Have you ever heard of a business that is satisfied with slow, unproductive workflows or business processes? What about a business that is unconcerned with staying ahead of the competition or slow time-to-market with new products? Has any client ever asked their business for a slow, disjointed process? No business or client would find such scenarios acceptable. In reality, operational efficiency is of the utmost importance for any successful, growing enterprise.
First, it is important to understand that data fuels all parts of a business. Ensuring high-quality data is key to achieving business goals, productivity, efficiency, and competitiveness. With increasing volumes of data being housed across the enterprise, firms are discovering that operational efficiency can only be achieved with strong data governance and data management frameworks in place.
Exactly, what is operational efficiency? The contemporary definition of operational efficiency entails streamlining operations, digitizing front-to-back workflows, and reducing manual processing of exceptions and errors – using data and automation. By lowering costs, of processing inefficiencies, and errors, firms improve client experience, increase profit, and avoid revenue leakage. Operational efficiency is not a one-off project—it needs to be improved and monitored cyclically and consistently using effective data management and data governance.
Firms face significant bottlenecks when trying to increase their operational efficiency.
Fragmented Landscape and Siloed Enterprise Knowledge
Currently, the enterprise landscape is hugely fragmented, and enterprise knowledge is siloed. Data is stored and managed across disparate systems, applications, and workflows. As a result, firms must often manually comb through and verify large volumes of scattered data, which becomes extremely laborious and resource-intensive. If errors are found, people must perform manual investigations to reconcile the error(s) across the enterprise—often leading to duplicate efforts and false positives. Without a unified view across the enterprise landscape, people only have fragmented pictures of data housed in their systems.
Beyond actual data, enterprise knowledge is also siloed. Enterprise and organizational knowledge is owned and operated by specific people in different departments. When it comes to decision-making, the appropriate Subject Matter Experts (SMEs) must be brought together from various areas of an organization. There are often duplicate groups performing the same functions and producing the same information across business units, clients, and regions. This disparate information must be brought together through phone calls, e-mails, and other manual forms of communication. When trying to solve these inefficiencies, not everyone understands a firm’s available tools; therefore, they implement their own solutions, causing more silos and a lag in productivity.
E2E Data Quality Challenges
Many enterprises do not cohesively monitor the quality of their data throughout the enterprise or throughout the data’s lifecycle. Data quality is measured within individual systems, applications, or workflows. The smallest error or discrepancy from a system storing, enriching, or transforming inconsistent data can quickly trickle down the pipeline and pose significant challenges to operational efficiency. To find the source of the error or exception, enterprises must manually investigate and trace the data’s various transformations, which is extremely time-consuming. Without end-to-end (E2E) data quality and data traceability, firms continue to produce low-quality data that is replicated and used throughout the enterprise.
One key dimension of data quality is data consistency. Due to information silos and the subsequent siloed knowledge, firms often lack a standardized data format. Bottlenecks and inefficiencies occur when data formats, taxonomies, and business rules are inconsistent. Exorbitant resources must go into preventing and investigating low-quality data to resolve these inconsistencies.
Manual Creation and Update of Data Dictionaries, Glossaries, and Catalogs
Data dictionaries, glossaries, catalogs, and rule repositories are the foundation of an enterprise’s data management and data governance frameworks. Most firms employ a passive or separately managed data governance model, which requires constant manual creation of these fundamental components. When underlying data or rules change, data stewards must perform manual updates to maintain consistency. This extra step can be expensive and timely, resulting in stale or inaccurate data that must be fixed manually.
With data fueling all aspects of a business, firms must implement an effective data governance model to achieve operational efficiency. Effective data governance provides processes and rules to ensure that poor data quality is identified and addressed on an ongoing basis across the enterprise. By consistently checking the quality of data throughout its lifecycle, firms will not only reduce errors but also prevent manual investigations and duplicate efforts.
A functional data governance tool will also successfully help break down data silos by providing E2E visibility across the firm. By bridging the gaps and gaining an enterprise-wide view into their data, firms can foresee and identify missed decision-making, regulatory compliance issues, and possible digital transformation opportunities. Ultimately, these possibilities rely on increased operational efficiency, thus making it the foundation for enterprise success and growth.
Most data governance solutions are extremely manual and require constant creations of the foundational blocks of data management frameworks. Effective data governance perpetually and automatically creates and updates data dictionaries, glossaries, and catalogs. Additionally, other data governance solutions try to understand, corral, and force large amounts of change, instead of allowing the people in the firm to evolve and understand their data. Through effective data governance, people within the enterprise can learn to evolve, giving the firm insights to drive change and efficiency quickly. This is true digital transformation, where people within these enterprises want to start doing something.
The PeerNova® Cuneiform® Platform is an active data governance and data quality tool that enables E2E trust and transparency of data and business flows for increased operational efficiency.
The platform ensures perpetual data quality (including data timeliness and data consistency) and E2E visibility across the entire enterprise landscape, ultimately reducing duplicate false positives and manual investigations. The Cuneiform Platform perpetually applies Data Quality and Timeliness rules across live data, regardless of where it is stored in the enterprise. Through automation and ongoing data quality checks, the solution not only remedies existing data issues but also continuously manages and monitors data throughout its entire lifecycle across siloed sources.
The solution uses ongoing data quality checks to track, group, and prioritize exceptions across tools in real-time for optimal efficiency. Exceptions are flagged immediately and corrective actions quickly—and often automatically—begin. The platform builds, updates, monitors, and optimizes continuously using automated tools for metadata and rules management. This results in faster root-cause analysis, increased operational efficiency, and lower operational costs.
Additionally, creating a standardized data format is an extremely huge undertaking. PeerNova’s solution allows firms to reap the benefits without having clean data to begin with. Employees and clients can continue to speak and work in their own language with their own data formats, while still being able to map the information across the enterprise.
The Cuneiform Platform provides E2E active lineages across workflows by automating metadata generation and maintenance. The enterprise knowledge in the firm is memorialized in the platform to provide communication efficiencies with a single source of truth. Individuals can realize change more quickly when complete knowledge is captured and not siloed. The enterprise does not have to find specialized people to make a quick decision, because the knowledge is represented by the rules and the rule results.
With PeerNova’s Cuneiform Platform, enterprises can quickly recognize and resolve exceptions, exceed client and stakeholder expectations, and identify the most promising digital transformation opportunities. By bridging the gap between silos, ensuring ongoing data quality, and providing E2E visibility, enterprises have increased operational efficiency, reduced risk, and better decision-making abilities.
If you are interested in learning more about how the Cuneiform Platform can increase your operational efficiency, please be sure to get in touch with us and request a demo today.
