5 Top Issues In Data

1. Organizational mislocation 

Data professionals, such as data managers, may be placed in an organization in a way that (without substantial intervention) can hamper their ability to do what is necessary. When placed where overarching corporate data strategies can be developed and implemented with managers who provide financial and human resources, data professionals and data itself can thrive.

2. Expectations 

When the vision for data presents overwhelming or impossible expectations for funding or timelines to complete, managers look for other solutions - even if they are temporary. Data-centric projects must be presented within the framework of an organization's business needs and capabilities. Selling too much (and under-delivering) sets the expectation that data managers are "empty suits" who don't deliver on promises. Promise and deliver realistic, valuable outcomes.  

3. Value 

Data projects must set measurable goals and specific objectives tuned to the organization's business plans and objectives. Upon completion, metrics that make success or failure clear should be available. Data outcomes should be verified against the strategic and tactical goals of the organization. Many project goals are overly tactical and distant from management's needs. 

4. Semantics 

Assumptions made that the same text string (or word) means the same thing to everyone (internally and externally) cause miscommunication, project failure, and business outcomes that fail to meet objectives. Industry-standard semantic disambiguation's help organizations map local terminology to a global Rosetta Stone. Industry has recognized this as a major issue for some time. Industry experts have collaborated in PPDM Work Groups, resulting in "What is a Well," "What is a Completion," and "What is a Facility" in an effort to standardize definitions of Well and Facility Components. These can be found at https://ppdm.org/ppdm/PPDM/IEDS/PPDM/IEDS.aspx  

5 . Data Quality and Trust 

Quality is a path to trust, which is the real goal of data projects. When trust drivers such as transparency, metadata, provenance, and processes are missing, data trust is compromised, even if data quality may be thought to be high.

Any data issues described above can make or break the decision to proceed with a new project or monitor and update an existing one. The co-founders of WellDatabase, John Ferrell and Joshua Holt, have recognized the importance of accurate data from the inception of our product. They, themselves, had worked with data for a number of years and fully understood the ramifications of making bad decisions based on incorrect or incomplete data. This led them down the path of putting strict data rules in place that question and cleanse the data when errors or omissions are reported by the operator or the regulatory body that initially collected the data. To learn more, click here.