Knowledge Graphs - Costs & Obstacles
I just read the Knowledge Graph Implementation: Costs and Obstacles to Consider whitepaper from Ontotext, and my favorite section was on the ‘psychology of data management’. It’s interesting to learn that implementing semantic standards and knowledge graphs faces hurdles mostly from middle management rather than top executives.
These hurdles arise from self-interest, as some managers prefer control and focus on immediate goals instead of broader organizational needs. Additionally, bureaucratic barriers within organizations, driven by risk aversion and cost reduction, hinder the adoption of new approaches. Overcoming these obstacles requires addressing resistance to change and demonstrating the value of knowledge graphs across different areas of use.
The whitepaper included some direct quotes from interviews where participants talked about the inhibitors to adopting new technologies and approaches. Some of the quotes that really stood out for me included:
"Business units at varying degrees of sophistication with regards to data literacy."
"Lack of technical expertise to move beyond a proof of concept."
"Management is lost and very cautious about any decision."
The challenges highlighted in these quotes are common hurdles faced in the adoption of new technologies and approaches in data management. Varying degrees of data literacy among business units require efforts to bridge the knowledge gap and promote a shared understanding of data.
Overcoming the lack of technical expertise beyond the proof of concept stage is crucial to scaling and maximizing the potential benefits of innovative solutions. Additionally, it is essential for management to overcome caution and embrace decision-making that supports the organization's growth and encourages the adoption of valuable technologies.
By addressing these challenges head-on, organizations can drive successful implementation and harness the full potential of data-driven strategies.
Check out the full whitepaper here . Share your thoughts in the comments.
Well done and thanks for the love sharing
We see it everyday in our KG implementation practice adoption cycles Kate Strachnyi. Our approach of demostrating value in an incremental process by answering compentency questions (compelling and meaningful business questions that couldn't be answered previously or required considerable manual effort) has helped in turning heads of middle management and sponsors. Keep in mind the broader business objectives while getting access to the right data sets early and SME engagement will help accelerate the process. Driving localized value in tandem with enterprise model mindset and execution connects it all contextually. One of the side effects, along the way to semantic adoption is improving data integrity. Removing data clean up (saving time) tends to be overlooked. This represented considerable value to a client in the business of selling accurate data. https://www.semanticarts.com/case-study/simplifying-symbol-management-with-facets/
?? Award Winning Data Governance Leader | DataVenger | Founder of LightsOnData | Podcast Host: Lights On Data Show | LinkedIn Top Voice 2024
1 年Oh wow, added to my read. Thanks Kate Strachnyi
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1 年Good read. Thanks for sharing, Kate Strachnyi
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1 年I think you’re ONTO something! ??