These days, data is increasingly regarded as a strategic business asset, but is your data actually being managed and handled as a strategic asset?
In the past, the emphasis was primarily on strict control of data quality and processing, as required by regulations. These compliance-driven control measures have long since ceased to be the primary objective. In today’s world, you want to derive maximum value from the data you access or own, or you want to optimise business processes based on objective analyses.
It may be that your colleagues are spending time building complex analysis models or dashboards, but you do not know how reliable the insights they generate are or how you can maximise their value. Or perhaps you are looking for the data organisation structure that best suits your company and your employees.
Where do I start and what is my end point in terms of data control?
You can find a lot of information online to confirm the importance of data control. But how and where should you start? We help our customers structure their data strategies, from an accurate picture of their current situation to their long-term objectives.
When drawing up a strategy or roadmap of this kind, we start with an analysis of your existing organisation and the level of maturity in your control and use of data. We use standard measuring instruments to evaluate your business – for example, the better practice KPMG frameworks, or global standards like CMMI’s DMM or the DAMA DMBoK.
After we have established a picture of the current maturity of your control and use of data, we focus on your strategic objectives in terms of data; do you need to comply with particular regulations or do you want to optimise the value of data? Do you have problems with data quality or is the issue that your business units speak very different languages (or data languages)?
What is your company’s level of data maturity?
How data-driven is your business actually? What is your level of data maturity compared to your peers? We help you answer those questions with the help of the KPMG Data Maturity Assessment. Contact our specialists to request an Assessment. You will find contact details at the bottom of the page.
Wat hebben we nodig om een datagedreven organisatie te worden? Hoe staan we ervoor in het kader van datagerelateerde wet- en regelgeving? Staan data en digitalisering wel op de agenda van de directie? Het is lastig om te beoordelen in hoeverre een organisatie bezig is met de juiste datamanagementactiviteiten. Advanced Data Management helpt daarbij.
Configuring a successful data management organisation
Do you have a good idea of where you are headed as a company in terms of data use and management, but are you still finding it hard to find the right people within the organisation? Do your employees take to little or no ownership of the data and does the quality of data leave a lot to be desired? Or do you have a mature data management team that is nevertheless not working in accordance with standardised processes and agreements?
Thanks to our years of experience in organising data management, we have a large collection of data organisation templates which we can apply to your business. Do you work for a start-up with ten people or a major international player with 10,000 employees? We have experience at both levels, and with every type of business in between.
We also have a database of processes and associated configurations for various common applications within our Powered solution. This makes adopting the better practice Powered model even more straightforward.
If you are looking for help designing processes, roles and responsibilities for working with data within your organisation, or if you need help overcoming internal resistance to implementing those processes, KPMG is the partner for you.
A condition for digitisation is control of structured and unstructured data. The following questions are important in this regard: what data do we need to control, how should we configure our organisation accordingly, what is the most critical data and what do we need to do in order to improve data management? Data governance is centred on policy, management and a broad vision and approach to data management within the organisation.
However, in many organisations, the quality of data is still the cause of disruption to processes. Rectifying errors in data retrospectively takes a lot of time and effort. It is therefore important for the effectiveness of business operations that data should meet the specified quality requirements. We offer solutions for setting data quality rules and controlling data quality and data definitions.
Controlling (master) data in business-critical processes
Do you face a challenge in terms of low data quality, is your data incomplete or incorrect, or are you launching a migration between systems? Are you looking for help modelling your data or harmonising master data within your business?
KPMG offers an attractive solution for operational data management in the form of solutions that can speed up processes, such as common business rules, common quality rules and even, for firms in particular sectors, standard data definitions (ontology).
Transferring data from the ‘old’ system to the ‘new’ one is a very complex process. In order to avoid surprises during the migration, we will develop and implement a migration strategy, perform mapping between legacy systems and recipient systems and develop and implement a control framework.
In an IT landscape in which multiple (source) systems make use of the same master data, controlling that data can often be a challenge for businesses. With a structured and proven master data management approach, KPMG delivers a long-term solution for managing and controlling your master data and assuring the reliability of management information.
The goal of Document & Content Management is to save, manage or destroy data such as drawings, e-mails, documents, lists or physical files in a structured manner. This gives you more control over legal liability. We provide support in developing customer-specific meta-data models that bring structure to the unstructured data landscape and implement tooling in order to manage unstructured data.
Accelerating data management initiatives with the right tooling
Speeding up a digital transition often takes more than just good processes, agreements and a division of roles. The desire for reliable insights leads to a big emphasis on control of analytical environments. To this end, we work with numerous suppliers of data management solutions for measuring and improving data quality, supporting data governance processes, recording data flows (data lineage) and increasing the findability of datasets and attributes in data catalogues.
At KPMG, we have created a solution suite for this purpose, called KPMG SOFY. This suite contains many of the basic components for controlling data and provides an excellent starting point for supporting the control of your data.
For this reason, it is important to define the data architecture, where various data objects are located in the system landscape and what the relationships between those data objects are. Designing, improving, documenting and maintaining the data architecture is becoming ever more important for businesses. This is partly the result of an increased desire to access data, integrate data sources and exchange data with external parties and legal requirements for insight into data flows within the organisation.
These platforms are usually designed to quickly deliver value for employees by providing access to a lot of data, performing rapid analyses and enabling them to answer ad hoc questions. In time, these initiatives often come up against challenges in terms of control. It proves impossible to find data, carry out analyses and compare results. KPMG has developed a framework to exercise greater control over data platforms in which repeatable reports, ad hoc analyses, data exploration and experiments are performed.
Do you have questions about Enterprise Data Management or do you want to know more about how we can help you? Then contact our specialists.