It is true what they say – “Change is the only constant.” Today, the Finance sector is morphing due to many forces – Fintech, Blockchain, shared economy, changing customer needs, and regulatory challenges. But the biggest of all is the internal urge of large financial firms to cater to the under-banked customers and unbanked population which is 1.7 billion adults in the world (source World Bank).
This rigid aim of financial inclusion and increasing customer transactions compelled globalized financial firms to rethink and reinvent their operating models to acclimate to the changes Updating the IT operations model to get ready for the ‘new normal’ appears to be obvious from the Financial Services Technology 2020 and Beyond report by PWC. Both run-the-bank and transform-the-bank ideas are inevitable and go hand-in-hand from an IT perspective. Let’s take a deeper look into one of the silent, yet critical run-the-bank technologies – Workload Automation/Batch Processing – for smooth banking operations. Of all the tech in IT, Workload Automation/Batch Processing is considered a “classic” technology because it has been frequently used for automating end-of-cycle tasks. These tasks include computation of credit scores, invoice processing, credit settlements, settlements of trades, calculations of market value or market risks at the end of the day, etc. It has been the finance sector’s favorite “motorcycle” for more than 40 years, simply because it is fast and efficient.
As financial giants progressed towards further globalization and grew (in)organically, their IT became more distributed and heterogeneous. Hence the size, complexity, and scale of batch operations grew commensurately. The unmanageable batch operations not only drove job failures, constant SLA violations, and consequent business outages but also snatched away the flexibility of giant financial firms to execute business transformation initiatives. In the quest for financial inclusion, the need to provide uninterrupted customer experience by managing batch surprises will only multiply in the recent future.
Hmm…But Is There A Case?
Many Fortune 500 Financial firms rely on us to modernize their businesses. For example, a Fortune 100 Banking institution with a global presence, serves a customer base larger than the population of countries like Brazil. They rely on the timely execution of over 1,000,000 batch jobs to execute transactions for 700 critical applications and multiple business units. In the past, batch failures in this environment have resulted in SLA violations and thus, heavy penalties.
Ok, But What Was the Key Challenge in Solving these Issues?
Challenges to fix batch outages were aplenty, leave aside from the task of predicting job failures and estimating business consequences. Cross and hierarchical dependencies among jobs, diversified holiday calendars due to geographic spread, and lack of understanding about performance metrics of jobs managed by various schedulers were top challenges. They hindered the IT team’s ability to investigate and fix batch operation outages within the stipulated SLAs. Moreover, lack of knowledge about the impact of modification in batch processing to incorporate new business initiatives was highly risky, putting batch operators into the thralls of reaction to the recursive suspension of operations.
How Was Workload Operation Modernized?
The answer is ignioTM AI.WorkloadManagement. By leveraging AI for processing Big Data from batch processing, ignio understands and predicts the behavior of workload operations.
- ignio™ created a unified view of ~50,000 batch jobs, cutting across different business functions and different batch schedulers. These jobs were responsible for 1,945 business-critical deliverables (200+ tier-1 and 1700+ tier-2).
- ignio used data mining and machine learning to understand historical trends, patterns, and dependencies, and predicted future behavior of batch jobs.
- ignio generated ahead-of-time notifications of potential SLA violations and enriched these notifications with insights about causes and possible fixes.
- ignio also provided a ‘what-if’ analysis engine to enable batch teams to assess the impact of change.
- Batch operations teams received sufficient time to take corrective actions to save SLAs.
Results Should Be Business Assuring, Right?
With ignio, the financial institution embarked on a transformation journey to make their workload operations transparent, agile, and stable. Using ignio, they now have:
- End-to-end transparency across 50,000 batch jobs
- > 95% accuracy in predicting potential SLA violations with an average look-ahead time of > 4 hours
- > 90% prevention of potential SLA violations
Modernization of Workload Management is Critical to Next-Gen Financial Service Offerings
Successful IT leaders in global financial firms must opt for solutions that capitalize on the bet of AI and Automation to re-imagine their IT operating model. The benefits include maximizing stability, agility, and predictability of Workload Automation. They must strategize to use analytics for modernizing classic Workload Automation to support their corporate strategy of staying competitive in the constant-changing industry.
To learn more visit https://digitate.com/ignio-ai-workload-management/ or request a demo.