What is Data Analytics?
Data analytics is an umbrella term for a number of different ways that data can be analyzed. Data analytics can occur in the cloud or on-premises, and can use methods from statistics, data mining, predictive modeling and machine learning.
Data analytics tools may include spreadsheets, databases, statistical programs and online dashboards. These tools can provide insights about customers, competitors and business performance. They can also help businesses make more strategic decisions by showing trends and relationships between factors, such as sales versus weather patterns.
Data analytics often starts with descriptive analysis to provide insights into what happened, why it happened and how to fix it. Predictive analytics looks at what might happen next, while prescriptive analytics recommends specific actions to take or decisions to make based on predictions.
Applications of Data Analytics
Data analytics has many applications. In general, a business analyst will take a large amount of data and use it to identify trends or make predictions about the future. Many other functions of a business rely on data analysis to make decisions- in fact, almost all of them will. In addition to business intelligence, the function that focuses purely on data analytics to make decisions for the business and understand it further, engineering, sales, marketing, and product will all take insights for analyzing data to make better informed decisions.
One specific type of data analytics is known as web analytics, which involves aggregating data about who visits a website and what they do when they get there.
Types of Data Analytics
There are several different types of data analytics strategies including:
- Descriptive analytics: This type of data analytics strategy is used to give business leaders an insight into what happened at some point in the past. It involves analyzing historical data to see if any patterns can be identified.
- Diagnostic analytics: As the name suggests, this type of data analytics strategy is used to diagnose problems and issues that have occurred within the business. While it can't tell you what caused a particular problem, it can identify possible culprits, such as a change in management or an increase in competition.
- Predictive analytics: This type of data analytics strategy is used to assess what might happen based on what has happened in the past and what is happening right now. It is often used when making business decisions or planning for the future.
Using Data Analytics in Business
In the context of business, data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics (DA) is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists to verify or disprove scientific models, theories and hypotheses.
Data analytics can be considered a subset of business intelligence (BI), which refers to a broad set of applications and methodologies for collecting, storing, analyzing and providing access to data to help enterprise users make better business decisions. In addition, BI can include the presentation layer that provides end users with tools such as dashboards, scorecards, reports and alerts when important changes occur within an organization's business environment.
Why is Data Analysis important?
- Maximizing profits and ROI. Understanding how your efforts as a business or company are truly coming to fruition is made easier when it's quantifiable- which data analysis does. The importance of making well informed decisions based on historical performance, along with the powerful future predictions that data analysis can provide, is highly valuable to companies small and large.
- Automating strenuous processes. As with everything, there is always room for human error. Data analysis can automate some of this human error, therefore eliminating it completely and ensuring consistency across the organization. It is also essential for scaling up DataOps.