7 Steps to Get Your Data Consolidation Project Off the Ground

Messy data kills efficiency and creates confusion. Here’s how you can create order out of chaos and get your essential data organized and under control.

John Edwards, Technology Journalist & Author

January 22, 2024

4 Min Read
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Anthony Brown via Alamy Stock

Data consolidation is the process of combining data from disparate sources, cleaning and verifying it by removing errors, and then storing it in a single location, such as a database or data warehouse.

Data typically flows into organizations from multiple sources and in various formats. Data consolidation unifies data, allowing organizations to efficiently plan, implement, and execute business processes and disaster recovery strategies. With all critical data centralized, adopters gain a 360-degree view of their essential business information. Data centralization also improves data quality, while accelerating process execution and simplifying information access.

Data consolidation may sound like a daunting undertaking, but it can easily be broken down into seven basic steps. Here’s what you need to know.

Step 1 - Update your data inventory

Check to your data inventory to see if it’s up to date, says Kathy Rudy, chief data and analytics officer with technology research and advisory firm ISG in an email interview. If not, make it current.

An accurate data inventory sets the baseline for not only what needs to be consolidated, but which systems will hold the data, as well as the data’s format, type, characteristics, size, how it’s being currently used, location, and its owner. “From there, an organization can define the scope and scale of the consolidation effort,” Rudy suggests.

Related:Why Now is the Time to Consider Data Consolidation

Step 2 - Establish consolidation goals

It’s important to outline the consolidation project’s goals, Rudy says. Is consolidation an effort to improve data quality, create a new way to leverage data across systems, improve analytics, offload data for cost savings, meet regulatory requirements, or any other specific reason or reasons? “If you want stakeholders to come along on the journey, you’ll need to have clear objectives that make business sense and justify the effort of the consolidation project,” she explains.

Step 3 - Assess the project’s demands

A big mistake when launching a data consolidation project is underestimating the complexity and challenges related to integrating and consolidating disparate data sources, says Ken Sardoni, senior vice president, learning programs, with CompTIA, profit trade association that issues IT certifications, in an email interview. “This often stems from inadequate planning and preparation, including not clearly defining the project’s goals and scope, not thoroughly assessing the existing data landscape, and not properly allocating resources.”

Step 4 - Consider the business impact

Related:Feasting on High-Quality AI Data

Data consolidation is often launched as an exclusively IT project. Yet such an approach negates the benefits of fully aligning the initiative to measurable business outcomes, says Rex Ahlstrom, CTO and executive vice president of innovation and growth at enterprise data management firm Syniti, via email.

Businesses today seek agility, which requires a new level of data quality, Ahlstrom says. “As digital transformation continues, companies must keep business goals front and center, saving money while making more money, reducing risk, and increasing sustainability,” he explains. “A sound data quality program, in conjunction with a data consolidation project, can assist with all these objectives and more.”

Step 5 - Establish resilient governance policies

Enterprises embarking on a consolidation initiative should establish clear data governance policies and procedures outlining roles, responsibilities, security measures, and access protocols, advises Kyle Fox, CTO at aerospace, defense, and government services integrator SOSi, via email.

Ongoing and repetitive data quality checks are crucial to identify and rectify errors or inconsistencies, guaranteeing the accuracy and reliability of the consolidated data, Fox says. “Continuous monitoring and evaluation of the data consolidation project are essential to assess progress, identify areas for improvement, and ensure alignment with evolving business objectives.”

Related:How to Safely Delete Obsolete Data

Monitoring should include tracking key performance indicators, conducting regular reviews, and soliciting stakeholder feedback. “By adhering to these best practices, organizations can effectively manage and utilize their consolidated data to drive informed decision-making and achieve their strategic goals,” Fox suggests.

Step 6 - Address security issues

There are two things most enterprises can’t tolerate: outages and security issues, says Chiara Regale, senior vice president of product management, at network management software developer Forward Networks, via email. She notes that security, network, and cloud teams often work in the dark, trying to protect networks that are poorly documented and full of blind spots. “Human minds are no longer capable of analyzing all the variables,” Regale warns.

Regale recommends deploying a digital twin, which not only creates a virtual copy of the network, but can also analyze, normalize, and contextualize data while showing exactly how the system is behaving over time. “This provides the people running the business with the context and actionable insight they need for successful decision-making,” she explains.

Step 7 - Provide team training and education

Organizations often underestimate the need for comprehensive training and awareness campaigns to educate end-users about the new consolidated system, its security protocols, and potential risks, says Adhiran Thirmal, senior solutions engineer at cybersecurity services and training firm Security Compass in an email interview. “By acknowledging security complexities and planning proactively, organizations can reap the benefits of data consolidation while minimizing risks and safeguarding valuable information.”

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Data Architecture

About the Author(s)

John Edwards

Technology Journalist & Author

John Edwards is a veteran business technology journalist. His work has appeared in The New York Times, The Washington Post, and numerous business and technology publications, including Computerworld, CFO Magazine, IBM Data Management Magazine, RFID Journal, and Electronic Design. He has also written columns for The Economist's Business Intelligence Unit and PricewaterhouseCoopers' Communications Direct. John has authored several books on business technology topics. His work began appearing online as early as 1983. Throughout the 1980s and 90s, he wrote daily news and feature articles for both the CompuServe and Prodigy online services. His "Behind the Screens" commentaries made him the world's first known professional blogger.

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