Why Data Analytics Is Important

Why Is Data Analytics Important?

You’ve probably heard that data is a big deal. It’s big business, and if used correctly it has big value. Corporate data management is becoming more of a strategy these days. Businesses are seeing the benefits of data analytics as it relates to business intelligence. And better decisions lead to more efficient business operations, which lead to better business in general.

To get to that place, businesses are placing an emphasis on data analysis. In fact, the global data visualization market revenue is expected to increase to $7.76 billion by 2023, which is a 9.47% growth over the previous 5 years. Learn how we are leveraging data to make informed decisions and predict future outcomes and why big data analytics is getting even bigger.

How Do We Analyze Data?

There are a variety of ways to analyze data. Some of these methods use algorithms, predictive analytics, machine learning or artificial intelligence (AI). Others require advanced analytics or data science to make sense of unstructured data.

Here are 4 ways data analysts and data scientists extract patterns and trends from complex data:

1. Data Mining

Most simply stated, data mining is a process used to extract usable data from a large dataset. Data mining involves data collection, warehousing and computer processing. In order to segment and evaluate the data, data mining uses advanced algorithms.

Real-Life Scenario: Data mining is often used in the health care industry during patient clinical trials. The algorithms can evaluate behavioral patterns of large amounts of data for interpretation, knowledge building and decision making.

2. Text Analytics

Text analytics is the process of drawing meaning out of written communication. Usually, text analytics software relies on text mining and natural language processing (NLP) algorithms to find patterns and meaning.

Real-Life Scenario: Text analytics is used to build the auto-correct function on your mobile device. It will not only correct your spelling, but also predict what you’re going to type next based on linguistic analysis and data pattern recognition.

3. Data Visualization

Data visualization presents a clear picture of what the data actually means. Using bar graphs, pie charts, tables and other visuals, data visualization makes the data easier for those making business decisions to comprehend.

Real-Life Scenario: Data visualizations are part of our everyday lives on IoT devices – and you probably don’t even realize it. Think about the exercise rings on your smartwatch, the energy-use trends from your smart thermostat and the weekly screen time charts on your phone.

4. Business Intelligence

Business intelligence (BI) is the end game. It leverages analytics tools to convert data to actionable insights. Often paired with data visualization techniques, BI provides decision makers with detailed intel about the state of the business.

Real-Life Scenario: Retailers use BI technology to capitalize on customer trends and extend customized offers in real-time. You've likely been on the receiving end of this if you've enrolled in any type of customer loyalty or rewards program.

Key Technologies in Data Analytics

Data mining, text analytics, data visualization and business intelligence are different ways we can analyze data. But let’s dig a little deeper. There is a plethora of analytics tools available to help data analysts and data scientists do this.

Let’s take a look at some of these key technologies:

Data Analytics Tool How It’s Used
Artificial Intelligence Makes decisions that can provide a plausible likelihood in achieving a goal
NoSQL Database Delivers a method for accumulation and retrieval of data
R Programming Assists data scientists in designing statistical software
Data Lakes Accumulates data without transforming it into structured data
Predictive Analytics Predicts future behavior via prior data
Apache Spark Generates big data transformation via Python, R, Scala and Java
Prescriptive Analytics Provides guidance about what to do to achieve a desired outcome
In-Memory Database Saves time by omitting the requirements to access hard drives
Hadoop Ecosystem Ingests, stores, analyzes and maintains large data sets
Blockchain Distributed ledger technologies have proven valuable in managing data challenges
Microsoft Excel Aggregates data to create reports and easy-to-use dashboards

Different Components of Data Analytics

Generally, there are three stages of data analytics: collection and storage, process and organization, and finally, analysis and visualization. In other words, it starts with identifying the data, then progresses to organizing it in a way that makes sense, and ends with identifying patterns and trends that mean something.

But when it comes to business, we can take these stages a bit further. To start, before we begin sourcing data, we need to engage in some business analytics. We need to ask questions about our objectives and desired outcomes before we identify the type of data we need to gather.

We also need to consider the people and the processes making this analysis happen. Do we need more qualified people? Do we need more training? And how will we share our findings internally and externally?

As businesses are continuing to make digital transformations, the components of data analytics can be seen more as a comprehensive data strategy, with the following components:

  1. Address the specific business needs.
  2. Determine where the data exists and how it will be gathered.
  3. Take inventory of the technical infrastructure needed to support the sourcing of data.
  4. Identify how to turn data into actionable insights.
  5. Look at the necessary processes and required skillsets of your people.
  6. Ensure the right people have access to the right data.
  7. Define the business value by creating a roadmap.

Having a plan in place ahead of time will result in good data quality and help your business achieve its data analysis goals.

What Is Data Analysis Used for?

By now you know that effective data analysis is used to make better business decisions. But what does that really mean? There are many decisions made within a business. What exactly is data analysis used for?

We Use Data Analysis for Product Development

Businesses that produce a product or offer a service rely on customer data to determine what comes next. For example, e-commerce companies have a huge pool of potential customers. They rely on demographic data, past purchase data and even payment data to decide what products and services appeal to different groups of people.

We Use Data Analysis for Targeted Content

In the digital age, content is huge and businesses are stepping up their game. Quality data can inform marketing campaigns and pricing strategies. Learning what your customers want can also drive your social media content and open the door to other initiatives like webinars, events and partnerships.

We Use Data Analysis for Efficient Operations

Data analytics doesn't always have to be about the customer. Sometimes, a business can garner insights into its internal operations via the data. These findings can lead to automation projects that will streamline operations and better align the business for growth.

Benefits of Data Analytics for Businesses

Speaking in even broader terms, big data can have a big impact on a business’s future. Once you have a data strategy in place and results in hand, you suddenly have the invaluable trait of industry awareness. Hold onto that knowledge as you keep track of economic developments and identify opportunities for your company to grow – and continue growing.

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