Maximizing Business Value through Effective Data Management: A Vasco Point of View


Are you struggling to realize the full value of your company's data?  You are not alone. This is why we have focused our efforts to get a grip on data and unlock more value with effective data management. 

The Importance of Data Management in Modern Business Operations

Data plays an ever more important role in modern business. Organizations with strong data capacities can use data both commercially and operationally:


  • Finding opportunities for new product/market combinations
  • Determining profitability of specific products/services or specific customer segments
  • Determining where to invest and where to save
  • Developing new services by making data an asset, for example offering analysis to customers, providing advanced selfcare, increasing customer flexibility


  • Automating processes (which is only possible if data is reliable), making operation faster, more efficient, and more predictable
  • Improving customer service, by always having the right and actual data available
  • Improving quality by consistent data- and consistent process-reporting

Top performing organizations are actively pursuing one or more of the above goals, however, are frequently limited by poor data quality and availability. For example, an organization may want to improve their customer self-service options, but data in the back-end systems is too scattered, ill formatted, does not have the right structure and contains errors. This needs to be solved before any kind of self-service can be implemented.

Data Management: The Vasco Approach

Looking at data management purely as an IT responsibility might be tempting but will never be successful. Good data management requires an approach that consists of 4 pillars: 

  • Organization
  • Processes
  • Culture
  • IT

Data management should always be a business responsibility. The entire organization needs to be aware of the target situation and the importance of data management for the company. If not, attempts in one area will be nullified by setbacks in another, and the attempts to improve will turn out to be frustrating and take much longer than predicted.

This is why any data management ambition will start with aligning very precisely on the strategic goals that the organization is aiming for, a proper assessment of the current situation and a clear joint roadmap on how to get there. Again, this roadmap should not be an IT roadmap, but a business roadmap that needs to be centrally managed and monitored, supported by ownership from all involved departments: A business roadmap includes not only the commercial part of an organization, but also operational departments e.g., Customer Support, Delivery, Logistics.

Common Challenges

So, what are the most common challenges that we see in organizations that want to make their data work for them? Although there are large differences between one organization and another, the following groups are often found:

  • Data is spread over different IT systems with limited integration, resulting in data that is inconsistent, and a 360 view is hard to achieve.
  • The same objects have a different data structure or typology in different parts of the organization, making it very complex to create a combined truth.
  • The data that is stored was never intended to be used in a different way than its initial purpose. For example: It lacks certain structured fields to create proper insights or categories, or it contains internal comments, making it unsuitable to publish online.
  • Departments use the data purely for their own processes and did not care about proper registration at the time, resulting in an unclear administration of actual assets, contracts, etc.
  • There are no clear definitions of measured entities (for example: Customer, Order, Contract), resulting in conflicting business reports on the same subject.

Implications of poor data management

If the data landscape is not in a good shape this has quite significant implications, to name a few:

  • Blocking innovation and thus limiting the agility of the organization
  • Missed commercial opportunities
  • Reduced customer quality
  • Missed revenue
  • High operational cost and limited options to improve
  • Incomplete insights and control over data security and data compliancy

If an organization is profitable, these issues may sometimes be taken for granted and accepted: it’s important but not urgent (yet). However, once the market changes, the organization will not be able to respond swiftly and in time. Especially in the current volatile rapidly changing markets, agility and insights are key elements for an organization to survive and grow.

Key Components of Effective Data Management
(What do we at Vasco see with our clients?)

Data Governance

As mentioned, setting up effective data management is a responsibility for the whole organization. And it’s not a one-time project but a continuous way of working that should be anchored in clear structures:

  • Policies and Procedures: Establishing clear policies and procedures for data management, including data access, usage, and security protocols.
  • Data Ownership & Stewardship: Assigning roles and responsibilities to individuals or teams responsible for overseeing data quality, security, and compliance.
  • Cultural Alignment: Create awareness, belief, and commitment at critical points in the organization to make the change work.
  • Compliance Framework: Implementing a framework to ensure adherence to regulatory requirements and industry standards.

Although data processing is by definition happening within departments, some parts of the governance need to be centralized, for example registering which department/team has ownership of which data and which compliance rules apply (e.g., data retention and data privacy regulations). 

Setting up such a structure can mean assigning new roles to existing people in the organization, sometimes part-time. Especially when using part-time roles, it is crucial that enough time is reserved to execute the new responsibilities and measures are implemented to keep everyone connected and involved, for example by creating (virtual) teams that meet up to share progress and issues.

Data Quality Management

Data governance is only useful with clear targets. So, this means defining what good data looks like and setting goals for the data quality levels. The required data quality level can differ per object or attribute, depending on its role and relevance to the organization’s goals. 

Implementing data quality management consists of the following steps:

  • Data Standardization: Establishing standards and guidelines for data formatting, naming conventions, and classification to maintain consistency. This is also referred to as meta data management. Without such definitions it is impossible to improve because there is no aligned way of working and different people may have different opinions on what the data in each system should look like.
  • Data Profiling: Analyzing and assessing the quality of data to identify inconsistencies, errors, and redundancies. It can help to create data quality dashboards to monitor whether quality rates are improving towards the agreed target quality level.
  • Data Cleansing: Implementing processes and tools to correct errors, remove duplicates, and enhance data accuracy.
  • Process enhancements: For each data quality issue that is found, you need to find out where in the process the data quality issue arises and adjust the involved process to prevent this from happening in the future.
  • IT adjustments: For example, implementing system validations that prevent data errors, implementing structured data fields or implementing integrations between IT systems.

The above steps can be seen as sequential, but in reality, it will be more like continuous improvement. Once the quick wins are solved, more specific issues come up and will need to be tackled as well. The data stewards play a key role in this process as they know their data the best and know which processes or exceptions cause the issues and what is needed to solve those. 

Data Integration and Interoperability

Having better data quality does not necessarily mean having better insights. The data architecture in an organization can be complex: Different IT systems may each contain parts of the puzzle. If IT systems are not integrated, this will make it very hard to reach company wide data quality, because the same object (for example Customer) may have different attribute values in different systems.

It is always preferred to have a level of system integration that ensures that each data element only has one master location, and all other occurrences are (automatically) synchronized with the value of that master. However, depending on the situation, this is not always achievable (for example because of costs and/or time). In these cases, decisions need to be made if and when such integrations are being developed (roadmap).

Even when systems are integrated, the data needed for specific insights may still be scattered across multiple IT systems, as each IT system has its own role. In that case a central data platform or data warehouse can help in making the various data sets available and allowing for reports or dashboards that combine data from different sources. Many organizations have some sort of data warehouse, but there is always a choice to make on how many systems and objects from these systems to include. This of course depends on the specific needs and goals of the organization.

This results in the following components to set up a data architecture.

  • Data Integration Architecture: Designing a robust architecture to facilitate seamless integration of data from disparate sources and systems.
  • ETL (Extract, Transform, Load) Processes: Implementing ETL processes to extract data, transform it into a consistent format, and load it into target systems.
  • APIs and Middleware: Leveraging APIs and middleware solutions to enable interoperability between different applications and data sources.

Data Security and Compliance

Besides the commercial and operational aspects of data management, security and compliance are nowadays essential aspects that must also be taken into account. The impact of security and compliance has been rapidly growing over the last years. With the increase in the use of internet, with integrations across organizations, working from home, working with different devices, privacy regulations, cybercrime, etc., the complexity of this topic has also grown. The main elements to consider from a data perspective are: 

  • Access Control: Implementing role-based access control (RBAC) mechanisms to restrict access to sensitive data based on user roles and permissions.
  • Data Retention: Based on regulations certain data may only be stored for a certain period, or on the opposite, some data should at least be stored for a specific amount of time.
  • Data Encryption: Encrypting sensitive data both at rest and in transit to protect it from unauthorized access or interception.
  • Auditing and Monitoring: Establishing auditing and monitoring mechanisms to track data access, changes, and security incidents for compliance purposes.


Effective data management is crucial for almost every organization that wants to grow and survive in the current dynamic marketplace. It is a prerequisite for being able to innovate, increase profit and reduce costs, but also to ensure dealing with security and compliancy matters properly. It can unlock the full potential of the overwhelming amounts of data many organizations have.

Many organizations realize that their current state of data management is holding them back and plan to take actions to improve this. Implementing effective data management is something that should be done in a structured program approach. It needs to be supported by the complete organization, given its strategic importance and because data processing and data ownership is spread across all teams.

Vasco Consult helps organizations in realizing their data management ambitions, combining a business-oriented approach with its proven track record on implementing effective data management processes, together with their clients. The Vasco method starts with the Vasco Data Scan, in which the organization’s strategic goals and resulting data management targets are defined, the current situation is assessed and a roadmap for improvement is defined. Based on the result of the scan, the Grip on Data project can be started, in which Vasco and the client together realize the envisioned data management set up, including data governance, data quality management, data integration and data security and compliance.

Want to know more about data management and what Vasco Consult can do for your company? Get in contact by mailing to

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