Being overwhelmed by ever-increasing volumes of data, businesses currently struggle to store, organize, govern, and utilize it efficiently, which results in data breaches, inaccuracies, analytics errors, and other data-related issues. To overcome these complexities and maximize the value of enterprise data for companies, business leaders seek new, more efficient ways to manage their information, which drives the popularity of such technologies as data fabrics and data lakes.
While data lakes are more mature and widely adopted, a comparison between data fabric vs. data lake reveals no definitive winner, as each data management model serves specific purposes within modern data infrastructures.
Below, we will cover the concepts of a data lake and a data fabric and highlight three use cases for each technology to showcase how they can help you enhance your data management strategy.
What Is a Data Lake, and How Can Companies Use It?
A data lake is a centralized repository that stores large amounts of raw data generated and collected by a company. Unlike traditional databases that keep structured data, data lakes can store data in any format, whether it is structured (including customer records, point-of-sale records, and data from online forms), semi-structured (such as API responses or configuration files, etc.), or unstructured data (multimedia files, emails, etc.).
Data lakes can ingest data from different sources via data pipelines, either on a schedule or in real time, and store it in a raw or refined format for different purposes, from real-time analytics and reporting to ML model training and data science.
Let’s now explore the three most common use cases for data lake solutions:
Optimizing Cold Data Retention
Since cloud-based data lakes offer nearly unlimited scalability, companies can utilize them for the long-term retention of refined or raw data, regardless of its volume, at a relatively low price. Operating in a heavily regulated industry, a U.S.-based software company, Acrometis, had to retain vast volumes of rarely accessed data. The company decided to migrate its data assets to a data lake, which turned out to be the most cost-efficient option for long-term data storage. Now, Acrometis stores more than 50TB of structured and unstructured data in a data lake, which helps the company ensure data compliance with industry-specific data privacy regulations while minimizing IT expenses.
Streamlining Self-Service Reporting
By adopting a data lake integrated with a business intelligence platform, a company can enable its employees (including data analysts, data scientists, and average business users) to analyze data faster and more efficiently. Teams at Wipro, an Indian-based multinational technology company, used different on-premise systems to generate 500 reports either manually or as scheduled jobs, which was too inefficient (teams did not have a single version of truth; they also had to spend much time on report generation and dissemination). The adoption of a data lake enabled Wipro to establish a consolidated data environment, implement data access controls based on user roles within an organization, and accelerate business processes associated with reporting. Over 10,000 employees can now automatically generate reports based on the latest data and visualize it via intuitive dashboards.
Empowering Advanced Data Analytics
Artificial Intelligence models require vast sets of complete, high-quality data to train on to function effectively and provide accurate and meaningful analytics output. As data lakes store large amounts of diverse data, which can also be automatically transformed into a format suitable for analysis, they can serve as a robust foundation for organizational AI and ML initiatives.
Nestlé USA, a subsidiary of the renowned food and beverage company, had to constantly aggregate data from different siloed systems and clean the data from duplicates before analyzing it with an advanced analytical engine. To mitigate this bottleneck, Nestlé USA decided to retire its legacy siloed systems and integrate its structured and unstructured data from 10-plus sources into a data lake. Being complemented with AI engines, the data lake now enables 800-plus sales representatives to analyze in-store visits accurately, which already helped Nestlé USA increase sales by 3%.
What Is a Data Fabric, and How Can Companies Use It?
A data fabric is a data management architecture and software solution that connects multiple disparate data sources and business tools into a single digital system, centralizing and optimizing data management processes across a company. Simply put, a data fabric fulfills the role of an end-to-end data management solution, which enables users to access and use data from different sources, as if it were stored in a centralized location, but without moving it to this data storage physically.
Although data fabrics do not have a single, universal architecture and structure, they all share one common characteristic. Any data fabric relies on metadata to discover data across diverse on-premises environments, cloud environments, or hybrid clouds, map relationships between it, and provide a unified view of data for users. Also, through the use of metadata, data fabrics can automate enterprise data integration and transformation processes, which enables users to examine and utilize data without spending time on preparing it.
Now, let’s discuss three real-life applications of a data fabric:
Fostering Self-Service Data Analytics
Implementing self-service analytics can be challenging, especially when business users must analyze vast volumes of data stored in diverse formats and locations. Fortunately, a robust data fabric solution can help a company mitigate the complexities associated with self-service analytics.
Centrica, a UK-based supplier of gas and electricity, stored billions of rows of data, including trading data, billing data, and pricing data, across its disparate systems. The company required a unified analytics and reporting platform that would enable users to discover and analyze all this data efficiently, so it decided to implement a data fabric. Now the data fabric automatically collects data from ERP, billing, CRM, and other systems, runs multiple quality checks to ensure data quality and reliability, and delivers the refined data to different teams via data orchestration workflows. As all these processes take only tens of seconds, the data fabric significantly accelerates insight generation and decision-making.
Leveraging Data for Customer Insights
A data fabric can work as a central platform for managing, integrating, and analyzing vast amounts of customer data stored across different systems and environments. An AI-enabled data fabric can help a company build comprehensive customer profiles to gain a 360-degree customer view, better understand customer behavior and preferences, and serve their needs more efficiently, all without physically transferring customer data from its original location.
Heritage Grocers Group, an American food retailer, has implemented a data fabric complemented by an AI data analytics framework to gather and analyze point-of-sale (POS) data across 115 grocery stores, thereby studying consumer behaviors more accurately. The company is now able to process 1.3 terabytes of POS transaction data and three billion item transactions efficiently, which helps it anticipate future consumer needs, meet varying consumer demand, and provide better customer service.
Enhancing Data Governance and Data Privacy
If a company cannot efficiently govern large amounts of data stored across different environments and business applications, it can encounter challenges related to data quality, regulatory compliance, and broader operational concerns. As data fabrics operate based on a metadata-driven architecture, which facilitates data discovery and control at an enterprise-scale, they can be instrumental for companies aiming to keep their data assets consistent, secure, and reliable.
AP Pension, a Denmark-based pension company, possessed decades worth of business and investment data, but as corporate data management processes were siloed, AP Pension was not able to govern and provision the data efficiently. As Jacob Rønnow Jensen, head of data platform at AP Pension, said,
“Our wealth of data, rather than being an asset, became a challenge. It was clear; we needed to rethink our systems. We needed a consolidated and democratized approach to analytical data in accordance with our principles and digital strategy.“
The adoption of a data fabric solution helped AP Pension gain a consolidated view of its data scattered across data warehouse systems, data lakehouse systems, and other locations, and establish an efficient centralized process for data governance, data curation, and data security.
Final Thoughts
If you struggle to manage large data volumes and experience data overload, data quality issues, and other data-related concerns on a regular basis, it may be time to switch to a more robust and modern data management model, such as a data fabric or a data lake. Although each model serves a different purpose, both can help you optimize data management efficacy, reduce data administration costs, and improve analytics.
