Strategic advice to leverage new technologies
Technology is at the heart of nearly every enterprise, enabling new business models and strategies, and serving as the catalyst to industry convergence. Leveraging the right technology can improve business outcomes, providing intelligence and insights that help you make more informed and accurate decisions. From finding patterns in data through data science, to curating relevant insights with data analytics, to the predictive abilities and innumerable applications of AI, to solving challenging business problems with ML, NLP, and knowledge graphs, technology has brought decision-making to a more intelligent level. Keep pace with the technology trends, opportunities, applications, and real-world use cases that will move your organization closer to its transformation and business goals.
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Ben Porter uses several case studies to show how organizations have made progress in amplifying the value of analytics by demonstrating three actions: recognizing how value is created, focusing on delivering that value, and understanding the changes that must be adopted to ensure long-term value. He describes the four fundamental requirements for successful analytics projects (sponsor, tools, team, and project/problem) and closes with the critical assertion that value creation from analytics requires teamwork between IT, business, and analytics professionals.
Michael Papadopoulos and Philippe Monnot take a deep dive into ML projects. They address the “very powerful tendency to anthropomorphize ML and AI, imbuing it with human characteristics.” As we increasingly describe them in human terms, we often fail to make a critical distinction in the way humans and machines interpret the world.
In this Advisor, we discuss how AI impacts the teaching and learning experience and the quality of education. We also briefly explore a number of AI technologies that are being applied in education to achieve these advances.
The authors look at how to achieve robust analytics through four lenses. They begin by identifying effectiveness as the first hallmark, echoing the previous critical objective around finding an important business decision to be solved in order to deliver relevant value. They describe efficiency as the next critical element. The authors' third criteria for robust analytics is around minimizing risk by monitoring threats and opportunities in both internal and external environments. Finally, they describe ethics as the fourth tenet of robust analytics.
Frank Contrepois discusses human emotions and our connections to data. He shares how some companies manage to convert data into money and how other organizations can learn from and potentially replicate the approach (or at least the outcome). But this approach comes with some warnings, which he outlines. Contrepois closes by supporting this point: focus on a defined business problem and act by building an analytical model that improves decision-making confidence.
Avishan Bodjnoud highlights the typical challenges of skill sets, data literacy, data governance, process, resources, and senior leadership buy-in that affect most traditional organizations. She explains why organizations that simply establish a standalone analytics entity and expect immediate results are often disappointed. Bodjnoud closes with an important discussion on the need to understand the reasons for resistance to change and then manage them — critical elements of any analytics project or digital transformation effort.
There are myriad approaches that can help analytics projects deliver on their promises and potential. The articles in this issue of Cutter Business Technology Journal provide insights into some of these approaches. As you contemplate how to ensure that your organization derives value from your next analytics initiative, it is imperative to develop your own path forward, determining what tactics you feel will work best in your environment.
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