One of the leading causes of poor data quality is the siloed enterprise landscape. As the amount of data grows, so do the presence of silos. A silo is a singular collection of data or information that isn’t effectively shared across an enterprise. Data is acquired from and stored in, disparate systems, applications, and workflows throughout the enterprise infrastructure. Each of these data sources consists of scattered, outdated, and duplicate records, leading to extensive disarray and disconnection in business processes. Firms need to implement an effective data governance tool to unify silos.
Enterprise silos arise naturally over time as departments and sectors store data in their own format and location, using inconsistent technology and purpose. Silos can also occur because of mergers and acquisitions, reorganizations, and the adoption of new technologies without the decommissioning of older ones. Additionally, disparate systems are created because existing architectures for given business purposes are implemented prior to appropriate data governance tools, practices, and policies being in place (i.e. online transaction processing vs. analytics) . Finally, data silos are created to make data work for that particular department or group of individuals and their business purpose.
According to a study on information silos, 48% of respondents said they feel their company struggles with managing information silos, and 67% cited that navigating through different systems and locations to find and verify the most current versions of documents or files negatively impacts their productivity. (Source).
Enterprise silos create significant barriers to efficient, compliant, and successful data management. With fragmented views, firms cannot ensure enterprise-wide data quality or understand fit-for-purpose data. People at large firms need a 360°-view of the data to gain actionable insights and realize latent opportunities (or threats) to make timely business decisions. Today, users have to take the data and manually review it against data dictionaries, catalogs, rules, other data sets, and also through one-on-one conversations with subject matter experts (SMEs). Access to enterprise-wide information is necessary to maximize operational efficiencies and discover new digital transformation opportunities.
To unify data or knowledge silos and improve data quality, firms must implement an effective data governance framework. Existing data governance tools fail to provide enterprise-wide visibility or traceability of actual data across siloed sources because they strictly focus on metadata. The resulting fragmentation continues to allow low-quality data, which results in increased operational costs, higher risks, and ineffective business decisions. Additionally, data silos undermine productivity, hinder insights, and hinder collaboration.
Enterprise silos pose significant challenges to building an effective data management framework due to the following barriers:
Lack of E2E Data Quality
Using existing static metadata tools, enterprises are unable to monitor data quality automatically throughout the data’s lifecycle. Data is measured and reconciled within individual siloed systems, applications, or workflows instead of across the end-to-end landscape. This broken process is then augmented with manual review processes. Each of these data sources consists of scattered, outdated, and duplicate records. The lack of E2E data quality quickly trickles down throughout the enterprise affecting operational efficiency, regulatory compliance, and timely decision-making.
Lack of a Single Source of Truth
When data is stored in disparate systems, it can quickly become outdated and stale. Someone may be using data that is not sourced properly or with an unknown origin. Without a single source of truth, firms can struggle to identify the genesis (or first entry) of the piece of data amongst the fragmented landscape. The single source of truth needs to be a guide for the people in the organization so that they can bridge communication gaps, but still work with their external constituents. There’s no assurance that external parties will conform to a firm’s “standard” metadata, expressions and rules (and always a need to traverse across terminology and models).
Inability to Gain Enterprise-Wide Insights and Identify Business Trends
Due to the siloed nature of data, firms cannot collectively gather and view data as a whole. Therefore, gaining enterprise-wide insights and identifying business trends becomes extremely difficult. Discovering business’ efficiencies and taking appropriate actions cannot occur without a unified view of all data, metadata, rules and analytics.
Increased Resources and Efforts
Enterprises must manually comb through and verify large volumes of data, which is both resource-intensive and laborious. When data is incorrect or causes errors within one silo, people must perform manual investigations to reconcile the error(s) across the enterprise. These efforts can lead to duplicate investigations and false positives, resulting in increased operational costs. In many cases, the people are also working in different areas and must spend considerable time just understanding that a rose by any other name is still just a rose.
To unify silos and improve data quality, enterprises must implement an effective data governance model. Data governance outlines the roles, rules, processes, and best practices an enterprise must follow to ensure data quality and proper use of its data. A functional data governance tool can successfully assist to break down data silos by providing E2E visibility across the firm.
Currently, most enterprises use passive data governance. This poses a vast amount of data quality challenges due to static metadata tools that only organize and manage data within the individual silos instead of across them. Additionally, this static approach only fixes the incorrect data, instead of fixing the process or rules that created the error. Therefore, firms must take a more active approach to their overall data governance and data management strategy to ensure perpetual data quality and E2E visibility.
Increased Operational Efficiency and Reduced Cost
Within an enterprise, various groups perform the same functions across business lines, clients, and regions (which is extremely counter-productive). Silos of information must be brought together through phone calls, e-mails, and other manual forms of communication. Additionally, not everyone understands the tools available to solve certain inefficiencies, and therefore, implement their own solutions, thus creating more silos.
With an effective data governance tool, enterprises can gain an end-to-end view of all their data and guarantee ongoing data quality, ultimately streamlining processes and reducing operational costs.
Regulatory Compliance
Tracking down data is difficult for businesses when the data is scattered across systems, applications, and workflows. To stay compliant, enterprises must be able to make specific quality assertions about their data—a significant challenge when data is stored in multiple systems. With regulations such as GDPR and CCPA, regulators require firms to efficiently add, move, or remove pieces of customer data when required. Silos make ensuring that data fields and attributes are appropriately managed very difficult. For CSDR, all parties must have full visibility to ensure that they have sufficient insight across pre-trade, trade, and post-trade events. Without a unified view of their data, firms cannot be assured that their data quality has the three C’s, nor can they perform root-cause analysis quickly for regulators. In fact, according to DTCC, large buy-side firms will likely need 25-50 additional resources to address and fix exceptions in trade data to ensure timely settlement, and therefore avoid the fines associated with CSDR (Source).
An effective data governance tool provides enterprise-wide data quality and end-to-end visibility which ensures that businesses can confidently locate, alter, and submit their data to regulators for accurate and compliant reporting. Additionally, firms can more easily identify and solve errors or exceptions in their trade data.
Better Business Decisions
A lack of enterprise-wide data quality and fragmented landscapes have made decision-making more difficult. Data silos produce incomplete views of essential business information. For the business to make a decision or approve a change, the right SMEs must be brought together. These SMEs are often separated across departments, sectors, and silos, limiting collaboration and enterprise-wide knowledge.
Effective data governance provides decision-makers with a unified and holistic view across the enterprise landscape. SME’s can confidently assert the quality of their data, regardless of where it is stored in the pipeline. With high-quality data and end-to-end visibility, decision-makers can quickly identify and address actionable insights and business trends.
PeerNova’s Cuneiform Platform is an active data governance tool that unifies and standardizes data across disparate systems, applications, and workflows while providing E2E visibility across the enterprise.
Through active data governance, the solution perpetually applies Data Quality and Timeliness rules across live data, regardless of where it is stored in the enterprise. Using this dynamic approach, the platform creates E2E, integrated, and active lineages across disparate sources, making the solution significantly more efficient than other data governance tools. Through automation and ongoing data quality checks across silos, the solution not only remedies existing data issues but also continuously manages and monitors data throughout its entire lifecycle. Ultimately, the platform breaks down these silos, ensuring E2E data quality and providing a unified view of all data and metadata.
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 provide E2E visibility to break down enterprise and data silos, please be sure to get in touch with us and request a demo today.
Sources:
Consolidating data silos: What enterprises need to know to harmonize data. Link
GlobalCapital, and GlobalCapital. “Learning Curve: The Settlement Discipline Regime and the Sell Side.” GlobalCapital, Link
