Data Excellence – Finding the Way
DATA leverages technologies from IoT to AI/ML and from AR/VR to Edge Computing into productivity boosters. Without data, digital technologies are useless.
Combined with data, technology increases customer value, improves customer experience and enhances operational efficiency.
“Data is the digital transformation spearhead”
However, the fact that data is everywhere and connects to everything results in systemic complexity. Finding the way to Data Excellence becomes THE management challenge of our era.
The bad news is that this is not easy but requires systematic and disciplined approach. The good news is that achievement in something difficult leads to sustainable competitive advantage.
“Future winners are created thru Data Excellence”
Data Excellence
Data Excellence is not about empty slogans. And it must not be about technical details that only seasoned data professionals understand. Rather, Data Excellence is about business impact.
That objective is met by defining Data Excellence thru five strategic measures:
To stay focused, all data related planning and implementation is to be based on these five measures.
However, measures cannot be directly worked on. They are elusive. To get something actionable, we need to shift our focus on data capabilities.
“Data Excellence is elusive – data capabilities are actionable.”
Dependency between excellence and capabilities
The way to excellence goes thru systematic data capability build-up. The very first step is to undertand the relationship between the two: How Data Excellence depends on data capabilities. It’s worth noting that Value differs from other measures with its direct and indirect dependency on data capabilities.
Picture 1: Data Excellence vs. Data Capabilities – dependency overview
To achieve excellence, data capabilities have to be treated holistically: Looking at all capabilities rather than settling for randomly selected ones.
Adapted from Data Capability Maturity Model, the complete data capability set is this:
2. Processes and Methods
3.?Data Stack
4.?Platforms and Architecture
5.?Data Governance
6.?Structure and Talent
7.?Strategy and Culture
领英推荐
8.?Use Cases
However, as revealed by Picture 1 above, to get our hands on actionable data capabilities, this level of granularity is not adequate. What is needed is fine structure of each data capability domain.
Pinpointing capability details is essential to understand the dependency. That leads to leverage: Identifying actionable data capability elements with highest potential. That is, how to best achieve Data Excellence with prioritised investments in selected capabilities.
Data Capability 360 Assessment
At first, the amount of missing data capabilities may appear as overwhelming task. With so many options and so much to do, the burning question emerges as: Where to start?
Answer can be found by considering each capability shortcoming as a constraint. Something that prevents us achieving Data Excellence. Theory of Constraints is useful concept to prioritise corrective actions.
“Finding the way to Data Excellence thru constraint identification, analysis and elimination”
Data Capability 360 Assessment consists of three steps:
Step 1: Constraint Identification – Going thru capability domains one-by-one by applying fine structure for each
Step 2: Constraint Analysis – Focusing on selected capability elements and looking for root causes rather than observing symptoms only
Step 3: Action Planning – Building shared view on current state and agreeing on corrective actions on capability gaps
Picture 2: Identifying key constraints is the essential starting point
Integrated and Aligned Data Strategy
Most companies are not in data business. Rather, they are in product, service or combination-of-the-two business. Hence, the role for data is basically straightforward: How to create higher customer value with products and services. And how to improve customer experience across all Customer Journey touchpoints - created by those products and services but also by business processes and functions.
If we able to do that, we differentiate from competition. Many good things result from that. For example, increase in pricing power leads to healthier margins and improved profitability. This is what business expects from data.
The second key domain is operational efficiency. That is, how data is to be used across business processes and functions like Marketing or Production. This too leads to better profitability. This time thru gains in cost-efficiency.
Data strategy that is integrated with business strategy is explicit and sufficiently detailed in What and How. It shows the way data is to be deployed for better products, services, processes and functions.
“This is what business expects from data: Leverage for profitable growth”
Finally, good data strategy adds clarity to data capability build-up. This relates to strategic choices on key capabilities like operating model, technology and data platforms, and talent acquisition.
The three data strategy elements – differentiation, efficiency and capability build-up – need to be aligned. In other words, data capability build-up must be rooted in strategic objectives in terms of products, services, processes and functions. And vice versa: No point of planning for data boosted offering or operations if data capabilities are not there to back them up.
“Good data strategy is integrated and aligned”
Systematic and disciplined execution
Data capability build-up deals with systemic complexity. Dependencies are many and devil is in the details. What’s more, “If the band you're in starts playing different tunes, I'll see you on the dark side of the moon.” That is, need for alignment is not only technical but also organisational.
All that leads to requirements for systematic and disciplined execution. Strategic change management for capability build-up must satisfy those requirements. The two elements serve as good starting point: Integrated Data Strategy and Data Capability 360 Assessment.
Summary
Data connects to everything: products, services, processes and functions – and to digital technologies used to make them better. Resulting systemic complexity creates management challenge that needs to be met head-on in order to remain competitive.
Data Excellence is a useful way to stay focused on business impact rather than getting lost in technical and organisational data capability details. At the same time, to grab something actionable demands attention to data capabilities themselves. The key is to understand dependency between excellence and capabilities.
Theory of Constraints provides useful concept for focus and priority. Combined with data capability fine structure, path to Data Excellence is about eliminating constraints one by one: thru Constraint Identification, Constraint Analysis, Action Planning and Systematic Execution.