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From Raw Data to Profitable Insights: Tools and Strategies for Successful Data Monetization

Data monetization has become an increasingly important topic in the world of business and technology. As companies collect more and more data, they are realizing the potential value that this data holds. In fact, according to a report by 451 Research, the global market for data monetization is projected to reach $7.3 billion by 2022. This is achieved through various strategies such as selling raw or processed data, providing analytics services, or creating new products based on the data.

There are many different approaches to data monetization, each with its own unique benefits and challenges. However, many organizations struggle with how to effectively monetize their data assets. In order to effectively monetize data, businesses need the right tools and strategies in place. These tools help collect, analyze, and visualize data to uncover valuable insights that can be turned into profitable opportunities. Below is a short list of essential tools used for successful data monetization.

 

  • Data Collection Tools: The first step in data monetization is collecting relevant and accurate data. This requires efficient and effective data collection tools such as web scraping software, API integrations, IoT sensors, and customer feedback forms. These tools help gather large amounts of structured and unstructured data from various sources like websites, social media platforms, customer interactions, or even physical sensors.

 

  • Data Analytics Platforms: Analytics tools play a crucial role in making sense of complex datasets by identifying patterns and trends that would otherwise go unnoticed. By leveraging these platforms, businesses can gain valuable insights that can be used for decision-making processes. They provide powerful reporting dashboards that allow businesses to visualize their KPIs with interactive charts, graphs, or maps helping them understand how their products are performing in real-time.

 

  • Business Intelligence Tools: These are applications designed specifically for reporting and dashboarding purposes. They allow users to input raw or analyzed data from various sources and present it in a visually appealing manner through charts, graphs or maps.

 

  • Customer Relationship Management Systems: CRM systems are essential tools for gathering customer-related information such as demographics, purchase history or behavior patterns. By analyzing this data, businesses can better understand their customers and tailor their products or services to meet their specific needs.

 

  • Data Management Platforms: DMPs are software solutions that help businesses to store, and manage large volumes of data from different sources. They allow for the integration of various data types, such as first-party and third-party data, which can then be used to create targeted marketing campaigns. It also provides features such as real-time processing capabilities, automated workflows for cleansing and transforming data, ensuring accuracy and consistency.

 

  • Data Visualization Tools: Data visualization tools help businesses present data in a compelling and visually appealing manner, making it easier for decision-makers to understand complex information quickly. These tools provide interactive dashboards, charts, maps, and graphs that can be customized according to the needs of the business.

 

  • Artificial Intelligence & Machine Learning: AI & ML technologies can help organizations extract valuable insights from their data by identifying patterns, predicting trends, and automating processes. AI-powered chatbots also enable businesses to engage with customers in real-time, providing personalized recommendations and increasing customer satisfaction.

 

  • Cloud Computing: Cloud computing provides scalable storage and computing power necessary for processing large amounts of data quickly. It also offers cost-effective solutions for storing and managing data as businesses can pay only for the services they use while avoiding expensive infrastructure costs.

 

  • Demand-side platforms: DSP help organizations manage their digital advertising campaigns by targeting specific audiences based on their browsing behavior or interests. These platforms allow businesses to use their data to segment and target customers with personalized messaging, increasing the chances of conversion and revenue generation.

 

  • Monetization Platforms: Finally, businesses need a reliable monetization platform that helps them package and sell their data products to interested buyers easily.

Data is certainly more than you think! It’s a valuable resource that can be monetized across your organization. So get in touch with us and learn how data monetization can transform your business.

How Technology can Enhance and Elevate Business & Employee Performance?

Technology has been advancing at an ever-increasing rate over the past few decades, and it has had a profound impact on how we live our lives. It’s no wonder, then, that technology is also having a huge impact on performance and enhancingperformance in both individuals and teams. Nowadays, the high level of performance is achieved by automating repetitive tasks, providing real-time feedback and analysis, facilitating communication and collaboration, enabling remote work, increasing efficiency and accuracy, and providing access to a wealth of information and resources. Additionally, emerging technologies such as artificial intelligence and machine learning are continuously helping to optimize and streamline complex processes & operations and decision-making leading to better outcomes and increased productivity within companies.

 

Below are the most common technologies that are used to enhance business performance, including:

 

  • Cloud computing: Provides access to on-demand computing resources, allowing businesses to scale up or down quickly, reduce costs, and increase flexibility.
  • Big data analytics: Is helping businesses make more informed decisions by analyzing large data sets to identify trends and patterns.
  • Artificial intelligence and machine learning: Helping business to automate routine tasks, make predictions, and optimize processes to improve efficiency and productivity. If integrated correctly, AI and AL can play a significant role in performance enhancement by analyzing vast amounts of data to identify patterns and insights to make predictions that humans may not be able to detect. For example, AI and ML can be used to optimize manufacturing processes, predict equipment failures, and analyze customer behavior to improve marketing strategies.
  • Internet of Things: These technologies are used to collect and analyze data from connected devices, providing insights into performance and enabling proactive maintenance.
  • Customer relationship management software: CRM software can help businesses manage customer interactions, improve customer service, and identify new opportunities for growth.
  • Collaboration and communication tools: These tools can help teams work together more effectively, whether they are in the same office or working remotely.

By leveraging these technologies, businesses can streamline processes, increase efficiency, and gain a competitive edge, resulting in increased revenue, profitability, and customer satisfaction.

How Technology can Enhancing and Elevate Business & Employee Performance?

Let’s now have a look on the most common technologies that are used to boost employee performance, these include:

 

  • Performance management software: This type of software can help track employee progress, set goals, and provide feedback and coaching to improve performance.
  • Learning management systems: These systems can help employees acquire new skills and knowledge through online courses, webinars, and other forms of e-learning.
  • Employee engagement platforms: These platforms can provide a forum for employee feedback, recognition, and collaboration, helping to increase employee motivation and satisfaction.
  • Data analytics and reporting tools: These tools can help managers track key performance metrics, identify areas for improvement, and make data-driven decisions.
  • Collaboration and communication tools: These tools can enable employees to work together more effectively, whether they are in the same office or working remotely.
  • Personal productivity tools: These tools can help employees manage their time and tasks more efficiently, reducing stress and improving work-life balance.

By leveraging these technologies, organizations can create a more engaging, productive, and efficient work environment, resulting in higher employee satisfaction, retention, and overall business performance.

Physical & Cloud #DataProtection: Best Practices for your #Backup and #RecoveryProcess

Data, one of the most valuable assets of organisations. Massive data is the new currency. Thanks to advancements in technology and connectivity, data creation is skyrocketing. According to IDC, Global DataSphere Forecast, 2021-2025, the global data creation and replication will experience a compound annual growth rate (CAGR) of 23% over the forecast period, leaping to 181 zettabytes in 2025. That’s up from 64.2 zettabytes of data in 2020 which, in turn, is a tenfold increase from the 6.5 zettabytes in 2012. These data are stored in ever-increasing environments and connected devices, therefore backup and restoring the capability of an information system is a real challenge to ensure business continuity and the availability of associated data.

Volume of data created and replicated worldwide

What must IT departments do to fulfill the data security mission? Well, the data security policy is at the heart of each business concern and should be a fundamental part of their security strategy. Planned security measures can then create tactical and operational rules through the joint efforts of security and storage teams. To this end, storage must be an integral part of the company’s security strategy.

 

To achieve these objectives, a company must establish a cluster around the following five essential aspects:
• Allocation of responsibilities;
• Risk Assessment;
• Development of a data protection procedure;
• Communication of data protection procedure;
• Execution and testing of the data protection procedure.

 

  1. Allocation of responsibilities

The goal is to make storage security a fully-fledged feature of the IT security architecture. Even if the company decides that the responsibility for backup or storage security rests within the storage team, it must nevertheless integrate any safety measures in this area with task to secure the rest of the infrastructure. This integration will contribute to the establishment of in-depth protection. It is also advisable to share responsibility for extremely sensitive data. It’s, therefore, better to ensure that the person authorizing access is not the same as the person responsible for enforcement.

 

  1. Assessment of storage risks in the area of ​​IT security

#Managers must review each step of their backup methodology to identify security vulnerabilities. Can an administrator secretly make copies of backup tapes? Are they stored in boxes accessible to everyone? Is there a rigorous end-to-end monitoring chain for backup tapes? If critical data is backed up and transported, vulnerabilities of this nature could make it easy prey. If the risk analysis reveals many vulnerabilities, the company must seriously question the encryption of its data.

 

  1. Development of an information protection program that guarantees the security of company data, at all times, wherever they are

Multi-level protection should be adopted by taking existing best practices for the data network in order to apply to the storage network, while adding specific layers adapted to the characteristics of the archived data, for example:

  • Authentication: application of multi-level authentication techniques and anti-spoofing (anti-identity or address spoofing).
    • Authorizations: access rights according to roles and responsibilities (as opposed to total administrative access).

It is imperative to duplicate backup tapes because it is never good to depend on a single copy of the data. Despite the longevity of the bands, they are still exposed to environmental and physical damage. A common practice is to perform nightly backups and then store these off-site tapes without any verification. Recommended best practices include duplicating backup tapes and then storing offsite copies.

Magnetic tapes remain the preferred storage mode for backups because they are economical and offer sufficient capacity to back up an entire operating system on a single cartridge. When stored properly, archival tapes have a lifetime of more than 30 years, making them an exceptionally reliable storage medium.

 

  1. Communication of the procedure to be applied with regard to the protection and security of information

Once the procedure for protecting and manipulating sensitive data has been defined, it is important to ensure that those responsible for their safety are informed and trained. Safety rules are the most important aspect of assigning responsibilities. Functional managers need to be aware of risks, countermeasures, and costs.

Data loss and intellectual property theft affect the entire enterprise, not just the IT department. As such, the Director of Security must undertake a data security approach by training the different functional frameworks in the risks, threats and potential harms arising from security breaches, as well as the cost of the various possible countermeasures in this area. In this way, company executives can raise awareness about the cost/benefit of investments in data security.

 

  1. Implementation and testing of Data Protection and Security Plan

Securing data is not about technology but about the procedure. This is why it is essential to test the procedure. In addition, as the growth of the company is accompanied by an evolution in security and data protection needs, IT security practices must also evolve. Once the complete security plan has been developed, defined, and communicated to the concerned team, only then it’s the right time to implement it. IT team must ensure the implementation of the tools, technologies, and methodologies necessary for the classification of information. New technologies may be required to classify information or label it with metadata so that it is backed up according to appropriate rules and procedures.

Once in place, the procedure must be tested, both concerning backup and restore. The test is to introduce, into the process, any possible and imaginable danger, whether it is the loss of a tape or a server, network problems, equipment or filing of data or any other scenario which could affect the company’s performance.

It is advisable to carry out tests with personnel who are less familiar with the procedure, to ensure that it can nevertheless be applied without difficulty in the absence of the usual supervisor (due to illness, holidays or departure).

Master Data Strategy: How to achieve greater operational efficiency and improve the customer experience?

Master Data Strategy How to achieve a greater operational efficiency and improve the customer experience

Without a doubt, the corona pandemic has led to a holistic rethinking in many areas of the company. Companies have implemented solutions that make their employees work easier, help them to reduce overall costs, and improve existing business processes and their customer’s experience in parallel. All this can’t be done without good master data. Master data is at the heart of all operational processes. Sourcing, product development, manufacturing, shipping, marketing, and sales all depend on the ability to efficiently collect, manage, and share trusted data on time.

 

Master data management also helps to automate and control error-prone manual processes, enable transparency and insights to make better operational decisions and so organizations can improve the quality of products and services, and accelerate time-to-market.

In order to achieve increased productivity, profitability, and business performance while reducing costs, one must not ignore the quality of the master data, regardless of whether it is customer master data, supplier master data, or article master data. Only superior quality data has a decisive influence on the efficiency of business processes and the quality of corporate decisions. Outdated, incorrect, or missing master data can lead to a loss of sales or weaken the reputation of the customer or supplier.

 

What mistakes can one make in master data management?

 

Management is not involved

Without the support and coordination with the management, the master data management project is doomed to failure. The support of the management right from the start is the only way to dissolve cross-departmental thinking. The senior management officer must ensure that the project team can not only streamline the management of data across departments but also that business processes and procedures can be adjusted across departments if necessary. Such huge changes are rarely received positively, so effective communication in change management is necessary.

 

Master data management is not an IT issue

Master data management is not a technical challenge or problem that only the IT department can solve. This topic must be addressed by the specialist departments. Only the various specialist departments know the content-related requirements for correct and up-to-date data. And they know their own business processes in which the various data are generated or changed. IT can help with the selection and the implementation of MDM solutions, but the specialist departments must take on the technical part here.

 

The long-term vision of the MDM project

As with any project, the MDM project also needs good management within the organization based on a correct goal matrix and a long-term vision for data management. However, this must not tempt you to create the scope of the project in such a way that it is no longer possible to carry it out quickly and efficiently. Agile project management makes it possible for achieving the goals step by step. With an unrealistic project scope, the entire project can quickly fail, and you end up with no result. Most of the time an experienced project manager, possibly external, can help get the project off the ground.

 

Organizational and cultural changes are ignored

No matter how good the project, the goals, and the vision, it will fail if all the different parties in the organization are not brought on board. Those affected and opinion leaders play a key role in the success of the project. The project team often gambles away its own success by doing everything in a quiet little room and in the end, everyone is surprised by the new solution, the result = is rejection. Good change management communication to the affected groups is an essential component of building awareness and support for organizational change and achieving long-term success.

 

The goal of mastering data management is the optimization, improvement, and long-term protection of data quality and data consistency. The main problem is when the master data is stored redundantly in different databases. This leads to time-consuming and costly data comparisons or the introduction of a central MDM system that, as a central data hub, provides the data for all other systems.

Data Management: Cost of poor data quality

Organizations are collecting and generating more information/data than ever before. This information/data is used in almost all activities of companies and constitutes the basis for decisions on multiple levels. But, simply having a lot of data does not make a business data-driven, because issues related to data quality maintenance are infecting numerous businesses. Companies are witnessing that not only the data is growing rapidly in scale & importance but also in complexity. The topic of data quality and what companies should do to ensure a good level of data is one of the biggest priorities within companies that are always being worked on. Since poor data quality affects, among other things, business processes, it can lead to wrong decisions and make it more difficult to comply with laws and guidelines (compliance).

 

Organizations around the world gather so much data that sometimes it’s impossible for them to differentiate the valuable and outdated or inaccurate data. Studies have also shown that the data stays stuck in different systems in inconsistent formats, which makes it unreliable or impossible to share with other team members. According to Gartner’s research, “the average financial cost of poor data quality on organizations is $9.7 million per year.” In other words, the cost of poor data quality is 15% to 25% of revenue.

MASTER DATA MANAGEMENT

Having quality data means getting the right answer to every question. This requires that data is constantly checked for errors, redundancy, and usability. In addition to avoiding errors and gaps, it is also about making data available to every concerning person in a uniform way and making it as easy to use as possible. Master data management (MDM) helps companies to ensure that their data is accurate, trustworthy, consistent, and shareable across the enterprise and value chain by enabling greater data transparency & empowering you to drive better decisions, experiences, and outcomes that benefit your business and your customers.

 

Basically, master data management creates added value on two levels: on the one hand in the administrative areas, for example through more efficient master data maintenance processes or also in IT projects; on the other hand, through increased transparency in the operational areas and thus improved controllability. The benefit in mastering data processes is reflected, among other things, in the reduced effort involved in searching for data, less internal coordination effort, and the fact that there is no duplication of work when changing data or making initial entries. Furthermore, clean master data forms the basis for scalable automation options and reduces the effort for migrations.

 

Mastering your data challenges also delivers a significant competitive advantage. And as the pace of innovation accelerates, the importance of mastering your data will only be beneficial for your business. The benefits of MDM in the administrative and operational areas as well as for compliance ultimately increase the competitiveness of companies. Last but not least, good data quality ensures the satisfaction of customers, suppliers, and employees.

CRM Automation: How can you elevate your Customer Relationship Management?

CRM Automation How can you elevate your Customer Relationship Management

The automation of companies has become a trend of our time. The modern market has set its own rules. The perfect product alone is no longer enough. In addition to the ideal product, customers also demand impeccable service. If a good product and competitive pricing are no longer enough to gain customers’ loyalty, then a personalized experience will have to be your differentiator. The competition is very strong, so a modern entrepreneur should pay close attention to customer management. Because one thing that 2021 has taught us, is to add good service to even the best product. Thus, automation is one of the most effective ways to effortlessly streamline your business.

 

The corona pandemic has further accelerated existing trends in automation. More and more companies want to automate their processes with the help of their CRM. The trend of collecting, unifying, and transforming customer data is becoming difficult and time-consuming without a proper data integration tool, but new offerings are finally making it possible for small and medium-sized businesses to do the same. One of the biggest benefits of automation is that instead of manually filling out all the documents, your team can let the software do it automatically. This gives them the opportunity to spend their valuable time on more important things. The software is programmed to work flawlessly, saving you a lot of problems and headaches and making the choice between tradition and automation obvious. CRM is becoming more and more synonymous with automation. The global CRM market is poised to reach about $113.46 billion by the end of 2027 (Globe Newswire, 2021).

 

As CRM takes care of a significant portion of the work processes; you don’t need to hire a big team to do the job but some entrepreneurs are still afraid to introduce the innovation because they are not sure if this solution really works and will be accepted by their team. Along with the fear of change, often companies don’t have an integrated approach to contact information and use different customer relationship management solutions that don’t communicate with each other. Without data integration tools, the process becomes difficult and time-consuming. In such a situation, employees need to log into multiple systems, download multiple sets of data to create their own unified customer database.

 
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On the other hand, the organizations which have successfully adapted to the new situation, use new tools and technologies to review and adapt their sales process to close more deals. Businesses that implement CRM automation experience an average of a 451% increase in qualified leads. (Annuitas Group). The CRM system, allows them to manage all contacted data of their customers and business partners in a structured and clear manner. They can record interactions and collect all important information about them along the customer journey, the journey from lead to purchase. Also, by bringing valuable data together, they are able to better understand and respond to the needs of their customers. Without electronic support, this mass of data cannot be handled at all.

 

Software for consolidating customer data easily connects disparate silos so data can be shared across systems for trend analysis, better decision-making, and greater customer satisfaction. For instance, using chatbots for communicating with customers, automating helpdesk tickets, or using automated email workflows to assist prospects in the sales process, customer management automation is on the rise. This automation enables companies to provide quality customer service while optimizing operational costs. The tools for this are also becoming easier and cheaper, which is why this is becoming more and more interesting, even for smaller companies.

 

However, it’s very important to choose software that is suitable for your team. Many CRMs look too mysterious but work as simple appointment schedulers. Therefore, the demand for quality software is very high among both small and medium-sized businesses. Well-designed software is user-friendly, which makes it easier for managers to explain the importance of the new software to their employees. It should definitely be done, and in this case, it’s not complicated.

A fully-integrated CRM system offers significant benefits but you must look for implementation options, scalability, adaptability, business value, and, of course, best value for money. The focus must always be on the individual requirements and needs of your company and the associated tasks, but also on employees who will use the system in the future.

The Data Modelling Techniques for BI

The Data Modelling Techniques for BI

Business applications, data integration, data management, data warehousing and machine learning – they all have one common and essential component: a data model. Almost every critical business solution is based on a data model. May it be in the areas of online trading and point-of-sale, finance, product and customer management, business intelligence or IoT, without a suitable data model, business data simply has ZERO value!

 

Data models and methods for data modelling have been around since the beginning of the computer age. A data model will remain the basis for business applications for the foreseeable future. In the area of ​​data modelling, the basics of mapping complex business models are developed. In order to model data successfully, it is particularly important to understand the fundamentals and relationships between the individual topics and to reproduce them using examples. Data needs a structure, without it, it makes no sense and computers cannot process it as bits and bytes.

 

What is the business intelligence and why is it important?

 

The concept of business intelligence first appeared in the 1960s. Business intelligence, also known as BI, is a collective or generic term for the various sub-areas of business analytics, data mining, data infrastructure, data visualization and also data tools. In summary, BI analyses all the data generated by a business and makes reports, performance measures, and trends that helps management in decision making.

 

BI is essential when it comes to optimizing business processes and positioning yourself successfully for the future. As the goal of BI is to provide you with company data from all of your company areas, so can use it for the company’s efficiency & increase productivity and react to changes in the market. With business intelligence, you are able to identify and evaluate data and ultimately react to achieve goals.

 

Data modelling techniques – an overview

 

The following is an overview of the various data modelling techniques:

    • Flat data model: in this very simplest database model, all data is in a single two-dimensional table, consisting of columns and rows. Columns are assumed to have a similar types of values and in the row, elements are supposed to have relational value to one another.

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    • Hierarchical model: data is stored in a tree-like structure. Data is store in a root or top-level, directory that contains various other directories and files.

 

    • Network model: This model is very similar to the hierarchical model but the hierarchical tree is replaced by a graph. In this model, the records are connected to each other and their allocation takes place via a link table. In this manner, the hierarchy is maintained among the records.

 

    • Relational model: This model represents the database as a collection of relations. A relation is nothing but a table of values. A predicate collection over a fixed set of predicate variables, the possible values ​​or combinations of which are subject to restrictions.

 

    • Star schema model: A star schema is a database architecture model where one fact table references multiple dimension tables, optimized for use in a data warehouse or business intelligence.

 

    • Data Vault Model: Entries with long-term stored historical data from various data sources, which are arranged in and are related to the hub, satellite and link tables. At the core, it is a modern, agile way of designing and building efficient, effective Data Warehouses.

 

2021: Ensure Your Business Growth by Becoming Data-Driven Company

ensure Your Business Growth by Becoming Data-Driven Company

 

In 2021, government agencies and businesses will need to be able to make decisions based on current/real-time data faster and more accurately than before. Because: due to COVID-19, markets, supply chains and customer behavior have changed in recent months, only data-driven businesses are able to respond quickly and effectively in a rapidly changing world. In order to transform into a data-driven business, it’s not only important to understand the importance of data quality and governance. But it’s also key to drive a data strategy that is aligned with your business strategy. By integrating analytics into business strategies, businesses can transform data into decisions that improve lives and results.

 

A study of more than 3,500 business executives and senior IT decision-makers across the UK, France, Germany, and the Netherlands found a gap between companies using data to inform decisions during the pandemic and those who are not. The YouGov survey, commissioned by Tableau, asked executives of small, medium, and large businesses about their use of data during the pandemic, lessons learned, and confidence in implementing long-term business change. For executives in data-driven companies, a majority (80%) believe they had a key advantage during the pandemic.

 

These leaders are also deeply committed to the important role data plays in the future of their business. A large majority of 76% plan to increase investments in data literacy; especially after the long bumpy ride we have all been on since the start of 2020. Additionally, 79% are confident that they will ensure business decisions are supported by data. The results show that non-data-driven companies are slower to grasp the meaning of data in these uncertain times. Only 29% see this as a key benefit and 56% say they will reduce or stop investing in data skills. Additionally, only 36% are confident that the data will support business decisions.

 

“This year has accelerated change for businesses and ushered in a fully digital world faster than anyone could ever have imagined. Data is at the heart of this digital world,” said Tony Hammond, Vice President Strategy and Growth EMEA. at Tableau. “In this age of data, our research shows that data-driven companies see clear benefits and are more confident about the future of their business. As a result, they really rely on the power of their data. Companies that haven’t woken up to it run the risk of falling behind. But businesses big and small can rest assured that it’s not too late to harness the power of data – the time is now.”

 

When asked how it helps to be data-driven during the pandemic, company leaders recognized several benefits. At the top of the list are: more effective communication with employees and customers (42%), the ability to make strategic business decisions faster (40%) and improved collaboration between teams for decision making and problem-solving (36%).

 
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“We started building data skills in our company in 2013, and due to the pandemic, we have definitely benefited from these functions,” explains Dr. Dirk Holbach, Senior Vice President and CSCO Laundry & Home Care at Henkel, one of the world’s leading consumer goods and industrial companies. “For example, within a few days, we were able to record all of our personal protective equipment controls so that each facility can see how we are equipped in this regard so that our business can continue to operate. I am confident that we will take some good lessons with us in the future, especially when it comes to working together. “

 

For all respondents, the key takeaways from the pandemic are: the need to be more agile (30%), prioritize and implement projects faster (26%), and access to more accurate, up-to-date, and cleaner data (25%). Jay Kotecha, the data scientist at full grocery brand Huel, said of his data strategy: “Our data-driven strategy helps the company respond to consumer behavior and enables us to pivot and react faster and more clearly. It’s about empowering the entire organization through data. Employees examine data from across the company and turn it into insights we can act on, whether it’s sales projections, sales effectiveness, or marketing spend. “

 

Across Europe, the results show that just over half (56%) of business leaders consider their companies to be data-driven, while one in three (38%) think they do not. These results indicate a clear way for organizations to leverage data to support business resilience and decision making during this time. German companies are taking the lead with 62% as their business is data-driven, while the UK lags behind with just 46%.

 

The promise of digital change is based on the ability to harness the power of technology to grow your business, open up new markets, and acquire new customers. It also means that you need to understand all of the data (the digital exhaust – the trail of data left behind by browsing the Internet) that new customer experiences create. A data governance strategy as well as information and data quality management as an integral component of management systems significantly supports the achievement of the organization’s goals, ensures compliance conformity, increases throughput, and supports organizations in the transformation to a data-oriented culture. Organizations thus secure their competitiveness and can expand this further through increased data intelligence.

 

Sources:

Data-driven companies are more resilient and confident.

Data-driven businesses vastly more optimistic – research

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