Taming the Data Flood: How Data Consolidation Empowers Businesses

by Bill Tolson

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Organizations are grappling with numerous data governance and information management challenges as vast amounts of digital data flood into organizations at a record pace. These challenges are fueled by the new regulatory retention requirements, rising ransomware and malware attacks and the growing adoption of generative AI. To make matters worse, companies must continue to capture and manage regulated files, including those containing PII, which is now subject to additional data rights and scrutiny.

Across all industries, the pressing discussion revolves around handling the continuous influx of legally responsive and regulated data. On average, organizations contend with data from over 400 sources in various formats, making data sharing and consumption both laborious and time-consuming.

A strategic response gaining traction is data consolidation — bringing data from multiple sources and formats into a unified location, such as a centralized file share or cloud-based archiving system. This proves effective for managing semi-active and inactive data.

Ongoing data consolidation offers many benefits, including simplifying information management processes and expediting e-discovery and regulatory compliance responses. To illustrate, envision trying to cook a meal with ingredients scattered throughout every room except the kitchen. Businesses, already stretched for bandwidth, cannot afford the inefficiencies of searching for responsive e-discovery or regulated data across numerous repositories – each with its individual search application.

This consolidation process yields quick organizational benefits, including enhanced decision-making capabilities and more efficient e-discovery responses. Moreover, it improves employee productivity by eliminating the need to waste time searching across numerous repositories. Storing semi-active and inactive data in a secure, low-cost cloud repository or archive reduces capital expenses, minimizes redundant data and enhances search accuracy and consistency.

Data consolidation and its role in data privacy compliance

The data privacy landscape has intensified globally since the enactment of the EU GDPR privacy regulations in 2018. A significant number of foreign countries and thirteen U.S. states (as of the writing of this blog) have implemented laws to fortify the protection of PII.

In fact, 137 out of 194 countries have passed laws to enhance the security of PII. While the US government has not yet passed a federal data privacy law, it is believed it will be passed in the next couple of years. States’ data privacy laws include the:

  • California Consumer Privacy Act (CCPA)
  • Virginia Consumer Data Protection Act (VCDPA)
  • Colorado Privacy Act (CPA)
  • Connecticut Data Privacy Act (CTDPA)
  • Utah Consumer Privacy Act (UCPA)
  • Iowa Privacy Act (IPA)
  • Indiana Consumer Privacy Protection Act (ICPPPA)
  • Tennessee Consumer Data Protection Act (TCDPA)
  • Texas Privacy Act (TPA)
  • Florida Data Privacy Act (FDPA)
  • Montana Privacy Act (MPA)
  • Oregon Consumer Privacy Act (OCPA)
  • Delaware Data Privacy and Information Protection Act (DDPIPA)

Note: On January 8, 2024, the New Jersey Legislature granted final passage to a comprehensive data privacy bill, Senate Bill 332, on the last day of the 2023 legislative session. The bill awaits final action from Gov. Phil Murphy, D-N.J., who has 45 days to approve, and would take effect one year after its enactment date.

With the ongoing global expansion of data privacy regulations, data consolidation is becoming a pivotal strategy in managing the increasing volume, velocity and variety of data. It plays an essential role in information management and data privacy compliance. Data privacy laws will continue to drive increasing data security requirements as well as evolving corporate information management practices.

Data consolidation benefits include:

Data consolidation can be a valuable tool for improving data privacy and security, including:

  • Increased transparency and accountability:
    With a single view of all/most enterprise data, organizations can more easily track data flows, identify PII and analyze its past and current use. This transparency facilitates compliance with regulations that require data access rights and usage disclosures.
  • Simplified access and deletion requests:
    When data is scattered across tens or hundreds of storage repositories, fulfilling data subject requests (e.g., access, rectification, erasure) can be cumbersome and costly. Additionally, the risk of not finding and deleting all instances of a data subject’s PII dramatically raises the risk of hefty fines, higher legal costs and negative PR. Data consolidation streamlines the search process by making locating and managing specific PII related to individual requests more accurate and straightforward.
  • Enhanced data security and anonymization:
    Centralized data stores can be more effectively protected with robust security processes and technologies, reducing the risk of breaches, unauthorized access and theft. Additionally, data encryption, anonymization, pseudonymization, redaction and data masking become easier by applying consistent techniques across the entire dataset.
  • Streamlined data subject identification and classification/tagging:
    Consolidated datasets allow for automated identification and classification of personal information (i.e., PII, location and biometric data) based on defined search criteria. This helps ensure ongoing compliance with data privacy regulations that have specific requirements for handling different types of data.
  • Improved data minimization (defensible disposition) and retention practices:
    By identifying and eliminating redundant data, organizations can minimize the overall amount of corporate sensitive data and PII they hold, reducing the compliance burden and potential ransomware extortion risks. Consolidated data also facilitates efficient data retention management, ensuring data is deleted after the designated period.

Data consolidation must be implemented thoughtfully and with robust and auditable safeguards as data privacy regulations further expand in a complex environment. For example, data privacy regulations are not all the same and will vary across jurisdictions. Companies must understand the specific requirements relevant to their PII collection and operations.

The four steps of data consolidation

Data consolidation can be a manual process requiring IT or legal staff to search all repositories looking for semi-active and inactive data metadata and determining if the files should be retained/moved/consolidated.

Obviously, a manual process is not feasible for many larger organizations and, truthfully, not as accurate as an automated systematic approach because of the need to rely on a group of employees with diverse backgrounds and education. The more efficient and accurate process is to incorporate automation and programmatic policies to get the job done.

However, whether you are pursuing the manual or automation route, there are four main steps for data consolidation processing:

  • Data extraction: Data is gathered from various sources, such as CRM systems, databases, SaaS applications, marketing tools, financial applications and potentially employee-managed devices.

  • Transformation: If needed, data is cleaned, standardized and formatted to ensure consistency and compatibility. This may involve resolving inconsistencies, removing duplicates and potentially converting data into a standard format.

  • Loading: The identified and transformed data is loaded into a secure central repository, such as a data warehouse, data lake or cloud archive.

  • Data management: The consolidated data is monitored and managed to ensure accuracy, security, accessibility and compliance.

Common tools and processes

There are many data consolidation processes to choose from. Some of the most common include:

  • Extract, Transform, Load (ETL): The traditional approach where data is extracted, transformed and then loaded into a target repository or system.
  • Extract, Load, Transform (ELT): A more modern approach where data is first loaded into a target system — such as a cloud archiving system — and then, if needed, transformed. This process is usually more efficient for large datasets.
  • Data integration platforms (DIPs): Tools that help automate data consolidation by providing pre-built connectors and transformation capabilities.
  • Cloud repositories and archives: Centralized and managed repositories for storing and managing large volumes of data from various sources and formats.
  • Data security tools to ensure ongoing data protection, such as role-based access controls, data encryption, and, in some instances, immutable storage for ransomware and extortionware attacks.

Data consolidation improves information management and compliance

Data consolidation is an essential process for growing organizations because it allows them to manage and visualize large datasets from all their departments and locations in a more comprehensive way. This helps companies perform data analytics, make predictions, detect business errors, detect intrusions (breaches) and make decisions based on valuable historical information extracted from their ongoing operations. Additionally, a timelier example includes using consolidated data stores to provide ready access to large data sets that can be used for custom corporate generative AI training processes.

Consolidating data from diverse applications with various formats and purposes can result in faster recognition of data duplication, reducing overall storage costs, increasing employee search productivity and reducing e-discovery costs.

Corporate data consolidation is crucial at a time when the amount of corporate data being collected, generated and shared is increasing.

The main benefit of data consolidation is that consolidating/archiving data improves both ongoing information management and data security. It reduces the number of storage repositories that must be searched and eliminates file disparities before data is used. This saves time, improves employee efficiency and adds value to the company’s analytical operations.

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Bill Tolson
Smarsh Blog

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