Harvard Business Review’s Post

All too often we see Data Scientists who know all about algorithms but nothing about business. In addition, the Business team often stimatizes the Data Science team as nerds and geeks, further adding to the segregation between Data and Business. To make things worse, insights derived from data ofren challenge established power dynamics within an organization, making resistance to change a very real challenge to be overcome by the Data Science team. Organizations who seek to extract value from their Data Science teams must be aware of the interplay between these three forces. Managers must actively foster the integration between the data-guys and the business-folks. ❎ The real challenge is not data wrangling, model building or deployment. ✅ The real challenge is navigating corporate culture to impact the business.

Do they? Technical assignments require more than technical expertise to succeed. Other crucial factors include: Comprehending business industry, organisational relations and interaction issues. Establishing trust with decision-makers. Effectively understanding and conveying results. Calmly and systemically address the concerns of those impacted. #softskills #strategicinsights #dataliteracy #complexitymanagement

Very interesting discussion highlighting the pitfalls of AI & data related projects and worth considering as the industry evolves. “It was immediately clear that the biggest successes stemmed not simply from technical excellence but from softer factors such as a deep understanding of business problems; building the trust of decision makers; explaining results in simple, powerful ways; and working patiently to address dozens of concerns among those impacted. Conversely, otherwise excellent technical work died on the vine when we failed to connect with the right people, at the right times, or in the right ways.”

Starting with the "why" is essential for every career field. Understanding the reason we do what we're hired to do is the first step to delivering excellence. Working in a silo, no matter your position, discourages individuals from openly discussing ideas and prevents cross-functional collaboration. When you have a deeper understanding of your organization and its mission, you can provide more relevant and beneficial products, services or data points to contribute to the success of the business.

Data alone isn't the hero, it's the "why" it serves that creates value. Shiny dashboards are worthless if you don't focus on the strategic goals fueled by insights. #datadriven #strategyfirst

data science to solve business problems

When your tool is a hammer, everything looks like a nail. At a tactical level, I see this with technical pros who get wrapped around the axle on implementation detail rather than focusing on "good enough." For instance, insisting on a ML solution to a problem that can be solved for (say) 90% of cases in more traditional libraries. This bogs down timelines and budgets for delivering value.

In this perceptive article, the authors dissect the persistent challenges faced by big data and AI projects, skillfully navigating beyond the allure of technical excellence. They underscore the pivotal role of softer factors like a nuanced understanding of business problems, building trust, and effective communication in achieving successful outcomes. The authors pinpoint the tendency of data scientists to prioritize their craft over solving tangible business issues and advocate for their seamless integration into the daily fabric of corporate operations. Through insightful recommendations, they urge managers to set clear business objectives, hire data scientists attuned to organizational challenges, and emphasize end-to-end accountability. The article concludes with a compelling real-world example, illustrating the transformative power of relatable communication in establishing trust. Overall, it's a thought-provoking piece that champions a holistic approach to data science, intertwining technical acumen with business acuity. #whatdoyouthink ?