What Is Data Analytics?

As businesses all over the world continue to collect, track and store data, the practice of data analytics is becoming increasingly important. But what do we mean when we refer to data analytics? In short, data analytics is the broad field of using data and tools to provide insights to make informed decisions.

Terms like data analysis, data science and business analytics are often used interchangeably as industry buzz words. In actuality, these terms refer to specific subsets of data that play important roles in the stages of data use. There are many moving pieces that go into the proper management of data in the digital age, and data analytics should be treated as a comprehensive program – not just point tools for specific purposes.

What Is the Role of Data Analytics?

Data analytics helps people make sense of raw data. Companies use data to improve their internal operations and to better understand their customers. Typically, this is done in three stages.

The three stages of data use include:

1. Collect and store: Customer transactions, social media streams, M2M sensors and business activity
2. Process and organize: Calculate, transform, process, protect and augment
3. Analyze and visualize: Dashboards, reports, predictive analytics and ad-hoc queries

Let’s take a look at how each stage works and how each one feeds into the larger umbrella we call data analytics.

Data Infrastructure

The individuals responsible for building a data infrastructure are the people designing, building and maintaining the technology needed to collect and store data. This is the first stage and requires the skills of storage engineers, business intelligence (BI) developers, data warehouse analysts, network administrators, server administrators and data architects.

Data Management and Processing

Once the data is collected and securely stored, companies need qualified IT pros to design and maintain how the flow of data is then processed and organized within the organization. Database administrators, systems analysts, developers, and data architects and engineers are some of the job titles that fit the bill.

Data Interpretation and Visualization

The final stage of data use is interpretating it for actionable insights and decision making. This advanced stage requires advanced analytics. For example, data scientists use sophisticated data modeling techniques to build new datasets. Roles like business analyst and data analyst also work in this stage.

One part of the data use process doesn't exist without the other. When these stages come together, a comprehensive data analytics program benefits the entire organization, from marketing campaigns and sales goals to research, development and finance.

What Are the Types of Data Analytics?

Each area of the organization requires different types of data analytics to succeed. It's the job of the data function to translate algorithms, unstructured data and data points into clear trends and metrics.

The IT industry typically recognizes four types of data analytics:

1. Descriptive analytics
2. Diagnostic analytics
3. Predictive analytics
4. Prescriptive analytics

Each type of data analytics answers a specific question.

Descriptive Analytics

Descriptive analytics answers questions about what has happened in the past and what is happening right now. Answering these questions provides a current snapshot by identifying trends and patterns. This type of data analytics leverages current and historical data.

Diagnostic Analytics

Diagnostic analytics focuses on the why. Why are these trends and patterns happening? To do this, diagnostic analytics concentrates on the data identified by descriptive analytics to discover the factors or reasons for past performance.

Predictive Analytics

Predictive analytics look ahead to the future to answer the question: What is likely to happen? This type of analytics is considered pretty advanced, and often depends on machine learning and/or deep learning. Techniques like statistical analysis and forecasting help businesses predict the future.

Prescriptive Analytics

Prescriptive analytics takes everything into consideration and asks: What do we need to do? Often, this involves testing and other techniques to recommend specific solutions that will drive a desired outcome. Prescriptive analytics utilizes machine learning, algorithms and business rules.

Why Is Data Analytics Important?

Simply put, organizations have been collecting data for quite some time. But poor data management or insufficient data analysis is impacting the bottom line. Most companies have data silos in every department, limiting the ability to build a holistic view of corporate data. Moreover, new data sources – like social media and smart devices – necessitate a more efficient structure and analytics process.

Data is a critical resource that needs to be handled properly. A solid understanding of the data function, techniques and tools provides the context for building a comprehensive big data analytics strategy.

What Are the Analytical Tools Used in Data Analytics?

The world is saturated with all kinds of data. The question is: How do we extrapolate what we need based on data quality? Choosing the right tool can be a challenge for data scientists and data analysts.

Before selecting a tool, do your research. Ask yourself questions like how popular is the tool? Is there a learning curve? How is the tool marketed? How much does it cost? The below table highlights some popular data analytics tools.

Because these tools are widely used in data analytics, it's important that those looking for a career in data are well versed in at least of few of them. SQL is the industry standard and possibly the most important skill for data analysts. SQL is able to handle large datasets that Excel simply cannot. That said, fluency in Excel is still essential. Advanced Excel skills, like writing macros and using VBA lookups, are commonly used for smaller projects and quick analytics.

There's also a whole set of professional skills that data analysts need to succeed. Making connections in complex scenarios is a critical thinking must-have. Then taking those connections and creating a compelling story that engages others within the organization through your presentation skills is key. After all, the data doesn't mean much if it's not used to drive business decisions.

What Is the Career Growth in Data Analytics?

In a 2017 survey by the Business–Higher Education Forum, nearly 70% of U.S. executives said they would prefer job candidates with data skills by 2021, and the demand for analysts will only grow as our world becomes more and more digital. According to Cyberstates, the projected growth for data scientists is 30% by 2030 Although there are slight differences between the responsibilities of a data analyst and a data scientist, the growth rate for data roles in general is skyrocketing.

Data analysts can come from many parts of an organization, including the technical and the business sides. Data visualization is a skill that can translate across departments. Many other individuals and teams throughout an organization rely on data and may need data analytics skills to drive business decisions. This includes people who work in marketing, finance, business operations and more. If you find yourself reviewing data, trying to understand what it means and using it to make decisions for your organization, that’s data analytics.

For organizations that have a specific data function, there are four distinct job roles that can help fill out a growing data team:

1. Database administrator: The most traditional of the data roles, many database administrators (DBAs) start out within a software development function. Although the role focuses heavily on using SQL and scripting to manipulate data residing in relational databases, there is often an infrastructure component to this role as well. Common skill clusters include database administration, SQL databases and programming, and systems administration.
2. Data analyst: While the DBA may currently be the most common role, data analysts lead the pack in terms of demand. Most companies are ready to move beyond the basic insights that come from standard database manipulation, and they are looking for someone to connect the dots between data and business operations. Common skill clusters include data analysis, business process and analysis, and data visualization.
3. Data scientist: Just as the field of data is the most nascent of the four technology pillars (infrastructure, cybersecurity, software development and data), data scientists are the newest entry. The key differentiator between data analysts and data scientists is the use of statistical modeling to drive predictive insights for the future. Common skill clusters include statistics, big data and machine learning.
4. Data architect: These individuals may come from any specialized role, but they have at least a working knowledge of each data discipline along with some familiarity with other technology functions. Since the role of the data architect is to plan and build the appropriate systems, they are familiar with business tradeoffs and leadership. Common skill clusters include project management, system design and implementation, and communication.

What Is the Future of Data Analytics?

While the scope and depth of data analytics is relatively new to many organizations, there are some industries that have been relying on data analysis for quite some time now. For example, the use of data analytics in health care is already very common. Effectively predicting patient outcomes can result in properly allocated funding, which, in turn, improves diagnostic techniques.

We even utilize predictive analysis in our homes. Widely used internet of things (IoT) devices, like smart thermostats, appliances and fitness trackers – just to name a few – collect meaningful data points from us to predict our behavior and further advance home automation.

Not to mention our friends, Google Search, Alexa and Siri. The virtual assistants we've become accustomed to use natural language processing (NLP) to understand and process natural human language in real time. NLP has the potential to make both business and consumer applications easier to use. It also has the potential to change how data queries are made. Used in conjunction with artificial intelligence (AI), NLP could possibly help professionals solve global challenges like clean energy.

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