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In the rush to modernize and make the most of technological advancements, many enterprises have turned to the cloud as a silver bullet for their data challenges. Data quality, the underlying foundation of analytics and AI, has long been a silent obstacle in the path to achieving actionable insights.
But does migrating to the cloud truly address these deep-rooted data quality issues, or are we merely transferring our digital woes from one platform to another? In this article, we’ll discuss the importance of data quality, how data silos harm data quality and why cloud migration is only one part of the solution.
The Challenge of Data Quality
Data quality issues can be insidious. From the redundant and inconsistent to the incomplete and inaccurate, poor data quality is the bane of trusted business analytics and decision-making. The rise of AI and machine learning has only amplified the importance of high-quality data; algorithms are only as good as the data they’re trained on.
Even in cases where companies license off-the-shelf AI solutions instead of building their own, poor data quality still presents a challenge. AI copilots and chatbots are quickly becoming widely used tools in the day-to-day work of more and more people across departments and job functions. As these tools become normalized and more widespread, the data these tools use to help automate routine tasks and generate quick insights must be accurate and up-to-date.
Consider one example of what can happen when an AI tool hallucinates. Air Canada made headlines in early 2024 when it was reported that an AI chatbot the company was using for customer service “lied” to a customer about the company’s policy on bereavement fares. The customer sued, and the British Columbia Civil Resolution Tribunal ruled against Air Canada — a decision that caused damage to both the company’s reputation and its balance sheet.
Uniqueness in Data: A Prerequisite for Quality
Uniqueness — or the principle that each piece of data should live in only one place — is a foundational aspect of data quality. However, in many organizations, data is duplicated across data silos.
This not only leads to poor quality data across the enterprise, but also to operational inefficiencies as people from different departments who can’t access certain systems end up duplicating work. In many cases, data is intentionally siloed for a specific reason, but doing so leads to several unintended negative consequences.
To correct this problem, many data leaders try to break down data silos, and migrating data into one consolidated, cloud-based platform is one of the approaches they take.
But Does the Cloud Truly Solve This Siloed Problem?
Some data leaders assume that migrating to the cloud naturally reduces data fragmentation and inaccessibility. But in reality, data silos are a product of organizational structure, information management practices and lack of collaboration. Whether data resides in on-premises servers or in the cloud, the essence of siloed data remains if the problem is not addressed at the core.
Data in the Cloud = Data Without Problems?
Cloud storage systems are designed with redundancy and fail-safes to preserve data integrity, but that’s not the same as ensuring each piece of data is unique, up-to-date and consistent. The structure of data storage in the cloud, while robust, doesn’t address the systemic issues that create data duplication and inconsistency.
The Real Solutions for Data Quality
To tackle data quality head-on, enterprises need to go beyond the location of their data stores. The real solutions lie in implementing robust data governance and master data management (MDM) systems and practices.
- Establishing data governance: Data governance empowers organizations to manage data as an asset. It’s about defining what high-quality data means for your organization and putting in place the policies, standards and processes to ensure data meets those criteria.
- Master data management (MDM): MDM enforces the policies defined by data governance, using technology and processes to ensure that an organization’s master data is consistent and accurate across the enterprise. This includes their most critical data domains like customers, products, employees, locations and more.
The real power of this two-pronged approach lies not only in the quality of data it produces, but also in the usability of that data. With clearly defined data governance policies in place, MDM lets you create accurate, consistent, up-to-date and de-duplicated data that can be used by downstream systems and end business users for initiatives like improved insights and analytics, AI adoption, regulatory compliance and more.
In this way, data governance paired with MDM delivers real business value — boosting operational efficiency, improving customer experiences, capitalizing on previously unrecognized business opportunities and helping to enable AI adoption across the enterprise.
Cloud as Enabler, Not Savior
That being said, cloud technologies enable better data governance and MDM by providing scalable storage, powerful processing capabilities and the potential for real-time integration. However, these tools are only effective if they are part of a comprehensive data management strategy that includes data governance and MDM.
For example, Profisee MDM lets you integrate data from any source, be it Snowflake, Azure Data Lake Storage, Salesforce or your ERP system. Profisee is also available as a true SaaS deployment built for any cloud, so you can deploy it where your organization already works.
Unlocking the Potential of Quality Data
Once data is properly governed and managed, it can be a powerful asset. Integrated, consistent and accurate data can unlock the potential of analytics, AI or any data-driven strategy a business hopes to implement. The cloud is a critical component of this, but it’s not the solution by itself.
Overcoming the Cultural and Organizational Hurdles
The challenges presented by data quality are often as much cultural and organizational as they are technical. Moving data to the cloud may require significant technical expertise, but it also necessitates a shift in mindset and working practices to fully communicate the benefits of a cloud-based approach to data management.
Rather than blaming poor data quality on a lack of data literacy in the organization, data leaders should focus on promoting the tangible business value a comprehensive data strategy can deliver. Everyone in the organization needs to understand the benefits of data quality and how it has a direct impact on their day-to-day.
The Path Forward
For enterprises grappling with data quality issues, the path forward is clear:
- Recognize the cloud as part of the solution, not the full solution
- Invest in robust data governance and MDM technology and practices
- Cultivate a culture that values data and its quality
- Implement technologies that support your data quality goals, whether in the cloud or on-premises
By taking these steps, organizations can effectively tackle the data quality challenges that have plagued them for too long. Only then can they fully harness the power of their data, regardless of where it resides.
Profisee MDM Lets You Overcome Data Quality Hurdles — So You Can Finally Make It Happen
Unlike traditional solutions, Profisee is an adaptive MDM solution that conforms to the way you work — not the other way around. With native integrations for Microsoft Purview and Fabric, Profisee makes MDM easy so you can create gold medallion data that’s consumption-ready for the initiatives you’ve wanted to pursue for so long.
Get a custom demo today so you can see how Profisee features like data integration, data stewardship, matching and survivorship, relationship management and workflow automation can help you overcome your data quality challenges.
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