Figure 1: Hurdles for data value creation
Each of these hurdles is represented as quadrant boundaries in Figure 4. Archetypes presented in this article are at various stages of overcoming some or all of the hurdles presented above.
Archetype #1: The legacy state
Business and tech behavior: Data analysis within these organizations centers around a conventional data warehouse architecture with some combination of enterprise data warehouse (EDW) and / or a collection of subject-area data marts (Figure 2). These patterns of data development usually impose heavy up-front quality and governance criteria that severely limit what type, condition and proportion of data is made available for analysis. The consumption of information is predominantly via business intelligence (BI) tools, which are typically used to handle only historical analyses. Therefore, any links between past events and future decisions are not structured and captured in enterprise systems. Even worse, these decisions are often arrived at via the proliferation of informal BI artifacts such as “spreadmarts” — or user created spreadsheets, that may not be well governed.
Insights gleaned from these artifacts often rely on the reader’s interpretation because the structured analysis doesn’t clearly frame either predictions or decisions for execution. These predictions, decisions and actions are commonly ad hoc and manual, and are therefore not easily explained, understood, audited, repeated, reviewed or refined. This tends to severely limit the sophistication of any one decision, as well as the granularity that can be achieved across different decisions. Likewise, it often takes weeks or months to define and execute decisions based on these artifacts, due to the lack of a defined implementation process.
Even where decisions can be arrived at more quickly, delays in sourcing data tend to remain. Data is primarily sourced from operational systems using batch extraction, transformation, and load (ETL) jobs that run nightly or weekly, meaning that information latency is — at best — one full day after business events occurred.
While BI capability is “table stakes” for most companies to maintain parity with competitors, it’s not a differentiator. Companies that exclusively rely on data warehousing approaches and BI to power the intelligence cycle fall into the ripe for disruption or intelligence envy quadrants of the Intelligence Maturity Model.
State of Intelligence Cycle: There are many opportunities for improvement in intelligence speed and effectiveness in these organizations — for instance by adopting modern, adaptive data architectures to ease ongoing access to all forms of data; use of advanced analytical capabilities to generate clear predictions; and better business and technology collaboration methods to frame more effective decisions. In such organizations, the conversion of data into information for analysis isn’t very effective. Decisions and actions are rather ad hoc in this state and most decisions are instinct- not data-driven. Often data analysis is performed post-facto rather than during or before decision making. Hurdles exist across all four quadrants.