Atlan: Effortless Data Governance For The Modern Data Stack
The entire data management space is going through a paradigm shift.
The data world is slowly converging around the best of the tools for processing large amounts of data, a.k.a the Modern data stack
Data governance for the Modern data stack needs a rethinking.
This is where Atlan comes to your support A data catalog and data governance solution built for agility, trust, and collaboration.
Centralized Data Governance And Decentralized Execution
The final model combines different aspects of the systems listed above. In this model, there’s an individual or team that controls the master data, but each team creates their own datasets to contribute information. This means that both management and team members are responsible for collecting and sharing internal data. This is great for larger businesses who are looking to streamline data to their management teams.
Now that we’re familiar with what data governance is and how you can implement it, let’s talk about some best practices to consider when creating a data governance framework.
Define The Scope Outline Compliance Requirements And Clear Any Misinterpretations
This is the stage where you should standardize the rules, standards and definitions around all data assets. Standardizing avoids math errors like that of the NASA orbiter and gets rid of questions like:
- What does this field mean?
- Why are there two fields with the same value?
- Should I record our international sales in USD or Euros?
While it may be too complex for the entire organization to remember all the rules and follow them to the T, the folks responsible and accountable for ensuring governance should be well-versed.
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Who Is Responsible For Data Strategy
If you have a Chief Strategy Officer or Head of Strategy, they should be responsible. You can partner with these resources and help them understand the power of the data your organisation holds. Business stakeholders need to be engaged from the CEO down to ensure data in your organisation is used to unlock enterprise ambition and value.
How Atlan Implements Data Management
Heres how users achieve it using Atlan:
Users can define rules for automatic storage or deletion of these logs as per the compliance standards.
Preview of access logs in Atlan
Now lets look at access. You can use a single dashboard to monitor access and grant or deny access rights immediately.
User groups based on functions or roles, and governance allowing actions via policies using Atlan
As a result, your managers dont have to wait for weeks to get access to essential customer data informing their business decisions.
Technology also makes it easier to implement governance programs with automation. For instance, if a particular data asset is labeled as sensitive, then every transformation, algorithm, or report using that asset will also inherit the same classification and security controls.
Heres how Atlan propagates governance policies through lineage.
Propagation of policies through lineage in Atlan
There are endless use cases of how using one platform for governance and data management can make them effortless and democratize data for teams across the organization. Thats how organizations can go from data governance vs. data management to effective data governance with data management.
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What Are The Challenges Of Data Governance
An effective data governance framework brings with it many clear advantages, including increased protection against data breaches and cyber attacks, improved ROI on data analytics, reduced data management costs, and the democratization of data management responsibilities throughout the entire organization. That said, establishing a data governance framework also brings with it certain challenges:
Compliance & Risk Mitigation
Data compliance is the process of adhering to regulations regarding data protection. This helps to secure sensitive information from unauthorized access and in reducing penalties for non-compliance. Besides, it also ensures regulatory compliance with privacy, security, and legal requirements.
Risk mitigation Identify risks related to data and establish controls to mitigate their risk exposure. By doing this, organizations can formulate plans to reduce risk exposure.
Pillar 4 will help organizations address the identified risks.
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Normative Laws And Regulations
Every successful data governance process will need to develop and follow uniform rules and regulations to secure the data and guarantee it gets handled according to all relevant external laws. These standardized rules and regulations, which will be developed at the data governance council level and implemented by the data steward, will provide criteria for all aspects of data usage.
Your Guiding Question: Are There Gaps To Address In Your Own Data Governance Framework
With so many priorities to manage across a modern business these days, Data Governance is one that justifies being high on your list. The 5 pillars discussed here, are the key focus areas within this deep subject area. Every company needs to consider them. Whether you build this capability internally or outsource the expertise will depend on your company size, risk profile, and many other factors.
So where to from here?
I challenge you to ask yourself these questions:
- Do you send unencrypted customer PI data to external parties?
- Do you suffer from inaccurate reports or have an ineffective measurement framework?
- Are you positive youre not unnecessarily exposing sensitive data?
- Would your customers be happy knowing how you use their data and with whom you share it?
- Do you know if errors are preventing reliable integration between systems?
Any company that uses customer PI data should consider an external data audit or review. Especially if you felt uncomfortable reading this article!
The Lumery specialises in reviewing, fixing, and enhancing modern digital enterprise stacks. We get to know your business, your desired outcomes and evaluate the key areas of People, Process, Tech & Data, to identify the gaps and provide solutions to meet your needs.
Talk to us today about how we can help you uncover areas for optimising the way your business approaches data management.
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What Is A Data Governance Policy And Why Is It Important
Data governance policies are guidelines that you can use to ensure your data and assets are used properly and managed consistently. These guidelines typically include policies related to privacy, security, access, and quality. Guidelines also cover the roles and responsibilities of those implementing policies and compliance measures.
The purpose of these policies are to ensure that organizations are able to maintain and secure high-quality data. Governance policies form the base of your larger governance strategy and enable you to clearly define how governance is carried out.
Q: Is Data Governance A Program Or A Project
A: Data governance should be viewed long-term strategic business program, not a single short-term project. Implementing data governance requires structural changes to a companys current data policies and practices, in addition to redefining the roles and responsibilities of data handling personnel.
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How A Math Problem Cost Nasa $125 Million
Way back in 1999, NASA lost its because of a translation problem. Unfortunately, spacecraft engineers didnt make the switch from the Imperial to the metric system for their measurements.
It is very difficult for me to imagine how such a fundamental, basic discrepancy could have remained in the system for so long. I cant think of another example of this kind of large loss due to English-versus-metric confusion. It is going to be the cautionary tale until the end of time.
John Pike, space policy director at the Federation of American Scientist
What if they had a central data repository complete with a glossary that provided adequate context and standardized processes that governed the recording and storage of all data? The Orbiter might still have disappeared, but discrepancies in data wouldnt be the cause.
Thats why data governance, along with a solid framework, is so important. However, before we move on to the nitty-gritty of a data governance framework, lets quickly recap the concept of data governance.
Assess Projects After Completion
After each data governance project is completed, you shouldn’t merely pat yourself on the back and move on. After all, if the project wasn’t successful in achieving your goals, it will need to be adapted for the next initiative. Run some tests on your data to note changes and discuss with your team what processes should be streamlined and which ones need to be readjusted.
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S To Make The Transition Easier
Data Governance frameworks organize people, processes, and technologies together to create a paradigm of how data is managed, secured, and distributed. But the path to a Data Governance framework often seems difficult to embark upon. Here are three steps to help you get started:
- Cleansing the data has the effect of streamlining the work it is used for. It is the process of repairing or removing inaccurate, corrupted data, or data that is incorrectly formatted. It also removes duplicate data. There is data cleansing software, though small amounts of data can be cleaned manually. After the data has been cleansed, it is important to continue the process as new data comes in. Data cleansing can be done with the help of automation and should be included in the Data Governance framework.
- Automated data transformation tools can be used to translate a variety of data formats into the standard format used by the organization.
- Organizing data makes it easier to find and use. Arranging and classifying the data makes it more accessible. The data should be arranged in the most orderly and user-friendly way possible, allowing anyone with appropriate access to easily find what they are searching for.
Challenges Of Data Governance
Though the rewards are great, creating a data governance solution may feel difficult. Some of those challenges include:
Company-wide acceptance. Since data spans across multiple departments, there needs to be clear leadership from the top down as well as cross-functional collaboration.
Poor data management. If your data management is structured from an incomplete data governance program, the data will be unsecured and siloed as well as having undisciplined processespossibly leading to massive data breaches and non-compliance.
Standardization. Organizations need to find the right balance between governance standards and flexibility.
Aligning stakeholders. You’ll need to work hard to convince stakeholders of the value of your dataproviding transparency to stakeholders will persuade them to invest in your organization’s governance and securities budgets.
Assignment of responsibilities. There might be struggles with deciding who and who shouldn’t have access to particular segments of data. Creating a system of who sees what and when will help you and your team eliminate potential issues.
Your data governance strategy both the technical and business aspectsneeds to be accepted by everyone in the company. And to ensure you have a successful strategy, you’ll need to implement best practices and principles into your data governance program.
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Data Governance Framework Components
The policies, regulations, procedures, organizational structures, and technology implemented as part of a governance program make up a data governance framework. Additionally, it outlines the programs mission, goals, and metrics for success, as well as decision-making roles and accountability for the several components that will make up the program.
The Data Governance Institute states that the following are requirements for every organization for a data governance framework:
- A set of guidelines outlining how various parties collaborate to create and implement these guidelines
- Making and enforcing the regulations are individuals and institutional entities.
- Processes that will control data while generating value, controlling cost and complexity, and assuring compliance
The data governance framework of an organization should be established and distributed internally so that everyone engaged is aware of how the program will operate from the outset.
Data governance framework:
On the technical side, managing a governance program can be automated using data governance software. Data governance tools dont have to be a part of the framework to enable program and workflow management, collaboration, the establishment of governance policies, process documentation, and other tasks. Additionally, they can be used in conjunction with tools for master data management , metadata management, and data quality.
Benefits Of Data Governance
Most companies already have some form of governance for individual applications, business units, or functions, even if the processes and responsibilities are informal. As a practice, it is about establishing systematic, formal control over these processes and responsibilities. Doing so can help companies remain responsive, especially as they grow to a size in which it is no longer efficient for individuals to perform cross-functional tasks. Several of the overall benefits of data management can only be realized after the enterprise has established systematic data governance. Some of these benefits include:
- Better, more comprehensive decision support stemming from consistent, uniform data across the organization
- Clear rules for changing processes and data that help the business and IT become more agile and scalable
- Reduced costs in other areas of data management through the provision of central control mechanisms
- Increased efficiency through the ability to reuse processes and data
- Improved confidence in data quality and documentation of data processes
- Improved compliance with data regulations
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Start With A Small Sample Size
It’s best not to kick off your data governance program with a complex or long-term project. You might make errors or lose motivation from the team. Rather, begin with a smaller, more manageable project, like analyzing data for one team. Assess the state of the data, specifically its collection, storage, and usage, then decide how much of your budget will be invested in the initiative.
How Do You Maintain Data Governance
This is a great question, as many data programs end up being cancelled. The average tenure of a Chief Data Officer is 2 to 2 and a half years. This shows that maintaining data governance is not easy.
In order to maintain data governance, we must make measurable data improvements. We have to establish metrics and KPIs that demonstrate our work has value. If you set up your data strategy properly, youll be doing things with data that align against corporate goals. You need to deepen that and demonstrate the dollar value of the work your team is doing.
The critical change you have to make is along data value chains:
Governing data creation
Data is created by your staff, by your customers, by your systems and by your partners. In the absence of any rules, the data creators are free to type in whatever they like. This has a knock-on impact in our Critical Data Flows, and it negatively impacts our business processes .
Most of the root causes of bad data stem from bad data creation. Training staff, changing data capture processes and making it easier to input the right data is critical if we are to make lasting change.
Governing data consumption
We also need to educate the data consumers. Middle management are often part of the problem.
Just give me the data
Middle management, everywhere
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Command & Control Data Governance
- People must be assigned to data stewardship roles
- Forced governance of data
- Believes people can be more efficient and effective if they are recognized and rewarded
- Establishes common trust model
- Achieves business outcomes while managing risks
A people-first approach to governance has gained traction in recent years. Bob Seiner, an expert in data governance, explains that, the best approach is the approach that will activate your program, engage the data stewards, and lead to the demonstration of business value. The benefits will spring from the data governance approach an organization takes and how it involves the people responsible in that journey.
Now that weve identified the types of data governance, lets explore the common misconceptions of data governance.
Data Governance Framework Examples The Traditional Approaches
There are two traditional approaches to establishing a data governance framework: top-down and bottom-up. These two methods stem from opposing philosophies. One prioritizes control of data to optimize data quality. The other prioritizes ready access to data to optimize data access by end users across business units.
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What Is Cloud Data Governance
In multi-cloud or hybrid cloud computing systems, when data gets stored in various locations, and cloud data governance protocols such as permissions, guidelines, and metadata are inconsistent across databases, data governance takes on a new level of sophistication. Moreover, modern tools from a single software platform that enable data engineers, data governance and compliance teams to automate data governance, data access rules, and privacy protection can help data teams negotiate the complexities of data governance.
The activity of managing data availability, authenticity, consumption, and security in cloud computing systems to fulfill critical business objectives is known as data governance. These objectives most likely include the following listed below.
- Increasing the privacy and security of data.
- Access to sensitive data is regulated and monitored.
- Using timely data analytics to improve operations and corporate decision-making.
- Obtaining and ensuring compliance with data privacy and security standards on an ongoing basis.
- Data breaches and other cyber security threats get avoided.
Data Governance Vs Data Management #: The Processes
Data governance dictates how organizations decide about using data. So, its all about overseeing all the processes involving data and making sure that they dont break any laws or corrupt the organizations data .
As a result, the processes that follow are frameworks with rules and guidelines that ensure efficient oversight.
Meanwhile, data management is all about how organizations use data. So, it deals with storing, integrating, cataloging, prepping, exploring, and transforming to extract value from data.
Thats why the processes that follow are procedures that ensure that data is being used as outlined in the governance framework.
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