Harnessing the Power of Data Lakes: Consolidating Multiple Data Sources for Organizational Empowerment

Consistency, reliability, speed, and accuracy. These are the litmus tests of any enterprise data strategy. If an enterprise data architecture and tool fail to deliver these imperatives, then anything else they give is, eventually, toothless. But these four characteristics are hard to derive from any enterprise data solution. Especially when the explosion of data, the proliferation of Big Data, and rapid digitalization have made data an unwieldy power for many organizations. According to an O’Reilly Data Scientist Salary Survey, one-third of the data scientists spend time doing basic operations such as necessary extraction, transformation, and loading (ETL), data cleaning, and essential data exploration. This is precious time that should, instead, be spent on real analytics or data modeling. Enterprises need something clever, spot-on, and big enough to control, make sense of, and effectively use data.

Enter the land of data lakes. These are better, cheaper to build, and bigger than data warehouses. A data lake is a practical answer to solve the questions of the exponential surge of data in all directions. It’s just like a lake. A go-to-place and a specific spot that holds all the data you need- in a drinkable form. As a central data repository of all critical data insights. Not like an ocean. But like a lake.

Data Lake – An oasis of clarity and business value

With the added elements of analytics tools, machine learning (ML), dashboards, visualizations, advanced real-time analytics, big data processing, structured query language (SQL) queries, and high-level information models – these data lakes make data valuable and actionable. They consolidate multiple data sources’ sheer volume and diversity into one shared pool—an accessible, simple, and fluid central data repository. Unlike traditional data storage methods, a data lake is:

• Consolidated
• Well-managed, well-architected, well-tracked
• Secure and governance-aligned
• Designed for organizational needs
• Fitted with new technologies
• Equipped for fast and fluid data extraction
• Not siloed or fragmented
• Purpose-built for more immediate decision making
• Designed for action on data- not just for storing data

A data lake is structured in a very smart way. It houses raw, enriched, curated, and developed data in different layers- like conformance, standardized data products, and sandboxes. It is also clearly organized in tiers like operations, processing, distillation, and in many cases-HDFS (Hadoop Distributed File System). The various parts of a data lake focus and deliver on specific needs like data ingestion, storage, auditing, quality, exploration, and discovery. This is why a data lake gives a lot of advantages to data analytics and business decision-making. A data lake makes it simple and swift to store data, manage multiple data sources, convert it into a standard and easy-to-use format, extract it, and analyze it.

Key Gains- Out of a Data Lake

There are many benefits of Data Lakes for organizational empowerment:

1. Centralized Data Management
   1. Elimination of data silos and duplication
   2. Simplified data access and sharing
2. Improved Data Quality and Consistency
   1. Data cleansing and standardization processes
   2. Enhanced data governance and control
3. Advanced Analytics and Insights
   1. Ability to perform complex analytics on diverse datasets
   2. Facilitate data exploration and discovery
4. Scalability and Agility
   1. Ability to handle large volumes of data
   2. Flexibility to adapt to evolving business needs

Creating Effective Data Lakes

If you are now thinking of opting for a data lake, it’s better to consider some strategies for consolidating Data Sources into a Data Lake: