The batch style method of data processing relies on reports, and this means analytics can take hours or even days to show results. Traditionally, data analysis happened after data capture and storage. Business insights had to come from stored data.
Real-time data or streaming data is different in that it is continuously generated from a variety of different sources. Some examples of real-time data include health data, weather data, website activity and utility service usage. Streaming data enables real-time data analytics, which allows businesses to be proactive. They can prevent problems before they happen or seize opportunities to increase their ROI. Making decisions is more accurate, and they can take action faster.
Gain instant insights
The obvious benefit of real-time analytics is that there’s no need to wait. With traditional data, users would have a snapshot in time of information on a chart. Now dashboards reflect current data at all times, and users can visualize changes occurring in real-time.
- Real-time website analytics, such as page views, visitors, clicks and other metrics, appear straight away on dashboards.
- Real-time customer analytics makes it possible to view orders as they happen. This enables better tracking and identifying of trends. Businesses can even target customers with specific promotions while they shop.
- Aggregating information into a single system makes it much easier to gain useful insights than trying to gather information from various different sources.
GigaSpaces offers a next-generation ODS that supports the running of real-time advanced analytics and machine learning for instant insights. When something happens that can affect business operations, regulatory compliance or customers’ actions, it enables real-time responses and decision-making.
Quickly fine-tune operational processes
A failure in any operational processes can cause problems with suppliers, customers and stakeholders. When a business can easily keep track of production processes in real-time, it can quickly identify problems and fix them before they escalate.
- Many businesses today collect real-time data from video feeds or IoT sensors to monitor production lines. This helps them to quickly address any problems or backlogs that could lead to customer disappointment.
- Running predictive maintenance applications can reduce equipment downtime.
- Using real-time analytics can help with streamlining inventory management. Businesses can easily monitor the balance between incoming orders and the availability of materials.
- Businesses can use analytics to optimize their budgeting, financial planning, and forecasting.
Assess and prevent business risks
Data analytics are important for today’s risk assessment process with the increasing complexity of technology and external factors. The first step in assessing risks is to identify events that could adversely affect achieving any operating, compliance or financial objectives.
Traditional ways of identifying risks may not take into account all internal or external risks, especially emerging ones. Using data analytics can accelerate and improve risk assessment. For example, a global organization might use geographical sales data to identify potential high-growth areas for investment. It is impossible for businesses to completely prevent all risks but using data analytics enables them to anticipate and respond to adverse events more efficiently.
Increase business agility
Fast decision-making is essential, but business agility is about more than just making fast decisions. It also involves having strategic and tactical business goals. Some businesses need to be able to respond quickly to market fluctuations, and real-time data may be essential to their survival.
- Airlines need to manage prices and availability based on weather, oil prices, current events etc.
- Hotel chains need to respond to current events and other rapidly changing factors.
- Retailers have to respond to changing trends, demands and costs. They need scalability for peak periods without compromising performance.
Recruit and retain top employees
Recruitment has evolved greatly in the last few years and the use of AI-based tools and use of data is common in selecting the best candidates. Selecting high-performing candidates is possible with access to data analytics. They allow businesses to find, assess and select the best candidates. The data assessed may include factors such as educational background, the average length of employment etc. By analyzing the quality and quantity of applicants from various job boards, they can prioritize those that work best for their needs.
By pinpointing the hard and soft skills of top performers in the business, they can determine appropriate skills for potential employees. Recognizing bottlenecks in the recruitment process can help them to enhance hiring processes.
By using data analytics, recruiters can proactively plan their deadlines, budgets and targets. Customizable dashboards can track a candidate’s journey from start to finish. If a job posting yields low-quality applicants, it could be a sign that the job description is too broad or vague. If too many applicants abandon the process halfway through, it could indicate the process is too complicated. Actionable insights from data analytics can optimize the whole hiring process.
Improve customer experience and service
CRM systems can analyze important performance indicators such as demographics, shopping behavior, socio-economic and lifestyle information. This can help businesses to invest in effective advertising campaigns. They can determine what customers want and give it to them when they want it. Measuring marketing metrics, identifying consumer behavior and analyzing market trends all contributes to business profits.
Many businesses have invested heavily in integrating real-time data in their call centers. When call center agents look up customer records, they have access to information about what prompted the customer to call while they’re busy talking. It could be information about a local outage, faulty equipment or a canceled flight.
One of the best ways of achieving financial goals is for businesses to improve their customer service. For example, they can provide customers with real-time price comparisons at peak periods, such as the night before Black Friday.
Conclusion
Real-time analytics allow users to view and analyze data as it flows into the business. Any lags in decision-making can cost businesses time, energy and money. Most data has a short shelf-life, so the faster data can yield actionable insights, the more valuable it is. Businesses with the best information from data can make better decisions, take appropriate actions and stay ahead of competitors.