Business Analytics Module 1.2: Challenges, Maturity, and Success Pillars

Course Announcements and Session Logistics

• Attendance Requirements: To record attendance for the week $16$ session, students must post an interesting trivia or fact based on data and research in the communication and discussions section. This must include a reference and the student's surname as the title.
  • Example Fact: Historically, only about $43\%$ of Mount Everest attempts above base camp reached the summit, but in recent commercial seasons, success rates for foreign climbers can reach around $70\%$ to $76\%$.
  • Deadline: This is available from $12:00\,AM$ to $11:59\,PM$ on May $16$. Failure to complete this results in an absence.
• LinkedIn Incentive: Students are encouraged to reach $30$ connections and have their accounts verified for a $+5$ point incentive on the first seat work. This is an optional incentive, not a requirement.
• Course Schedule: The syllabus is uploaded through Module $4$ for advanced reading. A face-to-face quiz is scheduled for the following week covering Module $1.1$ and $1.2$.

Internal Challenges of Business Analytics

• Weak Executive Sponsorship: This refers to the lack of active support from top leaders (CEOs, department heads, managers) who control budgets and resources.
  • Impact: Analytics projects may become "side projects" without authority. If leadership does not reinforce data usage, the team may create reports that no one uses seriously.
  • Example: An analytics team builds a sales dashboard, but managers continue making decisions based on instinct or personal preference because the culture does not mandate data usage.
• Failure to Align BA Goals Against Corporate Goals: Analytics should not be performed for its own sake or just because competitors have teams. Every initiative must support a real business objective.
  • Alignment Examples:
    • Customer Retention: Focus on customer churn, repeat purchases, complaints, and behavior.
    • Reducing Operating Costs: Focus on process inefficiencies, delays, and productivity savings.
  • The Mistake: Measuring what is easy to measure rather than what is important. Business Analytics (BA) must answer: "How does this help the organization achieve its goals?"
• Weak Alignment from Technology Support Function: Analytics depends on hardware, software (Excel, Power BI, Tableau, SQL, Google Looker Studio), and data pipelines.
  • Conflicts: Delays and frustration occur if the business team wants daily updates but the IT team has not automated the data pipeline or if data is stored in non-communicating silos.
  • Solution: Constant collaboration where the business team understands the problem and the technology team ensures data is accessible, accurate, secure, and scalable.
• Lack of Formal Data Governance: Data governance involves the rules, standards, and ownership of data. It addresses who can access data and how it is cleaned or defined.
  • Metric Inconsistency: Without governance, different departments define metrics differently.
    • Example: Finance may define an "active customer" as someone who purchased in the last $30$ days, while Marketing defines it by the last $6$ months.
  • Consequence: Presenting conflicting numbers to management creates confusion and mistrust, leading leaders to revert to manual intuition.
• Weak Alignment of Analytical Resources: Resources include people (analysts), tools (Python, SQL), data (the "new gold"), budget, time, and skills.
  • Operational Issues: Highly skilled analysts often waste a large chunk of time on low-value tasks like manual Excel preparation instead of analyzing trends or recommending actions.
  • Organizational Issues: Different departments might hire separate analysts who work in silos, producing conflicting outputs without a standard approach.

External Challenges of Business Analytics

• Business Environment: Changes in economic conditions (e.g., inflation), new government regulations (e.g., Data Privacy Laws in the Philippines), and social trends affect how data is used.
• Competition: Competitors may use analytics more effectively to price products better, market efficiently, or serve customers faster. Organizations must remain at par to maintain a competitive edge.
• Customers: Customer preferences and behaviors evolve constantly. A strategy from $2$ years ago may no longer work today. Analytics acts as a monitor to detect these patterns and respond to external pressures.

Stages of Analytical Maturity

• Stage 1: Analytically Impaired: The organization has some data and management interest, but data usage is optional and unorganized.
  • Sari-sari Store Example: A small store manually recording prepaid load numbers in a notebook. While they collect data, they may not use it to adjust inventory unless they actively look for patterns (e.g., needing $15,000$ pesos of load inventory on Fridays instead of the usual $10,000$).
• Stage 2: Localized Analytics (The Terminal Stage): Functional silos exist where some departments (like Marketing) use advanced tools (Power BI) while others (like Finance) use manual Excel processes. There is no organization-wide uniformity, and efforts often stall here.
• Stage 3: Analytical Aspiration: Executives commit to building analytical capability by setting a timetable and aligning resources. This is considered the "baby" or "toddler" stage of maturity.
  • Challenges: The company is hiring analysts and buying software but still struggles with data quality, governance, system integration, and adoption.
• Stage 4: Analytical Companies: Data is regularly embedded in performance monitoring and operations. There is strong collaboration between business and analytics teams and clear metrics across the company.
  • Example: A retail company uses data to track inventory, predict demand, and measure customer loyalty as a standard operating procedure.
• Stage 5: Analytical Competitor: Analytics is a source of competitive advantage and part of the company's identity. They use advanced systems like Artificial Intelligence, predictive models, and real-time data.
  • Netflix Example: Every account is personalized based on interests (e.g., Anime vs. Action). By using data to recommend content and optimize pricing, Netflix ensures users stay "hooked" on their platform over competitors like HBO or Hulu.

Knowledge Check: Maturity Scenarios

• Scenario 1: A store using only a notebook for record-keeping is Stage 1 (Analytically Impaired).
• Scenario 2: WWW Gadgets using platform-based reports from Shopee and TikTok separately is Stage 2 (Localized Analytics) because data is limited to specific channels and not integrated.
• Scenario 3: PH Bank investing in software for $5$ years and hiring regional staff for data processing is Stage 3 (Analytical Aspiration) because they are actively building but still progressing toward maturity.
• Scenario 4: A company where analytics directly influences product strategy and helps them lead the market is Stage 5 (Analytical Competitor).

Seven Pillars of Business Analytics Success

1. Business Challenges: Every project must start with a clear problem (e.g., reducing customer churn, detecting fraud, or improving student performance). A project without a purpose results in unclear output.
2. Data Foundation: Data must be accurate, complete, consistent, timely, and relevant. "Garbage in, garbage out" means poor data leads to poor insights and ineffective recommendations.
3. Analytics Implementation: Choosing the right tool (Excel, Power BI, Tableau, Machine Learning) for the specific problem. The goal is to use the "right" tool, not necessarily the most complex one.
4. Insight: Results show what happened (e.g., sales dropped $20\%$); insights explain why (e.g., sales dropped because delivery fees increased). Analysts must interpret the "why" for decision-makers.
5. Execution and Measurement: Analytics is only valuable if it leads to action.
   • Measurement: Checking if the recommendation worked using Key Performance Indicators (KPIs).
   • Class Metaphor: In the classroom, a KPI is the $70\%$ passing grade. Achieving $69\%$ results in failure.
6. Distributed Knowledge: Insights must be shared across the organization rather than trapped in silos. If Marketing finds a successful bundle, Sales and Operations must know so the whole organization becomes smarter.
7. Innovation: Mature analytics is proactive, not reactive. It helps discover new opportunities, such as personalized loan offers in banking or optimized real-time routes in logistics (e.g., Grab).

Individual Seat Work Assignment

• Task: Watch the assigned YouTube video and identify examples for each of the seven pillars implemented by the featured company ($10$ points per pillar).
• Elaboration Question: Do you think the company has been successful in its decision-making based on Business Analytics? ($30$ points).
• Verification: Students may be randomly selected to explain their submission during the next session. Failure to explain the content (e.g., if generated by AI) will result in a zero grade.
• Deadline: Friday at $11:59\,PM$.