Does your enterprise’s current practices, methods, and processes allow you to make timely business decisions? To be successful, a business must evolve and flexibly mold its practices to the current climate and industry landscape. As a business or operations leader, you must consistently evaluate and improve business processes to help your enterprise grow. If you are a data governance or data management professional, you must implement effective frameworks to ensure your enterprise can govern and manage its data effectively and efficiently. Therefore, consistently updating both data governance and data management processes are vital for better and more timely business decisions.
As human beings, there is an innate tendency to forget why we are doing something. After all, we are all creatures of habit. When we learn certain routines or pick up practices from our predecessors, we absorb and continue them without much thought. Similarly in the enterprise world, methods that have been in place for quite some time become permanently accepted as processes. Eventually, because so much effort and thought goes into a process’ actual implementation, its organizational purpose can be easily forgotten. By focusing on how to implement it, one can forget why it was created in the first place.
If you look at your enterprises’ data governance and data management strategies, you may notice a similar pattern. Old and outdated frameworks continue to be used, without evaluating or analyzing their current purpose and effectiveness. Even today’s data governance solutions still present significant gaps that prevent business users and enterprises from reaching their goals—efficient and explainable business decisions. Therefore, it is important to pause and continuously re-evaluate current strategies, methods, and processes, measuring their effectiveness in furthering enterprise success.
Data governance started as an Information Technology (IT) discipline to understand the meaning of data in systems, but quickly evolved to include operations and business initiatives. A lack of end-to-end (E2E) data quality, fragmented landscapes, and the manual management of changes to the environment, have made decision-making more difficult. As technology transforms and becomes more pervasive, the importance of having effective data governance is apparent. Enterprises need rules and policies in place to better manage large amounts of data, while staying compliant to regulatory and maintaining operational efficiency.
Data governance promotes stronger data management to better inform business practices. When business teams are able to quickly assert the correctness, consistency, completeness, and timeliness of their data, they can gather insights and predictive analytics to make efficient and explainable business decisions. Ultimately, they can create new, evolving products and services to satisfy customers and spark organizational growth.
Currently, enterprises face certain organizational challenges when it comes to effective decision-making.
Complexities on Top of More Complexities
In the early 2000s, technology was evolving rapidly. Organizations had parts of their processes on mainframe, client server, and various versions of service-oriented architectures that were moving towards the cloud (Source). Each of these technologies expressed data in different ways, and transactions and information spanned across them.
Mergers and acquisitions resulted in people having to translate between technologies and the number of organizations that were combined. Each organization had its own way of describing its business, and these descriptions were embedded in their legacy systems and data.
Additionally, business models were coming together after the repeal of Glass Steagall in the ‘90s. This change added additional layers of complexity for firms to address. If you take all of these layers and multiply them, there are too many dimensions for anyone to comprehend individually. For example, one data source (like a trade) multiplied by three organizations, three business models, and four different technologies, results in 36 possible versions of a given type of data source. It is painstaking to link and rationalize all of that information today. This painstaking process is not memorialized and occurs repeatedly for the same information.
Managing Change
Data catalogs have existed since the introduction of technology, and have been maintained to understand the business. Since IT began, it has been a best practice to put the inputs and outputs of each process and technology implementation on paper. As a result, the business could approve the implementation for production to understand the automation supporting their business decisions. Over time, complex software development lifecycle (SDLC) methodologies began to manage changes to these environments.
Currently, when a business outcome is implemented across technologies and applications, it is difficult to see how it is going to work from end-to-end. Generally, it is time-consuming to refer back to paper specifications. This enterprise and organizational knowledge is held by certain people. The complexities described above create silos of information that must be brought together through phone calls, e-mails, and other manual forms of communication in order to confirm and decipher information. For the business to make a confident decision or approve a change, the right subject matter experts (SMEs) must be brought together.
Previously, organizations and regulators relied on metrics from SDLCs. Their focus was on the SDLC process and more immediate outcomes governed by command and control operational processes. The understanding of why some of those processes were in place had been long forgotten in the throes of execution. Both parties were focused on the actual process implementation versus its effectiveness.
Making Timely Decisions
In 2008, the financial crisis demonstrated that the current methods of decision-making across this complexity was not sustainable. Each reactive decision made by both the business and nation required efforts from IT, operations, risk, and many other areas.
After the crisis, many groups came together to solve different parts of this problem. In 2013, the Bank for International Settlements (BIS) designed BCBS 239, a framework that lists principles to strengthen banks’ risk data aggregation capabilities and internal risk reporting practices . BIS thought that an “effective implementation of the principles [was] expected to enhance risk management and decision-making processes at banks” (Source). This statement is a reminder that the purpose of governing and managing data is to allow for decision-making. A crisis or economic downturn shows that decision-making must occur quickly to support risk management. Additionally, this type of decision-making is also needed to capitalize on ripe opportunities, continuously improve the way enterprises operate for clients and employees, and grow holistically. Efficient and informed decision-making is not limited to the members of BIS; all firms in financial services need this capability.
Data governance and data management exist to allow enterprises to make better decisions that mitigate risk, encourage innovation, and reduce costs—and, ultimately, grow the business.
Not all data governance models provide the same results. Most current tools in the industry implement passive data governance versus active data governance. The existing data governance landscape has organizational challenges that limit effective decision-making due to managing complexities during times of change. Each time the business implements a new requirement to support growth, regulation, or cost reduction, SMEs must attest that the right data is used at the right time to make the right decisions.
Four challenges exist in current data governance solutions:
It is key that an effective data governance framework addresses these four challenges to ensure enterprises make timely and better business decisions.
As an active data governance tool, PeerNova’s Cuneiform Platform enables end-to-end (E2E) trust and transparency of data and business flows.
To ensure E2E data quality (correctness, consistency, completeness, and timeliness), the Cuneiform Platform perpetually runs Data Quality and Timeliness Rules on live data across enterprise sources. By continuously running rules on an E2E view, it becomes easier for joint decision-making to prioritize change. Enterprises can proactively act or react to extraordinary circumstances quickly. Additionally, having high-quality data results in clearer insights, increased actionable opportunities, and, ultimately, better business decisions.
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.
PeerNova’s Cuneiform Platform tracks and enables change controls on data, metadata, rules, and rules execution. When an organization makes any changes that affect data or its definition, they can see how those changes affect the different aspects of the complex environment. Businesses can then save time when making a decision, because they can see how correct or complete the data is and adjust their decision-making approach appropriately.
PeerNova’s solution enables API-driven hybrid cloud adoptions for greater interoperability for the exchange of data, metadata, and rules. Integrations and use of proven data simplify and progress digital transformation efforts.
PeerNova’s Cuneiform Platform is an active data governance platform that provides enterprises with a tool to simplify the environment, manage and prioritize change, and, ultimately, improve the timeliness and ability to make better business decisions. If you are interested in learning more about our solution, please be sure to get in touch with us and request a demo today.
How does your data governance program enable better business decisions today?
Sources:
“The Evolution of 13 Innovations in Enterprise Technology.” VMware Radius, 30 May 2019, Link
McDonald, Oonagh. “The Repeal of the Glass-Steagall Act: Myth and Reality.” Cato Institute, 1 Feb. 2017, Link
BIS. “Bank for International Settlements.” The Bank for International Settlements, 2020, Link
BIS. “Principles for effective risk data aggregation and risk reporting.” The Bank for International Settlements, 2013. Link
