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The Pause for Perfect Part 1: How to Approach Operations without Ideal Data Management


Driving new initiatives, including integrating new technology, is never easy. From overcoming countless planning and funding challenges to tackling resistance to change and technical difficulties; there is never a shortage of issues. In data management, the real issues appear straightforward when, in fact, the actual problem is different.  

Yet this doesn’t have to be the case. Data management is never perfect and it’s an evolving process. It should also never impede any improving effort. In other words, don't let perfect be the enemy of good – most issues are easily fixed if you have the right partners to help guide you along the way.

The next three blogs are dedicated to driving incremental change. We explore how imperfect data management can still lead to valuable initiatives starting with a view at what the ideal outcome is to begin with; then exploring data management tips to live by in your organization; and concluding with how to refine best and improve data management practices over time, and the numerous benefits this approach carries.

Start with the End

High-performing companies generally have high-performing data management principles. They routinely review and reimagine how their data drives business and generates value. This includes constant examination and reexamination of principles that lead to data excellence, which we explore in depth in our ebook

From building a purpose and a data strategy that is clear to the entire organization to define the behaviors and principles that put the management of data into play, building a sound data management approach leads to operational excellence and organizational success.  

A good example of an iterative data management improvement can be illustrated through this hypothetical scenario:


Imagine a medium-sized e-commerce company that initially has basic data management practices. Their customer and transaction data are stored across multiple systems with no standardized format, making it difficult to analyze customer behavior or predict sales trends effectively.

An iterative improvement process starts with:

  1. Assessment and initial cleanup, which includes an objective understanding of the current state of data and identifying key issues. This step includes conducting a data audit to assess the quality, structure, and storage of existing data and cleaning data by removing duplicates and correcting obvious errors.
  2. Standardization and integration then take place and involve developing and implementing standard data formats and integrating disparate systems by utilizing, for example, a cloud Data platform like Snowflake™. This approach may involve defining standard formats for data entry across all systems and implementing basic data validation rules to prevent future data quality issues.
  3. Standing up role-based access control ensures employees can only access data necessary for their roles; enhancing organizational control and security around vital data.
  4. Implementing a data management platform enables advanced features like data visualization, reporting, and analytics to help deliver continuous monitoring and feedback for actions such as setting up regular data quality reviews and audits.

As a result of these small incremental steps, our hypothetical company can more easily integrate solutions like AI or automation, and start to leverage data more effectively to improve decision-making, enhance customer experience, and drive business growth.  

Stay tuned for the next blog in the series when we explore the data management tips to get started on this path and how a company can evolve its data management capabilities step by step - addressing immediate needs while laying the groundwork for more advanced data utilization in the future.

For more information read our latest eBook The Future of Data Management.

Post by Steven MacLauchlan
Apr 9, 2024 7:30:00 AM


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