5 Benefits of Using Data Lakes for Big Data Analytics
With the advent of big data, sectors as varied as healthcare, finance, and manufacturing have leveraged a once-unmanageable deluge of information in ways that many of us have witnessed firsthand. Lower-priced quality goods, better medical outcomes, and safer, more agile financial systems have all resulted from the pioneering applications of big data principles.
However, the main challenge of wielding big data is in the name. As data sets grow larger, the ability to manage the information contained within them becomes progressively more difficult. Many organizations that attempt to handle extremely large data sets without the right infrastructure will eventually run into scaling issues that can hamper analysis and decision-making. This is where next-generation “data lakes” come in.
Data lakes are centralized data repositories that permit structured and unstructured data storage at any scale, holding raw data in its native format until it is needed. This is in contrast to conventional databases that require strict formatting so that data can be used. Compared to previous-generation storage frameworks, data lakes effectively reduce multiple burdens from data management and data analytics operations.
With a data lake, you can store your data as it is, avoiding the tedious step of restructuring it to fit specific needs. Once a data lake has been set up, you can much more easily run different types of data management activities, from AI training to visualizations, and much more. Data analytics can benefit immensely with data lakes directly permitting faster, more accurate, and cost-effective insights. Let’s explore some of the key advantages your organization will enjoy by updating its data storage systems to data lake infrastructure.
1. Superior Scalability over Legacy Systems
Data lakes are designed specifically to scale along with growing data volumes without compromising performance. This removes a massive roadblock that has historically limited the efficiency of data analytics. With a properly managed data lake, the need to manually restructure existing data to fit emerging needs is minimized.
2. Cost-Effective Storage and Analytics
Because there is a minimal need to structure data kept in data lakes, they provide a cost-effective solution for storing vast amounts of data. Also, unlike traditional databases, which can become expensive as data volume grows, data lake infrastructure is usually designed to be future-ready, reducing the need to constantly update the system once new data analytics requirements emerge.
These benefits combine to bring down the cost of data analytics and the costs associated with achieving business-changing insights. Even now, the reduced costs provided by data lakes have enabled smaller businesses to achieve the big data capabilities once reserved for larger institutions.
3. Enhanced Data Analytics
Because data lakes allow organizations to perform complex analytics on unprocessed data, they effectively permit information management systems to draw from a much wider pool of data points. More data points can lead to more accurate insights, giving decision-makers more confidence to act. Data lakes are especially valuable for organizations transitioning to machine learning and artificial intelligence in their systems. Such a combination can allow businesses to gain deeper insights into their data that would have been more expensive or outright impossible to obtain with older systems.
4. Holistic Data Consolidation, Access, and Governance
By integrating data from various sources into a single repository, data lakes can provide analysts and decision-makers with a much more holistic view of the organization’s data. This breaking down of silos simplifies data management, eliminating duplicated efforts and enhancing data quality by making it possible for organizations to more easily obtain a single source of truth for all their stakeholders.
Having a unified data source for everything has other more specific benefits for businesses. Data governance, compliance, and life cycle management are all vastly simplified by using data lakes. Importantly, security in a data lake framework can be better than in traditional setups since cybersecurity managers are no longer dealing with multiple databases with many more potential ingress points for malicious parties.
5. Flexibility
Data lakes support all data types, including structured, semi-structured, and unstructured data. This flexibility allows organizations to collect data from various sources (such as Internet of Things devices, for instance) and store it without the need to transform it first.
In practical terms, this means that you can simply focus on gathering data without having to second guess what you may need it for in the future. Once a specific data analytics need arises, you can draw the information from your data lake to quickly gain insights without having to go back, find potentially useful info, and restructure it for a specific purpose.
The Backbone of Future-Proof Big Data Strategies
The advent of big data has revolutionized how businesses across various industries operate, consistently providing deep insights that were previously only attainable through luck, intuition, or expensive investments in still-emerging data technology. Data lakes further extend the core benefits of big data by removing key friction points that have, up until recently, slowed down its wider implementation. As the volume and variety of data continue to grow, data lakes will likely play a lead role in enabling organizations to maximize the information they may already have.
