Executive Summary

The sixth edition of the Geoprofessionals Data Management survey achieved 704 responses from across the world and across a range of geoscience industries. This latest report explores the state of play with data management in the market, the key challenges being seen across the various industries, and a look to the future with emerging technologies.

Key takeaways

Over 70% stated that the data management is of high or critical importance to their organisation.

Data Management is viewed as an important issue across all industries, with over half of each individual industry stating its importance, however less than a third of respondents currently use an established data management framework with over half keeping data organised in various systems.

Over half of those without a data management framework are looking to implement one within the next 3 years.

Whilst the value can be seen in data management frameworks, there are also nearly 1 in 4 of those without an established framework who don’t know whether or not there is a plan to implement one, regardless of how important they find data management.

24% don’t have the information to make data driven decisions.

Almost 1 in 4 do not have the required information to support data driven decisions. When prompted as to what was missing, there was a specific call out for historical data as well as an integrated centralised database.

On average, 22 people interact with the data in an organisation.

With over 20 people interacting with the data in an organisation, challenges can arise such as difficulty knowing that the data set being used is the most current and best quality one available.

24% of geoprofessionals spend more than 30% of their time on Data management tasks.

Although a quarter of the market spend a large proportion of their time of data management, this figure shows a marked decrease vs 2020 where it was over a third.

Almost 80% claim to be early adopters of new technology.

Whilst almost 80% claim to be early adopters, this differs by management level, with Directors and above being much more likely to believe the organisation is an early adopter vs entry level employees.

58% stated that unmanaged historical data is a challenge for their organisation.

Unmanaged historical data was the most significant challenge for organisation, with respondents also having relatively less confidence in their organisation to handle it.

Almost 2 in 3 now use or would consider using AI, Machine Learning, Advanced Analytics and or Data Science Scripting.

There has been a significant increase in those using or considering the use of emerging technologies. With the highest use / consideration of use, almost one in two use or are considering the use of Data Science Scripting.

The need for a single point of truth has doubled since 2017. There has been continued growth in the reported importance of one point of access for all data / a single point of truth. In 2023 29% reported this as the most important outcome expected from resolving data management issues vs 25% in 2020, and 13% in 2017.

Introduction

The Geoprofessionals Data Management Survey is the sixth iteration of its kind, in field throughout May 2023 and receiving 704 responses in total. The survey was distributed to a randomised sample of Seequent contacts, and published on Linkedin and geo-professional sites. The survey and report have been drafted by a third-party research consultancy. This year, a total of 25 questions were asked on the topic of data management, covering areas such as:

Importance of geoscience data management and data types

• Key challenges in geoscience data management

• Barriers to geoscience data management success

• How geoscience organisations are overcoming their data management challenges

• Challenges faced when implementing a data management solution

• Level of readiness to leverage the Cloud for data management and access

• Main challenges when leveraging the Cloud for data management and access Data management frameworks

• Ease of data access

• Data practices (i.e. data science scripting, advanced analytics, AI, Machine Learning)

25 questions

704 responses