Driving Business Intelligence with a Robust Data Lakehouse

Driving Business Intelligence with a Robust Data Lakehouse
Driving Business Intelligence with a Robust Data Lakehouse

Enterprises that aim to gather valuable insights from their data in real-time can embrace data Lakehouse in their IT infrastructure.

Developing a robust data Lakehouse will enable businesses to get the best out of data lakes and data warehouses. If the organizations have a separate lake and warehouse in their IT infrastructure will need to migrate their data from one to the other. Maintaining a separate lake and warehouse in the IT infrastructure will increase the latency, efforts, and cost of managing data. 

Data Lakehouse equips enterprises with data structuring features using a metadata layer to streamline data management and business intelligence just like a data warehouse. Additionally, it also equips organizations with low-cost raw data stored in open file formats just like a data lake. Convergence of the best features of both the data lake and warehouse helps organizations overcome the limitations to provide enterprises with a competitive edge.

Data Lakehouse is a perfect way to minimize data migration efforts and acts as a catalyst in unveiling valuable data insights. CIOs should consider developing a data warehouse with all the robust tools like data streaming, machine learning, artificial intelligence, and analytics to make the most. Here are a few ways that enterprises can consider getting valuable insights from data Lakehouse:

Reengineering decisions to make the most accurate one

Many data-driven organizations are embracing reengineered decisions that bank on data, analytics, and artificial intelligence to get a competitive edge in the industry. CIOs should consider evaluating the decision-making process and design a robust framework to align the core decision that ensures business continuity. Furthermore, enterprises need to ensure they implement a robust decision-making framework that allows them to re-define decisions that align with the stakeholders’ vision. 

Also Read: Developing IT Infrastructure Resiliency in Industry 4.0

Simplify data governance and compliance

A robust data Lakehouse architecture helps businesses to develop one compute layer to execute data refinement and governance in a more efficient approach. CIOs should consider setting standard programming APIs to enable self-serve data registration and automate other tedious tasks to develop an effective data catalog and refine data. The self-serve data registration feature is a perfect way for large enterprises to reduce the work burden on the data governance and compliance teams. Enterprises should consider designing and implementing data quality frameworks throughout the workflows to ensure data quality always. 

Data democratization to ensure data quality 

Organizations that aim to have better data quality need to consider time value. Once data enter into the organization’s data repository, it has a high value the moment it is generated. Organizations that work on critical data entries in real-time will help them to make strategic changes immediately to make the most. 

Enterprises need to reduce the duplicate data entries in the data Lakehouse to ensure quality data democratization. Businesses with different data ecosystems will have the same data residing on different platforms. CIOs should consider developing a robust data warehouse to centralize the data and reduce costs and fewer resources to get valuable insights. 

Bank on data Lakehouses to gain valuable insights

Enterprises of all sizes, types, and sectors want to have efficient risk management; data is the most important asset for them. It is crucial to get actionable insights, minimize costs and protect against all the risks. CIOs should consider developing a robust data Lakehouse architecture that allows the data teams to have an efficient data ecosystem, integrate scalable resources, and complies with the cost and data governance policies set. Large enterprises that implement an effective Lakehouse architecture enable the data teams to seamlessly focus on gaining valuable insights from the data generated. 

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Nikhil S is a Tech Journalist with OnDot Media. He is a media professional with eclectic experience in communications for various technology media brands. He brings his eye for editorial detail and keen sense of language skills to every article he writes.