What Is Data Analytics? Data Analytics & Analysis 101
Learn more about data analytics—a field that involves collecting, organizing, and analyzing data to gain insights and inform decisions.
It’s estimated that businesses only use about 50 percent of the data that they collect. Data collected must then be analyzed—and it has to be analyzed to be used effectively.
Data analytics helps an organization get the most from the data that it collects. It can be powerful when used correctly or misleading without the right processes and tools. A deeper understanding of analytics will help you drive value for your organization.
What is data analytics?
Data analytics is a broad field that involves collecting, organizing, and analyzing data to gain insights and inform decisions. Data analytics can range from basic budgeting in Excel to advanced machine learning (ML) algorithms that predict customer behavior. By better understanding the patterns within your data, you can make informed decisions that lead to greater success.
Key takeaways
- Data analytics is the process of organizing and analyzing data to produce actionable insights and meet goals.
- Raw data is rarely useful—to become useful, an analyst must curate, normalize, and mine the data for relevant information.
- To transform data into actionable insights, analysts use a variety of data analytics processes and tools.
Types of data analytics
Analysts may use different methods to analyze data—and not every method is relevant to every data set. Choosing the right type of data analytics helps you get the most from your information.
- Descriptive analytics is the most basic form of analytics and can be used to answer, “What happened?” Descriptive analytics help you understand the current state of a business by summarizing data into useful insights. An example of descriptive analytics might be customer demographics, indicating who purchases from the company.
- Predictive analytics is used to answer, “What might happen?” It uses data from past and present events to forecast outcomes and trends in the future. A common use of predictive analytics is customer recommendations. Predictions provided by Amplitude, for example, helped Chick-fil-A change the appearance of their menu for specific users based on previous behavior and purchases to minimize friction during purchase.
- Prescriptive analytics helps organizations make data-driven decisions by answering, “What should we do?” It combines predictive insights with optimization algorithms to suggest a course of action. It’s an opinionated form of predictive analytics. Financial advisors may use prescriptive analytics to determine good investments. Not only is their algorithm predictive (“What stocks should I buy?”) but also prescriptive because the financial advisor wants to match certain risk model assessments (“Which of those stocks match my low-risk model?”).
- Diagnostic analytics is used to answer, “Why did it happen?” Diagnostic analytics helps uncover the root cause of a certain problem. An epidemiologist may use diagnostic analytics to identify commonalities between health issues to figure out what is causing them.
Most organizations will use multiple types of analytics to do different things—different types of analytics form an analytics toolkit that companies can use to further their goals and initiatives.
The process of data analytics
Data analytics begins with gathering of information, or data points, from various sources like your website, product, marketing campaigns, customer interactions, or financial information. There are various data analytics platforms, like Amplitude Analytics, that allow you to collect, track, and report on data easily and effectively.
- Collection: The first step to data analytics is collecting data from relevant sources. Analytics platforms help users collect digital data from a host of different sources, often combining various sources together, that you can then use for various analyses and insights.
- Tracking: Once you’re set up to collect data, you need to track it over time so you can review trends, patterns, drops, spikes, and analyze the factors that drive those to arrive at valuable insights. The more historical data you have in your data analytics database, the more accurate and useful insights you will get.
- Reporting: Data reports and visualization tools available in analytics platforms allow you to easily glean insights from your data. The goals you’re trying to achieve as a company or department will guide the kind of data you want to collect, compare, and report on to make data-driven decisions about your business.
Data analytics never happens in a vacuum. The more data sources and context you’re able to connect together, the richer and more accurate your insights will be. Data analytics platforms that allow teams to collect, track, and report on data cross-functionally, such as tracking data from marketing campaigns all the way through to product use, for example, can help teams understand the most valuable actions and interactions for their customers and double down on offering greater value.
Data collection and normalization
It’s becoming harder to collect and normalize data today partly because of the sheer volume of data and the proliferation of platforms that collect it. Data management is a field dedicated to collecting and protecting an organization’s data, especially very large data sets.
Modern companies may aggressively collect data across dozens of platforms. When data lives on multiple platforms, it has to be consolidated and normalized to create a complete picture. For example, you might run a marketing campaign across Facebook, LinkedIn, and Twitter. Facebook counts interactions differently than Twitter, which could give you a misleading picture of how each platform is performing.
Today, companies frequently use data lakes and data warehouses to collect, consolidate, and normalize their data. Data lakes and warehouses connect to multiple data resources to provide a centralized source of truth. Analytics platforms also collect and normalize data (and can sit on top of existing data warehouses)—such as robust marketing platforms that draw data from multiple channels to produce consolidated reports.
Data analysis and visualization
Most people can’t just read an Excel sheet of raw data. Instead, they need to see a visual interpretation—such as a chart, graph, or a well-formatted table. Data visualization is frequently the last step of data analysis and is an essential tool for making data readable and actionable.
However, data visualization can also be misleading if it’s not properly curated and controlled. Advanced analytics, such as statistical analysis, frequently involves nuances and complexities because how data is presented matters. A pie chart could show that the bulk of customer interactions originates from Facebook, even though the bulk of leads comes from LinkedIn.
The best data analytics tools create visualizations that are both readable and actionable. A line graph comparing customer interactions and leads against both Facebook and LinkedIn would show the organization that they should put more time and energy into LinkedIn (because the leads are coming from there) but that they should work on improving their lead generation on Facebook (because their customer interactions aren’t converting).
How data analytics is used
Even a household budget can benefit from data analytics. For example, how much does a person spend on eating out? Product, marketing, finance, and customer satisfaction all deal with data analytics—data analytics provides visibility into what’s happening in an organization.
Every organization across every industry uses data analytics—generally, when data is complex enough that it’s not easy to understand intuitively.
- Product: Product teams use data analytics to measure user acquisition, activation, engagement, retention, and monetization by analyzing user engagement and behavior within the product. Product analytics help businesses optimize the customer experience and journey to increase conversions and adoption of their product.
- Marketing: Marketers use data analytics to identify target customer segments, optimize marketing strategies, measure campaign performance, and analyze customer behavior (behavioral analytics). Data analysis helps marketers better understand their customers to make better decisions and design more effective campaigns.
- Business intelligence (BI): Businesses use data analytics to gain insights into their operations by analyzing key performance indicators (KPIs), customer lifetime value (CLV), inventory management, and financial analysis. Business intelligence helps businesses identify areas for improvement, optimize processes, and make more informed decisions.
- Financial projections: Companies use data analytics to forecast and anticipate future trends, customer behavior, and market conditions. Financial projections help businesses better prepare for the future and drive decisions through past performance or expected outcomes.
In these cases, looking at the raw data wouldn’t be helpful for the analyst. A marketer likely can’t look at sheets of demographic information to determine which customers are most interested in one product—they need a tool. This becomes especially true as we move into big data analytics, the process of data modeling enormous data sets.
Modern data analytics challenges
Data analytics is a more challenging field today in part because of the volume of data that’s being collected. Companies are collecting large volumes of data, and not all of that data is valuable. Still, the organization has to process its data and draw meaningful conclusions.
Those working with data analytics, like product marketers and marketing leaders, have to wrangle enormous amounts of data that’s spread across different parts of the technology stack, from social media platforms to marketing campaigns and from landing pages to in-product usage. Moreover, the data isn’t easily accessible by the entire organization, creating silos and making it difficult to align on decisions. Finally, a lot of this data is low quality, creating a lot of noise and making it hard to trust patterns and insights derived from them.
It’s important for those working in data-driven roles to keep up with technological advancements and new tools that can help them deal with these challenges and structure their data in meaningful ways.
Common data analytics tools
Organizations can benefit from data analytics tools to simplify their operations and take their data from its raw form to actionable information. For every application of data analysis, there is generally a specialized tool that will make the process much easier.
- Customer relationship management (CRM) tools help businesses manage customer relationships and track customer data. Features may include contact management, automatic emails, sales forecasting, and report writing. Some of the top CRM tools include:
- Product and web analytics track your product's and brand's performance across the web and in applications. Amplitude provides in-depth knowledge regarding how customers interact with your digital presence. Other product and web analytics tools include:
- Business intelligence (BI) tools help businesses make data-driven decisions. Features may include dashboards, analytics reports, predictive modeling, and data visualization tools. Some of the best-rated BI tools include:
Some analytics tools are fairly broad, whereas others are very niche, such as a medical analytics program that generates insights from MRIs. For an organization, knowing which tool it needs is critical.
Accelerate growth with the right data
Better data leads to better decision-making. Data analytics gives organizations the information that they need to improve their operations, from product to marketing to finance. But as the volume of data that companies collect continues to grow, it becomes more difficult to analyze.
Amplitude Analytics leverages machine learning to produce at-a-glance insights—fast answers to your most pressing analytics questions without the need for custom code. With Amplitude, you can analyze the entirety of your buyer’s journey alongside your customer’s relationship with your product. Get started today.
Pragnya Paramita
Former Group Product Marketing Manager, Amplitude
Pragnya is a former Group Product Marketing Manager at Amplitude. She led the go-to-market efforts for data management products. A graduate of Duke University's Fuqua School of Business, she is passionate about working at the intersection of business and technology and when time allows, cooking up a storm with cuisines from all over the world.
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