What Is Data Analytics? An Overview of Methods and Practical Uses

For years, businesses have struggled to collect and make sense of the data generated by what seems like a constantly expanding variety of sources. Without a comprehensive—and scalable—data analytics strategy, decision-makers will miss out on valuable insights that could help them improve operations, increase revenue, and stay ahead of the competition.

A plan is key. Using today’s analytics tools and techniques, businesses can dig into data sets to uncover industry and customer trends, patterns, and correlations that marketing, sales, and other departments can use to their advantage.

What Is Data Analytics?

Data analytics is the process of collecting information for the purpose of studying it to generate insights. High-level analysis is primarily performed by data scientists, but the latest data analytics platforms have tools, such as queries based on natural language processing and automated insights, that allow business users to dig into datasets.

Data analytics as a practice is focused on using tools and techniques to explore and analyze data in real-time or near-real-time to uncover hidden patterns, correlations, and trends. The goal is predictive and prescriptive analysis, using advanced techniques to make accurate, dynamic, and forward-looking forecasts and recommendations. Related business intelligence (BI) capabilities allow you to collect up-to-date data from your organization, present it in easy-to-understand formats such as tables and graphs, and disseminate resulting insights in a timely fashion.

Data Analytics vs Big Data and Data Science

Data analytics often overlaps with big data and data science disciplines, though the three are different. Data analytics uses big data as a key element to succeed while falling under the umbrella of data science as an area of focus. Additional differences are as follows:

Big data refers to generating, collecting, and processing heavy volumes of data. With data coming from databases, Internet of Things devices, social media and emails, and other diverse sources, data analytics systems can work best when integrated into big data stores. The greater the volume, the more context and data points feed into data analytics. In essence, big data is the fuel for the data analytics engine.

Going on with that analogy, a data scientist tunes the data analytics engine using training in data science. Data science is the study of how to use data to derive meaning and insight. A data scientist must possess a cross-section of math, statistics, programming, and other related skills to be able to build queries and models for data analytics projects.

Key Takeaways

• Data analytics is the practice of using analytics tools to derive insights from datasets to inform decisions.
• With data analytics, organizations can improve decision-making, streamline operations, and increase revenue.
• Still, data analytics projects can be resource-intensive from both a technology and skills perspective.
• Data analytics is different from business intelligence, big data, and data science, though connected to all three.

Four Main Types of Data Analytics

Before launching a data analytic effort, companies need to decide what they want to achieve: Do you have historical data to mine, to understand trends and patterns? Are you looking to make predictions, maybe even recommend actions to achieve desired results? Each type of data analytics serves a purpose and requires specific tools and techniques to succeed.

1. Predictive data analytics

Predictive analytics may be the most used type of data analytics. Businesses use predictive analytics to identify trends, correlations, and causation. The category can be further broken down into predictive modeling and statistical modeling, which go hand in hand.

For example, an advertising campaign for clever tee shirts on Facebook could apply predictive analytics to determine how closely conversion rates correlate with a target audience’s geographic area, income bracket, and interests. From there, predictive modeling could be used to analyze the statistics for two, or more, target audiences and provide possible revenue values for each demographic.

2. Prescriptive data analytics

Prescriptive analytics is where artificial intelligence and big data combine to help predict outcomes and identify what actions to take. This category of analytics can be further broken down into optimization and random testing. Using advancements in machine learning (ML), prescriptive analytics can help answer questions such as “What if we try this slogan?” and “What is the best shirt color for an older demographic?” You can test variables and even suggest new options that offer a higher chance of generating a positive outcome.

3. Diagnostic data analytics

While not as exciting as predicting the future, analyzing data from the past can help guide your business. Diagnostic data analytics is the process of examining data to understand cause and effect. Techniques such as drill down, data discovery, data mining, and correlations are often employed.

Diagnostic data analytics help answer why something occurred. Like the other categories, it too is broken down into two more specific categories: discover and alerts and query and drill downs. Query and drill downs are used to get more detail from a report. For example, say a sales rep closed significantly fewer deals one month. A drill down could show fewer workdays due to a two-week vacation.

Discover and alerts notify of a potential issue before it occurs; for example, an alert about a lower number of staff hours could warn of a decrease in closed deals. You could also use diagnostic data analytics to “discover” information, such as the most-qualified candidate for a new position at your company.

4. Descriptive data analytics

Descriptive analytics are the backbone of reporting—it’s impossible to have BI tools and dashboards without it. It addresses basic questions of “how many, when, where, and what.”

Once again, descriptive analytics can be further separated into two categories: ad hoc reporting and canned reports. A canned report is one that has been designed previously and contains information around a given subject. An example is a monthly report sent by your ad agency that details performance metrics on your latest tee shirt marketing efforts.

Ad hoc reports, on the other hand, are designed and run on the fly. They are generated when there is a need to answer a specific business question. These reports are useful for obtaining more in-depth information about a specific query. An ad hoc report could focus on your corporate social media profile, examining the types of people who’ve liked your page and other industry pages, as well as other engagement and demographic information. An ad-hoc report’s hyperspecificity gives a very complete picture of, say, your social media audience in a particular city at a certain time of day.

Benefits of Data Analytics

Data is generated by nearly everything these days, from smartphones to vehicles to industrial machinery. Individually, that data provides status updates from each source, but collectively it can offer insights on a level unheard of just a decade ago.

Common benefits of data analytics include the following:

• Informed decision-making: Better use of data can revolutionize an organization’s decision-making process. An executive’s hunch can now be verified through data, incorporating historical context while considering other direct and indirect variables for a clear view of how to proceed. Data integration is a key driver of this benefit. By pulling together data from across an organization as well as applicable external datasets, like publicly available data from sources such as local governments and universities, analysis can deliver much deeper context before making a final decision.
• Operational efficiency: When you have data for the end-to-end workflow of your operation, it’s then possible to break each department’s steps down and consider potential improvements. For example, analytics can tally metrics across your supply chain, highlighting areas where shipping problems, inventory loss, or unnecessarily higher prices are common. With this data in mind, supply chain management teams could theoretically adjust their approach to vendors, quality control, or inventory management.
• Enhanced customer experience: Analytics can create a better customer experience, from both operational functionality and customer individualization POVs. From an operations perspective, analytics can show what works and what creates problems in the customer workflow, highlighting where to invest in process fixes to maximize customer satisfaction. On the personalization side, analytics can build individual profiles, which then allow for stronger engagement via customized marketing, such as specific discounts or timed reminders.
• Revenue growth and competitive advantage: The more data an organization uses, the clearer its markets become—segmentation, seasonality, competitor trends, and other factors can come together to highlight areas primed for capitalization. With this type of analysis, more actionable options come into focus, from ways to leapfrog competitors to finding underserved markets to phasing out failing products and services. All of these actions feed into greater profitability, either by increasing incoming revenue or reducing expenses.
• Risk management and forecasting: An organization’s operational risks come in many forms, from fraudulent financial activity to cybersecurity to faulty processes. With data analytics, departments can translate risk identification into actual numbers that highlight the weakest or most anomalous findings. By expanding the scope and depth of identifying risk, organizations can reduce both direct and indirect financial impacts.

Data Analytics Challenges

Data analytics is loaded with benefits for organizations, but thorough and effective implementation comes with some hurdles. The following are the most common challenges when it comes to data analytics:

• Data quality and accuracy: Data analytics efforts only work if the underlying data is accurate, clean, and relevant. Thus, identifying quality data sources is one of the key early steps in any analytics project. To facilitate high-quality data, organizations must weigh key elements including relevance and accuracy of data sources, possible data format conflicts, and necessary data preparation/cleansing steps.
• Data integration and silos: Once data sources are identified, be they from internal or external sources, related datasets must be collected in a unified repository to realize the full benefit of data analytics. Getting those datasets into a repository requires a solid data integration strategy. IT teams must enable networks have an underlying infrastructure able to support integration and any required transformation/cleansing. Depending on how organizations are structured, this may require some negotiations with departments not accustomed to sharing data.
• Scalability issues: Data analytics projects can be resource-intensive. It can be beneficial for IT teams to inventory the individual components in the data pipeline and list tasks ranging from data integration to transformation and consolidation to repository connections to the analytics application itself. This is a bigger-picture process requiring IT teams to consider the impact of the project on the network. Depending on configuration, a resource-heavy setup may create significant difficulties scaling up as demand grows.
• Data privacy and security: On its own, a data analytics application does not present a significant security challenge. However, when all the pieces are connected, the analytics process can introduce vulnerabilities. Every time data transfers between environments, that presents risk. On a user level, role-based access is necessary to enable that sensitive data is not exposed. Privacy demands of regional regulations, such as GDPR, present compliance challenges. For the entire organization, basic usage protocols and guidelines are critical to enable general understanding of how to deal with data on that level. These points and more are constantly evolving, which means that IT teams must stay up to date with the latest risks and tools on each topic.
• Skills gap: Data science is a rapidly growing and evolving field. As demand for data analytics increases, so does demand for talent, meaning top candidates are often snatched up immediately and at top dollar. That skills shortage requires companies to get strategic when building out a data science team. Consider training up current employees, where feasible, and purchasing tools that empower users with self-service functionality.

Data Analytics Strategies and Solutions

If you want to build a more insights-driven organization, there are plenty of data analytics products on the market today. Ultimately, the ideal solution offers modern analytics tools that are predictive, intuitive, self-learning, and adaptive.

To support all the ways that your organization will use data, here are a few things to keep in mind:

• Opt for a platform that integrates analytics and data management capabilities. Such a solution is easier to provision and delivers business value faster while avoiding the compatibility and access issues of a legacy system that has separate solutions for reporting, discovery, analysis, and recommendations.
• A platform that resides in the cloud but can access data in on-premises and/or hybrid environments is key. Fast, easy access to data as well as the analytics system enables everyone across the organization to gain insights and make informed decisions.

In addition, practices we consider include the following:

• An end-to-end analytics solution: Look for a solution that supports the entire analytics process—from gathering data to providing insights and prescriptive actions—with security, flexibility, reliability, and speed.
• Leverage all data: Choose a solution that accesses and analyzes all available datasets—of any size and in any location—from applications, devices including Internet of Things sensors, departments, and third-parties, whether structured or unstructured, onsite or in the cloud. This complete view unlocks the full value of your data by uncovering patterns and relevant insights to help users make informed, data-driven decisions.
• Improve productivity and data integration: The ideal data analytics solution optimizes all the steps in your data workflows. That makes data and analytics processes faster. Advanced built-in capabilities, such as machine learning, accelerate model building. Ideally, efficiency will be enhanced everywhere in the process, including data gathering, discovering insights, and improving decision-making.
• Benefit from a single source of truth: For trustworthy analytics, insights, and results, data should be consolidated into a single source. Doing so allows for consistency and accuracy with a unified view of data, metrics, and insights.
• Accelerate data insights: Look for a solution with augmented analytics—such as embedded AI and machine learning—which can simplify, accelerate, and automate tasks, giving your decision-makers the power to dig deeper and faster. Ideally the system will automatically collect and consolidate data from multiple sources and recommend new datasets for analysis.
• Self-service analytics—free IT: To realize its potential as a business tool, analytics needs to be democratized. That means having a solution that doesn’t require IT assistance—anyone in your organization with the proper authorization should be able to use it. The ideal analytics solution is designed for self-service, with point-and-click or drag-and drop functionality and guided, step-by-step navigation that enables users to easily load and import data and analyze it from any angle.

  We think the best data analytics solutions offer users the ability to find, understand, govern, and track data assets across the enterprise based on metadata and business context. Doing so accelerates time to value and makes it easy to find fit-for-use data. Data discovery, collaboration, and internal processes can be enhanced with user-defined annotations, tags, and business glossary terms.

• Visualize data: Analytics has the potential to give you a detailed snapshot of your business landscape. To help make the most of that potential, you want a smart solution that can automatically transform data into visual presentations. This can enable you to see and understand patterns, relationships, and trends that might be missed with a spreadsheet of raw numbers. It also lets you create data mash-ups to get new, unique insights. Your employees can do that without specialized training, thanks to smart technology.
• Mobile analytics: You want a tool that can give your people access to the information they need even when they’re on the road. But not all mobile analytics solutions are created equal. Look for one that not only offers voice-enabled access and real-time alerts, but provides advanced capabilities to help your people be even more productive.

  These capabilities include creating mobile analytical applications with interactive visuals from a phone or tablet—without writing code. Or imagine a solution that looks at your digital footprint, knows you’re about to attend a meeting out of town, and delivers insights to help that meeting be a success.

How Automation and AI Transform Data Analytics

Millions of manually prepared spreadsheets are used across diverse industries, including finance, healthcare, and economics. Yet, according to ZDNet, 90% of all spreadsheets have errors that affect their results. Cut-and-paste issues, hidden cells, and other mistakes have cost businesses millions of dollars.

Traditional analytics solutions and processes can also cause delays in providing businesses with the insights needed to make timely decisions. Often, data is collected from multiple applications and platforms, requiring a corporate department to create the extract, transform, and load (ETL), connections, and interfaces; transfer data from one database to another; evaluate the data quality; and enter the data into spreadsheets.

All of these tasks consume precious time and resources.

In addition, with traditional solutions and processes, you usually need to be an expert in IT or analytics to conduct the analysis. It is not a self-service experience for the busy executive who requires end-of-month analytics. And that means waiting for the IT or analytics expert to provide what’s needed.

Automating analytics processes and putting the processes in the cloud can be a game changer for businesses of all sizes and in all industries. For example, a modern analytics solution with embedded AI and ML and an integrated autonomous data warehouse that runs in a self-securing, self-patching, self-tuning autonomous cloud can revolutionize decision-making.

When you’re working with a modern analytics solution, everything can be automated: Identify a few parameters of what you want examined, which model to apply, and which column you want to predict, and the tool will take over. Data can be ingested from multiple applications, platforms, and clouds. It can be gathered, cleaned, prepared, transformed, and analyzed for predictions—all automatically, accelerating processing and reducing the chance of human-created errors.

Choose Oracle Analytics and you’ll get a single, integrated platform that combines Oracle Analytics and Oracle Autonomous Database. It’s a simple, repeatable solution with the best elements of analytics and powerful autonomous data services. That means obstacles are removed, data is brought together into a single source of truth, and highly actionable insights are unlocked—fast—which makes it an ideal data analytics solution to guide strategic business decisions.

But remember: Companies that realize the full benefit of data analytics don’t stop at tools. They also work to develop a data-driven culture within the organization, where decisions are based on facts rather than intuition. The result is better growth, profitability, and customer satisfaction.

Data Analytics FAQs

What are the main types of data analytics?

The main types of data analytics are as follows:

• Predictive data analytics, which identify trends, correlation, and causation.
• Prescriptive data analytics, which predict outcomes and provide suggestions on course of action.
• Diagnostic data analytics, which reviews historical data to quantify why something happened.
• Descriptive data analytics, which reviews historical data to show a comprehensive review over a past event that covers all key facts.

Why is data analytics important?

As data is constantly generated from devices and databases in nearly all facets of both business and everyday life, data analytics presents a way to turn those heavy volumes into something meaningful. Thus, data analytics is important because it provides quantifiable evidence to drive decisions while also uncovering insights that can inform further strategy.

How can data analytics improve business decisions?

Before data analytics, business decisions were executed with limited context. For example, a marketing decision might be based on campaign data, but it would have been impossible to fully factor in sales data, competitive data, seasonal factors, and other types of contextual data because of the time and effort involved. With data analytics connected to a comprehensive repository of quality data, all of this can be synthesized into a clear view of a specific situation—and in addition to justifying decisions, data analytics can produce new insights by finding patterns buried deep within datasets.

What is the difference between big data and data analytics?

Big data refers to the generation, collection, and processing of heavy volumes of data from a wide range of sources. Data analytics is the study of data to derive insights. While analytics can be performed on a single, contained dataset, it works best with heavy volumes of data—in fact, the more data, the better.

What is the best type of data analytics?

The best type of data analytics for an organization depends on its stage of development. Most companies are likely already using some sort of analytics, but it may afford insights to make only reactive, not proactive, business decisions.

More and more, businesses are adopting sophisticated data analytics solutions with machine learning capabilities to make better business decisions and help tease out market trends and opportunities. Organizations that do not start to use data analytics with proactive, future-casting capabilities may find business performance suffers because they lack the ability to uncover hidden patterns and gain unexpected insights.