Data Strategy Journey: the 5 steps to evolve into a data-driven company

Data Strategy Journey


As detailed in the article DATA STRATEGY: THE KEY TO SUCCESS, implementing a proper Data Strategy within the company is crucial nowadays, and if well-planned, it leads to concrete results in strategic areas of interest for the business. Furthermore, we have discussed how Italian companies, despite having a decent growth rate, have a huge margin of maneuver, especially in certain business sectors and/or application areas. But what are the first steps for a company that is still immature in terms of data?

What is Data Strategy Journey?

 The goal of the Digital Innovation Observatories of the Polytechnic University of Milan was to define, through dedicated meetings with its Advisory Board and interviews with other companies and/or technological vendors, a framework that describes the development phases that companies can go through in their transformation towards a data-driven company. For each identified phase, some areas to work on are described, along with the respective actions to be taken to advance to the next phase. The aim is to develop a tool that:

  • Allows companies to map their progress in the transformation towards a data-driven company
  • Provides organizations with guidelines and concrete actions to implement to move to the next step of the data-driven transformation

The transformation phases

As anticipated, the needs can be diverse: improving decision-making effectiveness, refining financial management, optimizing operations (demand planning, supply chain), enhancing customer experience (personalization), managing business risks (security, compliance). To undertake this path, 5 phases have been identified: Discover, Start, Scale, Consolidate, Win. The following framework, the result of research findings, represents the ideal itinerary that companies could follow, focusing on 3 crucial analysis dimensions: Data Governance, Business Intelligence, and Data Science:

1. Discover

In the “Discover” phase, companies find themselves in an environment characterized by independent data silos, poor data quality, outdated technological infrastructures, and deficiencies in skills (Data Skills), leading to a general distrust of data throughout the organization. To overcome these difficulties and start a path that can be successful, it is essential to first identify the objectives to be achieved, the priority development areas, and then the resources to allocate. Furthermore, it is crucial to define monitoring metrics to assess the progress and effectiveness of the actions taken. Only through this phase of discovery and careful planning can companies begin to build a solid foundation for an effective and future-oriented data strategy.

2. Start

In this phase, the engine is already running: the first experiments begin, and the first responsibilities are established. For Data Science, there is a development reference involving a few company functions and using “artisanal” tools/processes to start the first experimental projects, albeit with total – or prevalent – support from external partners. Regarding BI, there is a small dedicated team evaluating the Data Visualization tools in use and having limited use of interactive reports. Finally, there is a reference for the development of Data Governance activities, which focuses on improving data quality, listing regulatory compliance requirements, and defining policies and procedures for accessing, controlling, and manipulating data on a first subset of areas identified as priorities. Companies in this phase, however, suffer from the total absence of a measurement system for the benefits obtained and encounter a practically nonexistent Data Culture in the company (both in terms of skills and trust in data).

Start Data Journey

3. Scale

Companies in the “Scale” phase have increased the areas involved and started a process of standardizing data-related activities. Business Intelligence has evolved to the point of having a central/distributed team that shares analyses mainly through interactive reports; Self-Service BI logics are used. For Data Science, a small team is allocated, KPIs are defined to measure benefits, and projects are active in various areas (marketing, sales, production, supply chain). There is also a dedicated team for Data Governance coordination, roles such as Data Owner and Data Steward are defined, access management policies are structured, and there is a Business Glossary.

Scale Data Journey

4. Consolidate

The key figure that identifies the transition from the “Scale” phase to the “Consolidate” phase is the existence of a Chief Data Officer (CDO/CDAO). The CDO/CDAO is responsible for managing and using data as an organizational and strategic asset. This role often has the task of acquiring and managing the necessary capabilities to drive innovation and transformation, generating competitive advantage through the use of data and analysis, and promoting a data-driven corporate culture.

In this phase, there is a Data Catalog, as well as a structured program of Data Ownership and Data Stewardship identifying distributed responsibilities within the LoBs. Data Governance is integrated into processes (by design). Data Science and Business Intelligence are part of a single ecosystem where specialized figures such as Data Analysts, Data Engineers, and Data Scientists are spread; projects implemented involve the entire organization, and there is monitoring of the actual use of the tools available to each employee, including non-technical ones. Data Culture is fairly entrenched, there are widespread specialized skills and structured training courses defined for different roles in the company. Data Architecture is complex and integrated, capable of managing different use cases and supporting automation activities.

Consolidate Data Journey

5. Win

The final phase seals the achievement of all the objectives set during the Discover phase. The data of companies that successfully complete this journey are of high quality with clear rules of access and cross-use. Data Observability and MLOps solutions are implemented for full governance of the products and information lifecycle; the process of transitioning from a “project” approach to a “product” approach begins, and collaborative guidelines are defined. Corporate infrastructure and skills are decentralized, activities to quantify the economic value of the available data are initiated, and Data Literacy is perceived as a set of “core” skills, so Data Culture is not only widespread but is a cornerstone of the corporate mindset even in the recruiting phase. In this phase, data is integrated into all business decisions.

Win Data Journey

Blue BI, with its decades of experience in this field, can accompany your company through all these phases, up to the “Win” phase. Contact us to discover how we can help you generate and leverage valuable data that underpins all business decisions!

We realize Business Intelligence & Advanced Analytics solutions to transform simple data into information of freat strategic value.


Table of Contents