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Data warehouse & BI

Ten years running and modernizing the SAP BI platform at Klabin, one of Brazil’s largest pulp-and-paper companies

Role: Business Intelligence Senior · Klabin S/A, São Paulo · 2008 – 2018 (transition to FP&A from 2016)

In one paragraph

Klabin runs the whole chain from forest to finished paper: it grows its own timber, produces pulp and paper in its mills, and sells to markets in Brazil and abroad. Every part of that chain generates data in SAP. For ten years I worked on the platform that turned that data into information for decisions, from building data warehouses for each business area to migrating the platform to new technology and keeping it secure, monitored and reliable.

The problem

Each business area needed reliable, timely data, but that data lived in different SAP modules and systems, each with its own rules. At the same time, the BI platform itself had to keep evolving: new SAP releases, growing data volumes, stricter security requirements and more demand for self-service reporting.

What I built

Platform modernization

  • Migration from SAP BW 3.5 to SAP BW 7.4 on HANA, moving the data warehouse to in-memory technology without interrupting the business.
  • One enterprise data warehouse: all of the company’s information integrated in one place, with data catalogs and aggregations, designed to grow with new business areas and data volumes.

Business areas

Data warehouses and reporting for the main areas of the company:

  • Finance: financial and managerial reporting, including automation of the managerial P&L.
  • Forestry: the timber operations that supply the mills.
  • Pulp and paper industry: production and operations in the mills.
  • Human resources: HR analytics on SAP HCM (see the talent-intelligence project below).
  • Across SAP: data from SAP ERP modules and other SAP solutions such as GRC, integrated into one platform, with business rules in ABAP.

Talent intelligence with machine learning (HR)

A new analytics area built on SAP HCM. We captured data on training, positions and other employee information, and used it to build a machine-learning engine that:

  • suggested the best candidates for open positions;
  • flagged positions at risk;
  • recommended training for each employee;
  • supported turnover reduction, improving the HR indicators.

Operations and governance

  • Security: the SAP BW security model, controlling who can see which data.
  • Backup structure for the BI platform.
  • Load monitoring and data housekeeping, so data arrived on time and the system stayed lean.
  • Better development and operations processes, making changes faster and safer to deliver.

Delivery and automation

  • Web and self-service reporting across the SAP BI front ends (BEx Web, SAP Portal, BusinessObjects and in-house web interfaces), bringing BI to users outside SAP.
  • Report automation, replacing manual report preparation.
  • ETL pipelines in Pentaho, Hive and Hadoop for data from outside SAP.

Team

  • Led BI project teams, with ownership of SLAs and data quality.

Impact

  • All of the company’s information integrated into one data warehouse, with data catalogs and aggregations ready for analysis.
  • Decisions moved from intuition and manual spreadsheets to analysis based on real, shared data.

Stack

SAP BW 7.4 on HANA · BEx · SAP Portal · BusinessObjects · ABAP · SAP ERP (ECC) · SAP GRC · Pentaho · Hadoop · Hive · machine learning

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