With the explosion of data, the most urgent challenge of organizations is how to know what they know. The central part of this challenge relates to building models or catalogs or knowledge graphs that allow data collection and storage (into common categories of integrated objects). The role of data warehouses comes into play here. Successful data warehouses create a common language that everyone in the business can interpret and use. Even though data warehouses are deemed important, they pose many limitations. They are mostly handcrafted and proffer serious implications such as high maintenance costs, restricted security, and ineffective data management, to name a few.
So, in the current business scenario wherein, all organizations are attempting to digitally transform, data warehouses clearly cannot serve the purpose. Enterprises need to move to faster and automated alternatives to savor maximum benefit. This is because relying on individual human minds to interpret the corpus of data can prove risky. To give an example, Data Lake is likely to turn into a data swamp when individuals are having the responsibility to keep track of all the data and the relationships between that data.
Many argue that AI is supercharging the way data is used and managed in business ecosystems. It helps organizations understand as well as map data through automation to facilitate better business decision-making. Further, various potential issues can be minimized that lead to lower sales and profits.
Here in this blog post, you’ll find how artificial intelligence can help organizations understand and map data effectively without compromising quality.
Artificial intelligence can impact four major areas of businesses from data management to data collection. Let's us delve into each separately.
Data Management: This involves taking steps to find what data one has, understand its structure and location, and gather all relevant information. But managing elaborate data with a general abstract understanding of the data model can impact a company’s overall efficiency. AI-based data mapping approach proves best when it comes to mapping that data and comprehend associated relationships. Further, it helps to understand and map all the data, companies can become more confident that all the data they need is available and integrated. Additionally, organizations can use the AI-powered data mapping mechanism to categorize data and find connections proactively.
Knowledge Graph: This entails establishing a graph representation to integrate disparate data and aid a specific use case. Knowledge graphs can be narrow and provide aid to a specific application. They can be wide like data warehouses or data lakes that allow organizations keep a track of everything. The challenge with these graphs is that many organizations are not aware of how to get all the data for identification and then perform an ETL process. It is nearly impossible for organizations to do this without understanding the relationships between data and identifying relevancy of new data in the graph. Artificial intelligence allows companies discover what’s in the data and affirm that every data source they need is available in the knowledge graph.
Assembling of Data: Many integration projects go wrong only because the data scientists’ team involved make an assumption that the incoming data is of high quality. They don’t confirm the quality of data beforehand, hence the algorithms that they use result in erroneous results. With an AI-powered data mapping approach, companies can determine the data quality, understand it as well as the relationships in toto.
Data collection in Data Lakes: Finally, artificial intelligence can help organizations get value from data lakes, and find relationships that companies didn’t understand or recognize as useful. In addition, AI can enable enterprises access backups to find out about what’s sensitive in the data and uncover anomalies and relationships they are not aware of.