Data Analytics - Public Speaking - Career Growth

Insights x Design Ep. 26 (Nisar Ahmed - Lead Decision Scientist at CVS)

Navigating Job Titles in Data Analysis

• The terminology used in job titles within the data field can often be confusing. Positions such as "data analyst," "business analyst," and even "data scientist" are frequently used interchangeably, leading to variations in job descriptions that may not align with the title. This discrepancy often reflects trends or buzzwords within the industry that shape how titles are assigned.
• Ultimately, the person hiring may select a title based on what is currently popular or appealing, rather than the most accurate description of job responsibilities.

"The job description often reveals that a so-called data scientist is essentially performing the tasks of a data analyst."

Nisar's Journey

• Nisar is the Lead Decision Scientist at CVS, and his background in data analytics spans two decades, particularly focusing on Tableau in recent years. His dual role as an instructor in Tableau certification programs and as the creator of content for his YouTube channel underscores his commitment to coaching others in data visualization and analytics.
• His experience showcases a blend of practical application in the healthcare sector and a passion for teaching.

"I have experience in analytics spanning 20 years, but over the last eight or so years, I focused really on Tableau."

Balancing Art and Science in Data Visualization

• You must balance the artistic and technical sides of creating effective dashboards and visualizations. Nisar explains that a deep understanding of design principles is crucial for successful data visualization. Relying solely on technical skills is not enough; practitioners must integrate design best practices to enhance communication and ensure effectiveness.
• Aspiring data visualizers must not only master the tools but also grasp the aesthetic elements, contrasting the general perception that data work is purely technical.

"In order to be good at data visualization, you have to balance both art and data."

The Interdependence of Data and Technical Communication

• In his experience as a technical writer, Nisar highlights its relevance to data analytics. He notes that data professionals need to translate complex data insights into easily understandable language for stakeholders. He asserts that strong written and verbal communication skills are vital for effectively narrating the stories data contains.
• The ability to articulate technical information is just as crucial as the analysis itself and contributes significantly to effective decision-making.

"A business analyst's job is essentially to translate technical data to the end users and make them understand what the data tells."

Becoming a Confident Communicator Through Experience

• Transitioning into public speaking, Nisar initially struggled with anxiety and discomfort. His journey toward becoming a confident communicator began with the practical use of Tableau, which provided him with opportunities to visually present data insights. Each training session he led helped lessen his nerves and build his confidence over time, proving that practice and positive reinforcement can enhance communication skills.
• Teaching others helped him recognize his own knowledge and abilities, thus improving his confidence.

"When you see that people appreciate what you teach, you begin to realize, 'Okay, I know what I’m talking about.'"

The Importance of Preparation in Data Analysis

• Analysts must be thoroughly prepared when discussing data, regardless of whether or not it was their responsibility to produce it.
• Analysts should take responsibility for any data that they use that is crucial for effective analysis and communication. He advises that while complete knowledge isn't always necessary, analysts should have a foundational understanding of their data set to effectively meet stakeholders' needs.
• Identifying the technical background of stakeholders is also essential, as it allows analysts to tailor their information in a way that meets those stakeholders' varying levels of understanding.

"You have to be more prepared; you can't go into this not knowing what you're talking about."

Best Practices for Data Visualization

• For effective visualizations, you must emphasize the importance of simplicity and clarity over unnecessary complexity. When starting, many analysts tend to overload dashboards with too many visuals and excessive colors, which can lead to confusion.
• Nisar recommends a focused approach with no more than four to five visuals on a dashboard and emphasizes the value of white space and color usage. Color should be applied purposefully, using traditional color associations where appropriate (e.g., red for negative, green for positive).
• The types of charts used must align with the specific story being told by the data, advising analysts to learn from the best practices demonstrated by others in tools like Tableau.

"There’s no focus; you need a focus. White space is good; only use color if there’s a reason to use it."

Handling Data Privacy in Healthcare

• There are unique challenges of handling data in the healthcare industry, particularly in relation to privacy concerns such as HIPAA regulations. The overwhelming amount of data can be both a challenge and an advantage.
• Data cleaning is critical. Nisar notes that raw data often requires substantial cleansing before use. This type of work can be labor-intensive, and although he doesn't personally handle all aspects of data cleaning, he understands its significance in maintaining data integrity.
• The role of data governance in ensuring compliance is paramount and he only works with data that has already been deemed appropriate for use by CVS.

"Privacy issues are always there when it comes to healthcare; you have to be very careful."

The Role of Data Analysts in Fraud Prevention

• Working on the insurance side of CVS, Ahmed discusses his involvement in developing dashboards for detecting fraud, waste, and abuse. He highlights the proactive role data analysts can play in identifying patterns and potentially preventing fraudulent activities before they occur.
• He notes that understanding trends in fraud and recognizing the types of fraud specific to certain geographical regions or specialties allows organizations to forecast when fraud may occur.
• By adopting a predictive approach, data analysts can help organizations save costs and mitigate risks associated with fraud.

"What we try to do when it comes to fraud is to forecast or predict when it's going to happen based on certain patterns."

Understanding the Role of a Lead Decision Scientist

• The concept of a "Lead Decision Scientist" can be confusing, as the title might imply skills associated with decision-making as well as scientific or statistical expertise. This often leads to a conflation of roles, where many might default to thinking of a statistician or a management-focused professional.
• Nisar clarifies that his role involves analytics rather than data science, distinguishing himself from traditional data scientists. He produces dashboards and analyses aimed at supporting decision makers, which his title aims to represent, although it lacks specificity.
• The title change from "Analytical Consultant" to "Lead Decision Scientist" also reflects an effort to redefine roles in analytics, aiming for clarity in internal functions rather than consultant perceptions which can suggest externality.

"The title in itself does not describe what I do."

The Importance of Job Descriptions Over Titles

• Nisar emphasizes that job titles can be vague and misleading, making it vital for candidates to focus on the job description and the key technical skills required instead of just the title. For instance, the role of a data analyst could be labeled variously, but the competencies needed often remain similar.
• He advises job seekers to recognize that descriptions may include many skills but to focus on specializing in a few key areas instead, especially in SQL, business intelligence tools (like Tableau), and Excel, which still play a significant role in data analytics.
• Furthermore, he shares insights on avoiding the pitfalls of just delivering outputs, stressing the importance of understanding stakeholder needs upfront to provide effective solutions.

"When you're applying for jobs, don't look at the title, look at the description."

Networking and Soft Skills for Career Advancement

• In a competitive job market, networking is crucial not just for job opportunities but for personal and professional growth. Nisar highlights that analysts should continue networking even after securing a position, as it enriches their experience and keeps them connected.
• He identifies soft skills, particularly communication, as extremely important for data analysts to differentiate themselves. While technical knowledge may be common, the ability to clearly convey insights and findings sets individuals apart in the industry.
• Nisar underscores the idea that being able to showcase one’s work effectively is as critical as the work itself, which encompasses both art and science.

"The soft skills, the communication—if you focus on that, it will set you apart from others."

The Importance of Business Understanding for Analysts

• Nisar Ahmed emphasizes the necessity for analysts to be inquisitive and to continually learn about the business they are part of. This foundational knowledge is especially crucial in fields like healthcare, where understanding stakeholders and industry language is vital for effective communication and decision-making.
• He recalls that, upon entering the healthcare sector, he had to quickly learn who the key stakeholders were and how to relate to them. Without this understanding, he felt his technical expertise would be undervalued.

"If you don't speak the same language, they won't listen to your message."

The Limitations of AI in Storytelling

• Nisar reflects on the current limitations of artificial intelligence in narrative creation. While AI tools like ChatGPT can provide useful insights and answers, he believes they lack the nuanced storytelling ability that human analysts possess. He stresses the need for analysts to edit and refine AI-generated content before sharing it with end-users.
• He sees AI as a tool that can enhance the efficiency of analysis but asserts that it cannot replace the human element of crafting a compelling story out of data insights.

"I would never take those answers word for word and then provide those to my end users."

The Comparison of Podcasting and Video Tutorials

• Nisar discusses his experiences with creating tutorials on YouTube versus hosting a podcast. He finds that developing comprehensive training materials requires significantly more preparation and editing compared to the relatively spontaneous format of podcasting, which he describes as a more conversational approach.
• Nevertheless, he appreciates both mediums for their unique contributions to sharing knowledge, reiterating that the effort required can vary dramatically between them.

"The training is where you need to really put more effort into it."

Happy Learning!

Andrew C. Madson and Michael Madson

Founders, Insights x Design