Lori Schafer in Forbes Tech Council: Harnessing Data With Integrity: Cleaning, Extracting And Unifying Data

Read the original article in Forbes Tech Council here.

Modern consumer-facing organizations rely on collaborative, data-driven decisions to fuel their business—yet the challenge is to do so with a keen focus on ensuring sound, well-maintained, accessible sources of actionable intelligence.

Effective companies understand the distinction between merely having data and being good stewards of it. The goal is to find ways not only to gather intelligence from multiple sources but also to connect, cleanse, enrich and organize data using smart, flexible processes that ensure accuracy and accessibility.

How does a forward-thinking yet nimble organization go about harnessing the power of disparate data sets? How can companies ensure they’re producing clean, extensible, enriched insights that are accessible to colleagues across an organization and partners alike? The answers are through governance and processes that help organizations clean and corral data.

Common Hurdles Of Data Extraction And Data Quality

The first step in data extraction is determining how to source information that is pulled, cleansed and enriched, which directly impacts the quality of the data a company can use. As the saying goes, “garbage in, garbage out.”

Data quality powers confident business decisions, and it needs to be extracted and enriched. Now, a major hurdle is if a company doesn’t have automated oversight to validate and harmonize incoming data. Without that safeguard, a data problem may have already taken root.

Data removal and transfer can be costly, as traditional methods of extracting data from one system and then inputting it into another can take months. The data may need to be reformatted, synthesized and then run through a gauntlet of convoluted batch-loading procedures. The problem of ensuring quality and control is further compounded by internal linking requirements to ensure data can be shared across departments. In essence, companies are not agile enough to quickly and effectively share data.

As CTOs and digital teams work to guarantee the company’s internal and external data are accessible and meeting quality standards, other challenges include:

• Navigating Silos: Data can come from various disconnected systems with incompatible formats or APIs that don’t play nicely together.

• Circumventing Limits: A legacy solution or platform inside a company might impose limits on data access by implementing restrictive APIs, data cubes or security frameworks, providing only a partial view of the data.

• Reformatting Data: Once data is collected, companies often need to standardize the information, tailoring it to match a company’s language, workflow and collaboration framework.

The hurdles can be high. Extracting, cleaning and unifying data can seem like a large mountain to climb; however, there are ways to scale the mountain safely, effectively and quickly.

Data Management Simplified

For companies seeking to accelerate their data processing, there are steps to take toward a smoother and more agile approach to data extraction and enrichment. Not surprisingly, as companies continue to explore and deploy GenAI, the technology is expected to play a role. New survey research from McKinsey found that 71% of respondents said their organizations regularly use GenAI in at least one business function, an increase of 6% from the 2024 survey.

The report notes that data governance is a leading application of AI. By leveraging GenAI in the ETL (extract, transform, load) process, companies can have a higher degree of confidence that granular inconsistencies can be identified and repaired and gaps can be filled and verified through inferred data patterns. The result is cleaner, comprehensive data at the time of loading.

As disparate data sources come together through intelligent ETL processes enhanced by AI, the next step is managing the security, authentication and role-level access of the different departments and users. GenAI can also help classify data based on sensitivity, department and type, enabling attribute-based access control. This function allows organizations to enforce highly granular permissions dynamically without manual intervention. For example, AI can automatically restrict access based on attributes like clearance levels or departmental ownership.

Navigating attributes and disparate data sources can be tough. Companies should also appoint a data governance leader to oversee ongoing processes, standards and security across the organization. Such a leader would oversee tools and processes that govern incoming and outgoing data sources, as well as usage patterns and emerging organizational needs. The data governor, so to speak, establishes protocols and guardrails that can prevent accidental or intentional data corruption, add or remove employee access and enable collaboration between departments or external partners.

Data Management And Culture Transformed

Modern CIOs and business leaders seek accurate data in real time. To achieve this goal, organizations must dedicate personnel, time and resources to modernizing and streamlining their data management activities. The requirement for clean, reliable and secure data that can adapt to the changing demands of internal and external partners has become a table stake for forward-thinking companies in this marketplace.

Organizations can explore AI and GenAI capabilities to accelerate the process. Additionally, they will need to instill a cultural mindset around protecting, cleaning and owning centralized data. Governance, standardized data goals and an AI-powered approach to smoothly extract, clean, format and unify data can help a company create data with integrity and deliver accurate results in real time.