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Data-Driven Approach in Practice: Implementation Guidelines for Real Results

August 10, 2023 Avvale

The data-driven approach plays a pivotal role in empowering circular business models. By harnessing valuable insights from extensive data sources, companies can optimize resource utilization, product lifecycles, and supply chain efficiencies. Data analytics enables businesses to identify patterns, monitor product performance, and predict demand, facilitating the transition from traditional linear models to circular ones. With data-driven decision-making, companies can implement innovative strategies, such as product-as-a-service, remanufacturing, and recycling initiatives, increasing profitability, driving sustainability, reducing waste, and fostering a more circular economy.

In this emerging business model aimed at generating more sustainable profits, data plays a pivotal role. However, many companies are not adequately prepared to navigate the growing data era and fully capitalize on the wealth of information at their disposal. As the importance of data-driven insights continues to grow, businesses must adapt and embrace effective strategies to harness and leverage the vast amounts of data available to them.

The Foundation of a Data-Driven Approach

Data-driven approach means having access to valuable information derived from the company's data to improve decision-making processes and implement increasingly effective business strategies. However, this definition should not be misleading, as its simplicity implies a series of aspects and procedures aimed at achieving a radical, purposeful change in the company's processes.

To become data-driven, it takes a well-structured path in practice. Merely using more powerful processing systems is not enough; instead, it requires embarking on a comprehensive journey that involves modifying how data is acquired, managed, and governed. Only at the end of this path can the data be made "actionable," providing useful insights to drive strategic actions and streamline decisions for improving business performance.


Implementing a Data-Driven Approach

A truly data-driven company can respond quickly and appropriately to the ever-changing economy and market. Moreover, it can optimize productivity, anticipate customer demands, and identify emerging trends in the business and market to develop more impactful strategies, increase sales, and generate new revenues. All this is made possible by the value extracted from data. Here is a four-step plan for a data-driven approach:

1. Executive Buy-In

The first step of a data-driven approach is to be aware of and explicitly state the desired outcomes. This primarily concerns executive leadership, who must be the driving force behind all processes leading to a new corporate culture, one that places data at the center of all strategies. It is also essential to ensure that employees view this evolution as a positive process that will bring real benefits, rather than an operational imposition.

2. Data Democracy

The pervasiveness of digital technology means that companies now collect a myriad of data from various sources both within and outside the organization. This data can come from IoT devices, CRM software, social media, and the company's ERP, among others. The research firm IDC estimates that 30% of collected data are stored in in-house data centres, 20% in third-party data centres, 19% in data centres or remote locations, 22% in cloud storage and 9% in other locations.

Addressing data dispersion is critical for a data-driven approach. After data sources have been identified, it can be housed within a single source of truth,readily available to equip all employees with the power to analyze and act.
It is worth noting that data fragmentation can also result from acquisition processes or the adoption of new systems or applications. In such cases, avoiding dispersion and eliminating duplication is also necessary.

3. Data Hygeine

Data stored in corporate systems are often not only dispersed but also come from different sources, leading to heterogeneity and differences in semantics. To be leveraged effectively, data must be aggregated, made homogeneous, and validated to ensure integrity, structure, and accuracy. As the saying goes, “to gain valid insights, one must have high-quality data.”

For this purpose, a platform that can connect to multiple databases and allows for ETL (Extract, Transform, Load) and data blending operations can be adopted to prepare and "cleanse" the data to create automated reports.

4. Accelerate Innovation

An important aspect of any data-driven company is that individuals in the business, or anyone needing to make informed decisions, perform the necessary data analysis to obtain sought-after insights without necessarily relying on IT.

It goes without saying that those handling analytics in a data-driven company should have appropriate skills, but technical specialization may not be necessary, as modern software tools often do not require it. However, it is essential that these tools have specific functionalities, such as data visualization through customizable and editable reports in self-service analytics mode. Finally, defining ad hoc Key Performance Indicators (KPIs) will be essential to measure results based on the objectives to be achieved.


Amidst today's dynamic business landscape, the data-driven approach acts as a potent catalyst for strategic evolution, further bolstering circularity. By extracting actionable insights from diverse information sources, this approach empowers organizations to make informed decisions, optimize operations, and foster innovation, all while advancing a circular and environmentally conscious future. As we chart a course toward a data-driven horizon, businesses secure enduring success, armed with tools to navigate complexities, gain a competitive edge, and champion circularity in an information-centric world.

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