November 2022 Summaries

Data leaders are increasingly adopting data observability to manage complex multilayered modern data architectures operating in cross-platform environments. A survey of 200 data leaders revealed that almost half experienced data pipeline failure due to quality issues or late error detection, with customer experience suffering as a result. Over 50% spend 1-6 days per month addressing data quality problems, and dealing with these issues interferes with other job responsibilities and higher priority projects. To improve their data performance and ROI on data investments, data leaders are seeking solutions that provide better insight into their data pipelines and plan to employ data observability tools in the next 12 months.

Data products are valuable tools that require high-quality, reliable data for effective product development and strategy. Building these products involves focusing on user needs, exclusivity of the data, and agility in identifying new opportunities to serve users. Data observability is crucial for modern companies as it helps monitor and manage data across various tools and applications, ensuring high data quality, integrity, and coherence throughout the data lifecycle. Choosing the right data observability platform involves considering factors such as compute and infrastructure, reliability, pipelines, and user experience. Acceldata's data observability solution can help organizations build effective data products by automating end-to-end data management for analytics, compliance, and security needs.

In 2021, the global economy faced significant costs due to the aftermath of the COVID-19 pandemic and natural disasters, leading insurers worldwide to incur over $130 billion USD. As a result, IT leaders in the insurance industry are turning to data observability as a means to optimize their data operations and reduce costs. Data is an internal commodity that can be leveraged by businesses for survival and success, with McKinsey research showing that data-driven organizations can increase customer acquisition metrics by 23x. Insurance providers have found significant promise in big data and data analytics, which has helped them increase revenue, retain customers, and improve processes to boost customer satisfaction. Data observability tools help address data problems across the pipeline layer, compute layer, reliability layer, and user layer, ensuring that data flowing across diverse channels and pipelines are monitored in real-time and alerting users in case of potential breakdowns. Acceldata's suite of data observability solutions helps eliminate potential risks associated with data quality errors and data pipeline breaks by monitoring them constantly and alerting teams in advance.

The Hadoop ecosystem is complex and challenging to operate due to its numerous components such as HDFS, Spark, Hive, and Kafka. To navigate this complexity and optimize the Hadoop environment, data leaders are turning to data observability solutions like Acceldata's Data Observability platform. This platform provides real-time intelligence of your data systems and automatically generates recommendations to optimize them. It helps track key metrics, predict and prevent incidents, identify over-provisioned resources, and align infrastructure costs with business priorities and requirements. Without such a solution, businesses risk unexpected downtime, sub-optimal workloads, and cost overruns that can impact their outcomes.

The increasing complexity and costs associated with modern data environments have led to the growing interest in data observability among data leaders and practitioners. Despite differences in their day-to-day responsibilities, CDOs and data engineers should be aligned around leveraging data to advance organizational goals. Data observability can help both roles by providing end-to-end visibility into data repositories and pipelines, enabling quick identification of issues, automating data quality monitoring, and supporting cost optimization. Acceldata's Data Observability platform offers these benefits without adding significant workload for data engineers.

The Finserv market is at a crossroads due to the COVID-19 pandemic and recession, with neobanking shaking up traditional banking norms. Financial institutions can leverage their existing data assets for critical insights into processes, product market acceptance, and risk management. By analyzing operational, transactional, social media sentiment, and customer satisfaction metrics, banks can improve customer experiences, optimize processes, eliminate redundancies, and mitigate risks resulting from human error. Artificial Intelligence (AI) is playing a significant role in this transformation, with global banking players investing heavily into future technologies like AI. Banks are leveraging data to evaluate credit risk, manage treasury operations, improve marketing and sales efforts, enhance security and payment operations, and ensure Data Observability. By adopting proactive approaches to data analytics and using multidimensional Data Observability solutions like Acceldata's, financial institutions can maximize the value of their tech investments and achieve business success through optimized data pipelines.

PhonePe, a Walmart subsidiary and one of India's largest digital payment platforms, has adopted data observability to improve its massive data infrastructure. The company faced challenges in managing system performance due to the complexity of their OLTP (online transaction processing) and OLAP (online analytical processing). Acceldata played a crucial role in enhancing productivity by minimizing downtime and optimizing data operations through data observability. PhonePe's adoption of Acceldata led to a 65% reduction in the cost of managing data warehouses, equivalent to five million dollars in savings within the first 18 months.

Data quality problems are prevalent in organizations due to various reasons such as schema changes, API call failures, and manual data retrievals leading to duplicate data. Poor data governance can also result in expensive data silos. Machine learning algorithms are significantly impacted by the quality of data. Migrations from on-premises infrastructure to the cloud introduce new challenges related to data management and quality. The rapid growth of data volumes and sources, coupled with a plethora of data tools, create fragmented and unreliable data environments. Legacy data quality strategies fail due to their inability to scale for today's larger data volumes and ever-changing data structures. Manual ETL validation scripts are not suitable for real-time data processing and require significant ongoing engineering time and effort. Acceldata's Data Observability platform provides an end-to-end solution that helps organizations continuously optimize their data stacks, offering features like data pipeline monitoring, data reliability assessment, performance tracking, and spend visualization. Advanced AI/ML capabilities enable automatic anomaly detection and root cause identification for unexpected behavior changes in the production environment.