Understanding Data in Your Organization
Organizations are always trying to get the most out of their data to ensure better decision making, sounder reporting, or even data monetization. But what does that even mean? How do you know if you're doing right?
This article is the beginning of a series that is meant to be a jumping-off point to better understand your data ecosystem. This is a wholistic view of data; we are not merely looking at the data itself, but the treatment and sentiment toward data across the organization. It is essential to consider all these factors when trying to build a sustainable data organization.
The four main pillars of a data ecosystem are Data Quality, Data Robustness, Data Culture, and Data-Driven Outcomes. Understanding these four areas is critical to getting the most out of your data. Below, I will define each pillar and its importance to getting the most out of your organization's data.
Data Quality refers to the accuracy and precision of your data. Is the data being input properly? Is there high enough coverage on data fields to trust the data? Good data quality is essential in order to know that insights drawn from this data can be trusted.
Data Robustness refers to whether or not data is available where and when you need it to be. It is also used to assess the fit of the tools and processes to the business needs. Practically, we will recognize these as the cadence of data input and data refreshes as well as the reliability of processes and tools.
Data Culture refers to the way that people in the organization interact with data. Who is able to interact with it? How do they feel about it? How do they speak about it? This is critical to understand if employees are willing and interested to work with data or if increased education and discussions are required to improve adoption of healthy data practices.
Data Outcomes refers to an organization's main goal for their data. Is it a revenue driver? Does it dictate decision making? Do they need it for fundraising? These questions will indicate how data is being used now, but also potentially areas for it to grow.
As you can see, these go far beyond just a remodel of the data warehouse or a redesign of data inputs. It is important to view all these factors because ultimately, even if the data is perfect, if employees do not use it to make decisions and drive outcomes, you will not be able to get the most out of your data.
In future articles, I will dive deeper into the questions we can ask to better understand these four pillars and how they are working in your organization — follow along for more!
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