The development of new applications and tools used to store, analyze, and process the continual influx of big data has been a boon for businesses across every sector. Information has become the number one resource to evaluate the current financial environment and forecast future trends.
According to Gartner, information will continue to be a crucial asset and as well as a competitive resource. By 2022, 90% of corporate strategies will use information as a critical asset and analytics as an essential competency.
The most common phrases associated with these activities are business intelligence and data analytics. While there are some similarities and areas of overlap, there are also inherent differences between these two terms.
Investopedia defines business intelligence as “the procedural and technical infrastructure that collects, stores, and analyzes the data produced by a company’s activities.” Essentially, it includes tools including graphs, charts, reporting, digital dashboards, and presentations. These are produced after data is fed into analytic software. Business intelligence allows the examination of data using these items to determine patterns and trends.
Business intelligence software tools process and present corporate data to users through web browser-based interfaces. This software offers real-time information that is accessible from any location through intuitive web-based platforms.
The Street lists three types of data used in business intelligence.
Structured data includes names, address fields, and headers that are entered into database programs.
Information that is not easily read by computers and may be difficult to organize into rows or columns.
Semi-structured data combines elements of both of the above forms.
On the other hand, data analytics involves the more technical aspects of processing data using statistical methods, programming, and formulas. Common methods include linear regression, frequency studies, network analysis, and correlation. Analytics strives to answer questions including “Will it happen again?” “Where is the trend headed?” and “What will happen next?”
There are four types of data analytics :
Predictive analytics use modeling and machine learning as well as data mining to analyze current and historical situations to forecast an event’s outcome. A typical application is collecting social media posts to predict positive, negative, or neutral sentiment.
Descriptive analytics uses reports and data dashboards to find the causes of successes and failures. Management reporting such as that for sales, marketing, and finance use descriptive analytics. Commonly used terms to describe this data include mean, median, and percentages. Businesses use this type of data to analyze consumer behaviors. This, in turn, helps guide marketing strategies.
Prescriptive analytics uses statistical models and machine learning to predict possible outcomes. It answers the question of what needs to be done by recommending actions.
Diagnostic analytics attempt to answer the question of why an event occurred. Solutions are based on historical data. Common methodologies used include data discovery, data mining, and correlations.
Data analytics software allows users to use various tools to perform analysis of complex and dynamic data. Factors to consider when selecting an analysis platform are its ease of use, scalability with company needs, and ongoing maintenance requirements. Ease of collaboration among colleagues and peers is also important.
Both business intelligence and data analytics are valuable assets to any organization. Without these essential tools, it is a challenge for companies to make sense of the mountains of big data that are continually generated through millions of transactions.
Helios can assist your company with our sophisticated yet easy to use data analytic services. Contact us to learn more about our complete line of analytic solutions that can help take your business to the next level.