Why Your Data Team Is Drowning in Requests—and How OLAP Can Save You

Problem Causes

1. Mindset

In daily operations, product managers rely heavily on data, often asking engineers for data directly and expecting a data secretary, which limits engineers' growth.

2. Requirement Approach

When engineers cannot act as data secretaries, operators submit data requests that start from a problem, leading to a loop of incremental requests for more fields.

Data requirements differ from functional requirements; they are numerous and fast‑changing, requiring a product manager to design data products, otherwise engineers fall into endless request traps.

3. Lack of Tools

Operators lack proper tools; existing reports are fragmented, each covering only a few fields, leading to endless new reports and long engineering schedules.

If reports aren't multi‑dimensional, the combination of dimensions and metrics becomes infinite, making it impossible to satisfy ever‑changing operational needs.

Wrong Directions in Data Construction

1. Purely Requirement‑Driven Development

Without a data product manager, engineers build a report for each request, stacking logic in RPT layers, which traps them in endless iterations.

2. No Modeling Concept

Missing dimensional modeling leads to ad‑hoc DWD/DWS/DIM tables with poor reusability, turning engineers into “SQL boys”.

3. No OLAP Concept

Poor abstraction of subject and data domains results in many scattered tables; the following diagram illustrates the conventional method.

Figure 1: Conventional data construction method.

4. “Mining” Behavior

Extracting only needed fields per request leads to fragmented tables and wasted effort.

5. SQL Monopoly

Engineers hoard personal SQL scripts, preventing shared maintenance and causing knowledge silos.

Solutions

Administrative methods like priority scheduling and value filtering only partially alleviate the issue; a real solution requires a dimensional‑model‑based data system and an OLAP multi‑dimensional reporting layer.

Figure 2: Method to handle massive data requests.

Without a solid data model, tools and knowledge empowerment are ineffective.

Building a robust dimensional model and OLAP reports solves most data retrieval problems; additional fact tables handle the rest.

OLAP Multi‑Dimensional Reporting System

As shown in Figure 3, the OLAP reports cover over 80% of data needs; the remaining 20% are addressed by specialized fact tables such as retention and attribution tables.

Engineers maintain a single OLAP table, while analysts can query ClickHouse directly for rapid results, achieving seconds‑level response times.

Report Usage

Dimensions: all common dimensions for the subject, often hundreds of fields, dragged to the right pane.

Metrics: core indicators, typically over 50 fields, also dragged to the right pane.

Filters: required partition fields like date and data flag; users drag dimensions and metrics into the filter area.

Report Construction

The OLAP system is built on a data warehouse; the core work beyond architecture design is dimensional modeling, which requires experienced model designers or data architects.

Knowledge Empowerment

A four‑course curriculum is proposed, covering data access, acquisition methods, knowledge acquisition, and event‑tracking, plus detailed reporting and SQL training.

Master data usage and business growth shortcuts

Build your own reporting system

Write SQL to extract data yourself

Understand business metrics and their meanings

Courses should be recorded as short video clips for easy consumption, allowing newcomers to learn at their own pace.