Development Path: From Data to Decisions – Data Analysis Path for Business

Who is this series for?

• For people whose daily work involves (or should involve) data on a regular basis.
• For those who feel they are drowning in an excess of reports and digital chaos that leads to no decisions.

What do we (consciously) NOT do during this Path?

• We do not teach statistics and AI model programming.
• We do not focus on clicking around in tools, because software changes quickly, while the logic of discovering the truth remains.
• We do not build dashboards just to have them.
• We do not implement AI solutions in isolation from the broader context of the company and the data workflow.

What are the business benefits for your organization?

Completing the entire Path allows organizations to achieve:

• Significantly shorter decision-making time.
• A decrease in useless reports in favor of valuable recommendations.
• The development of consistent data definitions that everyone understands the same way.
• An increase in the practical use of data within the company.
• Real and fully secure implementation of AI tools.
• Massive time savings for both analytical and business teams.

Module 1: Fundamentals of business process design

Main goal: The course teaches "translating" between business chaos and the world of numbers, based on the assumption that data without a question is just a cost.

Outcomes for the participant: You will stop creating "just in case" analyses, learn to distinguish reporting from analysis, and start conducting analyses that directly support business decisions.

Real-life success story: A sales team shortened their report from over a dozen metrics to just three key ones (analyzed for root causes), which meant that meetings started ending with decisions, not just a review of slides.

Content scope:

• The role of analytical thinking in an organization and the boundary between analysis and reporting.
• Formulating a business problem and identifying the decisions behind it using the "So what?" test.
• Building precise analytical questions and avoiding the most common mistakes.
• Distinguishing mere information from an "insight" (conclusion) and getting to the real root causes.
• Hygiene of analytical work, including mastering fast decision-making heuristics.

Module 2: From data to conclusions (Analysis and interpretation in practice)

Main goal: Transitioning from a raw set of numbers and digital chaos to the "Aha!" moment where you can see a story worth telling in the numbers.

Outcomes for the participant: You will gain the ability to explore data, connect it, design readable visualizations, and communicate conclusions in the form of specific recommendations.

Real-life success story: An operations team narrowed their analysis to a single question about the biggest delays and identified one bottleneck, allowing them to reduce process execution time without increasing resources.

Content scope:

• Data detox: preparing data for analysis, evaluating its quality, and cleaning it.
• Connecting the dots: integrating data from various sources and ensuring the consistency of metric definitions in the company.
• Data exploration, including searching for patterns, anomalies, and root cause analysis.
• Designing visualizations that do not lie and genuinely support decisions (avoiding so-called "pretty dashboards").
• Data storytelling, guiding the audience straight from data to accurate recommendations.

Module 3: AI as a partner in data analysis

Main goal: Understanding in which areas Artificial Intelligence genuinely helps in analysis, and where it is merely an expensive gadget, in order to do more, faster, and smarter.

Working with tools: Utilizing, among others, BI tools (Power BI, Tableau), Excel / Google Sheets with automations, AI tools (ChatGPT, Copilot, Gemini), and automation software (Zapier, Make).

Outcomes for the participant: You will speed up your work with data, automate repetitive processes, and learn to manage AI without losing control over quality and final decisions.

Real-life success story: A customer service team used AI to categorize thousands of reviews, which allowed them to identify product issues faster and shorten the company's response time – without the need to expand the team.

Content scope:

• Evaluating when manual work ceases to make sense and processes should be scaled or automated.
• AI as an assistant when working with unstructured data (analysis of reviews, emails, texts).
• Automation of reporting and data preparation.
• Quality control (Safety First), i.e., verification of AI results, validation, and mitigating the risk of so-called hallucinations.
• Smart implementation of AI in an organization and avoiding typical mistakes associated with it.

Marta Obłękowska (NobleProg)

• Role: Co-creator of the Development Path.
• Specialization: Designing strategic directions for team development, business transformation, IT courses, educational projects, and consultations.

Do you want to talk about the details of implementing the Development Path in your team?

• E-mail: marta@nobleprog.com
• Phone: +48 532 194 833

Anna Kotarba

• For 9 years, she has been successfully conducting courses on working with data.
• As a specialist, she helps companies turn data into accurate decisions by combining statistical analysis, data exploration, and predictive models to discover the most important business patterns.
• She works both at the stage of identifying "what is actually happening in the data?" and planning "what to do with it next?".
• She supports organizations in defining key variables, organizing data work, and implementing best practices.
• Privately, after working hours, she can most often be found traveling or hiking mountain trails.