Data-driven working: almost every organization is working on it or is thinking about it. But how can you actually deliver on the promise of data-driven work?
The promises of data-driven work are great. Organizations that make decisions based on facts and figures as much as possible are able to bridge the gap between customer needs and activities such as R&D, marketing & sales, and maintenance like no other.
In this way, an insight driven business leads to an improved customer experience, more profitability and / or a better service towards the citizen. That sounds wonderful on paper. But in practice, many organizations are now drowning in the abundance of data they collect. How can you actually use data in such a way that it contributes to your business?
Step 1. Determine the scope
Important tip for enthusiastic companies and organizations that no longer see the data forest through the trees: know what you are looking for to start with. Define for yourself what data-driven working should ultimately yield. And then look specifically at which data fits that purpose. A reservoir of data becomes much clearer if you as an organization clearly determine the scope: what are you really looking for?
Step 2. Take the business as a starting point
Many organizations that want ‘something’ to work with data-driven take the available data and the possibilities of new technology as a starting point. They then invent business applications.
In order to achieve real added value, the reverse order is better. What exactly do you want to achieve? What delivers the most business value? What do your customers want? Then search for the correct (existing) data. Or invest in technology that provides the right data.
Step 3. Start small
Many companies and organizations often start with grand vistas and sky-high ambitions. However, practice soon turns out to be more difficult. Therefore, keep it small, especially in the beginning. A small-scale pilot is often a good way to gain knowledge and experience. From there you can then scale up to the entire organization or parts thereof.
Step 4. Monitor the data quality
Garbage in is garbage out . To be valuable, data must be complete, accurate and consistent. Data that is not quite right can lead to less good decisions.
Good agreements about entering, cleaning and maintaining data are therefore indispensable. It is often useful to ‘entrust’ one or more people with monitoring the data quality. It may also be wise to invest in automated data entry; this reduces the chance of errors compared to manual input.
Step 5. Provide good security
In a data-driven organization, data is more valuable than ever. All the more reason to think carefully about how to properly protect this data capital. This primarily concerns technical measures, such as regular backups, a good firewall and encryption of data.
But just as important is attention to employee behavior. After all, people are often the weakest link in the security chain. Think carefully about who exactly has access to which information. And invest in safety awareness; for example through an internal campaign, or by putting the subject structurally on the agenda of the work meeting.
Step 6. Invest in people
Organizations that want to work data-driven cannot do without good expertise in this area. Do not wait too long to recruit data scientists and data analysts with specialist knowledge in the field of data analysis and access. They quickly find the right information in a reservoir of data.
Do not forget your sitting staff at the same time. Invest in training in data science, Artificial Intelligence (AI) and data visualization. In this way you ensure that data-driven working starts to live in the entire organization.
Step 7. Don’t forget the corporate culture
Culture eats strategy for breakfast . You can still make such nice plans and invest in IT solutions, if employees are not included in the transition to data-driven work, every innovation is doomed.
As a management, actively demonstrate that you believe in data. And keep communicating structurally about how data can help the organization. This prevents data-driven working from remaining the domain of a data team that is limited in size.
