Modern organizations are struggling with data transparency challenges. Data discovery helps to resolve this issue with data democratization at an enterprise level. Data democratization comprises enforcing privacy, analytics, and reliability valuations for compliance. Besides, gain data insights towards a successful digital transformation journey.
What’s data discovery?
It helps enterprises to detect, classify, and catalog important, sensitive data. This increases transparency and uses data for meaningful purposes. Data discovery is helpful in –
- Uncovering new insights reveal opportunities and increase business value.
- Lowering data exposure risk through mandatory compliance.
- Drive high-value business results employing data analytics.
Data discovery is important for the business intelligence process because if ignored data is difficult to find. If data is not centrally located or made universally accessible, then this chaotic practice will need months or weeks of stressful research to retrieve specific data. An efficient data discovery program can help to access the same data with a few clicks.
Data governance training course at Data Management Education Center can help in learning about how to implement data discovery protocols. With thorough data governance understanding, you can rapidly build a searchable data catalog to help data scientists and engineers to gain access and efficiently analyze the information.
Benefits of data discovery model
- The process of data accessibility is streamlined using a defined method to identify and pool the data from several resources.
- Data is made easy to understand via correct categorization.
- Data is searchable using keywords.
- Teams can collaborate as data is centralized and everyone can gain access to work with it, even if they are not responsible for entering it.
Data discovery is the first crucial step that enables to transform the raw data into improved data intelligence. For example –
- Understand customer needs and fulfill their needs better.
- New insights enhance business operations as well as accelerate the development of new products or services.
Six pillars of data discovery
- Data collection – On an enterprise level, there are massive data sources, which are hard to search and scan. Advanced scanners help to create an entire metadata repository to curate trusted data.
2. Data preparation – Raw data transformation to high-quality actionable data is a crucial process for data preparation. The raw data from multiple sources are collected and sanitized for analysis. Mature data management tools [extract, transform & load] help in fueling data discovery & analytics.
3. Data inventory – It lists all the available data assets to the data catalog. It comprises data repository details, data location, data types, and similar metadata. Manually hard to reform, so AI & L is used to speed up metadata inventory creation.
4. Data exploration – For better data intelligence and meaningful insights data exploration and evaluation are performed. Data visualizing tools help to resolve crucial questions.
5. Data lineage – Finding data location is not just critical but even understanding the movement of data is necessary. It can impact data governance proper use policy to reduce wrong exposure.
6. Data domain organization – Classification and tagging of different data types into data domains helps to derive insights.
Data governance webinars or workshops can help to understand how data discovery helps in data intelligence!
