Table 2. Data structure: from... | Emerald Publishing

Table 2.

Data structure: from first-order codes to higher-order categories

Higher-order category	Second-order theme	First-order codes (representative)	Source excerpt (paraphrased)
Fragmented standards and absent regulation	Inherited incompatible assumptions	“incompatible assumptions”; “locally optimized standards”; “no agreed deadlines or standards”	Analysts “inherit implicit assumptions embedded in data sets created elsewhere” (#34)
	Voluntary coordination failure	“working around missing standards”; “basic categorie differ across units”	Even product labels like “South” interpreted differently across business units (#49)
Underutilization due to legal ambiguity	Regulatory uncertainty blocking reuse	“privacy limits reuse”; “unclear anonymization”; “fragmented data sets”	Privacy agreements “strictly limit what data can be exposed or reused, even when analytical value is clear” (#31)
	Institutional risk aversion	“organizational boundaries prevent reuse”; “data left outdated and unmanaged”	Customers ask whether existing data can update contact info; organizational barriers prevent it (#41)
Human error as governance symptom	Assumptions replacing standards	“unclear provenance”; “unspoken assumptions”; “communication failures”	Errors stem from “misunderstanding about how certain quantities were meant to be counted” (#8)
	Bias risk from weak specification	“poorly specified practices”; “gender or skin-tone bias”	Weak standards introduce bias “that organizations are highly motivated to avoid but poorly equipped to prevent” (#33)
Time pressure and absent lifecycle regulation	Short-termism and siloed practices	“deadline-driven shortcuts”; “narrow problem definitions”; “proof-of-concept pressure”	Development teams “frequently focus on narrow, short-term objectives” under performance pressure (#35)
	Reuse sacrificed for immediacy	“rigid standards and expectations”; “little room for durable infrastructure”	Pressure to deliver proofs-of-concept “leaves little room for building durable data infrastructure” (#40)

Higher-order category	Second-order theme	First-order codes (representative)	Source excerpt (paraphrased)
Fragmented standards and absent regulation	Inherited incompatible assumptions	“incompatible assumptions”; “locally optimized standards”; “no agreed deadlines or standards”	Analysts “inherit implicit assumptions embedded in data sets created elsewhere” (#34)
	Voluntary coordination failure	“working around missing standards”; “basic categorie differ across units”	Even product labels like “South” interpreted differently across business units (#49)
Underutilization due to legal ambiguity	Regulatory uncertainty blocking reuse	“privacy limits reuse”; “unclear anonymization”; “fragmented data sets”	Privacy agreements “strictly limit what data can be exposed or reused, even when analytical value is clear” (#31)
	Institutional risk aversion	“organizational boundaries prevent reuse”; “data left outdated and unmanaged”	Customers ask whether existing data can update contact info; organizational barriers prevent it (#41)
Human error as governance symptom	Assumptions replacing standards	“unclear provenance”; “unspoken assumptions”; “communication failures”	Errors stem from “misunderstanding about how certain quantities were meant to be counted” (#8)
	Bias risk from weak specification	“poorly specified practices”; “gender or skin-tone bias”	Weak standards introduce bias “that organizations are highly motivated to avoid but poorly equipped to prevent” (#33)
Time pressure and absent lifecycle regulation	Short-termism and siloed practices	“deadline-driven shortcuts”; “narrow problem definitions”; “proof-of-concept pressure”	Development teams “frequently focus on narrow, short-term objectives” under performance pressure (#35)
	Reuse sacrificed for immediacy	“rigid standards and expectations”; “little room for durable infrastructure”	Pressure to deliver proofs-of-concept “leaves little room for building durable data infrastructure” (#40)