Why Data Centralization Is Vital for Today’s Companies

Data centralization brings together all data into one place so it can be more effectively managed and accessed. As businesses rely on a larger number of data sources than ever before, the importance of having a centralized approach to store and manage it has never been greater. This article explores the concept of data centralization and why having a platform that works across cloud providers and storage patterns is so vital for today’s businesses. 

Why Is Centralized Data Important?

Modern data applications and analytics rely on a wide variety of data spread across various systems. Data centralization unlocks the following benefits: 

Eliminates data silos 

Data silos make it impossible to gain a clear, unified view of business data. Data teams must track down and reconcile disparate data sets, creating inefficiencies. Centralizing data increases collaboration among teams and ensures that everyone has access to validated, complete data sets.

Improved data governance

Data governance guides the creation, evaluation, and use of data products within an organization. As concerns around data quality and data security grow, data best practices and governance policies provide a necessary framework for both ensuring that data is reliable and that processes and procedures comply with legal requirements and customer expectations of data privacy. Organizations that house data in multiple systems face additional challenges in maintaining data security and oversight. Centralizing data access creates a less complex environment for data, making it easier to monitor and control the use and protection of data.

Better data analytics 

Missing or incomplete data can skew data analytics, depriving decision-makers of valuable opportunities to capitalize on industry trends, recognize pending changes in the markets, and accurately predict future demand for products or services. Data centralization ensures that analytics efforts produce accurate, actionable insights by ensuring that all current, relevant data is immediately accessible for queries. 

Cost-effective, scalable data storage

A centralized data warehouse or data lake is less expensive than its on-premises counterpart. Unlike traditional data storage solutions, cloud-based options offer near-infinite scalability and flexibility. Organizations can dynamically adjust their data storage based on the needs of the moment. Without capacity constraints, organizations and their data can grow over time, free from the cost burden of future data infrastructure expansions.

Decreased maintenance burden

Cloud data lakes and warehouses are managed as a service, allowing data teams to shift their attention from managing infrastructure to extracting value from data. With managed services, teams can spend their time on important work such as building improved data pipelines and fine-tuning data analytics software to better meet the business needs of the organization.

Increased resilience 

Centralized data warehouses and data lakes provide built-in data protections that improve data resiliency. Advanced data recovery options such as service protection against node failures and failover protection across multiple availability zones guards against data loss.

Better shareability

When all data is stored in a single platform, that data can be shared more easily within the company, with customers, with data partners, and other authorized users. Storing data in separate systems creates a tangled web of data that’s cumbersome to share, even just internally. 

Expanded data sources

As the value and business applications of big data continue to grow, many organizations have turned to public and third-party data marketplaces. Data marketplaces provide access to relevant data from a variety of providers, allowing organizations to supplement their own data with outside sources—while retaining control of data privacy and governance. The result is enhanced data analytics capabilities that wouldn’t be possible using in-house data alone. 

Centralized Data and the Security Data Lake

A security data lake allows businesses to solve problems of scale, cost, and detection when it comes to data cybersecurity. By using a security data lake, companies can separate storage from compute to eliminate overhead costs, quickly scale as needed, centralize diverse data sets and sources in one location for easier analysis, and increase data processing flexibility.

Centralize Data with Snowflake

The Snowflake Data Cloud enables organizations to manage all of their data in one place, streamlining data centralization for even the most complex business data landscapes. Snowflake is a single, integrated platform delivered as a service. It features storage, compute, and global services layers that are physically separated but logically integrated. Data workloads scale independently from one another, making it an ideal platform for data warehousing, data lakes, data engineering, data science, modern data sharing, and developing data applications. 

In addition, Snowflake for Cybersecurity allows organizations to deploy a modern centralized security data lake within Snowflake’s Data Cloud to enable powerful analytics, accelerated detections, and speedy investigations.

With Snowflake, resource limitations and wait times are a thing of the past thanks to the ability to instantly spin up and down a virtually unlimited number of concurrent workloads against the same, single copy of your data. Eliminate the administration and management demands of traditional data warehouses with Snowflake’s fully managed platform.

See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial.