Technology has proliferated the business world in unimaginable ways. Looking back at previous decades, no one could have imagined that data management would take great strides. Nowadays, data management has evolved greatly through revolutionary smart technologies. Cloud-based innovative solutions have several benefits in the data management field. Here are some of the main advantages of using smart technologies to process data.
Improving business operations
Businesses are getting more data-driven and require insights before making decisions. The advent of smart technologies like Business Intelligence (BI) tools improves overall company performance. BI tools generate reports that help key decision-makers improve business operations.
Through AI-powered analytical engines, BI tools conduct a deep scan of all business operations. They consider financial, operations, sales reports, and other important insights. Once the data is aggregated on the BI tool, a report is generated flagging any potential harmful behavior.
Instead of manually assessing all reports and business operations, companies can easily get insight into where to improve. From then, key decision-makers can formulate a surgical approach to fixing any problems highlighted by the BI tool. Since the reports can be generated periodically, organizations can remain in good business health through BI insights. The improvements suggested by AI-powered BI tools factor in many variables related to business performance.
Access to actionable insights
In addition to improving business operational performance using BI tools, organizations can gain access to actionable insights. Using dashboard analytical tools, company executives, managers, and enterprise system developers can make tactical decisions. There are so many smart technologies that have the potential to power dashboard analytical tools.
The data management stack is flexible, and system developers design it according to each company’s requirements. Using a smart cache solution is an efficient way of processing insights for analytical dashboards. The core purpose of analytical dashboards is to provide insights in real-time for tactical decision-making. Using solutions like smart caching and distributed in-memory grids reduces latency, providing operational data in real-time. The insights from analytical dashboards are actionable at that very moment which is important for time-sensitive decisions.
These insights can be used on time-sensitive decisions like finding the best time to seize a financial opportunity. Gathering insights on market trends and other pertinent information and analyzing it using an analytical dashboard will yield better results.
Efficient data management
Data management plays a critical role in launching mobile apps for enterprises. Some companies have disparate data sources like legacy systems and cloud-based database solutions. If an application requires fetching insights from these disparate sources, an efficient data management system is necessary.
To facilitate data processing from disparate sources, enterprise system architects use the latest smart technologies for this purpose. Data architects have the option of using smart technologies like Operational Data Stores (ODS). The ODS can be part of a more elaborate data management system with structured access layers.
Smart technologies make processing insights more efficient within an Enterprise Data Warehouse (EDW) environment. For example, if an ODS is part of an EDW, modern application interfaces can use insights from both legacy systems and cloud-based solutions. The data is aggregated on the ODS and made available to applications efficiently. When data is written from application interfaces, the insights easily persist from the ODS to an EDW.
Real-time data processing
Consumers have grown very demanding nowadays. If an application or web app does not load quickly, it will most likely churn. Improving application loading speeds has never been more important. With intense competition from alternate service providers, if you do not improve loading speeds, customers can easily turn to other vendors. Therefore, improving loading speeds by redesigning data processing systems will contribute to customer retention.
At the same time, fast-loading applications will attract more customers, contributing to business growth. Business owners can improve loading speeds by implementing smart technologies that power real-time data processing. For example, using an ODS powered by a distributed in-memory grid expedites data processing.
Applications gain access to data in real-time, which significantly improves loading speeds. If the application is lightweight and is paired with real-time data processing systems, users will have instantaneous page loading. In this case, customer satisfaction rates are improved significantly since the loading time is reduced to a minimum.
Accurate and reliable insights
Although most customers expect fast-loading application interfaces which require real-time data processing, accuracy and reliability are still important. Organizations should find a way to strike a balance between fast-loading app interfaces and accurate and reliable insights.
One way of ensuring that data is accurate and reliable is by implementing a distributed system. The data is written on different nodes, making the insights available across that particular grid. Distributed in-memory data processing grids are a perfect example of a system that promotes accuracy and reliability.
With this data processing solution, if there is a system failure, the insights will still be available. Therefore, when an application fails, the data request will still be fulfilled by fetching the insights from a different node within the grid. This data processing system does not impede application loading speeds but still promotes accuracy and reliability. All of this is possible through the use of the latest smart technologies.
Improving customer experiences
Enterprise system success hinges on how customers receive the front-end interfaces. If an application provides a seamless experience, customer satisfaction rates will significantly increase. Smart technologies directly impact customer experiences, as mentioned above. For example, using smart technologies that improve data processing speeds will result in a fast-loading app.
Subsequently, customers will be satisfied with the application’s performance, which could lead to more users. At the same time, if an app does not behave well, enterprise system developers can quickly fix it using data-driven analytical dashboards.
Additionally, smart BI tools can assess sales volumes and CRM reports to figure out innovative methods of improving customer experiences. By understanding customer personas, you can easily improve the way customers experience your application. It is much easier to personalize service or product offerings with AI-powered tools. The effect smart technologies have is very widespread and can reshape customer experiences on enterprise applications and systems.