Developing a Winning Disruptive Strategy
- Disruption comes not from the usual suspects, but your peers, competitors, and also from outside the industry.
Developing a strategy for the unknown is the most challenging issue facing the CEO today. Why? Because digital disruption is the number one paranoia haunting today’s management teams worldwide and nobody knows where disruption will hit next.
Disruption comes not from the usual suspects, your peers and competitors, but from outside your industry which, let’s face it, means almost anywhere. A strategy for the unknown is a strategy for disruption. This is great news for everyone because disruption is no more than innovation in action.
To unlock the Asian market, register now to the iFX EXPO in Hong Kong.
It is great for the consumer, especially since a major factor behind the current uncertainty is the incredible shift in power to the customer over the last five years. Some of the customer-centric issues facing every enterprise in every industry are:
- Communicating and interacting through multiple channels
- Customizing products and services to a market of one
- Involving customers in product design
- Coping with customers that can source globally and compare prices globally
- Providing add-on digital services
- Dealing with the immediate influence of market opinion from anywhere in the world, any time.
Power to the people indeed. It is great news for the enterprise too - the industrial internet (or Industrie 4.0) will have a much bigger impact on digitalizing the world than the social or consumer phase ever had. And the business opportunities will be correspondingly large. We are still in the early stages of the IoT, and the who, the what, the how and the why of the industrial internet are all wide-open issues.
The Industrial Internet
And, to make things even more exciting (and believe me this is an exciting time to be in the software industry), the solutions customers are building are changing the world on a daily basis. The technology portfolio driving this change is expanding too.
The knock-on effect? The industrial internet, the greatest integration program man has ever seen, provides the backbone for the amalgamation of Big Data Big Data Big data refers to the collection of data that is too complex and too large for processing by standard database tools. There is no specific quantity of data, which is set as a minimum level to be considered Big data. Image the data collected on global credit card transactions. Many governments used Big data analysis to study the recent pandemic spread. The term Big data was first introduced in 1980 by Charles Tilly.The term Big data was primarily used in computer science, statistics, and econometrics and was made famous in Silicon Valley in the mid-1990s. What Big Data Can Do for YouBig data is the massive amount of data collected over time that are difficult to analyze and handle because the data sets are so enormous. The records are analyzed for marketing trends in business as well as in the fields of manufacturing, medicine, and science. The types of data include business transactions, e-mail messages, photos, surveillance videos, activity logs, and unstructured text from blogs and social media, as well as the vast amounts of data that can be collected from sensors of all varieties. Big data can also refer to the analytical challenge in deriving meaningful information from data in petabyte and exabyte volumes. For example, big data analytics breaks down the data sets into smaller chunks for efficient processing and employs parallel computing to derive intelligence for effective decision-making.Big data is used in a wide range of industries, sectors, or applications. This includes benefits for governments, healthcare, finance, education, media, internet of things (IoT), information technology, and others. Big data refers to the collection of data that is too complex and too large for processing by standard database tools. There is no specific quantity of data, which is set as a minimum level to be considered Big data. Image the data collected on global credit card transactions. Many governments used Big data analysis to study the recent pandemic spread. The term Big data was first introduced in 1980 by Charles Tilly.The term Big data was primarily used in computer science, statistics, and econometrics and was made famous in Silicon Valley in the mid-1990s. What Big Data Can Do for YouBig data is the massive amount of data collected over time that are difficult to analyze and handle because the data sets are so enormous. The records are analyzed for marketing trends in business as well as in the fields of manufacturing, medicine, and science. The types of data include business transactions, e-mail messages, photos, surveillance videos, activity logs, and unstructured text from blogs and social media, as well as the vast amounts of data that can be collected from sensors of all varieties. Big data can also refer to the analytical challenge in deriving meaningful information from data in petabyte and exabyte volumes. For example, big data analytics breaks down the data sets into smaller chunks for efficient processing and employs parallel computing to derive intelligence for effective decision-making.Big data is used in a wide range of industries, sectors, or applications. This includes benefits for governments, healthcare, finance, education, media, internet of things (IoT), information technology, and others. Read this Term. Big data itself provides the fuel for Machine Learning Machine Learning Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained through observational behavior. For example, the process of learning begins with observations or data.This includes examples and indirect experience or instruction to help detect patterns in data. In doing so, the goal is to make better decisions in the future based on the examples that are provided. In an ideal set of circumstances, computers learn automatically without human intervention or assistance and adjust actions accordingly.Machine learning can take two different form, i.e. supervised or unsupervised learning. Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. As such, the system is able to provide targets for any new input after sufficient levels of training. Learning algorithm can also compare its output to find errors in order to modify the model accordingly.By extension, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data. Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained through observational behavior. For example, the process of learning begins with observations or data.This includes examples and indirect experience or instruction to help detect patterns in data. In doing so, the goal is to make better decisions in the future based on the examples that are provided. In an ideal set of circumstances, computers learn automatically without human intervention or assistance and adjust actions accordingly.Machine learning can take two different form, i.e. supervised or unsupervised learning. Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. As such, the system is able to provide targets for any new input after sufficient levels of training. Learning algorithm can also compare its output to find errors in order to modify the model accordingly.By extension, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data. Read this Term and artificial intelligence, the basis of the next generation of IoT applications.
This is why I say today’s CEO must form a strategy for the largely unknown, a constantly evolving strategy that can be changed, modified or replaced at a moment’s notice.
For the enterprise, and for government services, this means having your IT and business departments use common tools, speaking a common language and co-developing new applications as rapidly as the market demands.
It means having the ability to identify market trends early, spot individual business or external events in real-time and having the flexibility and agility to change your business operations to the new situation as fast as needed. Information, analysis and the ability to respond are key to future success.
The speed of adapting to change is of the essence. The faster the enterprise moves the easier it is to turn a business challenge into a new business opportunity. Only the slow movers will view digitalization as a threat. To do nothing is not an option.
The enterprise transformation demanded by digitalization dwarfs any other factors impacting the global economy - from low oil and commodity prices to sub-zero interest rates - these constantly changing issues themselves constitute a part of the unknown, they could be out of date by the time you read this.
This article was written by Karl-Heinz Streibich, CEO and Chairman of the Management Board at Software AG.
Developing a strategy for the unknown is the most challenging issue facing the CEO today. Why? Because digital disruption is the number one paranoia haunting today’s management teams worldwide and nobody knows where disruption will hit next.
Disruption comes not from the usual suspects, your peers and competitors, but from outside your industry which, let’s face it, means almost anywhere. A strategy for the unknown is a strategy for disruption. This is great news for everyone because disruption is no more than innovation in action.
To unlock the Asian market, register now to the iFX EXPO in Hong Kong.
It is great for the consumer, especially since a major factor behind the current uncertainty is the incredible shift in power to the customer over the last five years. Some of the customer-centric issues facing every enterprise in every industry are:
- Communicating and interacting through multiple channels
- Customizing products and services to a market of one
- Involving customers in product design
- Coping with customers that can source globally and compare prices globally
- Providing add-on digital services
- Dealing with the immediate influence of market opinion from anywhere in the world, any time.
Power to the people indeed. It is great news for the enterprise too - the industrial internet (or Industrie 4.0) will have a much bigger impact on digitalizing the world than the social or consumer phase ever had. And the business opportunities will be correspondingly large. We are still in the early stages of the IoT, and the who, the what, the how and the why of the industrial internet are all wide-open issues.
The Industrial Internet
And, to make things even more exciting (and believe me this is an exciting time to be in the software industry), the solutions customers are building are changing the world on a daily basis. The technology portfolio driving this change is expanding too.
The knock-on effect? The industrial internet, the greatest integration program man has ever seen, provides the backbone for the amalgamation of Big Data Big Data Big data refers to the collection of data that is too complex and too large for processing by standard database tools. There is no specific quantity of data, which is set as a minimum level to be considered Big data. Image the data collected on global credit card transactions. Many governments used Big data analysis to study the recent pandemic spread. The term Big data was first introduced in 1980 by Charles Tilly.The term Big data was primarily used in computer science, statistics, and econometrics and was made famous in Silicon Valley in the mid-1990s. What Big Data Can Do for YouBig data is the massive amount of data collected over time that are difficult to analyze and handle because the data sets are so enormous. The records are analyzed for marketing trends in business as well as in the fields of manufacturing, medicine, and science. The types of data include business transactions, e-mail messages, photos, surveillance videos, activity logs, and unstructured text from blogs and social media, as well as the vast amounts of data that can be collected from sensors of all varieties. Big data can also refer to the analytical challenge in deriving meaningful information from data in petabyte and exabyte volumes. For example, big data analytics breaks down the data sets into smaller chunks for efficient processing and employs parallel computing to derive intelligence for effective decision-making.Big data is used in a wide range of industries, sectors, or applications. This includes benefits for governments, healthcare, finance, education, media, internet of things (IoT), information technology, and others. Big data refers to the collection of data that is too complex and too large for processing by standard database tools. There is no specific quantity of data, which is set as a minimum level to be considered Big data. Image the data collected on global credit card transactions. Many governments used Big data analysis to study the recent pandemic spread. The term Big data was first introduced in 1980 by Charles Tilly.The term Big data was primarily used in computer science, statistics, and econometrics and was made famous in Silicon Valley in the mid-1990s. What Big Data Can Do for YouBig data is the massive amount of data collected over time that are difficult to analyze and handle because the data sets are so enormous. The records are analyzed for marketing trends in business as well as in the fields of manufacturing, medicine, and science. The types of data include business transactions, e-mail messages, photos, surveillance videos, activity logs, and unstructured text from blogs and social media, as well as the vast amounts of data that can be collected from sensors of all varieties. Big data can also refer to the analytical challenge in deriving meaningful information from data in petabyte and exabyte volumes. For example, big data analytics breaks down the data sets into smaller chunks for efficient processing and employs parallel computing to derive intelligence for effective decision-making.Big data is used in a wide range of industries, sectors, or applications. This includes benefits for governments, healthcare, finance, education, media, internet of things (IoT), information technology, and others. Read this Term. Big data itself provides the fuel for Machine Learning Machine Learning Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained through observational behavior. For example, the process of learning begins with observations or data.This includes examples and indirect experience or instruction to help detect patterns in data. In doing so, the goal is to make better decisions in the future based on the examples that are provided. In an ideal set of circumstances, computers learn automatically without human intervention or assistance and adjust actions accordingly.Machine learning can take two different form, i.e. supervised or unsupervised learning. Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. As such, the system is able to provide targets for any new input after sufficient levels of training. Learning algorithm can also compare its output to find errors in order to modify the model accordingly.By extension, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data. Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained through observational behavior. For example, the process of learning begins with observations or data.This includes examples and indirect experience or instruction to help detect patterns in data. In doing so, the goal is to make better decisions in the future based on the examples that are provided. In an ideal set of circumstances, computers learn automatically without human intervention or assistance and adjust actions accordingly.Machine learning can take two different form, i.e. supervised or unsupervised learning. Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. As such, the system is able to provide targets for any new input after sufficient levels of training. Learning algorithm can also compare its output to find errors in order to modify the model accordingly.By extension, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data. Read this Term and artificial intelligence, the basis of the next generation of IoT applications.
This is why I say today’s CEO must form a strategy for the largely unknown, a constantly evolving strategy that can be changed, modified or replaced at a moment’s notice.
For the enterprise, and for government services, this means having your IT and business departments use common tools, speaking a common language and co-developing new applications as rapidly as the market demands.
It means having the ability to identify market trends early, spot individual business or external events in real-time and having the flexibility and agility to change your business operations to the new situation as fast as needed. Information, analysis and the ability to respond are key to future success.
The speed of adapting to change is of the essence. The faster the enterprise moves the easier it is to turn a business challenge into a new business opportunity. Only the slow movers will view digitalization as a threat. To do nothing is not an option.
The enterprise transformation demanded by digitalization dwarfs any other factors impacting the global economy - from low oil and commodity prices to sub-zero interest rates - these constantly changing issues themselves constitute a part of the unknown, they could be out of date by the time you read this.
This article was written by Karl-Heinz Streibich, CEO and Chairman of the Management Board at Software AG.