13.10.2022 | Nikolay Valiotti
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Is it possible to create a lean governance program in a strict regulatory environment? Can you reduce its complexity without incurring undue risk? Where do you start? If you’re trying to build a foundation of trusted data within your organization, you’re probably asking these questions, too.
While data governance covers a lot of areas and serves a crucial role for any organization, it doesn’t have to be complex. In fact, it can fit naturally into your existing processes. This article will explain how to launch data governance activities that integrate well.
Learn all about data governance processes, tools, best practices, benefits of data governance for organizations and stories that data tells, and more below.
It’s easy to assume that terms describing information-related processes are interchangeable. Data governance, data management, and master data management get confused frequently.
So, before you establish a data governance process, make sure you understand exactly what you’re dealing with. It’s not concerned with execution and operationalization, and it’s not about decision-making. Data governance is about the roles, responsibilities, and processes that ensure the ownership of and accountability for data assets.
All programs have a data governance framework that provides a holistic approach to discovering and storing data. Think of it as the hub for different knowledge areas. When establishing a data governance strategy, each of the following facets of data collection and use should be considered.
With areas of governance defined, let’s move on to the steps for building data governance programs.
Important note: you shouldn’t be looking for a one-size-fits-all program because every organization is different. These are broad guidelines. If you’re not sure how these steps would fit into your data operations and overall business processes, you may want to discuss them with a data specialist.
Understand why data is important to your company. Answers might range depending on who you ask and whether they’re looking to improve internal data analysis, optimize operations, or create a long-term competitive advantage. So, consult with different people across the organization to have various inputs.
It may also be the case that some people don’t recognize the value of data for their work. As you formulate your data governance vision, communicate how your data processes might change across teams. The better people understand the role of data, the better governance program you can put in place.
As mentioned, governance programs may look different from one organization to the other. That also applies to the level of governance needed. Here are some of the factors to consider:
If you don’t have any kind of governance program, don’t try to govern every aspect of your data right away. Focus on high-priority data subject areas and accomplish the main objective of data governance in small steps.
The level and intensity should scale with the company. The bigger the size, needs, risks, maturity, and capabilities, the more demand should be placed on data governance.
A top-down approach usually works best. Identify leaders throughout the organization that can see the program through. They will be the ones to introduce the new vision for how data will be used, explain what’s expected from everyone, and remove barriers to implementation.
In addition to those who will advocate for change, you need to set up and fill roles for day-to-day operations. These are data owners that have direct data quality activities, oversee data repositories, etc. Other participants include data stewards, who are the governing body for most data-related decisions. Finally, form a steering committee to set and champion the overall data governance strategy, as well as hold other members accountable to timelines and outcomes.
Speaking of the committee, you need a formal data governance team charter, putting all the roles and responsibilities in writing. Other key components of a charter are listed below.
As the scope of data-related processes grows, make changes as needed, incorporating feedback and suggestions.
Workflows allow you to standardize how work gets done and ensure consistent results, and data workflows move data across business systems from one step to the next. Essentially, it means establishing a data supply chain.
As you try to manage a big data workflow, be prepared to encounter a number of different data governance tools and a vast assortment of technology components. Try to avoid using too many and make sure the ones you have are synced and integrated.
This is one of the most important steps—establishing appropriate controls for data quality and integrity. Bear in mind that different types of data go through different processes. So, people involved with documenting and implementing the appropriate controls should know which practices apply where.
Assign data controllers who will have full oversight over data storage, access, and updates. They will also handle personally identifiable information and enforce rules for the deletion of data.
As the number of your data sources grows, it will get increasingly more crucial to prioritize the ones you can trust.
A trustworthy data source is any source that offers reliable, independent, and, most importantly, updated data. For the highest data accuracy, it needs to be updated in real-time. Verifiable information will ensure regulatory compliance and correct data-based managerial rulings.
Before you can agree on authorized sources of data organization-wide, classify data in data models. These, in turn, will serve as differentiators for creating a catalog of data domains and sources.
Learn about relevant security laws and compliance requirements. Keep in mind that your organization is likely to fall under several regulations, so you can’t do away with complying with one. Examples of such regulations are listed below.
These and other regulations will inform your data security measures.
You should also look into excess access and storage locations. Ensure users have the appropriate amount of access to do their job (no more), and all sensitive data storage locations are protected from common threats and vulnerabilities.
By now, the data governance committee has established a data management glossary and key metrics, and the roles and responsibilities are defined. All that is left before roll-out is automating data governance principles and their associated components. This way, users will get a full 360-degree view of the data landscape in as little time as possible and avoid manually cross-referencing every data asset in every department.
Another benefit of self-service data governance tools is the ease of use. The key to creating a good data governance program is helping business users handle data without IT’s involvement.
It might take some time before data governance is fully internalized and ingrained. In the meantime, you should be prepared to do some extra work upholding the goals of the program. Leaders should be overseeing and closely managing data—specifically, the transition from non-governed data assets to governed data assets.
Get ready for data-related questions as you roll out:
Finally, no matter how successful your program turns out to be, don’t treat it as self-sustaining. Assess performance on a scheduled basis and adapt your program based on the changing needs and requirements. But it will get easier after you go through that initial cultural shift within the organization.
It’s rare to have tools specifically developed for data governance. Cross-functional data governance solutions are more popular and offer better value.
This tool acts as an ETL (Extraction, Transformation, and Loading) solution for the entire data ingestion lifecycle—starting from initial capture and necessary conversions to allocation.
A data integration tool is meant to collect structured and unstructured data from various sources and in different formats. Then it standardizes input based on the rules set out by the governance program and transforms available data assets, bringing them to high data quality. Finally, it moves them to their target destination.
Data processing tools help single out specific content types. As a result, users can get access to easily digestible information and extract valuable content. This supports one of the key principles of data governance—fostering an organized system to ensure clean, consistent data and building effective procedures for internal data analysis.
Even with a strong governance foundation, the work should be ongoing. It’s unreasonable for organizations to seek a quick fix for their governance woes. Brace yourself for a long journey of constantly assessing your program, understanding new challenges, benchmarking, and identifying areas of improvement.
These practices lie at the center of all successful data governance and stewardship programs:
Data tells a story, and most trying to decipher that story may already have certain data governance practices in place. But where they fall short is formally structuring all the processes and responsibilities. An exhaustive strategy shouldn’t leave anything that affects the integrity and security of data behind—people, applications, business units, or functions. That can only happen with systematic, formal control.
With a data governance team, you will standardize the answers to some important questions about data:
In addition to always having answers to high-level questions, an effective data governance strategy brings other benefits:
Start rolling out your new governance program slowly and build on the momentum. These ten steps should put you in a good position for a foundational program, which will grow and amplify the impact of data governance.
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