Data intelligence includes a framework with people and processes along with content designed to help people access, understand, connect, protect, and effectively use an organization’s data across all systems. Data is an important asset in the organization. Employees and the organization need to be data intelligent. Until an organization becomes data intelligent it cannot be data-driven. In this blog post we will discuss the traits of an organization that is data intelligent.
The data intelligent organization has many traits including:
- Has a unified data knowledge base - A common repository or knowledge base is necessary where organizational data assets and data products can be managed for consumption and reuse. There needs to be minimum content tipping point so that this knowledge base is accessed. An organization’s data spans multiple business domains and reports. This information needs to be accessible and defined consistently across the organization to be useful. Resource: Data Governance Framework: Knowledge Base blog post
- Has a data systems inventory – An organization needs to know what data systems it has and uses. Each data system should have an owner and information should be available on how to access the data system. Resource: Data System Inventory - Resources to Make It Happen blog post
- Has invested in data governance and data intelligence – One investment includes having a data governance framework in place which helps an organization and its members on how to think and communicate about data governance. And assists data stakeholders to come together with clarity of thought and purpose when working on data governance. The framework will allow an organization to scale data governance. Another investment includes the implementation of a data governance tool or tools such as the Data Cookbook. Data governance is too difficult to do without a tool in place to assist. Resource: Determine Your Data Governance Framework blog post
- Has implemented metadata management – Do not forget about the data about data, the metadata. Use metadata to maintain the transparency of data and information assets across the organization. Ensure that these assets can be easily found, used and trusted. Resource: Need Help with Your Business Glossary? Here are Some Resources to Help blog post. The purpose of metadata is not simply to describe data and information assets, but rather to proactively answer questions that consumers might have about them such as:
- Where did this data come from?
- How up to date is this data?
- How trustworthy is this data?
- What organization process(es) created this data?
- What transformations have been applied to this data, and why?
- What reports or dashboards display this data?
- Has established a data governance oversight management team - The organization has a team in place that is responsible for:
- setting and sharing actionable goals
- recruiting and assigning data stewards
- providing training in data governance goals, processes, and tools
- supporting data steward activity
- acting as data stewards when necessary
- creating standards, examples, and templates
- getting initial data-related content created
- resolving disagreements such as collisions
- performing the necessary administrative tasks including the administration of the data governance solution
Resource: Resources to Assist with Data Governance Oversight and Management blog post
- Has set up data stewardship - This trait involves the establishment of a data stewardship program and team. The ideal situation is to have people on staff who are experienced and knowledgeable about the organization’s data. You want to have data stewards in all the areas of the organization. For data stewards to be successful they need the proper knowledge and resources. The more successful they are the more successful data governance will be and thus the more successful the organization will be. It is helpful to have triage data stewards in place who can assist with the easy data governance related issues freeing up the more experienced data stewards. Resource: Data Stewardship is Critical - Here are Some Resources blog post
- Has users who are data knowledgeable – This trait is about data users who understand how to access data sources, work with data, interpret data and avoid data analysis errors. The organization needs to have a trained staff and communication with those involved, which is every data user at the organization. The oversight committee creates the proper training and communication. Resource: Here's Some Data Intelligence Resources Regarding Communication and Training blog post
- Has goals and roadmap in place – An organization needs clear goals regarding their data governance initiative with a pragmatic and prioritized data governance roadmap. In the organization there needs to have buy-in for these goals and this roadmap. Resource: Setting Goals and Creating Your Data Intelligence Roadmap recorded webinar
- Has data-related training – This training should be for all employees in data intelligence, data governance, and data policies as well as having data governance as part of the new employee onboarding process. New employees must understand that data governance and good data management practices are part of their job expectations and responsibilities. Communication is key in the data governance process and providing a new employee with the right information at their start is important. Resource: Here's Some Data Intelligence Resources Regarding Communication and Training blog post
- Has data processes in place – These processes are for data consumers and data stewards. One of these processes should be a data quality issue submission that then goes into a data quality resolution process. Also, an organization should have automated content synchronizations in place to save staff time. An organization should have points of engagement in place to make it easier for data users. The organization should take a data help desk, customer service, and just-in-time approach to data governance. Resource: Evaluate Your Data Governance Processes blog post
- Has enabled data-driven insights – This trait involves linking data governance with the organization’s reporting, advanced analytics, data warehouse and business intelligence efforts so that information can be accessed. These tools should be documented. And the critical reports and integrations should be documented as well. Resource: Link Data Governance with Your Technology Projects – Here are Some Resources blog post
- Has established data policies – This trait includes having documented policies such items as data security and privacy that are communicated to employees. Sound data policies need to be in the data knowledge base on how data can, and cannot, be used. These policies need to be in alignment with what is possible with the data, what is good for the organization and what is best for the customers. These policies need to be known, easy to access and transparent. Resource: Here are Some Resources About Data Policies blog post
How many of the above traits does your organization have currently? A data intelligent organization needs to understand and work on the above traits to achieve data intelligence success. Some organizations believe that data intelligence is nothing more than a video that they ask their staff to view, whenever they have some spare time. This approach is ineffective. You need to have a pragmatic, just-in-time, customer service, and help desk approach to data intelligence. Data Intelligence is an ongoing journey and not a short-term project. New data-related content is always being added to the data governance knowledge base. And existing data-related content is always being improved. Processes are created and improved so that data consumers can make requests and report issues easier. Data stewards are becoming more knowledgeable about handling these requests. Data intelligence becomes part of the organization's culture. Make sure that data and information assets are published and accessible across the organization, and make sure that people know where and how to find them. Educate users on where and how to find good data, how to tell good data from bad data, how to avoid common data usage errors, how to determine when the results of analyses may be incomplete or incorrect, and how to report data errors and problems for quick resolution. Also, make sure that less-trustworthy data are identified and improved. Hope you found this blog post beneficial. Additional data intelligence resources can be found here.
IData has a solution, the Data Cookbook, that can aid the employees and the organization in its data governance and data intelligence efforts including data requests. IData also has experts that can assist with data governance, reporting, integration, and other technology services on an as needed basis. Feel free to contact us and let us know how we can assist.
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