Overview: Business analyst in Practice
This article from Revology Analytics explains business analyst in the context of modern pricing analytics and revenue growth management. It draws on real engagements with mid-market and enterprise clients to turn business analyst from a buzzword into a measurable commercial capability. Read on for the full perspective, and see our related reading for additional depth.
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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:
Business analysts/managers/directors with domain expertise and foundational data science skills.
Full stack data scientists / ML engineers who are hybrid software engineers/data scientists.
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.
Technology / Coding (e.g., Excel, Tableau, Power BI, R, Python…)
Analytics (e.g., cohort analysis, customer lifetime value, marketing effectiveness…)
Foundational machine learning (clustering, regression, ensemble models, association rules…)
Industry and domain expertise
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.
Frequently asked questions about the business analyst of tomorrow
Who is the business analyst of tomorrow?
It is a business analyst, manager, or director who combines domain expertise with foundational data science skills, sometimes called a citizen data scientist or business scientist. These professionals bring advanced analytics together with functional expertise in pricing, sales and marketing, finance, or supply chain to uncover hidden value, and the future of analytics belongs to them.
How is the data science role splitting?
Years of hype and misaligned expectations, especially at traditional non-tech companies, have split it into three categories: business analysts, managers, and directors with domain expertise and foundational data science skills; full-stack data scientists or ML engineers who are hybrid software engineers; and data engineers who make the work of the other two substantially easier.
What skills does a modern business analyst need?
Five competency areas: technology and coding, such as Excel, Tableau, Power BI, R, and Python; analytics, such as cohort analysis, customer lifetime value, and marketing effectiveness; foundational machine learning, such as clustering, regression, ensemble models, and association rules; industry and domain expertise; and effective communication with a sense of urgency, a results orientation, and empathy.
Why do soft skills matter so much in analytics?
Strategic communication and stakeholder management are often undervalued and left out of training programs, yet their absence is the common thread among brilliant data scientists who fail to make an impact despite best-in-class technical skills. As advanced methods like PyTorch, TensorFlow, and AutoML become democratized, the field needs more structured thinkers, data storytellers, and practical communicators.
What should companies look for when hiring analytics talent?
Hire strategic, systematic thinkers and effective communicators with a demonstrated history of driving measurable value. For more junior roles, look for people who balance soft skills with technical and analytical understanding, and invest in upskilling them in the right competencies. Data scientists, in turn, should hone communication, influencing, data storytelling, domain expertise, and strategic thinking.
How does a business analyst create value across the organization?
By communicating effectively across functions and business units, the business analyst of the future can design solutions that meet each group’s needs. That takes a solid analytics solution plus the ability to convey its less tangible value so everyone supports its deployment. These analysts also spot where analytics-informed decisions can improve marketing spend, price realization, or supply chain efficiency.
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.
Related Reading
- Pricing Strategies to Counter Tariff Impacts
- RFM Analysis as an Important Revenue Growth Analytics Capability – Part 2
For broader industry perspective on pricing analytics and revenue growth management, see McKinsey’s Growth, Marketing & Sales insights.
