Empower Data Warehouse and Data Lakes Using Knowledge Graphs

Hal Varian, Chief Economist at Google, proclaims, “The ability to take data — to be able to understand it, process it, extract value from it, visualize it, and communicate it — is going to be a hugely important skill in the next few decades.”

Data warehouses and data lakes are repositories or cloud storage solutions where different types of data are stored.

Data Warehouse

Data warehouses store highly structured historical data that can be processed for a defined purpose.

A data warehouse stores data in a structured format in tiers, also called data marts, because the data is stored in a shelf-like structure. It’s a central repository of pre-processed data for analytics and business intelligence. Data warehouses use data from transactional systems and relational databases, and frequently accessed data is stored in fast storage spaces such as SSD(Solid State Drive) drives.

Benefits of Data Warehousing

• There is little or no data prep required, which makes it significantly easier for analysts and business users to access and analyze this data.
• Since accurate, comprehensive data is more readily available, organizations can convert information into insight more quickly.
• Data that has been unified and harmonized provides a single source of reality, fostering confidence in data insights and decision-making across business divisions.

A data warehouse is also not free from its own constraints, traditional data warehouses have the disadvantage of being primarily built for structured data storage and retrieval, making it difficult to properly handle unstructured or semi-structured data. This constraint can make it difficult to uncover links, insights, and trends across multiple data sources.

Knowledge graphs allow for flexible and dynamic representation of data, making them suitable for capturing both structured and unstructured data. It can help organize data in a flexible and intuitive way and provide tools for exploration, querying, and visualization, a knowledge graph can help analysts to uncover insights and patterns in the data that would be difficult to identify with traditional database methods.

Use-case of how Knowledge Graphs can solve Data Warehousing Issues with a BFSI industry example

A knowledge graph can be utilized in the BFSI business to navigate and solve difficult data warehousing use cases. A bank, for example, can utilize a knowledge graph to connect various data points relating to customer transactions, customer profiles, credit history, and other topics. They can then find patterns and insights that were previously invisible.

Here are some specific applications of a knowledge graph in the BFSI industry:

Fraud detection: A knowledge graph can be used to connect multiple data points connected to consumer transactions, such as location, time of day, and transaction amount, to detect fraud. This allows the bank to detect odd patterns of behavior that may indicate fraud.

Risk management: A knowledge graph can be used to connect many data points concerning a customer’s credit history, payment behavior, and other risk concerns. This allows the bank to identify consumers who are at high risk of missing loan or credit card payments.

Consumer segmentation: A knowledge graph can be used to connect multiple data points relating to consumer profiles, such as age, income, level of education, and transaction behavior. As a result, the bank may identify various consumer segments and develop customized marketing efforts.

Regulatory compliance: A knowledge graph can be used to connect various data points related to regulatory compliance, including KYC (Know Your Customer) requirements, anti-money laundering rules, and other regulations. By doing so, the bank can verify that all regulatory obligations are met.

Overall, a knowledge graph can assist the BFSI business in navigating and solving complicated data warehousing use cases. The industry can obtain insights and make better-informed decisions by connecting diverse data points and building linkages between them.

Data Lakes

Data lakes hold unstructured or raw data with low-cost storage and use data from IoT devices, websites, mobile devices, social media, etc. A data lake is a pool of structured and unstructured data that can be used to create pipelines for data analytics to find insights and make key business decisions.

Benefits of Data Lakes

• Massive amounts of organized and random information, such as ERP transactions and call records, can be stored economically.
• By maintaining data in its raw state, it becomes ready for use much faster.
• A greater range of data can be analyzed in novel ways to reveal previously unknown and unexpected insights.

Companies seeking to utilize their customers’ data collect massive amounts of data regarding their buyer behavior, demographics, preferences, and online activity, creating vast data lakes and enabling data hoarding. We can avoid a badly organized data lake by using a knowledge graph to create structure within the lake and facilitate analysis.

Let’s say our problem is like finding a needle in a haystack of information. How do we navigate the depth of information that is available to us?

For this, a knowledge graph helps in linking data from disparate sources, finding compatible components, and capturing expert knowledge.

Use-case of how Knowledge Graphs can solve issues with Data Lakes with a Pharma Industry example.

Let’s discuss the solution in detail with a drug discovery use case, wherein a data-driven approach to R&D can improve drug discovery success rates as well as manage clinical trial safety.

Knowledge graphs can be used in drug development to combine and analyze vast volumes of data from multiple sources, such as genomics, proteomics, metabolomics, and clinical trials. Researchers can quickly visualize and investigate the relationships between different things, like genes, proteins, pathways, and diseases, by organizing this data in a graph structure.

Drug repurposing is one application of knowledge graphs in drug discovery. Researchers can identify existing treatments that may have the potential to cure a new disease or condition by analyzing the relationships between different entities in the graph. For example, if a specific protein is known to play a role in both cancer and Alzheimer’s disease, a knowledge graph can assist in identifying medications that target that protein and have previously been licensed for usage.

Another use case is biomarker discovery. By analyzing the relationships between different entities in the graph, researchers can identify potential biomarkers that may be associated with a particular disease or condition. For example, if a particular gene is known to be involved in a disease, a knowledge graph can help identify other genes that are closely related and may also be involved, which could serve as potential biomarkers.

Key factors

There are four key factors that knowledge graphs help with in empowering data warehouses and data lakes:

Data integration: Knowledge graphs are useful in integrating data from various sources, even if the forms or structures differ. A knowledge graph can provide a common ontology that allows the mapping and integration of disparate data by building a semantic layer on top of the stored data.

Data discovery: By offering a unified perspective of the data landscape, knowledge graphs can assist users in discovering relevant data and insights. Using the knowledge graph as a guide, users can search for specific data sets or explore relationships between multiple data sets.

Data enrichment: Knowledge graphs can enrich data in a data warehouse or data lake by providing more context and metadata. For example, the knowledge graph can include information on origin, quality, and so forth to enhance data reliability.

Machine learning: By providing a structured representation of the data, knowledge graphs can be used to train machine learning models. The knowledge graph can be used by machine learning models to learn from past experiences and predict future events.

Overall, knowledge graphs have the potential to be an extremely useful tool for organizing and analyzing data in data warehouses and data lakes. Knowledge graphs can help users extract additional value and insights from their data by adding a semantic layer to the stored data. This disruptive technology is surely the next big thing to aid you in becoming your own data warrior.