Domenico Ambrisi explains how UniCredit Group has approached the challenge of creating a centralised data warehouse.
For a multinational company, the ability to effectively manage complexity is an opportunity to create value. This means keeping tight measurement and control of performance in a consistent and timely way; to strengthen business and IT collaboration and manage core systems.
To achieve the economies of scale required, this also requires taking particular care of global customer knowledge, product and process harmonisation, corporate governance and standardisation of policies and legal guidelines.
The approach taken by UniCredit Group to properly manage this complexity was to form the UniCredit Global Enterprise Services Program, a group of projects to take place over several years, with a high level of commitment to organisation, processes and architecture. The objective for UniCredit was to achieve a group data governance vision, supported by an integrated and centralised system, to be shared by the company's composite legal entities.
The programme's remit was to redesign the overall CFO information management process at all strategic levels within the group, ensuring coordination and coherence of all finance function projects.
One of the fundamental functions born of that process is the UniCredit DataWareHouse (DWH), a common asset available across the group. It delivers a high level of accuracy in a controlled and certified financial environment with the goal of improving the quality of data used for risk and financial reporting.
At legal entity level, the design and implementation of DWH across the group provides a reconciled data repository for all CFO-related functions. Standardisation and common data quality processes represent financial events and offer a consistent view of metrics and KPIs generated from standardised data sources, using common rules of calculation.
At group and divisional level the programme includes MIS model supports all group divisions, regions and holdings. This model covers profitability, cost and risk data at different levels of the organisation in detail. The necessary data are made available directly from DWH.
At holding company level there is a choice of platforms for the consolidation of accounting, management and profitability.
Challenges and enablers
Implementing UniCredit's DWH project proved highly complex due to initial differences in business requirements across departments, divisions and even countries. Terminology needed to be standardised and existing IT projects reconciled with the DWH project.
There were others challenges, mainly related to the company having different CFO governance models in different divisions, legal entities and regions, leading to an inadequate data management process, different supporting IT applications, and CRO calculation processes not fully integrated with finance function architectures.
These problems were solved by introducing common data governance rules and operating processes based on two key enabling factors: people and processes.
People and processes
One of the reasons for the project's success was providing synergies across the countries and business and IT working groups to generate a common view of UniCredit teams. The resulting data governance process is based on group policies and works according to a specific process that has its foundation in the following functions:
- The DW Steering Committee. This reviews and prioritises projects/ROI, provides funding and interacts directly with management.
- The DW Advocacy Team. This solves cross-functional issues, activates the relevant processes and provides management with the pertinent information.
- The Business Advisory Team. This produces official business requirements both horizontally and vertically.
- The DW Governance Team. This produces the official solution requirements and assesses the data quality and coherence.
To maximise the effects of DWH, a structured data management process was developed based on group policies. This cross-functional approach resulted in centralised processes with dedicated organisational units responsible for specific data in terms of quality, calculation rules, utilisation rules and IT solutions.
The objective of this approach has been the homogenisation and usability of the same data from all functions across the business.