DIGITAL & OPERATIONAL TRANSFORMATION
Data & InformationData is increasingly becoming a performance and competitiveness lever for organizations (new business models, improved operational model, better customer knowledge and easier decision making). However, many organizations are still insufficiently “data driven” due to a lack of strategy, exploitation or governance of their data. Pagamon supports you to ensure the success of your transformation by turning your data into relevant information.
Challenges
The digitalization of organizations has led to an explosion in the volume of available data, whether internal to the company (processes, operating procedures, operations), or resulting from interactions with its customers, partners, or employees…. Even if it appears that data collection has been considerably improved and structured, its exploitation to transform it into relevant, useful, usable and used information is more limited. High volume, diversity of sources, heterogeneity of formats, volatility, complexity of analysis: these are all factors that slow down many organizations in their transformation. However, data represents such a challenge for organizations that it is now referred to as the “black gold of the 21st century”. A good “data” strategy combined with robust and structured operating methods and organized governance can generate key information. This information can be used to improve decision-making, the relevance of offers, operational and commercial efficiency, and to generate new business models in the face of new entrants, whether they are historical or aggressive and agile start-ups.
But how do you become “data driven”?
Data-driven organizations make informed decisions in a data-driven business cycle. To exist, this cycle requires that the organization combines:
- A technological maturity that allows it to create and collect all relevant digital data, and to integrate and structure this data into information.
- A data fluency that allows it to draw accurate analyses and insights from data and information.
- Data literacy, which allows for decision making and the creation of action plans based on data analysis.
- And finally, an internal data culture to ensure the quality and security of data and to exploit its full potential.
Becoming and growing sustainably as a data-driven organization requires first and foremost a human transformation. This transformation is linked to the company’s vision and strategy.
The human and listening qualities of Pagamon consultants make them very good partners for supporting the digital transformation of a field as complex as the automotive industry. Well versed in business issues, they adapt quickly and know how to question some of our usual practices. I salute the spirit of synthesis and the quality of the deliverables of the consultants for their entire service.
Approach
Data Strategy & Organization
- Organization Data capabilities diagnosis
- Measuring the level of maturity of the organization and its employees regarding data
- Mapping and classification of the organization’s data sources and flows
- Identification of data optimization opportunities
- Data Management strategy definition
- Design of strategic data roadmaps
- Technology watch and benchmarks
- Change Management | Acculturation to data
- Sharing the vision, communicating tangible benefits, addressing resistance to change and involving employees in the continuous improvement process
- Measuring the level of adherence / acculturation through periodic surveys
- Training of employees adapted to their business context: management training, training-the-trainer, peer-to-peer training or independent learning
Use of information
- Identification of optimization opportunities
- Identification and localization of target data to be collected based on business needs
- Diagnosis of business optimization through data
- Evaluation | Business model initiatives
- Support in a Test & Learn process
- Methodological support, scoping and implementation of a Data Lab
- Animation of ideation and prototyping workshops
- Scoping, support and industrialization of initiatives (PoCs)
- Value-based management of initiatives
Governance
- Definition of data management policies
- Governance: procedures, roles, responsibilities, rules and indicators governing the effective use of data in the organization
- Documentation: standards, roles and responsibilities for consistent documentation across the organization
- Architecture: principles for standardizing data architecture across the organization
- Quality: practices, roles, and responsibilities for managing data lifecycle | tools to ensure data quality
- Implementation of data governance bodies (vision, strategic alignment, supervision, arbitration, data strategy, data culture)
- Definition and scoping of governance roles / responsibilities (CDO and possible relays, data stewards…), identification of profiles
Excerpt from our references
Agri-food
Automotive
Tourism
Poins of view
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