Data is an important asset at all organizations – no matter what their business, their size, or their structure. Without data, an organization would not exist, so the decisions made about data are critical. Many organizations have little understanding or visibility into its data. This post will cover the no brainer topic of the importance of data for any organization and stress the need for being able to discover your data and then get to know your data better.
Effective data discovery is not easy because of the increasing number of data systems, the interconnectivity between them and the diversity of the environments. Some data discovery is easy while other types are more difficult. When you know what data you have and where it is, you can capture, categorize, manage, protect, migrate, reduce, share, display, and repurpose information. These capabilities are critical for making well-informed decisions, including those on enhancing privacy, adding security, meeting regulatory items, achieving compliance, forecasting, controlling costs, hiring, firing, minimizing risk, increasing employee satisfaction, improving customer satisfaction, protecting intellectual property and list goes on.
For discovery and knowing you need to ask and get answers for data-related questions such as:
- What data systems are used at your organization?
- What data do you collect from people in your organization and those outside of the company?
- Who can access this data?
- How do we manage the data?
What does an organization do to discover its data and get better knowledge and control?
- Data System Inventory - Organizational data is created, moved, copied, modified, shared, and translated across many data systems including shadow systems. An organization needs to understand what data systems are used, who has access to these data systems, and what data is in these data systems. Therefore, a critical need is having an accessible, up-to-date data system inventory in place. Before you can do data governance policies addressing data privacy, integrity, security, and availability you must know what you have. Creating a data system inventory will require some question asking and research but should not require long heated discussion (not like a business glossary discussion could be). We have many resources regarding a data system inventory so we will not go into any further detail on it and just refer you to our data system inventory resources blog post.
- Common Knowledgebase - Understanding data requires a business glossary and data definitions. Having these knowledge areas will improve satisfaction especially with staff who will have a better understanding of the data. Having a common knowledgebase of data governance related content including business glossary, data definitions, data system inventory, reference data, report specifications, data quality rules, data policies and other information is critical. With access by all, a common knowledgebase makes data discovery and knowledge a lot easier. Business glossary is one of the harder pieces of data governance as often discussions (sometimes heated) are necessary to firm up the business glossary entries.
- Data Governance Framework - You need some structure around your data and thus the importance of having a data governance framework in place. Our recommended data governance framework is discussed in this blog post.
- Data Request and Data Quality Issue Processes – You need to have in place a data request process (new reports, new definitions, data exports, etc.) and a data quality issue submission process. Both processes are important for data discovery. Check out our data request resources blog post for more information. Also check out our data quality resources blog post for more information.
- Understand you user’s data behavior – This includes items such as data sharing agreements with others, specifications on integrations and data movement/changes (data lineage). Make sure that this information is documented.
- Data Governance Solution - Since your data is everywhere, you need a solution that can traverse data systems (such as the Data Cookbook). And have a framework, have the necessary processes and workflows in place and be able to manage the data governance related content and have it in a common knowledgebase.
- Automate and integrate where possible – Do not depend on manual entry. Too much risk of error. Add workflows for data stewards where possible, including the approval of data definitions and report specifications. Document all the integrations in specifications so you know what is in the integration and how the data was modified.
- Data Models - Bringing in your technical data models is easy with a tool such as the Data Cookbook (automatic and import).
Intelligent decisions stem from the ability to discover, classify, optimize, and take advantage of your data. The first step is finding or discovery of your data. Not sure who said, “knowledge is power”, but it is so true. After discovery, the next step is knowing. The better the knowledge you have of your data, the better your data will be. And that leads to better information which leads to better data-driven (or data-informed) decision making. Hope that this blog post reaffirms the importance of data governance and its related activities and content. With data governance (or as we call it data intelligence) you can discover your data and get to know it better.
IData has a solution, the Data Cookbook, that can aid the employees and the organization in its data governance, data stewardship and data quality initiatives. 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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