Data stewardship is essential for organizations to master. It takes you further than data management, and understanding the difference is key to collecting and using data well.
Unfortunately, many confuse the two and fail to make the most of organizational data. Answering “what is data stewardship” and understanding its role helps supply chain professionals deal with modern logistics challenges, including disruptions, limited budgets, and managing countless network partners. You can address these challenges with improved data management by recognizing incorrect, missing, or latent data.
What Is Data Stewardship in Logistics?
Data management happens on a more superficial level. Good stewardship of data is a complex and comprehensive process that includes all aspects of data, including:
- Collecting
- Viewing
- Applying
- Maintaining
- Following through
In addition, good stewardship involves data security. An organization’s data opens it to risk.
Organizations need an effective data stewardship system, yet many fall short of this goal. Data is challenging because it features a never-ending cycle of managing data while new data comes in. Organizations that do not have a good stewardship plan are likely to struggle.
It’s not impossible for a company to carry out data stewardship, and you may not need an entire team. Instead, this is where modern technology comes in. New technologies offer the potential to handle the challenges of data management that interfere with data stewardship. Cutting-edge technologies are capable of supporting organizations in improving their ROI regarding digital transformation and management.
Challenges Affecting Good Stewardship of Data
Organizations often face data management challenges that interfere with proper stewardship. To address the problem, start by understanding existing operations through standard benchmarking. It’s essential to understand what is data stewardship’s effect when it is poorly managed?
Incorrect data
Not all data leads to effective outcomes. Incorrect data negatively impacts decision-making and outcomes.
Latent or untimely data
Some data is too outdated to be relevant. It’s possible for data to no longer be valuable due to its age, and the data’s value can change quickly. Even recent data from the same or previous week may no longer be helpful. It’s important to make decisions from a complete look at needs going forward, using the most recent data possible.
Missing data
Incomplete data poses a challenge because organizations need a comprehensive viewpoint to understand fully what to do next. Using the right modern technologies with effective stewardship processes provides the data set required for the best decision-making.
Data integrity
Organizations need to rely on quality data when making decisions. In addition to being accurate and current, the data must be complete and functional. Good stewardship involves assessing data for whether it will help make organizational changes. This data needs to be distinguished from data that may be useless or bog down useful data.
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MercuryGate Supports Your Data Stewardship
Organizations often struggle to keep up with data. They struggle to utilize it to move forward correctly. They may veer off track from their goals by using incorrect, untimely data or otherwise lacking integrity. Creating and following proper processes in stewardship while utilizing technology to manage these processes improves your organization by allowing you to maximize your data.
MercuryGate offers cutting-edge technology to help your organization collect and use your logistics data effectively as part of a comprehensive data stewardship process. Learn more about how data stewardship works and its connection to logistics in our data stewardship in logistics e-book.
