The value of data is at an all-time high. The way you use the data in your organization directly impacts your bottom line. Your revenues, your market capitalization, your income, your cost — everything will be impacted based on how your organization is managing its data. There is no operational excellence without data excellence, and to ensure you are using this valued asset properly, a data governance framework must be part of your strategy.
What is data governance?
Data governance is a collection of processes, roles, policies, standards, and metrics that ensure the effective and efficient use of information that enables an organization to achieve its goals. It establishes the processes and responsibilities that provide the quality and security of the data used across a business or organization. Data governance defines who can take what action upon what data, in which situations, and using what methods.
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Typical Data Governance Questions
When considering your organization’s data, it’s natural (and important) to ask many questions. Those questions may run along these lines:
- Can my data be trusted?
- Who understands this data?
- I see this code in this data, what does that mean?
- Who does what in terms of data governance?
- Who should be able to change the data?
- What happens after changes are made?
All these types of questions generally open a Pandora’s box of processes and standards that are absent from the enterprise. A solid data governance framework will address these and other data concerns.
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Why do we need data governance?
It’s easy to get caught up in the notion that together big data and cloud can conquer anything. Forrester Research found that “74% of firms say they want to be ‘data-driven’”. But nobody wants to know any more details. The business side often assumes all is well when the big data is in the cloud, and that IT is just taking care of everything. This is a huge disconnect that many enterprises face today. To be able to really trust your data and become authentically data-driven, you need processes and standards around it.
Today’s regulatory climate is also a huge driver for data governance initiatives. From GDPR to HIPAA, and many others in between, there have been dozens of data protection regulations enacted around the world, and there are plenty more coming down the pike. Compliance to these regulations often requires data in specific formats, and most importantly, accountability of data.
Begin with a data maturity model
The best way to start your data governance framework is to apply a data maturity model. This model acts as a benchmark upon which you can review your current IT landscape and how well you manage your data against your people, processes, or technology — or all three combined. Many examples of data maturity models can be found online. The idea is to compare your current state to the desired state, which is predicated on your company goals and mission. Once you understand where you are today, you will have a clearer picture of the implement the strategies and policies you must implement to become a more data-driven company.
Building the data governance framework
A data governance framework can involve a lot of mini-initiatives including master data management (MDM), metadata management, data warehousing, data quality — it doesn't have to be driven by a single theme. Your data governance framework will depend on what your company wants to do with data governance. Consider your specific use cases. What is your company struggling with in terms of data? Is it reporting, data quality, data access, or something else? It is up to you to focus on what will provide the most value to your company. These initiatives can be at the enterprise level, or at the project level. Here are some key areas of focus to get you started:
- Standards and policy: This sort of program would collect standards, review existing them and check against the corporate standards. Another main activity is to define a data strategy for the company and provide support for any siloed projects trying to join the enterprise landscape.
- Data Quality (DQ): This kind of program deals with finding, correcting, and monitoring data quality issues in the enterprise. These programs normally involve software for profiling, cleansing and matching engines. DQ initiatives also lead to MDM projects, which define the master data and give a 360-degree view of domains such as customer or vendor.
- Data security and privacy: Every company should have compliance and regulations requirements, and this program would try to address these issues by setting access management rights, information security controls, data privacy procedures, etc. particularly for sensitive data.
- Architecture/Integration: This focus area aims to achieve operational efficiency by simplifying data integration architecture components such as data modeling, master data modeling, service oriented architecture (SOA), etc.
- Data Warehouses and Business Intelligence (BI): This program promotes the use of building data warehouses and data marts to support historical reporting and also futuristic reporting.
Self-service architectures: This kind of program takes into consideration the stewardship and data preparation challenges and aims to build workflows limiting the ‘shadow IT’ paradigm, which happens so often in organizations.
Three steps to ensuring data you can trust
A data governance framework organizes 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:
Step 1: Discover and cleanse your data
Your challenge is to overcome these obstacles by bringing clarity, transparency, and accessibility to your data assets. You have to do this wherever this data warehouse resides: within enterprise apps like Salesforce.com, Microsoft Dynamics, or SAP; a traditional data warehouse; or in a cloud data lake. You need to establish proper data screening so you can make sure you have the entire view of data sources and data streams coming into and out of your organization.
Step 2: Organize data you can trust and empower people
While step 1 helped to ensure that the incoming data assets are identified, documented and trusted, now it is time to organize the assets for massive consumption by an extended network of data users who will use it within the organization.
Step 3: Automate your data pipelines and enable data access
Now that your data is fully under control, it is time to extract all its value by delivering it at scale to a wide audience of authorized humans and machines. In the digital era, scaling is a lot about automation. In the second step of this approach, we saw how important it was to have people engaged in the data governance process, but the risk is that they become the bottleneck. That’s why you need to augment your employees’ skills, free them from repetitive tasks, and make sure that the policies that they have defined can be applied on a systematic basis across data flows.
The final task: Enforcement and communication
All of the topics above cover key focus areas to be considered when building a data governance framework. Building the framework is important — but enforcing it is key. A data governance process must be created on the heels of the framework to ensure success. Some organizations create a data governance council — a single department that controls everything. Smaller organizations may appoint a data steward to manage the data governance processes. Once your data governance framework is in place and is being rolled out, it needs to be communicated to all areas of the business. Make it resonate with employees by demonstrating how this framework will help carry out your company’s vision for the future. As with any new policy or standard, employee awareness and buy-in is important to success; and this holds true with the data governance framework.
There is certainly no shortage of data, and building a data governance framework is a challenge many enterprises face. With Talend Cloud, you can build a systematic path to get to the data you can trust. A path that is repeatable and scalable to handle the seemingly unending flow of data to make it your most trusted asset.