Data-Driven Decision Making and Trust
At best, data-driven decision-making, including the trending AI themes, can transform the way organizations operate and innovate. Connecting data-driven applications to organizations’ processes is a potential way to gain benefits in areas such as operations efficiency, productivity, creativity, and customer satisfaction. However, sometimes technical tools suffer from remaining loosely connected to daily work or may even get actively rejected by the targeted end users. Trust is a key factor when building sticking data-driven decision-making capabilities.
On the highest level, a foundation of trust is built by setting clear direction and objectives that everyone in the organization can understand - The targets need to be connected to real business needs. Secondly, internal and external stakeholders need to be on board as early as possible to enable the learning process and acceptance of new ways of thinking. Lastly, organization needs to have a proper platform that, together with the right technology selection, forms a basis for scalable and reliable tools.
Trust is the key when choosing gets hard
Data-driven decision-making, as the name suggests, can be an important aid that boosts decision-making capability. Often it is seen as a concept that helps build the understanding required to untie conflicts, have the courage to answer previously unanswered questions, or even find new relevant questions to be answered. Effectively, it eases decision-making by eliminating the poorest options and highlighting the most feasible ones.
Ultimately, decision-making is a process of making choices and we may think of it as a trust-building tool too: The more we trust in one option over the others, the more likely that option will be the way to go. With zero trust, we get paralyzed or must choose fully randomly, by intuition.
Paradoxically, the major issue that hinders the adaptation of data-driven decision-making concepts that eventually help build trust in daily decision-making, is the lack of trust itself. Many companies ponder whether the received benefits are greater than the required investment needed to build the capabilities. For example, to implement machine learning applications, companies must be able to build an understanding of the rapidly developing and wide range of technologies, invest in business and tech teams’ technical and collaboration competencies, and also transform the whole culture to enable technology’s more guiding role as an internal advisor.
Trust in the organization
I believe organizational culture dictates the maximum depth that technology can reach and get applied to in processes and daily problem-solving. To build trust towards technology in an organization, the first step is to build understanding and a clear strategy for reaching the main objectives of the organization. Typical well-working approaches are data strategy and digital transformation concepts that push tech and business stakeholders to truly co-operate and start the needed change management. The key is to discuss and decide on real business needs while dropping unnecessary tech jargon.
In addition, if the organization’s top management is not committed, there is little hope to drive any kind of transformation throughout an organization. This surely applies to digital transformation too. The main target needs to be engaging and involving the relevant stakeholders: Internal functions with managers and employees but more and more also external stakeholders including customers and partners. Only then, the full data-driven decision-making potential can be identified, direction can be set, and a prioritized roadmap can be built.
A few-point checklist to get started:
- Top management has to be truly committed to driving a data-driven culture
- Clear and measurable data-driven decision-making objectives that connect to company strategy have to be set
- A portfolio of data-driven decision-making programs and prioritized project roadmaps need to be visualized
- Honest and open communication is needed between stakeholders to break down silos and to build a common understanding of data-driven decision-making maturity
- A common platform is needed to serve data-driven innovation at least in terms of resourcing, tech expertise, infrastructure, and governance
Trust in the people
What comes to decision-making and development activities, team composition is crucial for success. A well-fitting team within and across organization functions needs different kinds of competencies to really start utilizing data-driven decision-making concepts in daily work. Everyone attending has to understand the point and be highly committed to common goals.
For motivation, some say that AI will split the employees in two: Those who adapt it to daily use, and those who do not. The first wave of early adopters has passed, and now the plane is boarding for the rest of the mass. Teams with AI or other data-driven decision-making capabilities outperform the ones without already in many areas. Understanding the full potential of data-driven decision-making helps to motivate personnel – it is so much more than just the hyped generative AI or large language model applications.
To have everyone on board, at least the following should be considered:
- Communicated vision and strategy of purposes of data-driven decision-making, so that each individual understands “What’s in it for me?”
- Clearly understandable and meaningful POC- and pilot projects that are aligned with the company’s strategy and real business needs
- Focused technology selection and continuous expertise building to support individuals’ learning
- Support to individual team members is crucial – to encourage learning, innovation, and also a safe environment to fail
By considering these topics, people can develop a sense of ownership, engagement, and collaboration in data-driven projects while leveraging their skills and expertise.
Trust in the technology
The third step is to ensure that the AI and data-driven solutions are trustworthy, meaning that they are reliable, accurate, transparent, well-governed, and secure. This requires proper design and development processes, as well as constant monitoring and evaluation of the made choices.
Some of the best practices to avoid losing trust and hope when building data-driven decision-making capabilities are:
- Use validated and up-to-date data sources
- Have clear ownership and responsibilities for the data
- Start by building relevant but not overly complicated models and tools
- Secure a continuous feedback loop with tech- and business stakeholders
- Build an understanding of technologies’ strengths and weaknesses
- Have sufficient exploratory analysis in place to explain what is happening
Getting started
When starting to build data-driven decision-making capabilities, considering the organizational, people, and technology aspects early on, organizations can mitigate possible trust issues towards the new way of thinking and technology adaptation. This is the key to gradually receiving more and more benefits and value out of data-driven decision-making concepts that nowadays can be considered as important factors of companies’ overall competitive advantage. While it is true that considering all three aspects within the roadmap requires a significant amount of planning and coordination, it will pay back in the long run.