What is Data Analytics? Everything You Need to Know

Data analytics is the process of making conclusions about information by analyzing raw data.¹ In business, you will use data and analytics in multiple ways—you'll optimize processes to improve financial performance or make data-driven decisions, like choosing which social media platforms are best for targeting specific audiences.

Because of technology, modern businesses have access to mountains of data. This data informs decisions in every department. It also allows you to make informed decisions to strengthen your brand and stay ahead of your competitors.²

This blog post will provide a comprehensive overview of data analytics.

Let’s review the types of data you will work with in a data analytics position, as well as where that data comes from.

You may feed structured data into a database to analyze it or use an AI-powered program to assess unstructured data. Structured data has consistent formatting with defined fields and relationships, while unstructured data includes video, text, and other unformatted files. Both structured and unstructured data can provide valuable insights if utilized efficiently.³

For example, if you needed data analysis for marketing efforts, your customer relationship management (CRM) database would include structured data on all your clients. Your social media mentions and customer reviews would be considered unstructured data but would offer you insight into your customers.

In the last example, your CRM database would be an example of internal data because you own it and enter it yourself. You might supplement this information with external data from industry reports, trends, and competitive analysis.

Sensors can also capture data. For example, if you were managing a wholesale business, you might place sensors throughout your warehouses and trucks to monitor your inventory and measure your average delivery time.

The parameters of data analytics aren’t limited to types and sources. Data analytics also refers to how you use data to make decisions. According to IBM, data analytics often fall into four categories:⁴

1. Descriptive analytics: An analysis of what happened
2. Diagnostic analytics: An assessment of why something happened
3. Predictive analytics: An analysis of what may happen in the future
4. Prescriptive analytics: An assessment of what you should do next

All of these categories are important for assessing your data points and drawing data-driven conclusions. For example, if you’re trying to measure the success of your past marketing efforts, you would use descriptive and diagnostic analysis. If you’re trying to plan marketing efforts for the next year, you should use predictive and prescriptive analysis.

When discussing data analytics, the term “data analysis” is often used interchangeably. However, data analysis should not be confused with data analytics. Data analytics encompasses all the techniques you use for trend forecasting and decision-making. It involves analyzing data to make predictions and strategize for the future. Data analysis involves assessing datasets for patterns and relationships. You use data analysis to summarize historical data and illustrate what happened in the past.

A data analytics process can be broken down into four key steps:

Start by stating your goals and collecting your data. Use a combination of internal and external data. You want a broad range of data to make informed decisions. It’s important to collect high-quality data and validate its accuracy. Otherwise, you could make critical business decisions with outdated or inaccurate information.

Next, you need to clean your data. This means checking your data for misspellings and removing redundant and irrelevant data. Your CRM database, for example, might include old customers who haven’t done business with you in years. You would remove these records to get insights into your current customer base.

The most efficient way to analyze your data is to use a program that automatically compiles the information and creates an analysis based on trends and patterns within the data.⁵

Finally, you can use the analysis provided by your software program to create data-driven solutions. These insights can be used to create a report for your management team or board and to implement actionable steps in your business strategy.

You don’t have to read through multiple databases and documents to develop your reports. Tools such as Excel, Python, and Tableau are invaluable for extracting information from piles of raw data.⁶ Newer data analytics tools, including Hadoop and Spark, let you work with large volumes of data.⁷ Hadoop lets you store and access your data from multiple computers, while Tableau and Excel primarily use localized data. Spark also works with large volumes of data, cleaning and transforming it so you can import it into data visualization tools, such as Excel and Tableau. Both Hadoop and Spark work with other programs run by AI to generate insights from large datasets.

Throughout your career trajectory, you will use analytics data in every aspect of business across multiple industries.

For example, you could use business intelligence data to streamline operations and save money without compromising product quality.⁸ Analytics and data could inform your marketing plan by helping you profile your best customers and better understand how to reach them.

If you're in a field like finance, you might use predictive modeling and analysis to show how the market is moving and how businesses are behaving so that you can help your customers make smarter investment decisions. In healthcare, predictive analysis could help you identify patients at risk of developing conditions or personalize care based on patient needs.

Data analytics does have its drawbacks. Many Americans are concerned about their privacy, with 42% stating they worry that companies are buying and selling their information without telling them, according to Pew Research.⁹

Another challenge involves scaling large volumes of data. You need the right storage systems and data processing methods in place to clean and format structured and unstructured data from multiple sources.

Without the right tools to manage your data, you run the risk of misinterpreting it. Analyzing too much or too little of a dataset can skew your results. Additionally, you want to make sure your sample set represents the whole population, or you could end up with inaccurate results. For example, if your target customers are 48% female and 52% male, but you’re analyzing a data set that is 20% female and 80% male, your analysis won’t guide you in the right direction to reach your audience.

Artificial intelligence (AI) and machine learning are rising in popularity among data analysts. The International Institute of Business Analysts recently published a blog on how AI and machine learning are impacting the field.¹⁰ Programs powered by AI can analyze data in record time—reviewing thousands of reports in seconds and identifying trends to make predictive analyses. Generative AI can also create a summary of relevant statistics with visualizations in seconds.

These tools are also letting businesses assess data in real time. New data analytics programs can scour the web for your brand name and develop reports as soon as someone mentions you. This allows you to more accurately predict how future trends will impact your business. You can plan your inventory, optimize your supply chain, and make other decisions that improve your ability to satisfy customers.

As a key decision-maker in any business, you have more access to data than ever. Understanding how to sort and analyze this data to gain insights helps you improve your revenues and operate more efficiently. Dig deeper into this field with an Online MS in Business Analytics from Santa Clara University. Our curriculum was developed by Silicon Valley professionals who understand big data.

You will learn the fundamentals of data analytics, such as statistics and calculus, along with current trends, such as predictive analytics and cloud computing. You can finish your degree in as few as 15 months and be ready to take the next step in your career. Contact an admissions outreach advisor today to get started.