Recommended actions for building successful Data Strategies - from Beginner to Expert

Dealing with Big Data is a necessity for every organization today. The strategic handling of data can help a company achieve competitive advantages and be successful in the long term. However, many companies start their data analytics journey as beginners and often struggle to become veterans. In this first paragraph, we provide recommended actions to help you move from beginner to routinier when it comes to data strategies.

From beginner to routinier

1. Develop a unified vision for dealing with Big Data and get the topic on the executive level agenda. - A unified vision is a must when it comes to building a successful data strategy. It is important that everyone in the organization has a clear understanding of where the company wants to go with Big Data. To do this, the topic must be put on the agenda at the executive level. Executives should understand that dealing with data is an important factor for the future of the company. This is the only way to provide the resources necessary to achieve the goal.
2. Consolidate existing initiatives around Big Data analytics to give them more weight, and educate employees specifically on the topic of data. - It is not uncommon for companies to already have initiatives around Big Data Analytics. To consolidate these and give the topic more weight, employees should receive targeted training on the topic of data. The training of employees should be supported by the management level. Training should be provided to improve understanding of data. In addition, data analysts should be hired to strengthen efforts in handling data.
3. Put a price tag on initial results and share success stories. - It is important to put a price tag on initial successes. Doing so will make resources visible and generate interest from senior leadership. In addition, success stories should be shared to build executive confidence in using data and to free up more resources. It is important to quantify successes and show the positive impact on the business.
4. Continuous improvement and evolution. - The rapid evolution of our digital world demands that organizations continually evolve their data strategies. Without regular review and adjustment of data strategies, the company will not be able to sustain competition for long. Therefore, it is important that companies do not stop after the first steps in dealing with data strategies, but continuously work on improving and adapting the strategy.

Management Summary

When dealing with Big Data, it is important to take steps to move from novice to veteran. Implementing a unified vision, training employees and sharing successes are important building blocks. Furthermore, it is necessary to continuously make improvements and adjustments to the data strategy. It will help companies that follow these steps to improve their competitiveness and succeed in the battle for market share in the long term.

From veteran to professional

1. Get in touch with the business and find out exactly what skills it needs. - The first recommended action for building successful data strategies is to connect with the business and elicit what specific skills it needs. In many cases, it will be necessary for businesses to look at what they need to make the technology work seamlessly and reliably. It may also be necessary to find several different skills and combine them into a single role.
2. Encourage business units to take ownership of their Big Data ideas and projects. - The second recommended action is for companies to equip specific business units with the capabilities they need to promote their data analytics and Big Data ideas and projects. By giving their business units more responsibility and resources in the area of Big Data, companies can foster innovation and ensure their technology is on the cutting edge.
3. Establish a leadership position in the form of a Chief Data Analytics Officer (CDAO). - The third and final recommended action involves establishing a leadership position in the form of a Chief Data Analytics Officer (CDAO). It is important that organizations have dedicated Big Data leadership positions to ensure that the technology is up to date and optimally supports business processes. This position can also serve as an interface between technology departments and business units.

Management Summary

Overall, building successful data strategies requires commitment and expertise. By making skills a key aspect of the business, giving business units more responsibility and resources, and creating dedicated leadership positions, companies can ensure that their technology is up to date and optimally supports their business processes. By following these recommended actions, you'll be on your way from being a veteran to a pro in data analytics and Big Data.

From professional to expert

1. Develop completely new services for your customers, not just products, based on Big Data Analytics. - Unlike simple products, services are more customer-centric and create a relationship between the company and the customer. With Big Data Analytics, companies can be able to develop advanced services by understanding customers' needs and requirements. Companies can collect and analyze data from their customers to understand which services are preferred by customers. Based on this, companies can then develop targeted services to meet customers' needs.
2. Rely on customized, personalized, and forward-thinking solutions to improve your customers' experience and increase their loyalty. - Customers today want personalized experiences that are tailored to their needs. Companies can address these needs by collecting and analyzing data from customers to understand their preferences and interests. By creating personalized experiences, companies can improve the value and experience customers have with their services, which can also positively impact customer loyalty.
3. Build a complete ecosystem for data mining and predictive analytics by sharing knowledge and resources with partners. - In today's business environment, companies don't always have the resources to collect and process all of their needed data. This is where partnerships come into play. Collaboration between companies can be very beneficial in gathering and analyzing data from multiple sources. This way, companies can perform faster and more accurate data analysis to make better business decisions.
4. Invest in training and education. - Data is a valuable resource, but it can also be very complicated. Companies should invest in training and education to ensure their employees are capable of collecting, analyzing and interpreting data. This is especially important because successful data strategies require a high level of expertise, and mistakes can lead to serious consequences.
5. Track and evaluate your progress and adjust your strategy accordingly. - A successful data strategy is not built overnight. It is an ongoing process that requires continuous monitoring and adjustments to meet the changing needs of the business. Organizations should constantly review and adjust their data strategy to ensure they are achieving the desired ROI.

Management Summary

A successful data strategy is essential to succeed in business. Companies should focus on customer-centric services, provide personalized experiences, build a comprehensive ecosystem, invest in training and education, and constantly review and adjust their progress. By following these steps, companies can move from professional to expert and build a successful data strategy.

The five cornerstones of any data strategy

The organizational structure and governance for data & analytics

The organizational structure and governance for Data & Analytics (D&A) is an important factor in today's business world. To make successful business decisions, organizations need to implement an effective data strategy. In this first part, we will look at organizational structure and governance for D&A and how it can support the business.

Most companies start with a decentralized approach to Data Science and then gradually develop a centralized approach. A decentralized approach can offer advantages in that it can motivate employee:s to make data-driven decisions and drive innovation. A centralized approach, on the other hand, can lead to better collaboration between teams as a unified data strategy and governance structure is established.

To take advantage of both approaches, many companies have opted for the hub-and-spoke approach. Here, data analytics skills are so tightly integrated with the business that business understanding and functional knowledge are balanced. This means that not only the Data Scientists, but also the business users:inside actively participate in data analysis and processing.

Another important aspect of organizational structure and governance for D&A is collaboration between teams. To collaborate effectively, clear responsibilities and roles must be defined. This can help minimize conflict and ensure smooth collaboration. A good example of this would be establishing a data governance structure to ensure that information is properly stored and secured.

Additionally, it is important to ensure that data and analysis processes are documented and standardized. This not only facilitates collaboration between teams, but also ensures that critical information can be accessed at any time. Through standardization, companies can avoid costly wrong decisions and react faster to changes.

Last but not least, a successful organizational structure and governance for D&A should be designed to support the needs of business units and customers. Customer needs can change rapidly, and it is important for companies to be able to respond quickly to these changes. A good organizational structure should be flexible enough to adapt to new market conditions and meet customer needs.

Management Summary

Overall, organizational structure and governance for data & analytics is an important factor in the successful implementation of a data strategy. Companies should ensure that clear responsibilities and roles are defined, documentation and standardization processes are in place, and business unit and customer needs are supported. An effective organizational structure can help companies make data-driven decisions and develop innovative solutions.

Dealing with human resources - A decisive factor for the success of data projects.

In a world where data plays an increasingly important role, companies can only remain competitive if they implement the right data-driven strategy. However, technology is not the only factor in implementing such projects. Rather, how human resources, skills and capabilities are handled also matters. In this paragraph, we will address the importance of optimally managing human resources when implementing data-driven projects.

It is indisputable that the success of large-scale transformation projects is related to the hiring and deployment of a company's employees. The skills and capabilities that exist within the organization have a significant impact on how successfully data-driven analytics can be used. 

Therefore, to create a data-driven decision-making process and increase the maturity of D&A, it is important to take an integrated approach to building expertise and skills. This approach should be carried across all levels and functions of the organization. Often, advanced training or education is only conducted in the IT department. However, this one-sided approach does not lead to the desired success, as the effective use of data is relevant in all departments of the company.

Accordingly, for the successful implementation of a data-driven project, management must also be sensitized. They must recognize the importance of using data and training employees in every department. This is the only way to establish a data-driven culture in the company.

Another way to improve the potential of a company's human resources is to use data analysts and other data specialists. These professionals help companies analyze their own data, identify patterns and trends, and make data-based decisions. 

To retain such professionals over the long term, it is important to create an attractive work environment. This is done, among other things, by providing access to the latest technologies and learning methods, attractive working conditions and offers, and adequate compensation and benefits.

Management Summary

In summary, the management of human resources plays a crucial role in the implementation of data-driven projects. An integrated approach that involves all departments and hierarchical levels, establishes a data-driven culture in the company, and promotes the use of data-based education and training is essential. In addition, engaging data specialists and creating an attractive work environment can help to fully utilize the potential of human resources. Only if companies find the optimal way to use their human resources can they remain competitive and ensure the success of their data-based projects.

The Operating Model for Data & Analytics - How it can help companies implement their data strategy.

In today's digital world, the handling of data and analytics is indispensable and a central component of modern business processes. Companies are faced with the challenge of effectively using and evaluating data in order to make informed decisions based on this information and achieve competitive advantages. The operating model for Data & Analytics is an important factor that determines how effectively an organization can handle data and what benefits it can derive from it. In this paragraph, we will explain how exactly the operating model works and how it can help organizations implement their data strategy.

The operating model for Data & Analytics covers all aspects of data management - from data collection to data analysis. A successful operating model ensures that the entire organization has a clear strategy and is able to access and use data quickly and efficiently. This means defining clear responsibilities and harmonizing processes. Collaboration between all the departments involved, such as IT, data management, CRM, and sales, plays an important role here.

A central question in connection with the operating model is who takes responsibility for the various aspects of data management. An effective solution is to set up a dedicated data department that deals exclusively with data generation, data preparation and data analysis. This department should have access to the company's entire data assets and be able to define and implement the operating model in close collaboration with management and the business units.

Another important component of the operating model is the use of analytics tools and platforms to facilitate data analysis and interpretation. Modern enterprises increasingly rely on data science and machine learning platforms to extract information from data and make effective decisions. Harmonized use of these tools within the operating model can help simplify access to data and improve the quality of data analysis.

A successful data strategy should also ensure that data can be captured and analyzed in real time. This means that the operating model must also provide an appropriate infrastructure that enables real-time data access. Such infrastructure includes data collection devices, databases, cloud platforms, and analytics tools that integrate seamlessly and provide unified interfaces.

Finally, companies should have a clear understanding of how data and analytics affect their business processes and operational decisions. This is the only way they can make the best use of their data strategy, identify value creation potential and achieve competitive advantages. The operating model provides the necessary structure for this and enables companies to use data effectively and continuously optimize their data strategy.

Management Summary

The operating model for Data & Analytics is an important factor for the success of companies in the digital age. It ensures that data strategy is effectively implemented and enables companies to gain competitive advantage. A successful operating model is characterized by clear responsibilities, harmonized processes, real-time infrastructure and effective analytics tools. By implementing an effective operating model, companies can improve their ability to analyze and interpret data and thus achieve decisive competitive advantages.

The Platform Landscape - How Companies Effectively Manage Their Data

In many companies, the data initiative begins with the introduction of different data platforms and technologies. However, this diversity quickly becomes a burden as it results in high costs for operations, interfaces, compliance and personnel. For this reason, more and more companies are looking to consolidate their platforms. A unified technology platform across the entire organization and all business units is the gold standard for effective data processing. In the following article, we will take a closer look at the enterprise platform landscape and explain the benefits of a consolidated platform.

Consolidating data platforms brings many benefits to enterprises. The most important benefits are:

• Cost savings: A consolidated platform reduces operational, interface and staffing costs.
• Improved efficiency: a unified platform provides a central point for data processing and promotes collaboration among business units.
• Increased data security: a consolidated platform enables organizations to easily meet compliance requirements.
• Better data quality: A unified platform enables organizations to unify and cleanse data, resulting in higher data quality.

An 80 percent solution has proven to be a successful approach to implementing a unified platform. A centralized data platform that addresses 80 percent of business needs can be implemented in most organizations. This gives business units room for custom solutions and exceptions.

Another important factor in implementing a centralized data platform is choosing the right technology. Companies should choose a platform that is flexible, scalable and easy to implement.

It is also important to consider the needs of the business units when implementing a centralized platform. Close collaboration between IT and business unit managers is imperative. IT experts can support the technical implementation, while business unit managers contribute their expertise regarding the requirements of the individual business units.

The success of a central data platform also depends on support from top management. A clear vision and shared understanding of the importance of data to the business can help drive platform adoption and collaboration between business units.

Finally, companies should regularly monitor and optimize the performance of the platform. Regular monitoring and analysis of data help companies identify potential issues early and address them quickly. Continuous optimization of the platform ensures greater efficiency and better data quality for the business.

Management Summary

Data platform consolidation is an important step for organizations to effectively manage their data. A unified platform offers cost savings, improved efficiency, greater data security and better data quality. Wise selection of the right technology and close collaboration between IT and business unit managers are essential to the success of a centralized data platform. Through regular monitoring and optimization, companies can ensure that the platform remains up-to-date and meets business needs.

The philosophy for data access

The way companies handle data access has become an important issue. Should all employees* have unfettered access to data or are certain restrictions necessary? It's about harnessing the full potential of the data, but at the same time ensuring that it is protected. Especially in strictly regulated industries like finance, companies need to find the balance between data access and data protection.

More and more companies are starting to recognize the value of data. Most companies want to collect and store as much data as possible so they can use it as a valuable resource. However, some companies forgo access to sensitive data to ensure that classified information is protected. 

In industries where sensitive data is processed, such as the financial industry, restricted data access is often the best option. Companies must ensure that all legal and regulatory requirements are met and that classified information does not fall into the wrong hands. 

However, if there are no specific regulatory requirements, companies should make access to data as open as possible. Especially in modern companies, it is important that employees have access to data in order to make quick decisions and work efficiently.

The importance of data access and data protection is increasing because it is important to gain the trust of customers, partners and employees. Companies not only need to respect the rights of their employees*, but also ensure that data is safe and secure.

It is also important to ensure that data access is consistent across all levels of the organization. Clear rules and policies must be established to ensure that data is only used by authorized individuals. Organizations should also ensure that their employees* are trained to ensure secure procedures and practices when handling data.

Management Summary

Organizations face the challenge of balancing data access and data protection. While it is necessary to keep data access restricted in certain industries, companies should make access to data as open as possible when there are no specific regulatory requirements. Data privacy and the trust of customers, partners and employees should always be at the center of companies' decisions. Clear rules and guidelines, as well as employee training, can make data access effective and secure.

The three stages of development in corporate data strategy

The economic importance of data has increased enormously in recent years. More and more companies are turning to a data-driven economy to increase the efficiency and productivity of their business processes and better understand their customers. But how can companies best build their data strategy and what are the stages of development? In this part, we will look at the three stages of development that companies can go through to optimize their data strategy.

• The first stage of development is the decentralized approach. Companies that are still in the early stages of their data strategy often do not yet have a centralized vision or implementation for their data. The decentralized approach is typical for companies that are still experimenting and focused on building out their data structure. However, the decentralized approach can lead to each department using its own data sources and systems, which can ultimately lead to redundancies and inefficiencies.
• The second stage of development is the centralized approach. Companies that have already implemented their governance approach to standardized data processes often follow set principles and have strict rules for common tools and platforms. In this approach, departments work together to create data standards and tools necessary for effective collaboration. This approach provides more control and governance to create a unified data infrastructure.
• The third stage of development is the embedded, decentralized approach. Organizations that have taken their data strategy the furthest rely on a combination of decentralized data management and centralized control. Departments can use their own tools and technologies, but governance policies ensure that data from all departments can work together and be integrated. This approach allows departments to respond quickly and flexibly to change, while also maintaining a consistent data infrastructure.

Management Summary

Developing enterprise data strategy is critical for effective collaboration between departments and for building a consistent data infrastructure. The three stages of development can help companies understand their own needs and adjust their data strategy accordingly. The balance between decentralized and centralized approaches plays an important role in optimizing the data infrastructure. Companies should focus on the vision of a consistent data infrastructure, but at the same time ensure flexibility to make quick and efficient changes.

The success characteristics of a successful data strategy 

Data has become the fuel of many successful businesses today. With the right data strategy, companies can gain valuable insights that help them make better decisions and give them a competitive advantage. However, a successful data strategy is not easy to achieve. To conclude, we'll discuss the characteristics of a successful data strategy so you can put your business on the right data trajectory. 

• The first key to success is driving from the executive level. - It is important that senior management views data analytics as a strategic tool that can directly contribute to the achievement of business goals. Only if this is the case can investments in modern technologies and analytics methods be sufficiently driven. Likewise, it is important that executives actively support the data strategy and demonstrate their own commitment to data analytics.
• The second critical component of success is a clear purpose and vision. - It is important to understand the purposes that the data strategy is intended to fulfill. With the help of a vision, a goal can first be clearly defined and a kick-ass strategy can be formulated with a great chance of success. This also allows everyone in the company to achieve the same goal. Only then can a successful data strategy be implemented. 
• The operating model is another important aspect of a successful data strategy. - It must be clearly defined and structured to ensure that everyone involved knows exactly what needs to be done. Responsibilities, tasks and processes must be transparent so that both subject matter experts and executives can focus on their work. A detailed breakdown of use cases is important to ensure that everything achieves the desired result. 
• Another feature of a successful data strategy is business unit involvement. - Companies with a centralized data strategy can pool expertise and resources and achieve critical mass. However, it is important that business units are actively involved in the data analysis process. Only in this way can different ideas and capabilities lead to new insights. Nevertheless, the decision-making power should lie with the executives to ensure a clear structure and unambiguous objectives.
• Last but not least, data & analytics is not only about technology, but also about culture. - Companies with a good corporate culture that promotes collaboration and exchange are more successful. A successful data strategy is only possible if the culture in the company is right and data analyses are actively used. This means that employees perceive data analysis as a valuable tool to support their work and actively participate in its development. 

Management summary

A successful data strategy is essential to making better decisions. Clear purpose, leadership, transparency and culture are the foundations of a successful data strategy. A successful data strategy can help increase a company's profitability and competitiveness. It is only through a combination of technology and culture that companies can be successful in gaining valuable insights from data. In the end, it is a combination of many factors that contribute to success.

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