Unlocking The Full Potential of Enterprise Data
This article is about unlocking the value of enterprise data 1 in industries 2 and aims at prompting discussions within the C-suite about the criticality of this important issue . The article takes a datacentric perspective and focuses on the necessity of managing enterprise data assets through :
• The different stages of Digital Transformation ,
• The different lifecycle tracks : transactional , operational , business , compliance , legal , and archival ,
• The migration of these data assets across dataspaces and systems .
The article posits that enterprise data assets can hold substantial tangible and intangible value , which can be realized in the present or the future . It also introduces the concept of Digital Enterprises — organizations that have achieved advanced levels of digitization and digitalization . With their digital DNA , these enterprises are better equipped to innovate , disrupt markets , and accelerate revenue growth . They generate vast amounts of rich and granular data in diverse modalities and from sources such as digital twins , AI , IoT systems , business systems , messaging and collaboration systems , and dataspaces .
This article also highlights the importance of harvesting , protecting and fostering the value of enterprise data assets , and proposes a path to innovative strategies , methods , and programs for managing the lifecycle of these assets and safeguarding their value .
1 DIGITAL TRANSFORMATION
The article begins by examining at a high level the topic of Digital Transformation ( DX ) in industry . This subject is not new as it has been debated extensively in the public domain , including publications from the Digital Twin Consortium 3 and Industry Internet Consortium 4 , but it is worth describing at a high level .
Definition : Digital transformation is the innovative , principled and strategic application of digital and connected technologies , coupled with organizational and process restructuring , to generate new value for the organization and its stakeholders .
There are many drivers for digital transformation , including :
• Evolving customer expectations
• Rising competitive threats , especially from digital-native competitors
1
The terms “ data ” and “ information ” are used interchangeably in this article ( in reality “ information ” is “ data with context ”)
2
Example : manufacturing , cities , power grids , healthcare , and many more .
3
DTC www . digitaltwinconsortium . org
4
IIC www . iiconsortium . org Journal of Innovation 33