What Is Data Analytics?

Leah Davidson
12-15 minutes

There has never been a more exciting time to work with data. The data analytics industry is predicted to reach almost $78 billion by 2023, and by 2020, there will be an estimated 2.7 million jobs open for data analysts and data scientists.

But what exactly is data analytics? And how does it differ from the other buzzwords out there?
Data Analytics Defined

One data analytics definition is: “qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements.”

Although some data analysts may look at large and complex data, also known as “big data,” a lot of work also involves smaller data, such as internal data sets and company records.

Data analytics is often confused with data science. Although similar in nature, data analytics is more concerned with solving problems through defined data sets, whereas data science requires the development of new models and algorithms through coding and programming.

Data analytics relates to business intelligence (BI). However, BI looks at the interpretation of past data, whereas analytics can predict the future as well.

When analytics extends beyond BI and covers areas like data/text mining, machine learning, forecasting, semantic analysis, sentiment analysis, neural networks, and pattern matching, it can be referred to as “advanced analytics.” Advanced analytics is autonomous or semi-autonomous and requires less human interaction for interpretation.
History of Data Analysis

Data has always been a part of our daily lives; with technological advances, we have just become more adept at collecting and making sense of it.

For a long time, governments have used censuses to gather data for urban planning and measurement of population growth. This data analysis used to take years to analyze, but it was expedited with the creation of tabulating machines that could read data via punch cards.

Relational databases were invented in the 1970s to retrieve information from databases through structured query language (SQL). Non-relational databases and NoSQL emerged in the 1990s as the internet took off and search engines like Google consolidated data into easy-to-read results from search queries.

Around this time, database and data warehousing gave rise to data mining, which involved procuring information from large, unstructured data sources. In 1997, NASA researchers coined the term “big data” to represent the massive amount of data generated by supercomputers.

In 2005, Google Analytics simplified the extraction of actionable insights from online data, such as time on site, new versus returning customers, audience demographics, and web page views and visits. In 2006, Hadoop was born, which was one of the first tools to process big data at scale.

With the release of Amazon Redshift and Google BigQuery within the last 10 years, data analytics has shifted to the cloud, and all industries, from healthcare to CPG to financial services, have started prioritizing data analytics in their business strategy to stay competitive.
What Is a Data Analyst?

As more industries and companies develop in-house data functions, data analytics is becoming an increasingly sought-after career.

A data analyst may be responsible for:

Data mining and setting up the infrastructure and systems to process, manage, and analyze data. This may include the ETL (extract, transform, and load) data function.
Data warehousing to build databases (storage systems) for easy data retrieval and access.
Running queries to answer specific questions and extract useful insights.
Cleansing data and making the necessary adjustments to ensure quality.
Creating reports to present information to business stakeholders.

Data analysts usually bring strong quantitative and analytical skills, an aptitude for communication and critical thinking, extensive experience with Microsoft Excel, and knowledge of certain statistical languages.

Data analyst salaries start around $54,000, but can pass the $100,000 mark for those with more years of experience and specialization.

(Learn more about the career path to becoming a data analyst and how it differs from that of a business analyst and a data scientist.)
Steps for Data Analysis

Each data analytics assignment is different, but they often follow a multistep process for sourcing, validating, correcting, and presenting data to answer business questions. The procedure may look similar to this:

Step 1: Understand what types of data you need to answer specific questions, and decide on the objectives of the study. When diving into ambiguous and unstructured data, you should come in with hypotheses to validate.
Step 2: Collect data, depending on your requirements. This data can include quantitative and qualitative analysis. Data analysts may collect this data internally through customer relationship management (CRM) and enterprise resource planning (ERP) software and external sources like census bureaus, APIs, Google public data, and third-party websites. Primary qualitative data is less commonly used by data analysts than quantitative data, but can encompass interviews and in-person observations. When collecting data, you will want to ensure consistency in your methodology (e.g., asking all interviewees the same questions).
Step 3: Clean data. With an initial data set, you may find missing, incomplete, or repetitive data, which can bias the results. You will want to check for outliers and ensure metrics, like the mean, median, mode, and range, make sense given the context. Sometimes you also need to convert data into a format that is readable by data analytics tools.
Step 4: Analyze the data through tools selected by your organization and check for the statistical significance of results.
Step 5: Compile the results. Once the data is collected and analyzed, data analysts are often charged with presenting it in an easy-to-understand format. Many companies develop internal dashboards that track key KPIs through graphs and charts.

Types of Data Analysis

There are a few different types of data analysis:

Descriptive analytics: This analysis seeks to explain what happened with variables, such as revenue, sales, cost, demographic data, etc. With descriptive analytics, the organization is looking for patterns and trends. For example, what happened with women’s clothing sales in the month of July?
Diagnostic analytics: Diagnostic analytics explains the “why” and “how” between a particular data set. For example, why did sales go up in some retail outlets and not others?
Predictive analytics: As the name suggests, predictive analytics is about predicting the future and what actions to take based on how variables are likely to behave. For example, will a promotion work at a store in X city based on the city’s characteristics and performance in similar geographic regions? Predictive analytics is the basis of machine learning and artificial intelligence.
Prescriptive analytics: Prescriptive analytics determines which action to take to improve a situation or solve a problem. For example, since the promotion did not work as well as anticipated with families, what can we do to drive growth in the coming weeks?

Techniques for Data Analytics

Depending on objectives and use cases, data analysts can choose from a variety of data analysis methods to validate hypotheses and check for patterns.

Regression analysis: Regression analysis is a statistical method that models relationships and correlations between different variables.
Factor analysis: The goal of factor analysis is to find a defensible structure underlying the data.
Cohort analysis: Cohort analysis requires the grouping of data by specific characteristics.
Cluster analysis: Cluster analysis ensures that the objects in one group are more similar to each other than objects in other groups.
Time series analysis: When data can be organized over time (for example, stock market prices), time series analysis predicts future behavior.
Text analytics: When analyzing emails, open-ended survey responses, or websites, text mining involves searching for patterns and summary information.

Tools for Data Analysis

At the core of data analytics lies rigorous mathematics and statistics. Today, in addition to statistical languages, there are many cloud-based software tools that adapt to different business needs.

Excel: For more basic queries, companies can start with Microsoft Excel, which supports formulas for pulling insights from data and coding languages like VBA.
SQL: Structured query language allows data analysts to extract and manipulate data from databases.
Tableau: Tableau provides an easy-to-use workflow for sourcing and preparing data for analysis. It also offers many data visualization templates (charts, graphs, maps, dashboards, etc.).
Rapidminer: Rapidminer integrates machine learning, text mining, predictive analytics, and other data manipulation functionalities.
R or Python: These languages are useful for statistical programming and are much more efficient and boast faster processing times than Excel.
SAS: SAS is a comprehensive software package for advanced analytics and statistics.
Google AdWords: For marketing-related data, Google Ads provides insights into how effectively advertising is reaching certain audiences and whether it results in desired behaviors (clicks, purchases, etc.). Google Analytics can also show how well your website or social media is performing in terms of traffic, visibility, and usage.

How Can Data Analytics Be Used?

Data analytics serves multiple purposes depending on the industry, but these are some of the most common problems businesses address through data analytics.

Sales forecasting: Based on sales growth, historical performance, and future market trends, companies can predict future sales numbers with greater accuracy.
Price optimization: Data analysis helps companies determine the price elasticity of different customer groups to different products and services.
Fraud prevention: Credit card issuers have historically used rules to identify suspicious transactions. With more advanced big data analytics and machine learning tools, it’s easier to detect and predict criminal behavior. This also applies to insurance, banking, and other industries concerned with high security.
Marketing segmentation, targeting, and positioning: Data analytics can show whether certain marketing techniques, such as a promotional campaign or social media infographic, are leading to the desired response. With CRM systems and demographic data, companies can acquire a 360-degree view of the customer and better understand their purchasing behavior, which can lead to custom recommendations and more personalized outreach.
Operational efficiency: By analyzing the different steps of a protocol, it is possible to identify bottlenecks and enact improvements. For example, adding an extra person at a particular shift could increase the speed of production or changing procurement orders could rebalance inventory supply and demand at retail locations.

The Future of Data Analytics

Data analytics has come a long way, and there are many more advances on the horizon.

With real-time analytics, data can be reported and acted upon in the moment—for example, by adjusting posted wait times at a restaurant or seats available at a concert. Automated analytics removes many of the tedious, manual tasks associated with inputting data. More data and software tools are becoming open source, providing a way for organizations of any size and budget to invest in data capabilities.

Machine learning, deep learning, natural language processing, and artificial intelligence are now enabling machines to learn from data and make decisions autonomously. Large technology companies are working on quantum computers that will revolutionize the speed of processing big data.

Leah Davidson

A graduate of the Wharton School of Business, Leah is a social entrepreneur and strategist working at fast-growing technology companies. Her work focuses on innovative, technology-driven solutions to climate change, education, and economic development.

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