Introduction 

In today's data-driven world, the importance of people and employees leveraging data cannot be overstated. Organizations that can effectively leverage data to drive decision-making gain a competitive edge in their industries. However, achieving successful data adoption is no easy task, requiring a delicate balance of technology, processes, and culture.

With the rise of artificial intelligence (AI) and other high-tech tools, it is tempting to think that these advanced technologies will solve all data adoption challenges. Yet, this is only half the story. A truly successful data adoption strategy requires the perfect blend of high-tech and low-tech approaches, with the two working in harmony to drive data-driven decision-making.

For the purposes of this article, high-tech tools will be considered recent additions to the analytics landscape such as generative AI, large language models, and neural networks that are accessible to the general end-user. Low-tech tools are simple, tried, and tested means for people to work together, ideate and collaborate. Examples include whiteboards, sticky notes, and simple pen and paper.

How AI is disrupting BI and analytics

The rise of AI has had a profound impact on the field of business intelligence (BI) and analytics. Advanced machine learning algorithms and natural language processing techniques enable organizations to derive more actionable insights from their data. AI-powered tools can automatically identify patterns and trends in complex datasets, allowing decision-makers to focus on the most relevant and impactful information. Moreover, AI-driven analytics can deliver insights in real-time, empowering organizations to make data-driven decisions at the speed of business.

These more advanced tools have been around for a while now, and are making an impact. However, the advent of generative AI being accessible to the mass market is utterly disrupting the analytics landscape. ChatGPT is changing how people make decisions. It is changing how people are becoming informed and accessing knowledge. Most of all, it is equipping people with expanded capabilities to be able to do more with less.

However, the advantages of AI do not come without challenges. Implementing AI-driven analytics can be a complex and resource-intensive process, often requiring significant investments in infrastructure, data management, and talent. Additionally, organizations must contend with the risks of algorithmic bias and other ethical concerns when using AI for decision-making. Added to that is the risk of not doing people relying too much on AI and not actually knowing their subject matter themselves. In such situations, the AI could recommend the wrong approach and the human operator would not have the wherewithal to know any better.

Learning from the past, what has worked

Despite the allure of AI and other high-tech tools, it is essential not to overlook the value of low-tech approaches to data adoption. Many time-tested low-tech strategies have proven to be effective in promoting data-driven decision-making, even in the absence of advanced technology. These strategies may include:

• Conducting workshops and design-thinking methodologies using structured frameworks to facilitate engagement and ideation
• Creating low-fidelity wireframes using sketches on whiteboards, PowerPoint, or other techniques like the Dashboard Wireframe Kit
• Providing targeted training and resources to help users better understand and utilize data in their roles

There is a time and place to use AI, and it is a tremendous accelerator in many ways, but an over-reliance on it can be used as a way to avoid sometimes difficult conversations that can only happen with people-to-people interaction.

Examples of two transformative AI technologies

According to Reuters, ChatGPT is the fastest-growing consumer product in history. Without question, organizations need to be having a strategy for how to both leverage it but to also set necessary guardrails around it. One profound application it has had is in the formation of business requirements for dashboards. With the right training, ChatGPT can produce robust requirements for any industry and even be targeted toward specific personas.

Perhaps less appealing but more dramatic is the use of an AI image generation tool called Midjourney. Given an appropriately well-defined prompt, it can produce visually beautiful dashboard designs that leverage brand colors. While not something that can be used directly, it can inspire the design and provide design cues for what a dashboard could look like.

The way forward and how to navigate

In a world where AI and advanced technology continue to revolutionize the Business Intelligence and analytics landscape, organizations must be strategic in integrating high-tech and low-tech approaches to data adoption. To do so, they should:

1. Identify the best high-tech and low-tech tools for their organization: Assess the specific needs and challenges of your organization, and determine which tools will provide the most value in addressing those needs. This may involve implementing AI-driven analytics tools to uncover deeper insights, while also leveraging low-tech visualization and communication techniques to ensure those insights are effectively communicated to decision-makers.
2. Develop strategies for integrating high-tech and low-tech approaches: Look for opportunities to combine the strengths of high-tech and low-tech practices, such as using AI to automate data analysis and then presenting the results in simple, easy-to-understand visualizations. By integrating these approaches, organizations can maximize the value of their data adoption efforts.
3. Every BI and analytics department must have a wireframing approach. People react to visual inputs, not dry requirements. They need to see what something will look like in order to give feedback. Low-tech wireframing provides that and is a profoundly low-cost means of reducing risk, wasted effort, and low adoption down the road.
4. Foster a culture of collaboration and continuous learning: Encourage team members to share their insights and learn from one another, whether they are utilizing high-tech AI-driven tools or low-tech visualization techniques. Create a culture where employees feel empowered to experiment with new tools and approaches, fostering innovation and data-driven decision-making.
5. Invest in ongoing training and education: Ensure that all team members have the necessary skills and knowledge to effectively use both high-tech and low-tech tools. Offer targeted training and resources to help users understand and apply data in their roles, and encourage continuous learning and development to keep pace with evolving technologies.
6. Measure and monitor the impact of data adoption efforts: Regularly assess the effectiveness of your organization's data adoption strategies, using a combination of quantitative and qualitative metrics. Use this information to refine your approach and identify areas for improvement, ensuring that your organization continues to drive value from its data.

Conclusion

The key to successful data adoption lies in striking the right balance between high-tech and low-tech approaches. Sure, use ChatGPT to create requirements for your dashboards, but vet these requirements in a low-tech workshop with the target end users and stakeholders. It is the blend of approaches that will both accelerate organizations but also grease the tracks of progress by minimizing friction between people.

By embracing the power of AI and other advanced technologies, while also recognizing the enduring value of low-tech best practices, organizations can create a data-driven culture that promotes collaboration, innovation, and meaningful decision-making. In this rapidly evolving landscape, those who can effectively unite high-tech and low-tech strategies will be best positioned to thrive in the age of data-driven decision-making.