While information systems have always been a critical component of effective business management, the phenomenal pace of technological advancement has made it increasingly difficult to keep up with best practices.
Today’s digital technologies have resulted in the development of information systems that are radically transforming the very nature of managerial work, the structure of organizations, and the way firms operate and compete in an ever-changing global marketplace. In turn, the importance of business analytics continues to grow.
Delivering Business Intelligence in Real-Time
Business processes must be efficient and responsive. Managers need real-time business intelligence and predictive analytics to make the right decisions quickly and confidently. This is a big change from the traditional business model, where decisions were made at the top and only after a laborious process involving numerous meetings, formal presentations, and pie charts over lunch.
Today, managers interact regularly with co-workers and partners located in cities and countries around the world. Technology-enabled mobility has made us increasingly agile in responding to customers, challenges, and crises no matter where they occur. Business teams have the capacity to connect wirelessly across multiple devices, access data stored in the cloud, and share that data via web meetings and live video feeds.
The growing importance of business analytics is evident in the most recent development in business intelligence: prescriptive analytics, which not only predicts future business trends but suggests the actions managers should take to capitalize on them. Forbes explains that prescriptive analytics removes much of the uncertainty managers encounter when planning marketing strategies, for example, by leveraging artificial intelligence and machine learning to identify which specific groups to target and the most effective media to use to have the greatest impact.
Why Analytics is Important for Your Business
The challenge business managers face is to apply the optimal analytics approach that supports decision-making in real-time while accommodating the ever-increasing amount and complexity of the data being analyzed. Business intelligence vendor MicroStrategy describes the four types of data analytics now used by companies to improve their business decision-making processes:
- Descriptive analytics presents a complete snapshot of business conditions as they were and as they are via data aggregation and data mining. The goal is to make existing data easily accessible to decision-makers throughout the organization, as well as to shareholders and investors.
- Diagnostic analytics digs deeper into the data to determine why current conditions exist and how future trends will influence those conditions. Probabilities, likelihoods, and the distribution of outcomes are applied based on attribute importance, sensitivity analysis, and classification and regression via training algorithms.
- Predictive analytics uses statistical models and machine learning to forecast future events based on models created from data generated through descriptive analytics. One use of predictive analytics is sentiment analysis, which projects how people will respond to new products and services based on opinions gleaned from social media.
- Prescriptive analytics builds on predictive analytics by recommending specific courses of action that are most likely to achieve future desired results. The forecasts depend on a strong feedback mechanism and continual iterative analyses to model various relationships between actions and outcomes.