Source: insidebigdata.com
While the benefits of working with big data are well established, the continuing growth of unstructured data is overwhelming many organizations. That’s because they have little idea of how to manage and use it in the best ways to generate value for the business.
Whether it’s used to inform business decisions, drive better processes, develop new services or improve customer experience, the poor use and management of big data can damage a business.
If your data warehouse and analytics tools aren’t able to use data effectively, it could result in poor business decisions based on inaccurate information, or poor customer experience that detracts from the brand or, worse, causes problems for customers that drives them away.
The organizations that know how to manage their data and make good use of it both internally and externally tend to perform better and generate greater trust and loyalty with customers.
To have accurate, timely data that will drive your business forwards, you need to have the right processes in place.
And these processes should use automation and orchestration to collect and evaluate the data to ensure it can be trusted when being used for decision making.
Automation – bringing coordination to the chaos
By definition, big data is taken from a range of disparate sources, so monitoring and tight coordination are key.
By automating extract, transform and load (ETL) processes, you can quickly, confidently and securely pull data from across the enterprise, regardless of the platform or technology, and rapidly feed it into your data warehouse, OLAP and BI tools.
This means you always have the information you need to inform decision making without any additional manual effort.
Modern workload automation software can also integrate data from multiple sources based on process dependencies and business requirements. The data can then automatically be sent to exactly where and when it’s needed, supported by a visual process monitor and automated documentation.
Big data without compromise
Many schedulers use the “new day” approach which requires a significant pause for downtime every 24 hours to move data and purge data from old job activity. This compromises or even stops real-time access to big data.
Modern workload automation avoids this problem by using a rules-based framework for detecting and responding to exceptions. As a result, the software automatically responds to events and takes remedial action to keep the process going.
No manual intervention or escalations are needed, meaning your big data activities are not compromised.
Another benefit that modern workload automation brings is that it mitigates the increased risk of errors and delays when new data is inevitably added to the mix through new sources and more effective collection. No additional manual effort is needed and the relevant business logic can be easily added to ensure the new data is effectively integrated.
Modern workload automation addresses many of the challenges associated with the collection and management of big data. It provides fast, accurate and secure collection of data from across the enterprise, formatting and storage without manual intervention, powerful exception handling, and high visibility into the state of your data.
By using automation to collect and manage your big data processes, you will truly exploit its value for the business.
