Enterprise Knowledge Graphs and the need for it

Today, most enterprises have a Business Intelligence and analytics teams. They address the time-sensitive, operational needs of the organization. Also, importantly, business decisions are taken based on insights discovered from these platforms. Often AI/ML helps project into the future. Most often, BI platforms and the need for a workforce is acknowledged and highly valued by CXO team. Data from various departments including sales and R & D flows into BI platforms via data warehouses/data lakes / lake houses. However, direct access to operational DBMS systems is still needed at times. Also, data may need to flow in reverse ETL from data warehouses to DBMS systems.

The above scheme is mostly accepted as necessary. In reality, adoption, data lineage, speed of insight generation and subsequent discovery varies. Often human insight is still ahead of the system. Here’s an opportunity for improvement.

One of the key additions to the above ecosystem could be Enterprise Knowledge Graphs. They can address a critical-need for ‘drilling-down’ into the data to arrive at the ‘nugget of gold’. This while feasible in current scheme, it is dependent on human skill. A skilled CXO might be able to get to the ‘insight’ with the right ‘SQL’ query (they may or may not write it though). This is not uncommon.

EKGs have the potential to bring together key ‘identities’ and their ‘relationships’ across organization. People, departments, products, customers, geography, time, research, language and inter-dependencies. The ‘operational’ facts can/should continue to come from data warehouse/data-lake/lake-house.

Can an organization achieve benefits of an EKG by leveraging investments in a ‘Master data management’ system? Yes, partially. In practice, ‘MDM’ is not brought into BI platforms, its siloed and has less visibility. ‘Mastering’ data is considered a data engineering act. Instead, an EKG system would address the organizational needs more holistically when it’s integrated into BI platforms through GraphQL.

Understanding needed for building an EKG is natural for any organization’s team. They know this intuitively. Skills and standards may be evolving. Web3 and subsequent conversations around semantic web are helping bridge the gaps. Most of these conversations are about blockchains. A necessary area that needs a focused effort, of its own, in the very near future. EKGs, though, can be built now and can provide value right away.

Let us know if EKGs, Semantic Web interest you. Here’s an open knowledge graph that can help you draw an analogy to your organizational needs. Write to us (email: madhulatha@vaidhyamegha.com). We are happy to help.

This post was later published on LinkedIn here.

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