Phase : Implementation Of The Data Governance Plan
Step 1: Ensure the Availability of Data
The data governance teams should ensure the availability of specific data assets they want to standardize and control. In large organizations, data is spread across different information silos like customer care systems, enterprise management applications, sales records, and even partner systems. All this data should be readily available in one place. Organizations might need to design an integration mechanism for these distributed data assets.
Step 2: Ensure Data Integrity for Implementing Data Governance
Data assets that are clean, standardized, and reliable are the crucial component of the data governance framework. To find the definition of clean and reliable data, start by asking teams that consume the data on a daily basis. Ask them which data format makes the most sense to them. Based on their input, embark on a multi-step data enhancement workflow as below.
Step 3: Enforcing Accountability and Adherence to Data Policies
Step 4: Continuous Feedback and Monitoring
Implementing a data governance framework is an iterative process. It can only be improved through continuous monitoring and feedback.
Egnyte The Best Tool For Data Governance
Our favorite tool for data governance is Egnyte, which tops our list of the best business cloud storage services. Its a good choice for both small organizations and large corporations, offering a lot of useful features that will benefit business owners who want to implement a data governance program.
Besides a 15-day free trial which readers should take full advantage of to try out the app Egnyte comes with three pricing tiers: Team, Business and Enterprise. Their names are a clear giveaway to which team sizes they are for, with the Enterprise plan best suited for large-scale data governance.
Egnyte handles file sharing really well, with near-instantaneous syncing across all devices and the desktop and mobile version of the app. You can assign roles to people according to the levels of access you wish to grant them. There is also the option to have several people edit the documents in real time, using the Microsoft Office and add-ons.
Besides sharing and file syncing, Egnyte also has some task management capabilities. Although it doesnt come close to the likes of Asana and other dedicated project management tools, the option to commit tasks and deadlines to files inside the app is a very welcome management feature.
What Is Data Governance And Gdpr
Data governance refers to the policies and processes that define the appropriate use of data as it flows into and out of an organization. Data governance is not implemented through a single technology but rather is a wide-ranging discipline that comprises people, processes, strategies, guidelines and tools in order to achieve its goals.
Specifically, data governance and data governance initiatives are concerned with ensuring that organizations maintain high standards throughout the data life cycle from creation to long-term storage, archiving and disposal for the purposes of internal policies as well as external regulations. This is important because successful data governance leads to the right decision-making based on the right data armed with accurate, consistent and up-to-date information about customers, markets and assets, an organization is able to act properly in response to new data-changing business conditions. Conversely, companies with poor data governance systems often find themselves floundering in fast-paced market conditions, paralyzed due to a lack of information or misled into making the wrong choices.
Data governance has become especially critical for global regulatory mandates such as the European Unions General Data Protection Regulation , which among other things, protects a consumers right to be forgotten, while imposing steep financial penalties of more than $20 million or up to 4% of annual worldwide turnover for violations.
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Your Data Sharing Workflows Are Outdated
Manual data sharing processes can be time-consuming and create bottlenecks that slow the movement of data through your organization.
If your organization is using email chains to submit data requests, obtain approvals, communicate access provisions, etc., youre probably experiencing long turnaround times for data requests that delay insights and reduce the impact of business analytics. A data governance tool can reduce your time-to-insights by streamlining the request process and making it easier and faster to connect your organizations data resources with data consumers.
Challenge : Data Silos And Inconsistent Implementations
Data silos are a big challenge to data governance. In many organizations, data might be owned by different teams and stored in various formats. Even with a data governance framework in action, some teams might fall behind and fail to adhere to the standard.
Solution: Data decentralization and a cultural shift.
The crucial step to achieve data governance is moving the data from silos and getting it into a centralized data governance framework. Also, data governance isnt just a project but an ongoing activity. Hence, there should be a cultural shift in the organization that favors high-quality data.
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Data Governance Use Cases
Effective data governance is at the heart of managing the data used in operational systems, as well as the BI and analytics applications fed by data warehouses, data marts and data lakes. It’s also a particularly important component of digital transformation initiatives, and it can aid in other corporate processes, such as risk management, business process management, and mergers and acquisitions.
As data uses continue to expand and new technologies emerge, data governance is likely to see even wider application. For example, efforts are underway to apply data governance processes to machine learning algorithms and other AI tools. Also, high-profile data breaches and laws like GDPR and CCPA have made building privacy protections into data governance policies a central part of governance efforts.
Determine A Data Governance Model
The next step is to create a data governance model for your team to work off of. This model should describe the hierarchy for who can view and distribute different types of data. This ensures that sensitive data is placed in the hands of your most trusted employees and isn’t shared without authorization. You can view one example of a data governance model below.
You should also describe your rules and regulations for data collection. Outline your standards for securing data as well as which channels you’ll use to obtain it. This will create consistency in your data collection which will lead to more reliable and accurate takeaways.
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What To Include In Your Data Governance Strategy
As discussed, data governance is a broad category. Consequently, there are a number of things included in a strong data governance framework. As a start, it should include policies, rules, procedures, and structures for data management.
To help ensure that these procedures are focused and working towards the right objectives, its a good idea to create some foundational documents to guide the process. These should include:
- A mission statement
- Metrics by which the goals will be measured
- Clear guidelines detailing who is responsible for various aspects of data governance
Further, throughout every phase of this process, its important to document all parts of the framework and share them throughout the organization.
In addition to the above policies and procedures, some enterprises make the decision to utilize data governance software to help with implementation and enforcement. While this isnt a requirement, many organizations decide to utilize this software to help support:
- Program and workflow management
- Development of policies
- Process documentation
Such software can be an effective way to ensure that plans are effectively developed, implemented, and enforced.
S Involved In The Data Governance Framework
1. Figuring out the mission: As with any project, every data governance activity begins with a mission statement. Why is the organization undertaking this? How much budget has been allocated? What are the long-term goals of this project? Is the organization going to focus on marketing decisions? These are some of the questions that need to be addressed at this stage.
2. Defining the success metrics: At what point in time do we declare that the data governance program is a success? This is a metric that needs to be determined even before the creation of the framework.
3. Identifying data assets: Various access points where data enters and leaves the system need to be identified, along with detailed information about the data itself. For example, what are the various operations that require storage and the use of unique customer IDs? Where and how is sensitive information such as social security numbers stored? Every attribute of each piece of data within the system needs to be scrutinized and documented.
4. Creating data rules and definitions: This involves getting inputs from employees across various departments and also from subject matter experts. Data rules are defined based on the goals set in the previous stages. The data from each access point and asset is defined with common terms to be used across the system. People involved at this stage include:
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How Do You Get Started With Data Governance
When many organizations set out to create a data governance program, theyre surprised to find that they already have a semblance of one. If you have data policies about records retention, a mandate to encrypt customer data, or restrictions against keeping corporate information on home computers and mobile devices, youre already on your way to building a data governance framework.
When youre ready to launch a formal data governance program, the first step is to consider where your preliminary data governance policies have fallen short and to begin working to remedy them. Your data governance team should choose projects that have the highest value a sensitive customer database that needs securing, for example as well as ones can be completed fairly quickly. With a few small projects under your belt, you can move on to broader concerns in the organization.
Each data governance project, small or large, will need to be built around protecting the accuracy, integrity and security of the data. Consider the appropriate platforms for each data source and set rules around how to access them and why.
Each governance project will also need to account for the risk of data loss or breach, which often means inviting various stakeholders into the governance discussion. For example, IT should not be setting governance rules around a finance database without closely involving that department.
Is An Erp System Enough To Manage Data Governance
Unfortunately, Dynamics 365 Finance and Operations does not have all the tools to execute your data governance strategy. This ERP application has the foundation for your data but tools for data quality management, master data management, and data workflow is what will help you streamline, improve your data quality, and manage and distribute master data.
Lets look at an example to understand this. Lets say you need to fill in the information about items that you source or manufacture in your ERP system. On the form, there are over 200 different fields and there are also some related forms with related information for that item. That would be overwhelming for majority of the users of the ERP. However, with a data entry workflow solution , you could guide your team and present various users only the necessary fields related to their job function. So, a data entry workflow tool would also be beneficial for collaboration purposes.
Lets say you do not have a validation check for address validation in your ERP, and an employee has mistyped a city name, or a street name. Then the goods will be sent to the wrong address, or maybe the invoice will be sent to an incorrect address. This will lead to delays and issues and will be a bad experience for your customer. If you have a data quality tool, then you could avoid such .
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Integrate Data Governance Into Every Department
Beyond transparency, you want stakeholders to buy into data governance. So once you have your framework in place, it should be integrated into every department at your business. This will ensure a consistent stream of data collection and will provide your marketing, sales, customer service, and product development teams with insights that will help them achieve goals.
For example, your data governance office should be informing your product management teams about consumer behaviors and product usage reports. This information should be readily available and influence how you design product updates. With data governance, customer insights are streamlined to ideal endpoints, improving productivity across your entire organization.
Do You Use Microsoft Dynamics 365 And Struggle With Managing Your Master Data
In an ERP like Dynamics 365, if we have all our information in the ERP, then we would just have one company where we have all the master data, and all kinds of operational companies. But you would need an MDM tool to set rules for security of the data entered. An MDM tool would allow you to open and block certain fields for different functions. The solution would also allow you to manage dynamic field security, by setting up dynamic rules for certain available fields for a user to enter data more efficiently.
An external MDM solution has its own rules, and all the other applications connected to it can also have their rules. But when it’s just in one environment like in the ERP system, then we need to define rules for each company and that might be cumbersome.
Our solutions at To-Increase, have an added complexity as our solutions are built inside the Microsoft Dynamics 365 ERP system. This works as a major advantage for organizations that want to connect their ERP and business systems and manage and distribute master data from within the ERP.
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.
Map Initiatives To Different Governance Frameworks
While most governance frameworks aim to provide transparency into various projects and initiatives, different frameworks are designed for different business objectives, be it tracking employee performance, preventing fraud, or protecting consumer data.
Heres a quick overview of some of the more common governance frameworks and the goals they support:
- COSOCommittee of Sponsoring Organizations of the Treadway Commission or COSO offers a general framework that focuses less on IT-specific initiatives and instead emphasizes risk-management and fraud prevention efforts.
- ITILITIL, or Information Technology Infrastructure Library, outlines five best practices for ensuring that your IT infrastructure supports your business operations.
- COBITCOBIT lays out 37 IT processes and defines the goals, metrics, and methods for measuring performance.
- FAIRFactor Analysis of Information Risk, or FAIR, is a framework that focuses on risk assessment and cybersecurity and aims to help organizations quantify potential risks.
- CMMICMMI, or Capability Maturity Model Integration, scores an organizations performance on a scale from 1-5, allowing companies to track performance over time.
Ultimately, as you start planning new initiatives, youll want to select a governance framework that matches the goals of that project and fits in with your internal culture and the processes your organization already uses each day.
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Alation Data Governance App
Alation was founded in 2012 and initially offered a data catalog platform to help organizations inventory and provide access to their data. Alation Data Catalog remains its flagship product, but the company released a companion data governance tool in September 2021. The Alation Data Governance App software is designed to simplify the process of providing secure access to reliable data in IT systems, including ones in both hybrid cloud and multi-cloud computing environments.
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Alation Data Governance App’s Policy Center feature can be used to create governance policies and view how they’re mapped to specific data assets. The governance tool also includes a data stewardship workbench that provides automated data curation functions and uses AI and machine learning to identify potential data stewards based on their data usage.
In addition, the data governance tool includes the following features:
- support for creating and configuring data governance workflows without any coding required
- a dashboard that the leaders of a data governance program can use to track its progress and
- an associated data governance service offering through Alation’s professional services unit.
Best Practices For Managing Data Governance Initiatives
Because data governance typically imposes restrictions on how data is handled and used, it can become controversial in organizations. A common concern among IT and data management teams is that they’ll be seen as the “data police” by business users if they lead data governance programs. To promote business buy-in and avoid resistance to governance policies, experienced data governance managers and industry consultants recommend that programs be business-driven, with data owners involved and the data governance committee making the decisions on standards, policies and rules.
Training and education on data governance is a necessary component of initiatives, particularly to familiarize business users and data analysts with data usage rules, privacy mandates and their own responsibility for helping to keep data sets consistent. Ongoing communication with corporate executives, business managers and end users about the progress of a data governance program is also a must, through a combination of reports, email newsletters, workshops and other outreach methods.
Communication and training are part of a set of seven data governance best practices outlined by Farmer in a second article. Some of the others include applying data security and privacy rules as close to the source system as possible, putting appropriate governance policies in place at every level of an organization and reviewing governance policies on a regular basis.
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