Data, Decision-Making and AI
The technologies that underpin modern society (e.g. IoT and sensors, social media) continue to drive a massive increase in data of all shapes and sizes, from internal to external, structured to unstructured, hard numbers to sentiment analysis. Even since we last addressed this issue, the amount of data has multiplied many fold, often exceeding our ability to manage it and make use of it. It could be argued that this is true in many areas of the enterprise, and that we all have real challenges around people, process and technology associated with the management, analysis and responsible use of data. Roles and skills are changing in enterprises, data-science organizations are being created, and the apparent need and desire for the democratization of data, its availability to all, is changing how we function.
This roundtable looked at how the availability and usability of data today is changing the nature of decision-making in business. It’s changing the quality and speed of decision-making, and also often where or what level in the organization the decision is made. As part of this, we examined the emergence of artificial intelligence (AI) and the implications of this for analytics, prediction and decision-making.
Key Insights Discussed in this Article:
- Data needs to be managed with the same discipline and skill as physical goods. With data-enabled products and services becoming commonplace, companies need to invest in developing and improving the same processes for data as they have for physical goods, including R&D and product management.
- All parties in the data chain need to benefit. Many of the new data-based products and business models rely on data owned by multiple parties: Without economic benefits to each of them, these businesses cannot scale.
- Data governance and data regulation bring more benefit than cost. As difficult as they are to implement, GDPR and similar requirements force structure and enable simplification – which are critical to business agility, responsiveness, and growth.
- The digital safety conversation is shifting from “privacy” to “trust.” The twin genies of data transparency and data sharing cannot be put back into the bottle: Companies will win and keep customers by demonstrating they can be trusted with how the use and protect personal information.
- The biggest obstacle to artificial intelligence may be human emotion. Automating physical processes and data mining through machine learning have had success, but higher-level AI is feared as a replacement by workers and not yet trusted by executives.