For large organisations that collect data from multiple sources, with teams and departments spread across the world, managing one of the assets with the most potential—data—is the first step on their path to success.
Customer satisfaction, identifying new revenue streams and the drive for improving performance has always been at the heart of a successful business. In today’s business landscape, the foundation of that success is a well-functioning data operating model. This is extremely important since the most significant barriers businesses face in unlocking the value from data are organisational. 
A good data operating model helps to break through the organisational and technical silos within a business. It builds upon the business model and addresses how data is being treated across organisational processes, all the way from data collection, cleansing and enrichment to the sharing and use of data. Across these different phases of the data operating model, the business and technical architecture play an important role as well, yet many businesses struggle when trying to transition from legacy- systems to newer technology or complement existing systems.
Key data processes:
Data collection outlines in which parts of the business model data is being collected. Simply trying to improve existing processes might not always be enough when the company is facing new challenges. Therefore, to design the target data operating model and the transformation roadmap, it is crucial to know the current expectations-reality gap, company’s strategic goals, how the data will be measured, and how these insights will drive value.
Data cleansing and enrichment
Good data quality is the cornerstone of insightful data analysis and good decision-making. Therefore, it is helpful to outsource the tedious and slow process of manual data cleansing to technology, which deals large amounts of multiple types of data faster and more accurately.
Sometimes the data at hand is not enough to get a complete picture and enable strategic decision-making. This is when data enrichment, in other words improving the raw data by bringing in new information from other data sources, can help in keeping the data accurate and up to date.
Enriched data is especially useful for sales & marketing since it can result in better-targeted marketing campaigns, and improved customer experience. With a good data operating model, enriched data can directly benefit the business and its strategical decision-making in a holistic way. Enriched data can also be used for predictive analytics enabled by machine learning.
Together, data cleansing and data enrichment are an integral part of ensuring the quality of data. Both processes are also necessary when standardising relevant data for it to be shared and used efficiently for its intended purpose. This provides the company with the possibility to share data across different silos and departments.
Data sharing and use
Depending on the intentions of using data and the specific interests of parties sharing data it can take different forms:
- A reciprocal exchange of data
- One or more organisation providing data to one or more third parties
- Several organizations pooling information and making it available to each other or to other third parties
- One-off-disclosures of data in unexpected or emergency circumstances
- Different parts of the same organisation making data available to each other
When it comes to the data operating model and data sharing, the main two questions to consider are:
- How does the data operating model assist in creating value with the help of data?
- How does the data operating model ensure that data is shared safely, without sacrificing security and privacy?
Unlike other assets, the value of data doesn’t depreciate when it’s shared, and rather its value increases with use. An example of this is the added value of improved customer satisfaction when they get faster and more personalised services based on the data they have allowed to be used by the service provider. That is why you should look for opportunities to share your data across your business ecosystem!
Business and technical architecture
To maintain the company in good health, its strategy needs to be translated into business architecture. While the business architecture outlines the key business capabilities that are required within an organization, the technical architecture is its key subcomponent, showing technologies used to implement and operationalise the company’s strategy and requirements for data.
A modular architecture that supports flexible technologies speeds up development and highlights the use of common components. This makes it easier for the business architecture to be enhanced and revised with the help of adequate technology and experts who can identify key improvement areas. Moreover, even within one organisation, people often require different views of the available data and information. This is when modelling tools such as Archimate can be helpful and provide different viewpoints from the business architecture, but with a common framework, to ensure consistency of data within business domains.
A well-functioning data operating model
With a good data operating model, making sense of data becomes easier and quicker. Having a clear sense of data flows, the stakeholders, and the technologies involved in each step of the data life cycle makes it much easier to ensure good practices in data governance and data security while freeing time for more strategic tasks like business analysis and decision-making.
When an organisation is more aware and transparent about its data operating model, good governance and security measures become less complex.
But with great data comes great responsibility. Your business should know when to share its data, but also when to provide lock-down security.
In the context of big data value cycles, the organisations exchange data mainly through contractual data-sharing agreements (DSAs). They often require the assessment of multiple, multi-layer agreements. The parties of DSA are bound to comply with requirements at two levels: obligatory rules arising from the regional or national laws, like GDPR, and contract-specific T&Cs.
The enhanced accuracy and consistency of data collected can be of sensitive nature, which is why some types of data enrichment websites providing additional information on individuals cannot operate in Europe, while they can be fully accessible to anyone in the USA.
Get a health check for your business!
Many businesses have several ongoing initiatives that aim to improve business performance. The danger is, these remain uncoordinated efforts inside different departments.
A good data operating model helps to break through these organisational and technical silos within a business. On the contrary, a poor data operating model can compromise decision making, generate unnecessary costs and compromise reporting and compliance. As easy as it might be to think you have time to deal with things later, it is always better to anticipate the future. The message is clear: if you want a health-check for your business then investing in a well-functioning data operating model should be one of your top priorities!
 The Age of Analytics: Competing in a data-driven world. McKinsey Global Institute in collaboration with McKinsey analytics. December 2016. https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/The%20age%20of%20analytics%20Competing%20in%20a%20data%20driven%20world/MGI-The-Age-of-Analytics-Full-report.ashx