No Data, No Chocolate

How do people make decisions? What data, facts, knowledge, insight does everyone use to make informed decisions? How do organizations ensure that the right decision is made at the right time? What digital tools can be implemented to help leaders make better decision? What data mining and business intelligence can help the decision-making process? Can business intelligence (and even artificial intelligence) help improving data analytics and human decision? Can it improve speed and accuracy of the decision-making process and contribute to helping human judgement?

To make the most of data, it is key to understand:

  • What are the relevant data sources: where and how is data created / updated and in which are the master data authoring tools; how often and how is the data updated and shared across teams and enterprise platforms.
  • How data is collected: how are analytics tools used, how is data aggregated and combined across multiple platforms, how often is data updated, released, and published for others to consume, in which location and in what format; what is the data continuity across business functions and enterprise systems (e.g. manual and systemic interfaces) and how secure these interfaces and applications are to access.
  • How data is used to make decisions: what constitute value for the organization, how is data analytics consumed to inform decisions, how is it consumed, how often, for what purpose, and how are analytics improved to learn from previous decision and business iterations.

Mining Value from Data: Understanding What Are the Relevant Data Sources

Many companies adopted a “data-driven” approach for operational decision-making. It is stating the obvious that “everyone relies on data to make decisions“, whether this is through rational interpretation of time-bound data, perceived relevant data, or experience-driven data (insight) that is only available to a sub-group or individual. The fact is that decisions are made by people, hence they must be driven by individual and shared insights, not only data, information and knowledge.

Intuitions relate to something that is known, experience-driven, perceived, understood or believed by instinct, feelings or nature without actual evidence, rather than by use of conscious thought, reason, or rational processes. 

Adapted from Matzler et al. (2007)

Understanding where (and how) data is stored is an important part of making better use of data. There are often multiple data sources with various systems and integrated platforms – often not really integrated at all, with a lot of duplication and secondary views. It is important to assess how data flows between different teams or functions, and the lifecycle of this data: i.e. how it is used over time.

Many refer to data integrity and accessibility in the context of being located in a “single source of truth” – which is often mis-interpreted as data needing to be stored in one single system; whereas it actually (to my mind) refers to authoring the relevant data set in the relevant source system(s) – which might in turn interface with one to another in order to share relevant data set, when mature, across multiple entreprise platforms.

Data Feeds Everything: Understanding How Data Is Collected

Typically, business users and related stakeholders can access data from various systems, though in many cases they barely trust it due to various technical and process limitations which can raise key concerns in industries where data is driving product and customer experience.

Data analytics are typically built-in platform capabilities combining “overlay” business intelligence tools to mine a number of independent systems. Understanding how and when data is collected, its maturity, the frequency of gathering it, and how it is combined in value-added information is key to establishing trust (and avoiding “data getting lost in Excel” once users decide to create their own uncontrolled external data summaries).

For example, a manufacturing Bill of Material (BOM) includes engineering attributes mastered in a CAD or PLM system, tooling data mastered in an MES system, supplier data and cost data mastered in an ERP system, and perhaps customer data mastered in a CRM system. The BOM overall data structure is a complex construct that is stored in multiple systems across the combined PLM-ERP-MES-CRM stack with various IT interfaces. Data mature at different speed and frequency across these sources, is made accessible in different ways to different functions and teams, hence the need to understand the required views and perspectives to tailor analytics.

Visual Data Dashboards: Understanding How Data Is Used To Make Decisions

Decisions are clearly informed by data, the ability to retrieve the relevant data at the relevant time, in the relevant format, accessible to the relevant people. Data that people can understand and trust.

Understanding the decision making process is as important as having access to the right trusted data at the right time. This allows to inform on the way that data needs to be presented and consumed by the relevant decision makers.

For example, in the world of digital twins, 3D and technical data is king. Having the ability to compile reports and overlay color-washing analysis against 3D data can help “visualize” issues and work on solutions in a more effective manner.


  • Matzler K, Ballom F, Mooradlan T (2007) Intuitive Decision Making. MIT Sloan Management Review

Author: Lionel Grealou

Founding Director & Consultant of Xlifecycle Ltd and contributor for

This content originally appeared on

Disclaimer: articles and thoughts published on v+d do not necessarily represent the views of the company, but solely the views or interpretations of the author(s); reviews, insights and mentions of publications, products, or services do neither constitute endorsement, nor recommendations for purchase or adoption. 

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Lionel Grealou

20th July


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