Even small and medium-sized businesses (SMBs) have data they could be analyzing to make better business decisions. Business intelligence (BI) is not just for corporations and big brands now that there are ready-made solutions for data analysis.
Previously, data had to be manually pulled into spreadsheets, custom calculations had to be created, and then data was exported into graphs for analysis. Few business managers had the skills or desire and most small companies did not have data scientists or analysts.
Today, many drag-and-drop tools exist that are able to pull data automatically and analyze and display it in visual format for actionable insights. But business owners and managers still need to understand what is being analyzed in order to draw valid conclusions using these new BI tools. Employees with training or analytical minds at every level can get insights from data that is currently untapped.
How To Use Business Intelligence
We have all seen business intelligence in use without realizing that is what it was. Ecommerce enhancements that suggest related products or upsells based on what other shoppers have purchased at the same time are examples.
There are many videos on YouTube showing how to use business intelligence solutions and to understand the power of data science and predictive analytics. Use these to make better decisions and grow your business.
Business Intelligence – Defined
The convergence of big data and analytics results in actionable decisions enabled by business intelligence (BI). By starting with end goals, it is possible to use business intelligence to increase sales and profits and reduce costs and expenses.
Using Google Analytics to draw actionable conclusions is an example of business intelligence. SMBs today can go much further using a combination of suggestions from a book such as Hyper business intelligence, and new tools that analyze their existing data.
Analytics 3.0 – The Future is Here
Businesses are not limited to traditional analytics platforms. New all-in-one data visualization software solutions like Datapine can pull data from multiple sources, both internal and external, into drag and drop technology allowing users to easily create interactive, custom dashboards.
Analytics 3.0 is evidenced by the way businesses provide users with the ability to personalize their BI experiences. Real-time monitoring provides users with the information they need to get an accurate overview of their businesses. Results can be displayed live in a visual interface at any time or via regularly emailed reports. Information is accessible 24/7 via a PC, mobile phone and/or a tablet.
Mobility, interactive dashboards and easy to use technology make business intelligence available to every business. One example of how to use it is to pull analytics data and sales data into a BI tool to compare external ad spend to internal sales to measure ROI.
Predictive and Prescriptive Analytics
According to The International Institute of Analytics:
“There have always been three types of analytics: descriptive, which report on the past; predictive, which use models based on past data to predict the future; and prescriptive, which use models to specify optimal behaviors and actions. Analytics 3.0 includes all types, but there is an increased emphasis on prescriptive analytics.”
These analytic disciplines provide awareness into the probability of a future event, recommending actions that could be taken, making them ideal for making business decisions.
Understanding Big Data – The History of Business Intelligence
Harvard Business Review provides this Analytics 3.0 review which includes more extensive information on the history of data and analytics. Here is a brief synopsis as all business owners should understand what these terms mean.
Business Intelligence – Analytics 1.0 – The 1950s
During the 1950s, tools were designed to collect information and identify trends, and patterns. These tools could accomplish tasks more quickly than was humanly possible. Data analysts generally refer to this early period of business intelligence as Analytics 1.0.
The majority of the business analytics tools at that time were small, structured, internal data sources. There was limited reporting ability and batch processing operations could take several months. Before Big Data arrived, analysts essentially spent more time collecting and preparing data than they did analyzing it. This early era lasted about 50 years, eventually leading to the dawn of Big Data.
Big Data Arrives – Analytics 2.0 – Mid-2000s
The mid-2000s brought with it the birth of the Internet and today’s social media staples Facebook and Google. Both Google and Facebook offered new data to analyze and a new way to collect that data. Although the term Big Data did not become common until around 2010, it was clear that this new information was much different that the small data from the past.
Big Data V. Small Data – What is the Difference?
Whereas a company’s own transactions and internal operations generated small data, Big Data was drawn externally, from the Net, as well as from public data projects and sources. One example of Big Data is the Human Genome Project. This new way of data collection signified the onset of Analytics 2.0.
Once Big Data arrived, the development of new processes and technologies to assist companies in turning their collected data into profit through insight was on the fast track. New databases (NoSQL) and processing frameworks (Hadoop) were developed. The open source framework Hadoop is specifically designed to store and analyze Big Data sets. The flexibility of Hadoop makes it the perfect tool to manage unstructured data (e.g., video, voice and raw text, etc.).
Data analysts during the Analytics 2.0 period needed to be competent in information technology as well as analytics. Having these competencies prepared them for the upcoming technological advancements during Analytics 3.0.
Analytics 3.0 is just one of the stepsresources/business-intelligence-guide-midsize-companies.pdf" target="_blank"> on the path to the future of business intelligence. The ultimate goal of business intelligence is to analyze data and boost a company’s performance level by providing staff members and business owners the information they need to make better decisions.
How Business Intelligence Can Benefit SMBs
SAP offers this free white paper on how business intelligence can benefit businesses of any size. BI assists research analysts, managers and other staff members in making informed management decisions faster. It enables sales teams and employees dealing directly with the public to provide reasons for their recommendations.
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