The Business Analyst of Tomorrow

Today’s Data Scientist is Tomorrow’s Business Analyst

Data science is an ever-evolving field, and its roles are also changing. As businesses increasingly rely on data to inform their decisions, there is a growing need for people with both the technical skills and domain/industry expertise to drive measurable value. 

Data science, AI, and ML have been overhyped for at least the last decade, resulting in often unrealistic and misaligned expectations between data scientists and employers (this is especially true for traditional, non-tech companies).

It has increasingly led to a split of the data science role into three distinct categories: 

1. Business analysts/managers/directors with domain expertise and foundational data science skills.

2. Full stack data scientists / ML engineers who are hybrid software engineers/data scientists.

3. Data engineers who improve the lives of #1 and #2 substantially.

I want to focus on #1 because there needs to be a growing movement around these citizen data scientists, business scientists, advanced business analysts, or whatever we want to call them.  

The future of analytics belongs to this new breed of business analytics professionals, who bring together advanced analytics and functional business expertise (pricing, sales & marketing, finance, supply chain) to uncover hidden value for companies. 

These individuals combine their competencies in the below five areas with a clear understanding of customer needs and market trends to create winning analytics insights and solutions. 

1. Technology / Coding (e.g., Excel, Tableau, Power BI, R, Python...)

2. Analytics (e.g., cohort analysis, customer lifetime value, marketing effectiveness...)

3. Foundational machine learning (clustering, regression, ensemble models, association rules...)

4. Industry and domain expertise

5. Effective communication skills, a sense of urgency with heavy results orientation, coupled with empathy

They can recognize opportunities for improved business outcomes through analytics-driven (or analytics-informed) decision-making and develop strategies that help improve marketing spend, price realization, or supply chain efficiency.

---

Master Strategic Communication and Stakeholder Management is Paramount

The role of strategic communication and stakeholder management (aka "soft skills) in the field of Analytics and Data Science is often undervalued and neglected in training programs.

I've been an advanced analytics practitioner, Analytics / Revenue Growth Management group lead, and commercial executive throughout my career. I've worked with and managed some brilliant data science practitioners who could not make an impact in their organizations despite their best-in-class data and technical skills. 

The common thread was a lack of "soft skills." You may argue that Analytics or Data Science practitioners shouldn't worry about that. I.e., there should be AI Translators, Product Managers, or Product Analysts to act as effective conduits between the Analytics teams and Internal Customers. 

Unlike most people in the data world, I'm an MBA-turned-coder who discovered the beautiful world of R/Python/Predictive Modeling during business school. One of the most underestimated and ignored pieces of our graduate school program was the "Organizational Behavior" and "Strategic Communication" classes – taught by two otherwise excellent professors and leaders in their field. Most of us wanted to take advanced finance classes, decision analytics, quantitative marketing courses, etc.

It took 5-10 years post-business school to realize that hardly any of us used a "Black-Scholes model" or ran a conjoint analysis manually. However, we would have benefitted tremendously from more strategic stakeholder management and organizational communication acumen. 

The analogy also holds for your data science teams:

Increasingly, advanced methods are being democratized (i.e., PyTorch, Tensorflow, various AutoML solutions, etc.), and this trend will continue. The data science and analytics field will need more structured thinkers, skilled data storytellers, and practical communicators laser-focused on solving significant problems and caring about driving measurable customer outcomes (i.e., results-focused).

My advice for data scientists is to start carefully honing their competencies in communications, influencing, data storytelling, domain expertise, and strategic thinking. Similarly, my advice for companies is to begin hiring data science and analytics talent who are strategic, systematic thinkers and effective communicators with a demonstrated history of driving measurable value for organizations. For more novice roles, focus on hiring talent who balance soft skills and complex technical and analytical understanding and spend the right resources to upskill them in the right competencies.

---

Summary

The Business Analyst of Tomorrow can think critically about data, identify patterns, and utilize their problem-solving skills to draw meaningful insights from large datasets. They possess expertise in coding languages such as Python and R and proficiency in using various data visualization tools like Tableau or Power BI to make sense of complex information. Moreover, they understand how and when to leverage foundational analytics and ML techniques and apply them effectively in strategic decision-making.

Having deep industry and domain expertise and excellent communication skills is a must for these professionals to impact an organization truly. 

By communicating effectively with stakeholders across different functions and business units, the business analyst of the future can design solutions that meet the needs of each particular group. It requires not only having a solid analytics solution but also conveying the intangible value proposition of the deliverable so that everyone is on board with its deployment.