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Similarity analysis, one of the metadata analytics capabilities, is used to identify and measure the degree of similarity or resemblance between objects or data points within a dataset. Similarity analysis plays a critical role in a data governance regime. What enterprises need is not just any similarity analysis tool, but an automated and comprehensive one. Orion Governance’s Enterprise Information Governance Graph (EIIG), a self-defined data fabric, offers such a tool as part of their next generation of metadata management platform. It enables enterprises to effectively enhance their decision-making, increase productivity, and optimize cost.

Better and Faster Decision-making

The ultimate goal of data governance or metadata management, of which similarity analysis is a part, is to enable organizations to make better and faster decisions. Sound decisions depend on trusted data, and trusted data is built on the comprehensive understanding of the data landscape. Without visibility into different types of data in a wide range of technology sources, the function of similarity analysis would be very limited.

For example, if you cannot cover the mainframe with all its complexity (PL1, JCL, COBOL, IMS, etc.), how can you identify duplicate or redundant data in this important system of records? The lack of this capability will make it very hard to ensure data quality and integrity which is essential for making accurate and preventing errors in reporting for compliance and other purposes.

On the other hand, if you don’t have support for Python and Java, you will have huge gaps in terms of identifying duplicates related to applications developed in such modern programming languages. If you are an application tester, you will not have the tool to help you decide what data sets are redundant. As a result, you will likely test everything, which is a waste of time and resources, and the efficiency of test automation will thus suffer.

Another aspect of comprehensive similarity analysis is the ability to automatically identify duplicates and redundancies of not only tables, but also ETL jobs, and reports. Enterprises’ data environment is complex, they cannot afford to have critical gaps in their similarity analysis.

Equally important is automation of similarity analysis. The whole process of ingesting metadata, cataloging, data lineage visualization, impact analysis, and similarity analysis should be an automated workflow. Such AI/ML powered automation ensures faster decision making.

Optimized Cost

One of the challenges enterprises are facing when deploying data governance tools like similarity analysis is the high and unpredictable cost. A key reason for such concern with cost is that too much manual effort is required. An automated and comprehensive similarity analysis allows enterprise users to easily identify duplicate and redundant information just by clicking data points in a knowledge graph. It does not matter whether they have hundreds of thousands of reports, millions of ETL jobs or tables. Such similarity analysis not only reduces storage costs by identifying and eliminating duplicate data assets, but also minimizes human intervention and thus lowers labor costs. In addition, it averts confusion caused by multiple versions of the same data and hence decreases the cost of inefficiency due to such confusion.

Enterprises can also realize savings because Orion Governance’s similarity analysis is an integral part of a self-defined data fabric. All capabilities such as data catalog, lineage, impact analysis and active metadata as well as similarity analysis are natively integrated as one product and one platform. This seamless integration eliminates the headaches of trying to manage a patchwork of different products and costs associated with it.

Boosted Productivity

Aforementioned better and faster decision-making and cost optimization are outcomes or examples of improved productivity. More specifically, EIIG’S automated and comprehensive similarity analysis enhances productivity in the following major ways.

First, it helps data citizens, without the need to involve IT or data governance specialists, to discover and access the most relevant and trusted data quickly. This automated and comprehensive self-service similarity analysis provides users with the ability to identify and duplicate data assets so that the time to get to the clean, consistent, and accurate dataset is greatly reduced.

Furthermore, EIIG equips users with the flexibility to choose the level of details and the type of datasets according to their roles and needs to conduct similarity analysis. This is possible because EIIG’s similarity analysis is performed in a self-defined data fabric with wide-ranging datasets of all types as well as hierarchical views.

For example, the BI team may focus on just the duplicated reports, the data integration team on redundant ETL jobs, a data engineer on similar tables. For the risk managers, they may use similarity analysis to detect fraudulent activities by comparing new transactions with historical records and identifying patterns of suspicious similarity. Compliance officers can use similarity analysis to identify and tag PII datasets to ensure compliance with data privacy regulations. All these could not have been achieved at scale without automated and comprehensive similarity analysis.

Finally, EIIG’s similarity analysis facilitates cross-team collaboration. EIIG establishes a federated view of the enterprise data landscape by providing detailed intra- and inter-system connectivity.

Similarity analysis conducted in this environment not only enables different teams or business units across the enterprise to see the “big” picture, avoid duplicated efforts in the future, but also afford them with a shared platform to collaborate more easily. One of the reasons for this productivity boost is that data from different systems can be more effectively mapped, combined, and thus better utilized by different teams working together to get more valuable insights.

In summary, enterprises can accelerate their data-driven innovations with EIIG’s automated and comprehensive similarity analysis. To learn more, please schedule a demo with us.

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