Data management

The foundation of digital finance transformation

How to get my data under control

Data is the oil of the twentieth century, the raw material for new technologies that is revolutionising business processes and new digital products – and the finance function is no exception. Data is redefining the role of CFOs: they are becoming strategic advisers, sparring partners, inventors and catalysts of innovation and growth at corporate level. They are responsible for business-critical data that can be analysed instantaneously, allowing extensive forecasts and simulations which open up huge opportunities for the finance function of the future.

All of this needs to be based on an effective and efficient data management system. Today, all relevant information has to be made available in every process step at the right time, in the right place and at the required level of quality. How the data is handled is crucial for success. Innovation through new technologies requires new and improved data, better organized data, data with fast access and data of excellent quality. 

The PwC Data Excellence Framework is the blueprint on the way to the new data-driven finance function of the future:

In addition to the fundamental challenges to robust data management, there are also significant barriers to innovation that need to be overcome to enable further development of data management. Our Data Excellence Framework addresses this combination head-on, helping to build a data management system fit for the future.

Data management – a holistic approach

We at PwC can help you overcome these hurdles by pursuing holistic, structured approaches and solutions while also taking your individual requirements into account. Under our approach, data management covers all processes from generation and validation to protecting and processing your data, to ensure accessibility, reliability and timeliness.

Data management framework

Data strategy

Data strategy defines the vision and roadmap for data management transformation to increase corporate value and act as the engine for business strategy across the dimensions of people, processes, technology and culture.

Your data strategy needs to be aligned with your business strategy – which defines your priorities for data management – whilst also reflecting the relevant (mega) trends,  innovations and technological developments that are creating new data potential.The orientation and goals of the data strategy are defined by means of assessments and expert interviews. Once the key data-driven accelerators and challenges have been identified for driving your strategy forwards, the design phase can start: strategic vision, interim goals and action plans are defined for the components of your data management system based on the dimensions of people, processes, technology and culture. A roadmap is then prepared for operationalising these aims based on capacity, resources and forthcoming initiatives. KPIs that allow strategy adoption to be measured and incentivised are then created to ensure long-term sustainability.

Your benefits:

  • Targeted and coordinated methodology for executing a successful data management transformation
  • Transparency on the value contribution of your data management initiatives
  • Operationalisation and consistency of strategic measures, along with measurable KPIs on strategy adoption for long-term sustainability

Data architecture

Defining a holistic data architecture to provide a harmonized and consolidated view of your data

Before this can happen, a comprehensive strategy needs to be developed on how all data generated in your company can be centrally gathered, stored and made available for further analytical steps. This must take into account master and transactional data as well as structured and unstructured data.

The goal is a standardised, company-wide data model,which can, however, be flexibly adapted to local requirements of our customers if necessary.

Your benefits:

  • Single source of truth
  • Greater transparency with regard to data origin and data use
  • Extensibility with new data sources
  • Avoidance of redundant data

Data governance

Implementing an organisational structure with clear policies, roles and responsibilities for handling data

The focus here is on the concept of "data ownership". The people responsible for data within your company need to be clearly identified and given the knowledge and skills they need to carry out their role throughout the process chain. It’s also important to ensure data governance is firmly anchored within your organisation by setting up a single point of contact for all internal and external data-related questions. 

We also recommend defining company-wide standard processes and methods for handling data.

Your benefits:

  • Reduction of administrative efforts by defining clear responsibilities
  • Company-wide, standardized handling of data
  • Improvement of stakeholder awareness in dealing with data management issues

Data process management

Controlling, harmonising and optimising business and data management requirements throughout the data life cycle

We support you in establishing standardized data management processes including controls to ensure compliance with process requirements. In addition, we advise on the selection and introduction of tools regarding modelling, metadata management, process documentation and reporting in your company.

We implement necessary boards to dissolve functional data silos and work with you to build ad-hoc reporting processes in terms of addressee-oriented internal and external reporting. 

Your benefits:

  • Cost-efficient, synchronised end-to-end implementation of (ad-hoc) requests
  • Improved support for corporate management through comprehensible reporting and data quality
  • Continuous improvement of data management processes in global companies

Data quality

Creating an assessment framework for evaluating data quality

Together we define individual data quality criteria, taking into account all processes and IT systems, in order to derive a target level for data quality. We then ensure that quality is continuously cross-checked against this target during day-to-day operations, and implement a data quality reporting system. Based on this reporting system, we support you in definingmeasures to sustainably improve your data quality.

In addition, we can also support you with short-term quality improvements that may be necessary, for example, in the course of an S/4HANA transformation.

Your benefits:

  • Future avoidance of data quality deficiencies.
  • Freeing up resources, as fewer employees are required for error research and correction
  • Improved basis for decision-making
  • Automated data quality management

Data security and privacy

Implementation of security measures and monitoring processes

A crucial aspect in this area is the definition of access rights based on a business-oriented role concept to protect your data from unauthorized access. We support those responsible for data in protection needs analyses and advise you in the conflicting areas of regulatory framework conditions and competitive business models.

We also take care of IT security aspects such as creating backups, setting up firewalls and anti-virus software, and performing regular updates.

Your benefits:

  • Minimization of risks, e.g. due to data manipulation or failure
  • Compliance with regulatory requirements (HGB, BDSG, GDPR, etc.)
  • Security with regard to the permissibility of data transfer (internal and external)

“Data & analytics are becoming increasingly important. The key success factor today is effective data management. Harmonizing and presenting a wide variety of data sources in an end-to-end process will be the decisive difference that makes successful companies.”

Felix Blume,Senior Manager at PwC Germany

Our Use Cases – How to establish Data Management as a key success factor

Data Management

To successfully manage a business, data must be reliable, accessible and up-to-date. As the amount of data grows exponentially, it becomes increasingly difficult for companies to ensure that this is the case. Data is held in different systems, but also on local databases of individual employees and is hard to combine with each other.

PwC can support companies in creating a professional concept for future-proof data management. We not only define process-related measures, but also work together to develop an optimized target architecture that not only connects the different data sources, but also enables users to access the information and perform analyses independently.

As many manual processes are automated, you will gain data quality and save time that can then be used for more in-depth analysis. Make decisions based on data-driven insights!

Data Warehousing with SAP

Do you need a reliable data source from which you can extract data in real time and make it available for your reports with a high performance?

We can help you to establish an SAP data warehousing solution of to optimize your existing solution. From initialization of your systems to the implementation of data models, we support you from strategy to implementation.

In the area of SAP BW as well as in advanced HANA Data Modelling, a team of experts with years of experience will work with you to find the best solution for your business. Together with subject matter experts from our PwC network, we can develop and implement customized data model concepts for any industry.

If you already have an SAP BW system that has become outdated, we can discuss the various migration paths to the current releases with you and work together in order to achieve the best possible solution.

Additionally, we offer training courses for your employees, which we can tailor to your individual needs. From basic training up to SAP BW/4HANA update training, we offer on-site as well as online training, but we also accompany your employees directly and train them in their normal workday.

Data Virtualization

Data volumes are exponentially increasing. One reason for this is the increasing number of source systems and the simultaneously increasing granularity of the data. Sensor data, social media information, weather data, geodata, but also the classic information from the different systems of a company should be evaluated together and presented in reports.

With the possibility of data virtualization, this is no longer a matter of concern. Data can be combined and reported without being physically stored a second time. A data virtualization layer that can be seamlessly integrated into your IT architecture, combines all data sources and provides the single point of truth for your reports.

The advantage of many data virtualization tools is that they are also designed for the business department. Employees can thus request and use harmonized information in self-service. In addition, the centrally managed data in the virtualization layer can be enriched and extended with individual, local information (e.g. from Excel/CSV files).

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Nico Reichen

Nico Reichen

Partner and Data & Analytics Leader, PwC Germany

Andrea Meyn

Andrea Meyn

Director, Financial Services, PwC Germany

Andrea Bönigk

Andrea Bönigk

Senior Managerin, PwC Germany

Felix Blume

Felix Blume

Senior Manager, PwC Germany