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Improving Time-critical Decision Making in Your Organization

Data analytics are revolutionizing organizations from all aspects you can ever imagine. From providing advanced applications for consumers to guiding important decisions companies make. Some of the decisions facing organizations could be time-critical and require insights at that very moment. The data processing and analytics field is continually developing solutions that also help those tactical decisions requiring real-time insights. These solutions are being designed to integrate well with current BI tools and analytical dashboards used by organizations. How can you improve your organization’s time-critical decision-making?

Implementing Operational Data Stores

Operational Data Stores (ODS) is the perfect solution to improve time-critical decision-making. Implementing an ODS to an enterprise system helps with providing operational BI, which is data currently relevant. Once the data is no longer operational, it is persisted in permanent storage locations and removed from the ODS. Therefore, using this solution provides insights only pertinent to making time-critical decisions.

Within the ODS realm, there are other technologies that advance this data processing solution. For example, distributed in-memory data stores provide more features and improve processing insights significantly. Depending on your enterprise data warehouse’s architecture, there are several flexible methods of integrating ODS solutions for real-time Business Intelligence reporting. Integrating this solution has also been made very easy and efficient with the latest AIOps technologies in the data processing field.

How to implement ODS for BI

It has never been easier to implement an ODS for BI tools and analytical dashboards. Advanced ODS vendors provide this solution as a SaaS offering that is easily integrable with enterprise systems. Some provide a no-code database integration that uses AIOps smart indexing to automatically integrate data sets to the ODS.

As a result, the integration is ongoing, and organizations currently under the process of cloud migration from legacy databases do not have to manually update data sets. The data distribution is also powered by AI and based on operational insights required by the BI tools. Any operational data relevant for time-critical decisions are stored on an in-memory grid or module to provide rapid access to it. Once insights are not operational anymore, they are moved to SSD-based storage locations.

Operational data visualization

Data analytics is all about visualizing insights into easily readable reports and dashboard results. Operational Data Stores do provide this for powerful BI tools even though it focuses on insights currently relevant at that time. The ODS has access to historical insights regarded as warm or cold insights stored on SSD-based storage locations or other systems. From then, the data can be queried and brought for operational data visualization.

Key decision-makers within an organization can see performance results from historical insights in real-time. Operational BI provides a peek into how the organization may be performing at that very second when compared to minutes, hours, or days ago. Powerful BI tools can use those operational insights and formulate suggestions of potential best decisions to make. Those suggestions are backed by data reports with visualized insights for easy readability.

Cost-effectively providing operational BI

The level of fast processing power required to provide operational BI can be quite expensive for legacy systems. Disk-based data processing is undoubtedly very expensive and more especially if the hardware is in-house. Instead of worrying about managing costly hardware, ODS solutions come in very affordable service offerings.

Organizations can have access to high-end data processing power using in-memory grids without investing in hardware and incurring maintenance expenses. Instead, ODS vendors provide it as a SaaS cloud-based solution. The SaaS solution is priced according to the requirements of each organization. It is very affordable and requires no personnel to take care of the infrastructure. All you need to do is integrate the data store with the current enterprise system you’re running for BI tools.

Autonomous scaling

Legacy data processing systems have the common downside of not being easily scalable. However, cloud-based in-memory data stores/grids provide freedom with scaling operations. This is especially important during peak performance times, where insights might come in abundance and require monitoring. Through AIOps, the ODS can autonomously scale and match the demand at that time. In the long run, this also contributes to making operational BI solutions more affordable.

Instead of paying a fixed price, the expenses can be reduced by paying only for the data processing power you used in that month. At the same time, you do not have higher data processing capacity even at times when it is not necessary. Autonomous scaling is a gamechanger for data analytics and puts ODS solutions at a major competitive advantage.

Data processing in record time

The main idea behind operational BI is to gain access to insights in record time. As a result, ODS systems were designed with this demand in mind, and power advanced data processing for operational BI at a rapid rate. The underlying technology behind processing operational data removes all bottlenecks that cause latency on database queries.

Even the fact that the data is stored on an in-memory module or grid improves database response times. Instead of using ETL, ODS solutions use HTAP, which is much faster and processes insights within milliseconds. That significantly improves the accuracy of BI reports and analytical dashboard results. Data processing for time-critical decision-making using ODS solutions increases report accuracy and timeous delivery.

Are these solutions reliable?

Enterprise system developers are concerned about reliability when it comes to data processing solutions providing insights to BI tools. If the data source is not reliable and some insights are lost, BI reports could be inaccurate, and that would impact time-critical decision-making. This begs the question, are in-memory ODS solutions reliable? These solutions are reliable, especially if the data is distributed in an in-memory grid. Distributed in-memory data stores provide a highly reliable operational insight storage location.

Instead of relying on one in-memory node, the grid hosts the data and provides highly reliable insights in record time. If there is a system failure, the enterprise BI tool can use insights stored on another node which is replicated once it is extracted from data sources. Subsequently, distributed in-memory data stores provide a unique service to organizations because high-speed data queries do not usually go hand-in-hand with reliability. With ODS solutions, organizations get the best of both worlds and benefit from real-time accurate and reliable operational BI for time-critical decision-making.