What does data-driven mean?
The expression data-driven literally means ‘driven by data’.
It implies, as its name suggests, decision-making based on the analysis and interpretation of data; a step beyond its mere storage or collection through digital sources.
The ability to have huge amounts of data available means that, when properly analysed, it can facilitate the understanding of certain business-related issues or be an aid in the design of future approaches.
Benefits of the data-driven approach
The fact of being able to make business decisions based on what the data says has numerous advantages.
Let’s look at some of them:
- User experience. Key issues for companies such as user experience or customer satisfaction benefit, since, by having more accurate information about consumer tastes or trends, they can be more successful in offering goods, services or products that are more tailored to their preferences.
- Accuracy. Having more accurate information allows decision-making processes to be more precise by minimising potential or possible errors and enabling more reliable and informed procedures to be developed.
- Agility. The use of data-driven tools increases both efficiency and productivity, which also indirectly generates significant time savings in decision-making.
- Helps in decision making. Having adequate data analysis can help to understand it better. It also serves to increase the degree of knowledge of the market and its objectives, as well as -thanks to predictive analysis- to be able to anticipate different hypotheses when making decisions. In addition, having reliable data can also minimise risks.
- Cost reduction. Although it is true that it is usually applied to customer relations or marketing, collecting data can be positive for all areas of the organisation. Among other things, it can increase productivity (both of staff and machinery, depending on the sector) or detect unnecessary costs and optimise available resources.
Challenges of data-driven
Although it is true that there are advantages to its application, this data-based decision making must also face a series of challenges in its implementation.
Let’s look at some of them
Poor data quality
Although it may seem obvious, data-based decision making starts from precisely that premise: having data.
Having inaccurate or incomplete data can potentially lead to the information being stored incorrectly and, consequently, the decisions made afterwards may not be appropriate.
In this situation, the data can be audited periodically to minimise these possibilities of error and thus increase the guarantees of integrity and accuracy.
Data literacy
Another basic premise for the correct application of data is that employees have the basic skills to use it properly. In view of this, training is presented as a potential solution through resources such as seminars and courses that underpin the learning of those who work in the company.
Resistance to change
Apart from the fact that there may be a greater or lesser degree of literacy among the workforce, another challenge is that employees may be reluctant to change. To this end, the benefits that the change can bring can be communicated to the workforce to overcome any possible resistance that may arise: learning about success stories can be one example.
Integration of diverse data sources
In this case, the challenge arises from the possibility of having numerous data sources, the consolidation of which can present problems, both due to the complexity of the process itself and the resources consumed by the task.
Security and privacy
As in many other issues related to digital transformation, privacy and security are two particularly sensitive issues. To this end, in addition to compliance with the respective regulations in force or the implementation of cybersecurity measures to guarantee data protection, security protocols can be periodically updated or data privacy audits can be carried out.
