Data Strategy: the key to success

Data Strategy: definition

By Data Strategy, we mean the implementation of a comprehensive and structured plan by an organization to effectively collect, govern, and utilize the information and data at its disposal. The Data Strategy is designed to achieve business objectives through strategic use of data, ensuring that the organization can derive value from them.

Having a Data Strategy pays off:

Increased profitability: companies with a solid data strategy record profits over 10% higher (Forbes).
Operational efficiency: companies that have established effective Data Governance can improve operational efficiency by 30-50% (McKinsey).
Customer satisfaction: 82% of companies have seen an increase in customer satisfaction following the planning of a defined data strategy (Experian).
Cost reduction: companies investing in Data Management & Analytics see a reduction in operational costs of 5-10% (Data & Analytics Global Executive Study).
Innovation: companies leveraging data for innovation have a 30% higher growth rate compared to those that do not (BCG).

Competitiveness: By 2024, over 90% of large companies will have a CDAO (Chief Data and Analytics Officer) to ensure strategic data management and remain competitive (Gartner).

The situation in Italy

As revealed by the research results of the Digital Innovation Observatories of the Politecnico di Milano, only 42% of large organizations claim to use analysis results optimally and widely. Furthermore, almost 1 in 3 companies conducts Business Intelligence activities through static reports or, in the worst-case scenario, spreadsheets (17%).

Regarding Data Governance, only 41% of companies have formalized a dedicated central team. Despite a significant growth compared to the previous year (36%), relatively few organizations effectively leverage the potential of the available data; effective data lineage is essential to organize them and ensure integrity and compliance at all levels.

New Architectural Paradigms: Data Fabric and Data Mesh

Among those who have already structured a corporate Data Strategy, the goal is to make it increasingly efficient and modern. For this reason, new architectural models such as Data Fabric and Data Mesh are gaining ground.

Data Fabric architecture allows the integration of different data management tools and platforms and orchestrates end-to-end processes using intelligence and automation. Through a cohesive structure, it promotes visibility, consistency, and efficient use of data in heterogeneous environments.

Data Mesh is an architectural paradigm conceived on decentralization, meaning heterogeneous domains that operate in a standardized manner and are subject to global governance. A Data Mesh architecture encourages local responsibility, data federation, and facilitates access to data, improving scalability, trust, and agility in the corporate data ecosystem.

Data Science for Business: where are we?

One in two companies, among large enterprises and SMEs, claims not to use data science tools to support strategic decisions. Although growing, the margins for improvement are still enormous; clearly, companies in an advanced state possess an additional weapon, a significant competitive advantage over immature companies or those in their early stages, both in terms of revenues and costs.

The most common application area for implementing data science projects is Sales (37%), followed by Marketing (27%), Production (23%), and Logistics/Supply Chain (16%).

Considering the sector, we notice that Banking/Insurance, Publishing, and Media companies are more likely to belong to the cluster of more advanced companies; those operating in the Utilities, Transportation and Logistics sectors are mostly considered cautious or enterprising (but not yet advanced), while immature or early-stage companies are represented by the Manufacturing, Retail, and Government/Healthcare sectors.

In conclusion, the direct experience of numerous companies shows significant growth in the use of data to implement a consistent and effective Data Strategy, and this happens for a reason, as data show how important this is for achieving business objectives. However, there is still a lot of room, especially in some sectors where intervening now with targeted investments can be decisive in outperforming the competition or making internal processes more efficient.

Blue BI, with its decades of experience, has distinguished itself for its cross-functional nature, which has led the company to obtain significant recognition through projects in all the aforementioned sectors: if you want to accelerate, don’t miss the opportunity and contact us!