Variety of data in terms of sources and formats is often a serious concern for businesses that consider tapping into the potential of unstructured data. The moment you go beyond the limits of table-bound, well-structured, numeric data and start using advanced machine learning capabilities in texts and images, things can go a little out of hand.
The most important factor in this respect is your data architecture. Whether your data management system can ingest data from multiple resources and whether your database is always synced with the data influx are things that control your ability to handle data better.
The impact of data lake implementation
Data lakes are envisioned for situations just like these. They afford you the ability to ingest data from multiple sources in different formats. They allow you to work on a copy of the data so that you can play around with it without tampering the original data source. Performing data lake migration also allows you to maintain data quality. A high-performance AWS data lake helps you maintain a single version of the truth with zero delays and inconsistencies.
Performing analytics become easier
Preparing the data for analytics is a huge part of an analyst’s job. Having the data on a single cloud-based repository makes it easier to clean and prep the data for machine learning models. The key in this respect is performance. A distributed file system cannot match the speed and performance of a cloud-based solution.
Climbing the AI ladder
Artificial intelligence integration is the name of the game in this decade. While every business wants to harness the power of artificial intelligence in terms of intelligent process automation, customer engagement, and data analytics, very few have the infrastructure required to implement AI with good effect.
The AI ladder consists of steps like collection, organization, analysis, and infusion of data. If you think about it, a data lake allows you to perform all these steps. You collect data from multiple sources; organize it by setting up policies and rules; analyze it by deploying machine learning models. And finally, infuse the resultant insights in decision making, application design, strategizing, or visualization.
Data lake provides you with the kind of flexibility that you need to deal with unstructured data.
Cost-effective solution
Storing petabytes of data on a platform like Salesforce, for instance, costs a lot of money. Moreover, the performance is often not as per your expectation. Migrating your enterprise data and streaming data to data lakes saves you a significant amount of money. Re-architecting your data governance capabilities in a data lake helps you cut down on maintenance costs and the cost of ownership. Data lake migration puts you in a win-win situation wherein you save money, save time, attain better performance and better analytical results while also growing your capabilities. It becomes easier for you to deploy machine learning to dig out insights that can go a long way for your business.
