Modern enterprises have to invest in data operations to ensure their business platforms become more nimble and agile. For over a decade, organizations have faced the challenge of infusing data and analytics within their business systems with unimpressive degrees of success. I’ve discussed these challenges from different perspectives in my earlier blog posts.
My first blog discusses the paradigm shift taking place in the data landscape and how distributed data lakes and the data fabric approach play a role in enabling business outcomes. In my second blog, I discuss the role cognitive solutions can play in powering data marketplaces and help solve major business problems. This is the third and final blog post in this trilogy and I’ll be discussing the ever-growing data operation needs of next generation enterprises which requires data at scale and on-demand.
So, what is the answer? Is it the cloud? Is it Big Data? AI? Data science? Or perhaps all of them? In this blog, I will discuss how the data fabric approach and cognitive powered data marketplaces can converge to help organizations achieve these goals.
Greater the Complexities, Greater the Data Operations
Organizations need data to be available, accessible, consumable, and actionable to be useful. However, with increasingly business complexity, achieving this outcomes is easier said than done. Organizations need customized solutions that resolve complexity and transform data into business insights at lower costs while ensuring data reliability and meeting all compliance requirements.
For data-sensitive industries like finance and pharma, this is a significant challenge as they work with large data sets under strict compliance requirements. Moreover, these complex industries face problems like fragmented data, legacy systems, outmoded technology and inordinate reliance on manual effort. In fact, most of them operate on mainframes or legacy systems that aren’t agile, or adaptable to modern solutions.
This gives rise to Shadow IT teams whose attempts at ad hoc solutions consume thousands of hours of additional effort to remediate the data across business systems. This effort costs these companies valuable resources and distracts focus away from core operations. Most organizations resolve this problem by eventually turning to outsourcing however this leaves them vulnerable to data security and compliance risks.
HCL’s Next Gen Data Operations Proposition
Next-gen data operations can help organizations reimagine their approach by combining the powers of data fabric and a cognitive solutions powered data marketplace. This leads to faster data remediate with greater accuracy and reliability and helps generate business insights within their mainstream data lakes at lower costs.
Our vision of a streamlined data ecosystem is a multi-phased process that can help organizations achieve remarkable agility and adaptability in their data operations. Our solution helps organizations deploy a new data operations and management architecture, helping them acquire valuable insights and offer the best returns on their data engineering and data operations investments.
Our next-gen approach is based on our data engineering capabilities, offering customers an ecosystem for efficient data ingestion and storage while also allowing seamless remediation at high quality. This approach phases out the need for Shadow IT and deploys an automated solution which generates valuable business insights while operating at faster speeds with greater accuracy.
Investments and Experience enabling better management of data operations
The acquisition of companies like Datawave empowers our existing capabilities and allows us to address our customer’s automation needs. This helps them shift their End User Computing systems into mainstream data zones using the commoditization of data services on data marketplaces.
Similarly, the acquisition of Stone-Bridge Envision helps our client’s understanding of business personas, allowing them to move towards agile ways of working, DevOps and most importantly, better adoption of mainstream solutions such as data fabric, cognitive data marketplaces.
Furthermore, our investment in Actian, enables us to create data zones for our customers that can be processed and consumed much faster while also not being reliant on traditional appliances. This effectively eliminates the need for end user computing systems as it integrates any existing parallel ecosystems into the mainstream.
This solution is in two parts: first - making sure data provisioning moves into mainstream either in data lakes or data warehouses or in the data engineering side of things, and second - to ensure automated operations for rapid consumption by various stakeholders.
HCL in Action: A Case Study
We have implemented these solutions for numerous clients around the world. For example, a major global banking organization came to us with a significant problem. The client wanted to save on the time and effort resources of managing, mediating, manipulating and processing data from across 1500 business systems that capture customer data. This costly and time consuming exercise was further complicated due to the large and complex in-flow of information streams.
The client wanted a solution that would help consolidate and process the data from these systems in an end-to-end automated system and leverage analytics to anticipate their customer’s needs. This would allow the organization to offer their clients financial products at every milestone of their client journey. We worked with the client and discovered that their existing systems were highly dependent on manual interventions to deal with issues like data duplication, data coverage, data integration, mapping between application and data warehouse, and a poor communication structure, data use very slow and cumbersome. Moreover, the client relied on various end user computing solutions that followed nonstandard custom apps that left the unwieldy shadow IT team struggling with poor maintainability and ownership issues. But most importantly, the data trustworthiness was low and unreliable. The organization had already made hundreds of millions of dollars-worth of investments in their RTB/CTB spends, in addition to the high cost of manual people-based solutions.
Our solution helped the organization prioritize their data remediation systems and processes, starting them of on a data transformation journey. We also made implementation easier through the use of our Fenix DevOps based operating models and innovative agile constructs.
As a result of our partnership, the baking giant was able to consolidate, map and warehouse data from across applications to various business segments like insurance and retail. They were also able to establish a trusted data source and streamline their 43 databases into 8 master data systems for their insurance product system.
We helped the client identify and fill in their repository gaps and e successfully migrate data from legacy systems to strategic platforms. Also, the application of our next-gen data operations to Item data, finance data, risk data, customer data and product was further made more secure and compliant to the organization’s data policies.
We are driven to help companies use data in the modern century. Our investments focus on areas where we can leverage our expertise in business process simplification and optimization using our proven business-first approach. By using a design thinking approach, we are able to better understand our customer’s business personas and behaviors.
With our “Relationship Beyond the Contract” philosophy, our customers are able to expedite their data preparation process through hybrid platforms for a distributed data ecosystem while also staying connected. This is the potential of next generation data operations and we are leading the way.