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What Is the Cuneiform Platform Anyway?

02 Sep, 2020  |  By PeerNova   |  Published in Articles,

What is the Cuneiform Platform Anyway?

Enterprises of all sizes and industries are embracing digital transformation by replacing older legacy systems with newer solutions, and refactoring their applications to take advantage of technologies like Big Data, Cloud, ML, etc. However, there is one key constant that firms cannot ignore—ensuring end-to-end data quality and process correctness for their mission-critical business processes. Ensuring data quality and process correctness across the enterprise is extremely difficult and costly, due to the inherent complexity of business processes, as well as due to the heterogeneous nature of IT applications in today’s global enterprises. Enterprises are forced to hire large teams to mitigate this complexity, which is unsustainable in the long-term. However, using cutting edge zero-code automation technology that PeerNova has pioneered, firms now have the ability to rapidly analyze and optimize data quality and process correctness at scale across the enterprise. Firms are now driven by insights and metrics that improve business performance, compliance, customer experience, and ROI.

What is the Cuneiform Platform Exactly?

At its core, the PeerNova® Cuneiform® Platform is a zero-code end-to-end (E2E) automation platform for enterprise business users, that enables them to rapidly define and deploy data integrity and process correctness controls across a firm’s workflows, applications, or business processes. The solution infers and provides an array of insights, so business users can appropriately identify, evaluate, and take automated or guided actions based on those findings. The guided or automated actions may relate to identifying and resolving data quality issues or exceptions that occur in various business workflows. Additionally, guided or automated actions can also be taken to reduce cycle times for processing straight-through-processing (STP) transactions, while seamlessly managing exceptions (thereby improving overall efficiency and reducing costs).

PeerNova’s Cuneiform Platform empowers enterprises to unlock data, improve performance, and discover digital transformation opportunities for increased regulatory compliance, operational efficiency, and faster and more accurate decision-making.

What Kind of Questions Can Cuneiform Answer?

PeerNova’s Cuneiform Platform can solve a variety of problems and help firms answer an array of questions surrounding their data.

When it comes to trade validation, firms have strong concerns about data sources and data quality issues. Consider the example of a particular business user to whom the platform is targeted. The targeted business user is someone who works in a large financial institution in the middle or back office. Consider a senior decision-maker such as a Managing Director (MD), who is responsible for trade validation. This person may have a specific focus on providing clean and consumable data for downstream applications like client reporting, regulatory reporting, risk reporting, liquidity reporting, etc. inside a financial institution. Reporting to the MD are the Operations Analysts and other operations team members who are responsible for performing various control functions that are part of end-to-end trade validation. These particular business and operations users are focused on gaining and providing metrics to help monitor and ensure data quality within and across their numerous data sources, workflows, and applications. More specifically, these users are focused on data quality issues occurring in particular data sources, along with any changes in data quality over time (both degradation or improvement). Ultimately, these users perform root-cause analysis, investigate the business rules or processes that are actually creating the data quality issues in the first place, and fix the relevant issues.

For trade validation, there are a variety of questions that the PeerNova Cuneiform Platform can answer.

  • What are the data sources where data quality is of most concern?
  • Which data sources exhibit data quality improvements/degradation over time?
  • Which data quality aspects/rules are of most concern?
  • Are data quality issues being resolved at a satisfactory rate?
  • How does one identify the root cause of exceptions and breaks faster?
  • How does one improve data quality metrics for the various sources in an automated manner?

When it comes to exception management, business users can use metrics and insights from the Cuneiform Platform to answer the following questions:

  • What are the different types of exceptions that occur frequently? Can these be grouped together by relevant business criteria?
  • Are exceptions happening from a particular type of instrument, trade facility, client, trading desk, or data source?
  • Are you going to address the exceptions by using people, fixing IT issues, or fixing a specific business or process rule?

Not only do business users look to broadly identify and resolve exceptions, they try to group them together to gain insights that enable them to transform their existing business processes. These users can then analyze whether the issues or exceptions are caused by frequent defects or shortcomings in existing IT, client, application, or operations processes. The Cuneiform Platform extracts the metrics and insights needed for this digital transformation, along with providing ways for business users to take corrective actions in real-time using existing 3rd party tools.

Challenges with Current Automation Tools

Over the last 40 years, the financial industry has experienced many mergers, acquisitions, and changes to the regulatory landscape. Coupled with the quick expansion of institutions globally and technological advances like cloud, mobile, and Big Data, the industry has faced serious challenges when it comes to data quality and automation. Currently, there are selections of tools that solve some of these problems; however, the majority of these tools present challenges when it comes to integration, interoperability, and implementation.

If you look at a financial institution, firms have previously used bespoke systems and integrated legacy systems to resolve their data quality or E2E process automation issues.

1. Bespoke (Custom) Systems
First, financial firms use bespoke (custom) systems, such as data preparation tools, data quality engines, workflow tools, etc, to solve these data challenges. Data preparation tools bring data into a staging area where data quality engines apply data quality rules to create exception reports. These exception reports are then tied to a workflow tool, where users assign issues to various operations team members for investigation and resolution. Meanwhile, a variety of other tools are tracking the progress of these exceptions across complex workflows.

While integrating bespoke systems in the enterprise landscape, financial firms experience a variety of challenges. These bespoke systems are independent tools that lead to significant integration obstacles when it comes to modern big data, cloud native, or API systems within an enterprise. Most of these bespoke tools were often built during a time when batch applications were more common, making it difficult for firms to maintain real-time E2E insights.

Ultimately, bespoke systems have high integration costs, a lack of interoperability with other systems, and complicated maintenance.

2. Integrated Legacy Systems
Despite innovative digital transformation, many enterprises still run and maintain integrated legacy systems, relying on decades-old software to power their business. Most of these systems are designed to be standalone, posing extreme difficulty when integrating with other pieces of software. This drastically reduces an enterprise’s flexibility and scalability. As a result, firms experience both integration and upfront costs when trying to resolve data quality and automation issues through legacy systems. Additionally, these legacy systems have little or no self-serve features, requiring significant IT effort in configuring the systems, and defining and maintaining the rules. Firms utilize system integration (SI) vendors (such as Accenture, Deloitte, and KPMG) and various outsourcing vendors (WIPRO, TCS, Cognizant) to customize, configure, and maintain their systems, which significantly increases the total cost of ownership.

Additionally, any challenges with legacy systems are typically viewed as IT problems, which means that firms need a large CIO budget and IT team. To resolve any issues, business users must provide concerns and desired requirements to their IT team, who reverts to the SI vendors to perform any changes or updates. This is extremely costly and time-consuming, not to mention resource-intensive if the IT team is capacity and budget limited. Most integrated legacy systems are not user-friendly and are extremely limited when it comes to operational and business-related insights, due to the lack of self-serve tools for business users.

Ultimately, integrated legacy systems have huge upfront costs, require SI vendors to customize, configure, and maintain the tool, and lack self-serve tools for business users.

PeerNova’s Cuneiform Platform: End-to-End Data Quality and Process Automation

Whether it is bespoke tools that banks have tried to integrate themselves, or using already-existing legacy systems, there have been serious challenges to achieve E2E data quality and process automation. PeerNova’s Cuneiform Platform is a unique solution that solves many of these issues by providing a zero-code platform for business users to quickly and efficiently automate critical business processes and achieve business insights at a low cost.

When comparing the solution to other tools, firms find that metadata tools do not perform data quality checks across data sources, workflows, and applications. Instead, they are a set of documentation tools that are used to manually maintain a catalog of all data sets. In the past, these tools have been extremely useful to financial institutions; however, they have become quite antiquated. PeerNova’s Cuneiform Platform automates the metadata information, saving time and resources in capturing and maintaining E2E metadata.

Application performance monitoring (APM) tools are one of the largest categories of enterprise solutions. An APM solution is targeted towards IT and DevOps users who may be running a data center or responsible for various workflow applications. APM tools provide these groups with various metrics and insights (such as CPU/memory/network utilization) that an IT user is interested in—i.e., where IT bottlenecks are and how they are affecting the business downstream. While APM tools provide automation for IT metrics and insights, the Cuneiform Platform provides valuable metrics and insights for business users. It offers business users the ability to obtain the business workflow and metrics they are interested in directly. Until now, business users have had to reach out to their IT counterparts to add this functionality into their legacy or bespoke systems.

As discussed, legacy and bespoke systems pose significant challenges when it comes to data quality and process automation. The solution provides E2E visibility and identifies data quality issues in real-time to transform the extremely complex trade validation process and streamline exception management and resolution.

The PeerNova Cuneiform Platform provides E2E automation and key business metrics and insights, so enterprises can quickly recognize and resolve exceptions, exceed client and stakeholder expectations, and identify the most promising digital transformation opportunities. In the long run, firms can ensure E2E data quality and process automation across the enterprise for increased regulatory compliance, operational efficiency, and decision-making.

If you are interested in learning more about how the Cuneiform Platform can help your enterprise, please be in touch with us and request a demo today. You can also listen to the supplemental podcast episode featuring PeerNova’s Founder and CEO, Gangesh Ganesan.


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