With the reduced cost of data storage corporations no longer have to struggle with the question of what data to store and what to through away. Corporations today are leveraging Data lakes to efficiently way for organizations to store, manage, process, and analyze big data. Monetizing data lakes, however, requires a well-defined machine learning model for delivering business outcomes for success. Machine learning techniques optimize data management in a data lake, improve the quality of data, and allow organizations to use data lakes for competitive advantage.
Machine Learning Combines Data Silos
A data lake captures data in raw format and has organizational level data from different departments and processes. Machine learning can effectively integrate data architecture and combine such silos. It does so by recognizing data types, structure, content and semantics. It then defines relationships between the data silos and harmonizes data types, value, scaling, format, and dimensions. This merged data can then be better used by businesses for analytics and extracting meaningful intelligence. The results of this data processing are made available in different forms — interactive visualization, predictive analysis, statistical reports, etc. In this way, machine learning integrates data silos to optimize the operational business processes and improve data quality.
Transforming Data Lakes to Data Hubs
Initially, the concept of a data lake gained traction as it offered companies a place to put all their information in and use it for analysis and extracting business intelligence. But with the incorporation of machine learning models, companies need more third-party connections and computational flow, which aren’t supported by data lakes. This has led to the increasing popularity of the recently developed data hubs. A data hub can be defined as a data lake that is integrated with cloud applications and machine learning. Several database providers have now begun releasing data hub services. A data lake that doesn’t incorporate concepts of machine learning is more internally focused, lacks flexibility, and doesn’t account for the entire cycle of data sharing. A data hub overcomes these problems. Experts claim that the data hub concept is actually “data lakes done right”.
Extracting Value From Data Lakes
Machine learning unlocks valuable insights by analyzing data. Going beyond creating reports and graphs, machine learning creates causal analysis and offers hypotheses for which variables and data domains are crucial to a particular business outcome. This is important for monetizing data, improving customer experience, and driving revenue growth.
With the advent of machine learning and its ability to enhance the functionality of a data lake, an organization’s data storage and analysis capabilities have vastly improved. Thus, it’s important that businesses looking to improve analytics embrace the powerful features of machine learning and improve results extracted from their data lakes.
Scalable machine learning architecture
An important aspect of the data lake is leveraging a machine learning framework like Apache Spark. This helps corporations with their data monetization and enhancing customer experience along with addressing other use cases.
With libraries like SparkML, PySpark, and SparkR Apache Spark is well-suited for large-scale machine learning tasks. Spark provides corporations with capabilities to execute algorithms for various statistical models.
Using the IBM Watson Data Platform business can incorporate data science. The machine learning models of R, Python or Scala enables Data scientists to explore what outcomes are possible.
By leveraging these tools corporations now have the functionality to look beyond just visual dashboards. By using machine learning corporations can undertake various types of analysis ranging from variance to causal analysis and predict an outcome.
To summarize, from data being stored in traditional relational databases, we are now moving towards big data, and the future is going to be incorporating specialized big data services in the cloud.
It is not a question of if or, all the technologies are important. But the future that we predict is going to be an amalgamation of big data technologies like Hadoop with Spark.