Business analytics (BA) has existed since World War II. Advancements in technology and the data revolution have only given analytics more importance in business operations. Business Analytics combines information technology, data, and statistical analysis techniques to derive insights from data and facilitate decision-making.
Business analytics employs data mining, modeling, AI, machine learning, and other techniques to analyze data and predict future outcomes. The power is in leveraging the information hidden within data. But first, a business analytics professional needs to know how to convert raw data into useful information.
Secondly, it is not enough for a professional to undertake a business analytics course. The information technology world is evolving fast. A BA professional must be keen on current trends and steer the company to gain a competitive edge.
Business analytics uses historical data to understand past business performance; identify problems, hidden trends, and insights; and make data-driven decisions to solve them. Business analytics is narrowly focused on addressing specific business needs. Due to the vast volumes of data generated from transactions, social media interactions, business systems, and more.
A typical business analysis process involves the following steps
- Identifying business issues to determine the goal of a business analysis process
- Gather data from various sources, e.g., business systems, social media, marketing campaigns, IoT devices, etc
- Mining data. This is the process of cleaning, processing, sorting, and integrating data into a central warehouse, data lake, or database to have a unified view of the data. Machine learning algorithms have become instrumental in this process as they discover hidden patterns and insights faster and more efficiently, especially for vast volumes of data.
- Determine the analysis method. The three main categories of business analytics are descriptive, predictive, and prescriptive analytics. These three are usually interrelated to provide complete business solutions. While descriptive analytics aims to provide insights into past events, predictive analytics focuses on forecasting future happenings, and prescriptive analytics determines the best possible solution/outcomes among those that have been proposed.
- Analyze the data using various statistical analysis methods to gain insight across business operations in the organization. Business analytics tools range from machine learning models, spreadsheets with statistical functions, visualization tools, and others
- Visualizing and reporting involve presenting analyses to allow for interpretation to be done.
Why is business analytics important?
Business analytics has a wide range of applications in business operations, including customer relationship management, marketing, and advertising, financial management, and strategic planning. The overall aim of business analytics is to harness information from raw data to facilitate decision-making, ultimately improving business operations in terms of productivity, efficiency, and profitability.
In addition, business analytics is essential for the following reasons:
- Business analytics helps convert raw data into valuable information whose consumption improves business operations.
- It has the potential to increase the revenue and market share of an organization. This is due to improved operations and better, more informed service delivery to customers.
- Provides quantifiable insights, trends, and roots to issues that the business can leverage to project future outcomes and take proactive courses of action to gain a competitive advantage. While access to information may be a constant for businesses, how well a business utilizes the information extracted from data determines its competitive advantage in a fiercely competitive market.
- Consolidation of data into a central repository puts all players in the organization on the same page to streamline communication flow and operations.
- Organizations use analytics software to create visualizations from data to make information accessible to all users and make it easy for them to discover hidden patterns and insights from data.
The Future of Business Analytics
Crunching numbers has never been more important to businesses than in this era of big data. Data carries the value that businesses need to optimize their performance. As we have seen, business analytics encompasses data mining, data aggregation, visualization, and prediction, elements that are critical for any business operation. Business analytics has evolved tremendously over the years and has been impacted significantly by technological advancements.
The purpose of business analytics in transforming operations, decision-making, and projecting future outcomes has not changed. Yet, a lot has changed in the way business analytics is undertaken. The future for BA is certainly tech, as is evident in the following noticeable trends.
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AI and machine learning in business analytics
The AI and ML revolution has not spared the field of business analytics. AI and machine learning have taken operations efficiency to a whole new level. Machine learning’s application in customer service, customer relationship management, personalized marketing, and cybersecurity. With machines getting better at performing tasks previously done by humans, self-service software is gradually taking over. Chatbots and voice assistants are not only offering customer support services, but they are also collecting data required for business analytics.
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The growing importance of data quality
Even the most sophisticated technologies need good-quality data to perform as required. Data quality is a function of two attributes, accuracy and reliability. Inaccurate data can easily lead to poor business decisions, which in turn affect business performance negatively. Thus, as technology continues to advance and more noisy data is generated thanks to a wide variety of data forms, the role of business analysts in data quality management becomes even more critical.
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The emerging role of data automation in business analytics
Away from manually collecting, processing, and storing data in a central repository which has proved to be an error-prone, time-consuming process. Data automation has gained traction with the increased volume and velocity of data being generated to the tune of zettabytes. Data remains valuable as long as it is processed and consumed in time. Data automation is the process of updating data to an open data portal automatically. The future of data management is automation. Automation also ensures efficient data processing and increased capacity to handle vast data volumes from multiple sources at a lower cost. Finally, automation frees business analysts’ time allowing them to focus on the more important tasks of analysis and interpretation.
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Predictive analytics takes center stage
Predictive analytics will play a significant role in shaping the business analytics field. As consumers demand personalized products, the focus shifts to predictive analytics. Predictive analytics enables organizations to anticipate customer needs and proactively design products to meet these needs. Good examples of predictive analytics use cases include recommendation systems, predictive maintenance in manufacturing, fraud prediction in cybersecurity, staffing prediction in healthcare, and more. Predictive analytics tools work by analyzing hidden patterns and trends in historical and current data using mathematical models and then using the findings to forecast future outcomes.
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Cloud computing and business analytics
On-premise systems no longer have the capacity and scalability needed to manage vast volumes of big data used for business analytics. Cloud computing offers greater flexibility, agility, computing power, and scalability. The cloud provides a better platform for hosting scalable interactive dashboards and other business analytics tools. In addition, cloud platforms are today used effectively for the experiment, development, and testing of self-service and advanced analytics software. What’s more, all these come at an affordable price tag for both start-ups and established businesses.
Conclusion
Business intelligence has advanced remarkably over the years as impacted by technological advancements and the explosion of big data. Today, business analytics is a far more powerful tool than it was traditionally.
Automation and emerging technologies like AI, machine learning, augmented reality, IoT, and the cloud will certainly play a significant role in shaping the future of business analytics. These technologies will streamline data collection from a host of sources, analytics, and consumption of information by businesses, data volume and variety notwithstanding.
