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Data governance: the 7 challenges of an innovation machine

Data governance the 7 challenges of an innovation machine.jpg
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1.    Involving the business lines to motivate them

Customer data, industrial and business information, financial data, etc. In an organization, there are many types of data, and they are constantly increasing with digital technology. What they all have in common is that they are the property of a business function or a support function. Historically, some data has been shared between functions (customer data between marketing and sales, product data between research and production, financial data between sales and finance, etc.), but today it is the complete cross-functionality of the organization that is able to create value through innovation.

However, can this transversality be decreed? Yes, partly, with regulations (and the associated fines) that impose global data management as part of a compliance obligation. This is the case, for example, with the GDPR, for which the company must prove its control and good management of all personal data processed within the organization.

But to be a real source of innovation, this transversality must be acquired: the business units must understand how sharing their data with the other stakeholders in the organization can bring them benefits, and therefore motivate them. The use of artificial intelligence (AI) provides a good example. By pooling all data for global governance, all business units can benefit from the disruptive innovations offered by AI: detection of weak signals, fine-tuned understanding of customer behavior, etc. based on stable, reliable, and controlled data.

If the proximity of the data science team to the business can be additional motivation, an evangelization and onboarding phase is essential. This is one of the roles of the CDO (Chief Data Officer), who is there to organize knowledge, promote exchanges, and ensure data reliability and compliance. He or she is also there to involve each data "owner" in the process. In short, to build this shared data culture, the CDO will have to find business sponsors - including at the board level - and set up a charter for the Data Governance approach, then organize the information, communication, and induction process for the various data owners.

 

2. Reconciling the technical nature of data governance with business needs

Data governance aims to know and catalog all the data of an organization, to evaluate and improve its quality and conformity, to provide it to the stakeholders who ensure the smooth running of the company. This is an extremely technical concept - data being an IT asset - whereas the priority is to respond to business needs and challenges. And it is this need, this concrete use case that must remain the starting point of any project: for example, detecting future customers with an appetite for a particular product, detecting the risks of customers leaving, etc.

Based on a defined use case, the business units, together with the data scientists, will select the most relevant business concepts and data dimensions. This "data shopping" is done first via the business glossary (concepts and related elements) and then via the data catalog, which is the concrete image of the data in the real systems (applications) - and therefore the data sources to be reused according to the quality, validity, freshness of the data, etc. This technical part is obviously essential and becomes more easily accessible to all actors via the business glossary.

MEGA HOPEX for Data Intelligence.jpg

3. Modeling the data life cycle

Data is not static; it has a lifespan. Therefore, a simple mapping is not enough: data governance requires modeling the entire data life cycle: creation, use, reuse, obsolescence, and destruction (GDPR). Objective: to link business process modeling and data governance, to save time and understand business issues.

 

In this context, to accelerate data governance, it will be necessary to rely on the business processes already known in the company (data entry and use in the various departments), the systems using this data, and on the company's risk management procedures (control of personal data for example).

Thus, the business process models each of the activities of the business stakeholders to concretely conceptualize the data that will then be used in the organization. For example, when setting up a credit offer in a bank, the financial advisor enters data (ID card, salary, family situation, medical situation, etc.) used for the entire process.

 

4. Promoting quality to enhance the value of data

Controlling data also means controlling its quality. Because only good quality data at the beginning of the collection process guarantees the quality of the use case at the end. For example, a credit agreement or an insurance cost depends on the data collected and used at the beginning on a given customer. This is the only way to ensure the performance of the service provided to the customer, as well as innovation, within the framework of an industrialized (production), reproducible and agile process.

Data quality is already measured in many client IT systems, in a heterogeneous and compartmentalized way. The implementation of advanced data governance must enable the construction of a repository of control and quality rules. This will avoid duplicating controls, concentrate all available measures, complement them, and set up improvement plans for priority data.

 

5. Facilitating the integration of standards and regulations

Standards and regulations are generally perceived as constraints that generate costs. However, they can also lead stakeholders to collaborate with each other, and thus represent opportunities for value creation.

However, new regulatory obligations are constantly emerging, while generally complementing previous ones. With each new development, effective data governance consists of not starting from scratch, but capitalizing on the controls already in place, to identify the elements that are only necessary and complementary to be integrated for an efficient implementation.

 

6. Making data governance a long-term process

New markets, new offers, new automated processes... the collection and processing of data is constantly changing: if the implementation of data governance is long and complex, it is never finished and must be a long-term process.

As in any project of this type, the first use cases must quickly demonstrate real efficiency ("quick win") to get the ball rolling. And it is the role of the CDO - through dashboards and indicators - to know how to communicate the results to his community to continue to build optimal data governance, to motivate and multiply the uses in the long term.

 

7. Establishing a culture of data sharing organization

In "digital native" companies, such as GAFAMs or start-ups, the data culture is native. Especially since the added value of these new leaders is generally determined and built on data. In other companies, it is a whole mindset that needs to evolve.

Change management is long and complex. It requires a lot of persuasion from the CDOs, who must rely on successful use cases to create a data sharing environment (data literacy) and thus foster innovation and offer new competitive advantages to the company.

How Airbus leverages data for growth.jpg

 

The role of Data Governance is therefore to promote the transformation of the company, its sustainability, and its renewal to face disruptions and market changes.

In other words, the biggest challenge for CDOs is to raise awareness so that all stakeholders move together towards innovation and value creation to ensure the survival and development of the company.

45723
1
Comment
ffourquet
MEGA

1.    Involving the business lines to motivate them

Customer data, industrial and business information, financial data, etc. In an organization, there are many types of data, and they are constantly increasing with digital technology. What they all have in common is that they are the property of a business function or a support function. Historically, some data has been shared between functions (customer data between marketing and sales, product data between research and production, financial data between sales and finance, etc.), but today it is the complete cross-functionality of the organization that is able to create value through innovation.

However, can this transversality be decreed? Yes, partly, with regulations (and the associated fines) that impose global data management as part of a compliance obligation. This is the case, for example, with the GDPR, for which the company must prove its control and good management of all personal data processed within the organization.

But to be a real source of innovation, this transversality must be acquired: the business units must understand how sharing their data with the other stakeholders in the organization can bring them benefits, and therefore motivate them. The use of artificial intelligence (AI) provides a good example. By pooling all data for global governance, all business units can benefit from the disruptive innovations offered by AI: detection of weak signals, fine-tuned understanding of customer behavior, etc. based on stable, reliable, and controlled data.

If the proximity of the data science team to the business can be additional motivation, an evangelization and onboarding phase is essential. This is one of the roles of the CDO (Chief Data Officer), who is there to organize knowledge, promote exchanges, and ensure data reliability and compliance. He or she is also there to involve each data "owner" in the process. In short, to build this shared data culture, the CDO will have to find business sponsors - including at the board level - and set up a charter for the Data Governance approach, then organize the information, communication, and induction process for the various data owners.

 

2. Reconciling the technical nature of data governance with business needs

Data governance aims to know and catalog all the data of an organization, to evaluate and improve its quality and conformity, to provide it to the stakeholders who ensure the smooth running of the company. This is an extremely technical concept - data being an IT asset - whereas the priority is to respond to business needs and challenges. And it is this need, this concrete use case that must remain the starting point of any project: for example, detecting future customers with an appetite for a particular product, detecting the risks of customers leaving, etc.

Based on a defined use case, the business units, together with the data scientists, will select the most relevant business concepts and data dimensions. This "data shopping" is done first via the business glossary (concepts and related elements) and then via the data catalog, which is the concrete image of the data in the real systems (applications) - and therefore the data sources to be reused according to the quality, validity, freshness of the data, etc. This technical part is obviously essential and becomes more easily accessible to all actors via the business glossary.

MEGA HOPEX for Data Intelligence.jpg

3. Modeling the data life cycle

Data is not static; it has a lifespan. Therefore, a simple mapping is not enough: data governance requires modeling the entire data life cycle: creation, use, reuse, obsolescence, and destruction (GDPR). Objective: to link business process modeling and data governance, to save time and understand business issues.

 

In this context, to accelerate data governance, it will be necessary to rely on the business processes already known in the company (data entry and use in the various departments), the systems using this data, and on the company's risk management procedures (control of personal data for example).

Thus, the business process models each of the activities of the business stakeholders to concretely conceptualize the data that will then be used in the organization. For example, when setting up a credit offer in a bank, the financial advisor enters data (ID card, salary, family situation, medical situation, etc.) used for the entire process.

 

4. Promoting quality to enhance the value of data

Controlling data also means controlling its quality. Because only good quality data at the beginning of the collection process guarantees the quality of the use case at the end. For example, a credit agreement or an insurance cost depends on the data collected and used at the beginning on a given customer. This is the only way to ensure the performance of the service provided to the customer, as well as innovation, within the framework of an industrialized (production), reproducible and agile process.

Data quality is already measured in many client IT systems, in a heterogeneous and compartmentalized way. The implementation of advanced data governance must enable the construction of a repository of control and quality rules. This will avoid duplicating controls, concentrate all available measures, complement them, and set up improvement plans for priority data.

 

5. Facilitating the integration of standards and regulations

Standards and regulations are generally perceived as constraints that generate costs. However, they can also lead stakeholders to collaborate with each other, and thus represent opportunities for value creation.

However, new regulatory obligations are constantly emerging, while generally complementing previous ones. With each new development, effective data governance consists of not starting from scratch, but capitalizing on the controls already in place, to identify the elements that are only necessary and complementary to be integrated for an efficient implementation.

 

6. Making data governance a long-term process

New markets, new offers, new automated processes... the collection and processing of data is constantly changing: if the implementation of data governance is long and complex, it is never finished and must be a long-term process.

As in any project of this type, the first use cases must quickly demonstrate real efficiency ("quick win") to get the ball rolling. And it is the role of the CDO - through dashboards and indicators - to know how to communicate the results to his community to continue to build optimal data governance, to motivate and multiply the uses in the long term.

 

7. Establishing a culture of data sharing organization

In "digital native" companies, such as GAFAMs or start-ups, the data culture is native. Especially since the added value of these new leaders is generally determined and built on data. In other companies, it is a whole mindset that needs to evolve.

Change management is long and complex. It requires a lot of persuasion from the CDOs, who must rely on successful use cases to create a data sharing environment (data literacy) and thus foster innovation and offer new competitive advantages to the company.

How Airbus leverages data for growth.jpg

 

The role of Data Governance is therefore to promote the transformation of the company, its sustainability, and its renewal to face disruptions and market changes.

In other words, the biggest challenge for CDOs is to raise awareness so that all stakeholders move together towards innovation and value creation to ensure the survival and development of the company.

1 Comment
Asish Kumar
Not applicable

Enterprise is now  making strategy for  SUSTAINABILITY as an part of enterprise solution . Data Integration and Enterprise Architecture  and EA Tools  will require to  create better Environment, Social and Governance (ESG)  platform. ESG platform must be integrated with Data  and EA models through EA tools. we need to provide  better Integration platform for tracking ESG  data. Data Protection and Privacy is also important for the organization. There  are multiple  products in the industry on Data Governance/Integration and Observability  integration of products  output must be at Enterprise Modelling level. EA tool company has better opportunity.