Appendix A
Information Management Maturity Challenges

The following information management maturity challenges are verba tim those articulated by participants in Gartner EIM Maturity workshops throughout 2016. Information leaders should anticipate these kinds of challenges and resistance, and take sufficient measures to avoid or overcome them.

Vision Challenges

• Information is seen as a source of power
• Information is managed in silos
• Data fiefdoms
• People spend time arguing about whose information is correct
• People spend time arguing about who owns data
• Little effort to seek uniform availability of information assets
• A general acknowledgement that information management (or lack thereof) is a serious problem
• Efforts by IT to formalize objectives for information availability are hampered by:
  • Culture
  • Contradictory incentives
  • Organizational barriers
  • Lack of leadership and executive support

• Different types of information are still treated and managed separately
• Poor awareness of exogenous data such as that from public data sources (open data), data brokers, or social media
• Information is seen as disposable
• Information is poorly linked across business areas or applications
• Information is not perceived as a fuel for innovation and transformation
• Information is not perceived as a competitive differentiator
• Information is not a central component of business strategy or enterprise architecture
• A focus only on Mode-2 (transformation) than on Mode-1 (operations)
• Customers and partners are not involved deeply in defining information uses
• The language of information is imprecise, and dialects differ throughout the organization

Strategy Challenges

• IT taking the reins for information management
• A static, annual planning process
• Too much planning, not enough doing
• Information hoarding or begrudged sharing by individuals and departments
• Lack of executive support and executive-level leadership (e.g., no CDO or similar)
• Obliviousness to what information exists
• Vision is lacking upon which to base a strategy
• EIM enablers have both process- and people-oriented gaps
• Being too focused on technology
• Executives are consumed by expense management
• Controls and standards get in the way of information enablement
• Corporate strategy is set without consulting information leadership
• An ill-defined or incomplete information management organization
• Lack of funding
• Information only being defined in terms of the value it brings, not its intrinsic characteristics
• No consideration of the larger info-ecosystem of partners, suppliers, and customers
• Information strategy being a separate work task, poorly linked to business operations

Information Metrics Challenges

• Measures and goals are purely subjective
• The achievement toward goals is rarely tracked, at least in any quantified way
• Information management is not an IT budget item, and certainly not an enterprise budget item
• Simple, often predisposed, cost-benefit models are used to justify independent information management investments, but not collectively
• Cost-benefit and ROI metrics and prioritization merely are part of IT projects
• Priorities and goals are based on:
  • Influence peddling
  • Failure prevention
  • User surveys
  • Expense reduction, or
  • Scaling infrastructure performance or capacity

• A proliferation of mostly incompatible nonfinancial metrics
• Qualitative metrics don’t link to business KPIs
• Data quality is not measured or performed only downstream for specific IT or business projects
• Information value, cost, and risk go unmeasured
• Information is not treated as an investment (e.g., an information portfolio)

Governance Challenges

• The culture allows few official (if any) policies to exist for the handling or use of most information, other than those required by law and industry regulations
• A lack of governance culture as evidenced by limited governance in other domains
• Ad hoc data quality efforts are favored or simply budgeted that way
• A lack of data definitions (metadata) result in low data trust and usage, and therefore no imperative for governance
• Emerging policies are mostly for particular information silos.
• Information is too easily copied, resulting in information sprawl
• There is no mandate or precedence for monitoring or enforcing policies, which leads to policies being regularly circumvented
• Information owners (or “trustees”) are assumed, not actually designated
• Limited authority for anyone entrusted with information to do anything about its quality or policies
• Lack of sponsorship or organizational structures and budget for information governance
• A “project” mentality in which information is merely an input or output for that process
• A fixation on policies, rather than a well-crafted, integrated hierarchy of precepts
• A fixation on control, rather than governance as an enabler of information
• A lack of defined information principles
• Lack of information inventory—not knowing what needs to be governed
• Data stewards without advocacy as part of their role
• Data stewards (for business information) who are part of the IT organization
• IT running the information governance initiative

People (Organization and Role) Related Challenges

• Information-related responsibilities are resourced on an application-by-application or project-by-project basis
• Business people are resigned to circumvent IT in an attempt to source and manage their own data, or must queue up in the IT backlog
• Lack of change management as part of EIM initiatives
• A lack of pooled or centralized database administrators, data administrators, information architects, data modelers, etc.
• The above resources being part of the IT department
• Information not being perceived as an enterprise resource or asset (hoarding)
• Lack of budget and executive sponsorship for such roles or structures
• A fixation on traditional information-related roles at the expense of emerging and increasingly important roles such as the CDO, data steward, information curator and information product manager, master data manager
• CDOs without budgetary authority or resources beyond a small “office of the CDO” team
• A lack of understanding of the larger information ecosystem beyond the immediate enterprise
• Information management being perceived as a cost center rather than as a revenue generator or business innovation enabler

Process (Information Lifecycle) Challenges

• A lack of understanding of information even having a lifecycle (Most managers and users of see information or part of it at a certain point in time, or they don’t see it at all as it’s embedded and obscured in systems and applications.)
• Information silos where it languishes
• Information is copied with wild abandon rather than being accessed in place
• A tendency toward local (single application or system) efficiencies
• Information is deleted early due to a lack of infrastructure or policy
• Infrastructure asset budgets are planned independently of information assets—which usually are not even budgeted
• Data integration may be effective in linking disparate sources, but efforts to semantically align them are lacking
• Metadata management is mostly manual (e.g., spreadsheets) and is specific to information assets for specific projects or applications
• A lack of interest or perceived business importance in defining information flows or architecture
• A lack of data architects owning or driving the definition of information asset lifecycles
• Little concern, planning, or innovation regarding older information
• An impression that information lifecycles are IT workflows or tasks rather than business processes
• No means to value information assets or balance this against their assessed risk

Infrastructure (Information Technology) Challenges

• A tendency to push information storage and processing capacities to their limits, rather than allowing for dynamic business needs or growth
• Application-specific technologies leading to technology-sprawl
• Redundancy in tools
• Lack of defined information architecture or reference models
• A preponderance of shelfware
• Unused licenses or superfluous capacity
• IT backlogs which perceptibly inhibit business performance
• Information silos limiting business interoperability, both internally and with business partners
• IT spending “spill over” as business units invest in their own tools
• Shadow IT organizations emerge
• Multiple or personal data extracts used for analytics instead of a common analytic structure (e.g., data warehouse or data lake)
• Applications are integrated but semantics and ontologies are inconsistent among them
• Overpaying for prior generation technologies with order of magnitude lesser capacity or performance
• Reliance on application-specific DBMSs for analytics
• Lack of a data-as-a-service architecture (e.g., logical data warehouse)
• Application- or department-specific master data, metadata, and governance/policy implementations
• Stubbornness and misconceptions about cloud security and privacy
• A focus on tools over solutions
• Non-elastic, non-scalable technologies
• Excess storage and processing capacity not shared among business units (i.e., “capacity hoarding”)