Column Level Data Lineage
Data lineage is a very hot topic these days, mostly driven by increasing regulatory requirements and data quality initiatives. Data lineage plays a critical role in better understanding the data itself: Where does the data come from? How was it modified and by whom? There are various perspectives and approaches out there. At Data Virtuality, we think that a comprehensive approach is needed for successful data lineage. The essential aspect is to make data lineage available together with the data. In this post we’ll highlight some of the major updates of the Data Virtuality Logical Data Warehouse 2.3
Starting LDW 2.3, you can see your data lineage with just one mouse click. Go to the number that you are interested in, click on the right button, and select “show data lineage”.
You will immediately see all the information about the data flow:
- Where the data originally comes from.
- How it was modified, e.g. through a WHERE condition, GROUP BY clause, etc.
- Who was the data owner of each step?
With this metadata information, you can instantly investigate if you see a red flag.
In today’s fast-changing business world, information became an actual production factor and data-driven decision-making an inevitable tool to withstand the growing competition across global industries and markets. Exploiting the power of BI/analytics and automating workflows is one way for companies to open new revenue streams while reducing costs by improving the efficiency of their daily processes.
And here lies the challenge. Nowadays, enterprise data is stored in different locations and comes in various, rapidly evolving forms such as:
- Relational and non-relational databases like MySQL, Amazon Redshift or MongoDB
- Flat files like XML, CSV or JSON
- Social Media or Website data like Facebook, Twitter or Google Analytics
- CRM/ERP data like SAP, Oracle or Microsoft Dynamics
- Cloud/Software-as-a-Service applications like Netsuite, Salesforce or Mailchimp
- Data lakes and Enterprise Data Warehouses
- Big Data
Businesses are faced with increasing volumes of data accompanied by growing data variety and velocity. This ultimately leads to further challenges like achieving trustworthy data quality, time efficiency in data management and self-service capabilities for data users. Overcoming these challenges efficiently and effectively became crucial for modern enterprises’ success.
Row-based Security and Column Masking
Next to data lineage, Data Virtuality introduces two features to advance the security management in the Logical Data Warehouse: row-based security, and column masking.
Row-based security allows to centrally control the data access. Based on preset conditions, data access for certain users/user groups can be determined. An example could be commission fees of salespeople. This kind of data is very sensitive and therefore needs special attention. With row-based security, the importance of data privacy can be reinforced.
Lastly, you have a third security feature at your disposal in LDW 2.3: column masking. Column masking works similarly to row-based security, just vertically. This feature allows to restrict data access to whole columns. The masking feature allows the users to mask the data such as personal data incl. address, telephone number, and account and/or credit card number in a customized way, e.g. hashing, anonymizing or making it invisible. Besides the privacy aspect, this feature allows to increase efficiency as the users can focus on the relevant data only as the irrelevant data is not accessible.
Web Business Data Shop
With the web business data shop, Data Virtuality takes the self-service BI concept to the next level. This is a web-based access point to data for non-technical data consumers. The users get a first overview of what reports with the accompanying data sets are available. A short description gives a quick insight into what the specific data set is about. This could also include specific information about the data that the data consumer should be aware of. Furthermore, the data owner is shown to support data governance functionality.
Finally, each data set can be directly accessed through the preferred data consumption tool such as Tableau, Power BI, Excel, etc.
This feature is subject to permission settings. A sophisticated security model in the background ensures that the users can only see the tables for which they have permission.
Metadata visibility through JDBC/ODBC based on permissions will increase the convenience and improve user experience in the Logical Data Warehouse. It ensures that data consumers only see data for which they have permission. This drastically improves user experience as the data consumer doesn’t have to deal with blank fields or similar.
Interested in seeing how you can get started?
Book a demo and test the features of the Data Virtuality Logical Data Warehouse 2.3