilled in data collection, data cleaning, and data mining.

  • Data visualization: Companies often expect data analytics candidates to be able to create visually appealing data visualizations using tools such as Tableau.

  • Soft skills: In addition to technical skills, data analytics professionals should also possess strong teamwork, leadership, critical thinking, and communication skills.

  • (Optional) Machine learning skills: For advanced analysis, some data analysts and other data analytics professionals will employ machine learning methods or utilize machine learning libraries like Pandas or Keras.

We’ve now covered what data analytics is and what you need to be successful in the field — but what kinds of opportunities are out there? In the next section, we’ll look at some real-world data analytics jobs.

What kinds of career opportunities are there in data analytics?

According to Technavio, the global data analytics market will expand at a 13.54% compound annual growth rate between 2021 and 2026 — almost $2 billion in growth difference — in large part due to the proliferation of advanced data technologies like machine learning. This means that the data analytics job market will continue to boom: the US Bureau of Labor Statistics foresees an additional 24,000 “Operations Research Analyst” jobs up for grabs over the next decade, 23% growth over a baseline of 5% growth for the US job market as a whole during this period.

Most jobs in data analytics are titled “data analyst,” but some with data analytics skills will also work as data engineers and data architects. Below, we’ll give you an idea of what these jobs look like and how much you can earn.

Data analyst

As we said above, a data analyst typically gathers data and readies it for analysis, and then analyzes it to yield business insights that the analyst will then communicate to relevant stakeholders, often through data visualizations.

According to, in the US the average data analyst salary is $81,719, far higher than the $58,260 that the average American earned in 2021.

Data Analyst — Dow Jones

New York, NY


  • Source and query data required for both ongoing business intelligence efforts and larger-scale data science projects.

  • Draw meaningful insights from large datasets—find creative ways those insights can be used to both sell subscriptions and improve the experience of reading Dow Jones products.

  • Support daily reporting on core business functions.

  • Use visualization tools to present complex data in a simple, engaging manner.

  • Assist in the management of long-term data projects. 

  • Effectively communicate with business stakeholders, identifying requirements and developing success metrics for developing and deploying machine learning applications.

  • Identify opportunities for new data projects.


  • Bachelor's degree in information systems, marketing, operations, or general management from a technically intensive business program. Graduates with relevant coursework or professional experience in business intelligence will also be considered.

  • 1 to 2 years of experience working in a data-related role is preferred.

  • Experience in standard analytics tools (SQL, Tableau, Adobe/Google Analytics)

  • Knowledge of coding languages (i.e., R, Python)

  • Interest in more advanced topics in analytics (machine learning, artificial intelligence)

  • Entrepreneurial attitude toward work and sweat the details.

  • Passion for news organizations and an understanding of the business models behind them.

Data engineer

A data analyst's main focus is on analyzing data, with tasks such as collecting, preparing, and storing data being secondary. On the other hand, a data engineer's primary focus is on efficiently organizing and transforming raw data to make it ready for analysis. In this way, a data engineer specializes in a specific aspect of data analytics.

For their specialization, data engineers can demand higher compensation. According to, in the US the average data engineer salary is $112,555.

Data Engineer — EquiLend

New York, NY


  • Build Spark pipelines required for extraction, transformation and loading the data from wide variety of sources using PYSpark and SQL.

  • Schedule and merge dependent Airflow jobs.

  • Collaborate with analytics and business teams to improve data models that feed business intelligence tools, increasing data accessibility.

  • Implement processes and systems to monitor data quality, ensuring production data is accurate and available.

  • Work closely with all business units and engineering teams to develop strategy for long term data platform architecture.

  • Design and develop machine learning and deep learning systems.


  • Bachelor’s degree in computer science, IT, engineering, or a related field.

  • Working knowledge of PYSpark and Airflow

  • Working knowledge in Python and other object-oriented languages

  • Working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases (Postgres, Oracle, Vertica, Deltalake)

  • Working knowledge of AWS services

  • Experience working with high-performance, distributed, in-memory database systems such as Ignite, Memsql etc.

  • Familiarity with machine learning libraries/frameworks (Keras, PyTorch, scikit-learn), plotting libraries (matplotlib, seaborn, Plotly), and Jupyter notebooks

  • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.

  • Experience with or knowledge of Agile methodologies such as SCRUM

Data architect

A data architect is responsible for designing efficient methods, protocols, and systems for collecting, preparing, and storing data that meet the specific needs, capacity, and resources of a business. As with a data engineer, this is essentially a specialization in an aspect of data analytics.

According to, in the US the average data architect salary is $124,771.

Data Architect — Scholastic

New York, NY


  • Analyze functional and non-functional needs related to data ingest, processing, storage, and analysis.

  • Conduct proof-of-concept efforts to evaluate designs, vendor and partner technology, and product prototypes.

  • Design appropriate storage structured for structured and unstructured data. 

  • Ensure that data solutions align with availability and performance goals.

  • Develop and implement data management strategies in support of non-production environments, with respect to data volume and complexity for multiple purposes (performance, functional testing) and maintain privacy and data security.

  • Contribute to partner and vendor selection and management.

  • Facilitate technical dispute resolution that cuts across multiple teams related to capture and landing data, data pipeline/processing strategies, and structured data design.

  • Document, communicate and solicit feedback on architectural decisions and recommendations.


  • BA or BS in a technical discipline or equivalent formal professional development

  • Career track progression in software development with an increasing emphasis on data processing, including pipeline development, database and data storage design, object storage, analytical schema design, troubleshooting and performance optimization

  • Expert-level experience designing and optimizing relational and non-relational databases and data stores

  • Strong data lake experience including ingest, processing of structured and unstructured data and related data cataloging and governance, storage, access, and cost optimization

  • Knowledge of data engineering and various data pipeline technologies for ETL/TEL such as map-reduce, job and dependency management, data lineage, and data lifecycle

  • Significant fraction of career experience specifically on data products or infrastructure serving millions of users

  • Experience in Agile environments and applying architectural methods that support Agile product development

How can you get started on a data analytics career path?

In this article, we’ve covered the different types of data analytics, core data analytics techniques and skills, and different career opportunities out there. If we’ve piqued your interest and you’re looking to learn more, we have some great options for you. 

If you want to know about how a career in data analytics can progress, check out our career path deep dive.

For educational opportunities to help you get started down a data analytics career path, check out our guides for short courses, bootcamps, and master’s programs.

If you’re looking for a break-down of how to enter the field, check out our step-by-step guide to becoming a data analyst.