Truth or Myth? Data must be replicated and isolated for secure downstream analytics
The article argues that although data replication and isolation are commonly practiced to ensure secure and consistent downstream analytics, this approach introduces complexity, resource demands, and delays, and ultimately, replicating data is unnecessary for effective decision intelligence since it can increase risk and complicate analytics without providing essential benefits.
Data replication is the process of storing the same data in multiple locations to improve data availability and accessibility. However, the necessity of replicating and isolating data for secure downstream analytics is questioned.
Replicating data for analytics and business intelligence preserves the structure of the source data, but it introduces extra steps and complexity. Organizations must allocate resources and establish procedures to maintain data consistency under these conditions.
According to Deloitte, "Data preprocessing such as cleansing and formatting it for analysis is time-consuming. Some estimates suggest that this can account for 80% of the effort in data analysis projects."
Ventana Research adds, "Analysts spend the bulk of their time on manual tasks such as preparing data for analysis (47%) and checking quality and consistency (45%) in the data rather than doing actual analysis."
Many BI tools require users to replicate data from source locations into a data silo or warehouse before analysis. The typical process involves copying data from various enterprise sources, cleaning it, and storing it in a separate, controlled environment accessible by the BI tool. In addition to security measures in the data warehouse, analysts may need to build content multiple times for different data sets to restrict end-user access.
The belief that data replication and security automation are necessary persists because organizations are told that replicating data keeps it consistent, reliable, and up to date. People are conditioned to avoid working directly with source data to preserve it. Analytics tools reinforce this by being designed to pull from a data warehouse rather than directly from the data source, claiming this improves speed and efficiency.
The truth is, there’s no need to replicate data for decision intelligence.
While data replication is a valid strategy for disaster recovery, it increases risk and causes unnecessary complications in analytics. Moving data to another location can compromise its inherent security and, depending on how it is shared (such as through intermediate files or insecure channels), can introduce additional downstream security concerns, like data falling into the wrong hands. There is also the risk of creating conflicting copies (data silos) and data latency, as extracted data is no longer up to date.
With the right decision intelligence platform, data can remain in its original location, avoiding unnecessary complexity and risk.
Decision intelligence is what’s next in analytics.
Rather than investing resources in data replication and patchwork security, teams can connect directly to any data source, query, and blend any amount of data instantly. Decision intelligence platforms enable this direct connection, streamlining analytics and reducing the need for data movement and duplication.
For more information on common BI myths and how decision intelligence can elevate analytics, refer to the Mythbusting Business Intelligence vs. Decision Intelligence guidebook.