Digitalization is an important enabler of two key industry trends: decarbonization and decentralization, which are necessary to enable the energy transition.

An integral part of succeeding with digitalization is the realization that data is a valuable asset and should be treated as such. Data is the fuel powering digitalization, and high data quality is a prerequisite for getting return on your digital investments. Poor data quality now has a real financial impact, and companies cannot afford to ignore it. According to Gartner, the average financial impact of poor data on businesses is $15 million per year¹. 

Many of our customers report that they cannot trust their data. This is also confirmed by a Deloitte survey, where more than two-thirds of executives (including CEOs) report that they are not comfortable accessing or using data from their tools and resources². 

On the brighter side, it looks like more and more companies are aware of data quality issues and the importance of solving them. According to DNV’s Energy Industry Insights 2022 report, 72% of respondents across the energy industry will prioritize improving data quality and availability in 2022. 

A key step on your digitalization journey is to focus on improving data quality and implement robust data governance processes to make sure that you can trust the data for critical decision-making.

What is data management and what are common challenges?

Data management ensures that important data assets are formally managed throughout the enterprise and that data governance goals are achieved to realize efficiency gains and value creation. In addition to focus on data quality, data management also covers management of data storage, retention, life cycle, legal compliance and intellectual property rights, value chains, metadata, business vocabularies, information risks and information security.

Common data management challenges include:

1. Not understanding the state of your data and how this affects your business
2. Not knowing how to start managing data and prioritizing improvement actions
3. Lack of an established data culture
   
   

Learn how we can help you to trust your data to gain real business value here.

^{1 “Follow these 5 steps to effectively design a compelling data quality improvement business case” Gartner, 2018.
2 Deloitte’s 2019 Becoming an Insight-Driven Organization survey}