Over the years, Big Data has transitioned from mere buzzword to major player across industries. Today, companies recognize that data has become crucial to strategy development and customer satisfaction.
Yet, because many still operate in silos, more brands must engage in data migration projects in order to consolidate complex systems and streamline internal operations. Such processes, however, can prove problematic, for there are numerous inherent risks involved.
Experian Data Quality's recent "Easing the Data Migration Burden" report examines the current condition of data migration to understand the risks of such projects and how to leverage quality information. According to the study, data migrations are becoming more and more prevalent, as this increased focus on data-driven insight has made combining information sources a necessity for most brands:
- Of the companies that engage in data migration projects (91 percent), most do so in response to a merger or acquisition, a de-merger or buy-out, system replacement, overall system upgrade, new system purchase, or regulatory changes.
- Ninety-five percent of respondents feel motivated to turn data into insight, with understanding customer needs, finding new customers, increasing the value of each customer, and securing future budgets as the principal drivers for such actions.
However, 85 percent of those polled have experienced one or more of the main data migration challenges:
- Lack of collaboration (38 percent) - Most companies fail to share knowledge and information across the organization, as many neglect to engage the right business stakeholders. Communication remains essential, as all involved need to be on the same page.
- Lack of standardization (37 percent) - During data migrations, businesses must combine a large number of data sources. However, because there are great differences in the way to store, standardize, maintain, and structure data, vast inconsistencies may arise.
- Poor system design (33 percent) - Often times, the system designed to house information fails to fit with each given department. Thus, such systems cannot manage or handle the types of data required due to poor scoping or overall lack of understanding.
- Inaccurate information (31 percent) - For many companies, poor data quality goes undetected. Instead, most neglect to use data profiling tools throughout the migration process, thus limiting comprehension of these issues and damaging data integrity at inception.
- Poor interpretation of business rules (24 percent) - Companies must create rules that fit a given purpose, for those created without thought to the data or business process will cause the brand to lose valuable information, as it will not satisfy the end goal.
Key takeaway: To ensure overall data health remains optimal throughout the migration process, companies must secure stakeholder buy-in and cooperation from the start. Collaboration fosters communication, thereby reducing confusion and minimizing exposure to the common challenges. Technology and business users, in particular, must be closely aligned during data migration, for business users need to clearly define their requirements so the technology team can gain clear understanding of the end goals. Organizations must also standardize their data by aligning terms and definitions in order to guarantee consistency and eliminate errors that could upset migration. Data has become the greatest asset of any given company, so brands must treat this information with the utmost care. Thus, data hygiene will continue to play an imperative role before, during, and after the migration process, for actionable consumer insight holds the key to growth and success.