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You’ve got to start with the customer experience

Anthony Shorten - Mon, 2018-05-21 05:47

Visionary business leader Steve Jobs once remarked: ‘You’ve got to start with the customer experience and work backwards to the technology.’ From someone who spent his life creating definitive customer experiences in technology itself, these words should carry some weight—and are as true today as ever.

The fact is that customer experience is a science, and relevance is its key goal. A powerful customer experience is essential to compete today. And relevance is what cuts through the noise of the market to actually make the connection with customers.

 

The fundamentals of success

For companies to transform their customer experience, they need to be able to streamline their processes and create innovative customer experiences. They also have to be able to deliver by connecting all their internal teams together so they always speak with one consistent voice.

But that’s only part of the story. Customers have real choice today. They’re inundated with similar messages to yours and are becoming increasingly discerning in their tastes.

Making yourself relevant depends on the strength of your offering and content, and the effectiveness of your audience targeting. It also depends on your technical capabilities. Many of your competitors will already be experimenting with powerful new technologies to increase loyalty and drive stronger margins.

 

The value of data

Learning to collect and use relevant customer data is essential. Data is the lifeblood of modern business—it’s the basis of being able to deliver any kind of personalised service on a large scale. Businesses need to use data to analyse behaviour, create profiles for potential new customers, build propositions around those target personas and then deliver a compelling experience. They also need to continually capture new data at every touchpoint to constantly improve their offerings.

Artificial intelligence (AI) and machine learning (ML) have a key role to play both in the analysis of the data and also in the automation of the customer experience. These technologies are developing at speed to enable us to improve our data analysis, pre-empt changing customer tastes and automate parts of service delivery.

 

More mature digital marketing

You can also now add in all kinds of technologies to the customer experience mix that are straight out of sci-fi. The internet of things (IoT) is here, with connected devices providing help in all kinds of areas—from keeping you on the right road to telling you when your vehicle needs maintenance, from providing updates on your order status to delivering personal service wherever you are, and much more—enabling you to drive real transformation.

Moreover, intelligent bots are making it much easier to provide high-quality, cost-effective, round-the-clock customer support—able to deal with a wide range of issues—and using ML to improve their own performance over time.

Augmented reality makes it possible to add contextual information, based on your own products and services, to real-world moments. So, if you’re a car manufacturer you may wish to provide help with simple roadside repairs (e.g. change of tire) via a smartphone app.

 

Always omnichannel

Finally, whether at the pre-sale or delivery stage, your customer experience platform must give you the ability to deliver consistency at every touchpoint. Whatever channel, whatever time, whatever context, your customers must all believe that your whole business is one person.

Indeed, as Michael Schrage, author of the Harvard Business Review, said: ‘Innovation is an investment in the capabilities and competencies of your customers. Your future depends on their future.’ So you have to get as close as possible to your customers to learn what they want today, and understand what experiences they are likely to want tomorrow. Work backwards from that and use any technology that can help you deliver it.

You’ve got to start with the customer experience

Anshu Sharma - Mon, 2018-05-21 05:47

Visionary business leader Steve Jobs once remarked: ‘You’ve got to start with the customer experience and work backwards to the technology.’ From someone who spent his life creating definitive customer experiences in technology itself, these words should carry some weight—and are as true today as ever.

The fact is that customer experience is a science, and relevance is its key goal. A powerful customer experience is essential to compete today. And relevance is what cuts through the noise of the market to actually make the connection with customers.

 

The fundamentals of success

For companies to transform their customer experience, they need to be able to streamline their processes and create innovative customer experiences. They also have to be able to deliver by connecting all their internal teams together so they always speak with one consistent voice.

But that’s only part of the story. Customers have real choice today. They’re inundated with similar messages to yours and are becoming increasingly discerning in their tastes.

Making yourself relevant depends on the strength of your offering and content, and the effectiveness of your audience targeting. It also depends on your technical capabilities. Many of your competitors will already be experimenting with powerful new technologies to increase loyalty and drive stronger margins.

 

The value of data

Learning to collect and use relevant customer data is essential. Data is the lifeblood of modern business—it’s the basis of being able to deliver any kind of personalised service on a large scale. Businesses need to use data to analyse behaviour, create profiles for potential new customers, build propositions around those target personas and then deliver a compelling experience. They also need to continually capture new data at every touchpoint to constantly improve their offerings.

Artificial intelligence (AI) and machine learning (ML) have a key role to play both in the analysis of the data and also in the automation of the customer experience. These technologies are developing at speed to enable us to improve our data analysis, pre-empt changing customer tastes and automate parts of service delivery.

 

More mature digital marketing

You can also now add in all kinds of technologies to the customer experience mix that are straight out of sci-fi. The internet of things (IoT) is here, with connected devices providing help in all kinds of areas—from keeping you on the right road to telling you when your vehicle needs maintenance, from providing updates on your order status to delivering personal service wherever you are, and much more—enabling you to drive real transformation.

Moreover, intelligent bots are making it much easier to provide high-quality, cost-effective, round-the-clock customer support—able to deal with a wide range of issues—and using ML to improve their own performance over time.

Augmented reality makes it possible to add contextual information, based on your own products and services, to real-world moments. So, if you’re a car manufacturer you may wish to provide help with simple roadside repairs (e.g. change of tire) via a smartphone app.

 

Always omnichannel

Finally, whether at the pre-sale or delivery stage, your customer experience platform must give you the ability to deliver consistency at every touchpoint. Whatever channel, whatever time, whatever context, your customers must all believe that your whole business is one person.

Indeed, as Michael Schrage, author of the Harvard Business Review, said: ‘Innovation is an investment in the capabilities and competencies of your customers. Your future depends on their future.’ So you have to get as close as possible to your customers to learn what they want today, and understand what experiences they are likely to want tomorrow. Work backwards from that and use any technology that can help you deliver it.

You’ve got to start with the customer experience

Angelo Santagata - Mon, 2018-05-21 05:47

Visionary business leader Steve Jobs once remarked: ‘You’ve got to start with the customer experience and work backwards to the technology.’ From someone who spent his life creating definitive customer experiences in technology itself, these words should carry some weight—and are as true today as ever.

The fact is that customer experience is a science, and relevance is its key goal. A powerful customer experience is essential to compete today. And relevance is what cuts through the noise of the market to actually make the connection with customers.

 

The fundamentals of success

For companies to transform their customer experience, they need to be able to streamline their processes and create innovative customer experiences. They also have to be able to deliver by connecting all their internal teams together so they always speak with one consistent voice.

But that’s only part of the story. Customers have real choice today. They’re inundated with similar messages to yours and are becoming increasingly discerning in their tastes.

Making yourself relevant depends on the strength of your offering and content, and the effectiveness of your audience targeting. It also depends on your technical capabilities. Many of your competitors will already be experimenting with powerful new technologies to increase loyalty and drive stronger margins.

 

The value of data

Learning to collect and use relevant customer data is essential. Data is the lifeblood of modern business—it’s the basis of being able to deliver any kind of personalised service on a large scale. Businesses need to use data to analyse behaviour, create profiles for potential new customers, build propositions around those target personas and then deliver a compelling experience. They also need to continually capture new data at every touchpoint to constantly improve their offerings.

Artificial intelligence (AI) and machine learning (ML) have a key role to play both in the analysis of the data and also in the automation of the customer experience. These technologies are developing at speed to enable us to improve our data analysis, pre-empt changing customer tastes and automate parts of service delivery.

 

More mature digital marketing

You can also now add in all kinds of technologies to the customer experience mix that are straight out of sci-fi. The internet of things (IoT) is here, with connected devices providing help in all kinds of areas—from keeping you on the right road to telling you when your vehicle needs maintenance, from providing updates on your order status to delivering personal service wherever you are, and much more—enabling you to drive real transformation.

Moreover, intelligent bots are making it much easier to provide high-quality, cost-effective, round-the-clock customer support—able to deal with a wide range of issues—and using ML to improve their own performance over time.

Augmented reality makes it possible to add contextual information, based on your own products and services, to real-world moments. So, if you’re a car manufacturer you may wish to provide help with simple roadside repairs (e.g. change of tire) via a smartphone app.

 

Always omnichannel

Finally, whether at the pre-sale or delivery stage, your customer experience platform must give you the ability to deliver consistency at every touchpoint. Whatever channel, whatever time, whatever context, your customers must all believe that your whole business is one person.

Indeed, as Michael Schrage, author of the Harvard Business Review, said: ‘Innovation is an investment in the capabilities and competencies of your customers. Your future depends on their future.’ So you have to get as close as possible to your customers to learn what they want today, and understand what experiences they are likely to want tomorrow. Work backwards from that and use any technology that can help you deliver it.

How APIs help make application integration intelligent

Christopher Jones - Mon, 2018-05-21 05:47

Artificial intelligence (AI) represents a technology paradigm shift, with the potential to completely revolutionise the way people work over the next few years. Application programming interfaces (APIs) are crucially important in enabling the rapid development of these AI applications. Conversely AI is also being used to validate APIs, themselves, and also to analyse and optimise their performance.

Wikipedia defines an API as a ‘set of subroutine definitions, protocols and tools for building application software’. In slightly less dry terms, an API is basically a gateway to the core capabilities of an application, enabling that functionality to be built into other software. So, for example, if you were creating an app that needed to show geographic location, you might choose to implement Google Maps’ API. It’s obviously much easier, faster and future-proof to do that than to build your own mapping application from scratch.

 

How APIs are used in AI

And that’s the key strength of API—it’s a hugely efficient way of enabling networked systems to communicate and draw on each other’s functionality, offering major benefits for creating AI applications.

Artificially intelligent machine ‘skills’ are, of course, just applications that can be provided as APIs. So if you ask your voice-activated smart device—whether it’s Siri, Cortana, Google Assistant, or any of the rest—what time you can get to the Town Hall via bus, it’s response will depend on various skills that might include:

  • Awareness of where you are—from a geo-location API
  • Knowledge of bus routes and service delays in your area—from a publicly available bus company API
  • Tracking of general traffic and passenger levels—from APIs that show user locations provided by mobile device manufacturers
  • Being able to find the town hall—from a mapping API

None of these APIs needs to know anything about the others. They simply take information in a pre-defined format and output data in their own way. The AI application, itself, has to understand each API’s data parameters, tie all their skills together, apply the intelligence and then process the data.

 

Everything is possible

That means you can combine the seemingly infinite number of APIs that exist in any way you like, giving you the power to produce highly advanced applications—and create unique sources of value for your business. You could potentially build apps to enhance the customer experience, improve your internal processes, and analyse data more effectively to strengthen decision making—and perhaps even identify whole new areas of business to get into.

 

How AI is being used to improve APIs

APIs are the ideal way of getting information into AI applications and also helping to streamline analytics—yet artificial intelligence also has a vital role to play within API development itself. For example, AI can be used to automatically create, validate and maintain API software development kits (implementations of APIs in multiple different programming languages).

AI can also be used to monitor API traffic. By analysing calls to APIs using intelligent algorithms, you can identify problems and trends, potentially helping you tailor and improve the APIs over time. Indeed, AI can be used to analyse internal company system APIs, for example, helping you score sales leads, predict customer behaviour, optimise elements of your supply chain, and much more.

 

How APIs help make application integration intelligent

Chris Warticki - Mon, 2018-05-21 05:47

Artificial intelligence (AI) represents a technology paradigm shift, with the potential to completely revolutionise the way people work over the next few years. Application programming interfaces (APIs) are crucially important in enabling the rapid development of these AI applications. Conversely AI is also being used to validate APIs, themselves, and also to analyse and optimise their performance.

Wikipedia defines an API as a ‘set of subroutine definitions, protocols and tools for building application software’. In slightly less dry terms, an API is basically a gateway to the core capabilities of an application, enabling that functionality to be built into other software. So, for example, if you were creating an app that needed to show geographic location, you might choose to implement Google Maps’ API. It’s obviously much easier, faster and future-proof to do that than to build your own mapping application from scratch.

 

How APIs are used in AI

And that’s the key strength of API—it’s a hugely efficient way of enabling networked systems to communicate and draw on each other’s functionality, offering major benefits for creating AI applications.

Artificially intelligent machine ‘skills’ are, of course, just applications that can be provided as APIs. So if you ask your voice-activated smart device—whether it’s Siri, Cortana, Google Assistant, or any of the rest—what time you can get to the Town Hall via bus, it’s response will depend on various skills that might include:

  • Awareness of where you are—from a geo-location API
  • Knowledge of bus routes and service delays in your area—from a publicly available bus company API
  • Tracking of general traffic and passenger levels—from APIs that show user locations provided by mobile device manufacturers
  • Being able to find the town hall—from a mapping API

None of these APIs needs to know anything about the others. They simply take information in a pre-defined format and output data in their own way. The AI application, itself, has to understand each API’s data parameters, tie all their skills together, apply the intelligence and then process the data.

 

Everything is possible

That means you can combine the seemingly infinite number of APIs that exist in any way you like, giving you the power to produce highly advanced applications—and create unique sources of value for your business. You could potentially build apps to enhance the customer experience, improve your internal processes, and analyse data more effectively to strengthen decision making—and perhaps even identify whole new areas of business to get into.

 

How AI is being used to improve APIs

APIs are the ideal way of getting information into AI applications and also helping to streamline analytics—yet artificial intelligence also has a vital role to play within API development itself. For example, AI can be used to automatically create, validate and maintain API software development kits (implementations of APIs in multiple different programming languages).

AI can also be used to monitor API traffic. By analysing calls to APIs using intelligent algorithms, you can identify problems and trends, potentially helping you tailor and improve the APIs over time. Indeed, AI can be used to analyse internal company system APIs, for example, helping you score sales leads, predict customer behaviour, optimise elements of your supply chain, and much more.

 

How APIs help make application integration intelligent

Antony Reynolds - Mon, 2018-05-21 05:47

Artificial intelligence (AI) represents a technology paradigm shift, with the potential to completely revolutionise the way people work over the next few years. Application programming interfaces (APIs) are crucially important in enabling the rapid development of these AI applications. Conversely AI is also being used to validate APIs, themselves, and also to analyse and optimise their performance.

Wikipedia defines an API as a ‘set of subroutine definitions, protocols and tools for building application software’. In slightly less dry terms, an API is basically a gateway to the core capabilities of an application, enabling that functionality to be built into other software. So, for example, if you were creating an app that needed to show geographic location, you might choose to implement Google Maps’ API. It’s obviously much easier, faster and future-proof to do that than to build your own mapping application from scratch.

 

How APIs are used in AI

And that’s the key strength of API—it’s a hugely efficient way of enabling networked systems to communicate and draw on each other’s functionality, offering major benefits for creating AI applications.

Artificially intelligent machine ‘skills’ are, of course, just applications that can be provided as APIs. So if you ask your voice-activated smart device—whether it’s Siri, Cortana, Google Assistant, or any of the rest—what time you can get to the Town Hall via bus, it’s response will depend on various skills that might include:

  • Awareness of where you are—from a geo-location API
  • Knowledge of bus routes and service delays in your area—from a publicly available bus company API
  • Tracking of general traffic and passenger levels—from APIs that show user locations provided by mobile device manufacturers
  • Being able to find the town hall—from a mapping API

None of these APIs needs to know anything about the others. They simply take information in a pre-defined format and output data in their own way. The AI application, itself, has to understand each API’s data parameters, tie all their skills together, apply the intelligence and then process the data.

 

Everything is possible

That means you can combine the seemingly infinite number of APIs that exist in any way you like, giving you the power to produce highly advanced applications—and create unique sources of value for your business. You could potentially build apps to enhance the customer experience, improve your internal processes, and analyse data more effectively to strengthen decision making—and perhaps even identify whole new areas of business to get into.

 

How AI is being used to improve APIs

APIs are the ideal way of getting information into AI applications and also helping to streamline analytics—yet artificial intelligence also has a vital role to play within API development itself. For example, AI can be used to automatically create, validate and maintain API software development kits (implementations of APIs in multiple different programming languages).

AI can also be used to monitor API traffic. By analysing calls to APIs using intelligent algorithms, you can identify problems and trends, potentially helping you tailor and improve the APIs over time. Indeed, AI can be used to analyse internal company system APIs, for example, helping you score sales leads, predict customer behaviour, optimise elements of your supply chain, and much more.

 

How APIs help make application integration intelligent

Antonio Romero - Mon, 2018-05-21 05:47

Artificial intelligence (AI) represents a technology paradigm shift, with the potential to completely revolutionise the way people work over the next few years. Application programming interfaces (APIs) are crucially important in enabling the rapid development of these AI applications. Conversely AI is also being used to validate APIs, themselves, and also to analyse and optimise their performance.

Wikipedia defines an API as a ‘set of subroutine definitions, protocols and tools for building application software’. In slightly less dry terms, an API is basically a gateway to the core capabilities of an application, enabling that functionality to be built into other software. So, for example, if you were creating an app that needed to show geographic location, you might choose to implement Google Maps’ API. It’s obviously much easier, faster and future-proof to do that than to build your own mapping application from scratch.

 

How APIs are used in AI

And that’s the key strength of API—it’s a hugely efficient way of enabling networked systems to communicate and draw on each other’s functionality, offering major benefits for creating AI applications.

Artificially intelligent machine ‘skills’ are, of course, just applications that can be provided as APIs. So if you ask your voice-activated smart device—whether it’s Siri, Cortana, Google Assistant, or any of the rest—what time you can get to the Town Hall via bus, it’s response will depend on various skills that might include:

  • Awareness of where you are—from a geo-location API
  • Knowledge of bus routes and service delays in your area—from a publicly available bus company API
  • Tracking of general traffic and passenger levels—from APIs that show user locations provided by mobile device manufacturers
  • Being able to find the town hall—from a mapping API

None of these APIs needs to know anything about the others. They simply take information in a pre-defined format and output data in their own way. The AI application, itself, has to understand each API’s data parameters, tie all their skills together, apply the intelligence and then process the data.

 

Everything is possible

That means you can combine the seemingly infinite number of APIs that exist in any way you like, giving you the power to produce highly advanced applications—and create unique sources of value for your business. You could potentially build apps to enhance the customer experience, improve your internal processes, and analyse data more effectively to strengthen decision making—and perhaps even identify whole new areas of business to get into.

 

How AI is being used to improve APIs

APIs are the ideal way of getting information into AI applications and also helping to streamline analytics—yet artificial intelligence also has a vital role to play within API development itself. For example, AI can be used to automatically create, validate and maintain API software development kits (implementations of APIs in multiple different programming languages).

AI can also be used to monitor API traffic. By analysing calls to APIs using intelligent algorithms, you can identify problems and trends, potentially helping you tailor and improve the APIs over time. Indeed, AI can be used to analyse internal company system APIs, for example, helping you score sales leads, predict customer behaviour, optimise elements of your supply chain, and much more.

 

How APIs help make application integration intelligent

Anthony Shorten - Mon, 2018-05-21 05:47

Artificial intelligence (AI) represents a technology paradigm shift, with the potential to completely revolutionise the way people work over the next few years. Application programming interfaces (APIs) are crucially important in enabling the rapid development of these AI applications. Conversely AI is also being used to validate APIs, themselves, and also to analyse and optimise their performance.

Wikipedia defines an API as a ‘set of subroutine definitions, protocols and tools for building application software’. In slightly less dry terms, an API is basically a gateway to the core capabilities of an application, enabling that functionality to be built into other software. So, for example, if you were creating an app that needed to show geographic location, you might choose to implement Google Maps’ API. It’s obviously much easier, faster and future-proof to do that than to build your own mapping application from scratch.

 

How APIs are used in AI

And that’s the key strength of API—it’s a hugely efficient way of enabling networked systems to communicate and draw on each other’s functionality, offering major benefits for creating AI applications.

Artificially intelligent machine ‘skills’ are, of course, just applications that can be provided as APIs. So if you ask your voice-activated smart device—whether it’s Siri, Cortana, Google Assistant, or any of the rest—what time you can get to the Town Hall via bus, it’s response will depend on various skills that might include:

  • Awareness of where you are—from a geo-location API
  • Knowledge of bus routes and service delays in your area—from a publicly available bus company API
  • Tracking of general traffic and passenger levels—from APIs that show user locations provided by mobile device manufacturers
  • Being able to find the town hall—from a mapping API

None of these APIs needs to know anything about the others. They simply take information in a pre-defined format and output data in their own way. The AI application, itself, has to understand each API’s data parameters, tie all their skills together, apply the intelligence and then process the data.

 

Everything is possible

That means you can combine the seemingly infinite number of APIs that exist in any way you like, giving you the power to produce highly advanced applications—and create unique sources of value for your business. You could potentially build apps to enhance the customer experience, improve your internal processes, and analyse data more effectively to strengthen decision making—and perhaps even identify whole new areas of business to get into.

 

How AI is being used to improve APIs

APIs are the ideal way of getting information into AI applications and also helping to streamline analytics—yet artificial intelligence also has a vital role to play within API development itself. For example, AI can be used to automatically create, validate and maintain API software development kits (implementations of APIs in multiple different programming languages).

AI can also be used to monitor API traffic. By analysing calls to APIs using intelligent algorithms, you can identify problems and trends, potentially helping you tailor and improve the APIs over time. Indeed, AI can be used to analyse internal company system APIs, for example, helping you score sales leads, predict customer behaviour, optimise elements of your supply chain, and much more.

 

How APIs help make application integration intelligent

Anshu Sharma - Mon, 2018-05-21 05:47

Artificial intelligence (AI) represents a technology paradigm shift, with the potential to completely revolutionise the way people work over the next few years. Application programming interfaces (APIs) are crucially important in enabling the rapid development of these AI applications. Conversely AI is also being used to validate APIs, themselves, and also to analyse and optimise their performance.

Wikipedia defines an API as a ‘set of subroutine definitions, protocols and tools for building application software’. In slightly less dry terms, an API is basically a gateway to the core capabilities of an application, enabling that functionality to be built into other software. So, for example, if you were creating an app that needed to show geographic location, you might choose to implement Google Maps’ API. It’s obviously much easier, faster and future-proof to do that than to build your own mapping application from scratch.

 

How APIs are used in AI

And that’s the key strength of API—it’s a hugely efficient way of enabling networked systems to communicate and draw on each other’s functionality, offering major benefits for creating AI applications.

Artificially intelligent machine ‘skills’ are, of course, just applications that can be provided as APIs. So if you ask your voice-activated smart device—whether it’s Siri, Cortana, Google Assistant, or any of the rest—what time you can get to the Town Hall via bus, it’s response will depend on various skills that might include:

  • Awareness of where you are—from a geo-location API
  • Knowledge of bus routes and service delays in your area—from a publicly available bus company API
  • Tracking of general traffic and passenger levels—from APIs that show user locations provided by mobile device manufacturers
  • Being able to find the town hall—from a mapping API

None of these APIs needs to know anything about the others. They simply take information in a pre-defined format and output data in their own way. The AI application, itself, has to understand each API’s data parameters, tie all their skills together, apply the intelligence and then process the data.

 

Everything is possible

That means you can combine the seemingly infinite number of APIs that exist in any way you like, giving you the power to produce highly advanced applications—and create unique sources of value for your business. You could potentially build apps to enhance the customer experience, improve your internal processes, and analyse data more effectively to strengthen decision making—and perhaps even identify whole new areas of business to get into.

 

How AI is being used to improve APIs

APIs are the ideal way of getting information into AI applications and also helping to streamline analytics—yet artificial intelligence also has a vital role to play within API development itself. For example, AI can be used to automatically create, validate and maintain API software development kits (implementations of APIs in multiple different programming languages).

AI can also be used to monitor API traffic. By analysing calls to APIs using intelligent algorithms, you can identify problems and trends, potentially helping you tailor and improve the APIs over time. Indeed, AI can be used to analyse internal company system APIs, for example, helping you score sales leads, predict customer behaviour, optimise elements of your supply chain, and much more.

 

How APIs help make application integration intelligent

Angelo Santagata - Mon, 2018-05-21 05:47

Artificial intelligence (AI) represents a technology paradigm shift, with the potential to completely revolutionise the way people work over the next few years. Application programming interfaces (APIs) are crucially important in enabling the rapid development of these AI applications. Conversely AI is also being used to validate APIs, themselves, and also to analyse and optimise their performance.

Wikipedia defines an API as a ‘set of subroutine definitions, protocols and tools for building application software’. In slightly less dry terms, an API is basically a gateway to the core capabilities of an application, enabling that functionality to be built into other software. So, for example, if you were creating an app that needed to show geographic location, you might choose to implement Google Maps’ API. It’s obviously much easier, faster and future-proof to do that than to build your own mapping application from scratch.

 

How APIs are used in AI

And that’s the key strength of API—it’s a hugely efficient way of enabling networked systems to communicate and draw on each other’s functionality, offering major benefits for creating AI applications.

Artificially intelligent machine ‘skills’ are, of course, just applications that can be provided as APIs. So if you ask your voice-activated smart device—whether it’s Siri, Cortana, Google Assistant, or any of the rest—what time you can get to the Town Hall via bus, it’s response will depend on various skills that might include:

  • Awareness of where you are—from a geo-location API
  • Knowledge of bus routes and service delays in your area—from a publicly available bus company API
  • Tracking of general traffic and passenger levels—from APIs that show user locations provided by mobile device manufacturers
  • Being able to find the town hall—from a mapping API

None of these APIs needs to know anything about the others. They simply take information in a pre-defined format and output data in their own way. The AI application, itself, has to understand each API’s data parameters, tie all their skills together, apply the intelligence and then process the data.

 

Everything is possible

That means you can combine the seemingly infinite number of APIs that exist in any way you like, giving you the power to produce highly advanced applications—and create unique sources of value for your business. You could potentially build apps to enhance the customer experience, improve your internal processes, and analyse data more effectively to strengthen decision making—and perhaps even identify whole new areas of business to get into.

 

How AI is being used to improve APIs

APIs are the ideal way of getting information into AI applications and also helping to streamline analytics—yet artificial intelligence also has a vital role to play within API development itself. For example, AI can be used to automatically create, validate and maintain API software development kits (implementations of APIs in multiple different programming languages).

AI can also be used to monitor API traffic. By analysing calls to APIs using intelligent algorithms, you can identify problems and trends, potentially helping you tailor and improve the APIs over time. Indeed, AI can be used to analyse internal company system APIs, for example, helping you score sales leads, predict customer behaviour, optimise elements of your supply chain, and much more.

 

GDPR: What are the priorities for the IT department?

Christopher Jones - Mon, 2018-05-21 05:46

All too often it is assumed that GDPR compliance is ‘IT’s problem’ because having your personal data and technology in order are such vital parts of it. But compliance must be an organisation-wide commitment. No individual or single department can make an organisation compliant. However, in planning discussions around GDPR compliance, there are clear areas where IT can add significant value.

 

1. Be a data champion

The potential value of data to organisations is increasing all the time, but many departments, business units and even board members may not realise how much data they have access to, where it resides, how it is created, how it could be used and how it is protected. The IT department can play a clear role in helping organisations understand why data, and by extension GDPR, is so important in order to realise the value of such data and how to use and protect it.

 

2. Ensure data security

GDPR considers protection of personal data a fundamental human right. Organisations need to ensure they understand what personal data they have access to and put in place appropriate protective measures. IT has a role to play in working with the organisation to assess security risks and ensure that appropriate protective measures, such as encryption, access controls, attack prevention and detection are in place.

 

3. Help the organisation be responsive

GDPR requires organisations to not only protect personal data but also respond to requests from individuals who, among others, want to amend or delete data held on them. That means that the personal data must be collected, collated and structured in a way that enables effective and reliable control over all personal data. This means breaking down internal silos and ensuring an organisation has a clear view of its processing activities with regard to personal data.

 

4. Identify the best tools for the job

GDPR compliance is as much about process, culture and planning as it is about technology. However, there are products available which can help organisations with key elements of GDPR compliance, such as data management, security and the automated enforcement of security measures. Advances in automation and artificial intelligence mean many tools offer a level of proactivity and scalability which don’t lessen the responsibility upon people within the organisation, but can reduce the workload and put in place an approach which can evolve with changing compliance requirements.

 

5. See the potential

An improved approach to security and compliance management, fit for the digital economy, can give organisations the confidence to unlock the full potential of their data. If data is more secure, better ordered and easier to make sense of, it stands to reason an organisation can do more with it. It may be tempting to see GDPR as an unwelcome chore. It should however be borne in mind that it is also an opportunity to seek differentiation and greater value, to build new data-driven business models, confident in the knowledge that the data is being used in a compliant way.  Giving consumers the confidence to share their data is also good for businesses.

 

The IT department will know better than most how the full value of data can be unlocked and can help businesses pull away from seeing GDPR as a cost of doing business and start seeing it as an opportunity to do business better.

GDPR: What are the priorities for the IT department?

Chris Warticki - Mon, 2018-05-21 05:46

All too often it is assumed that GDPR compliance is ‘IT’s problem’ because having your personal data and technology in order are such vital parts of it. But compliance must be an organisation-wide commitment. No individual or single department can make an organisation compliant. However, in planning discussions around GDPR compliance, there are clear areas where IT can add significant value.

 

1. Be a data champion

The potential value of data to organisations is increasing all the time, but many departments, business units and even board members may not realise how much data they have access to, where it resides, how it is created, how it could be used and how it is protected. The IT department can play a clear role in helping organisations understand why data, and by extension GDPR, is so important in order to realise the value of such data and how to use and protect it.

 

2. Ensure data security

GDPR considers protection of personal data a fundamental human right. Organisations need to ensure they understand what personal data they have access to and put in place appropriate protective measures. IT has a role to play in working with the organisation to assess security risks and ensure that appropriate protective measures, such as encryption, access controls, attack prevention and detection are in place.

 

3. Help the organisation be responsive

GDPR requires organisations to not only protect personal data but also respond to requests from individuals who, among others, want to amend or delete data held on them. That means that the personal data must be collected, collated and structured in a way that enables effective and reliable control over all personal data. This means breaking down internal silos and ensuring an organisation has a clear view of its processing activities with regard to personal data.

 

4. Identify the best tools for the job

GDPR compliance is as much about process, culture and planning as it is about technology. However, there are products available which can help organisations with key elements of GDPR compliance, such as data management, security and the automated enforcement of security measures. Advances in automation and artificial intelligence mean many tools offer a level of proactivity and scalability which don’t lessen the responsibility upon people within the organisation, but can reduce the workload and put in place an approach which can evolve with changing compliance requirements.

 

5. See the potential

An improved approach to security and compliance management, fit for the digital economy, can give organisations the confidence to unlock the full potential of their data. If data is more secure, better ordered and easier to make sense of, it stands to reason an organisation can do more with it. It may be tempting to see GDPR as an unwelcome chore. It should however be borne in mind that it is also an opportunity to seek differentiation and greater value, to build new data-driven business models, confident in the knowledge that the data is being used in a compliant way.  Giving consumers the confidence to share their data is also good for businesses.

 

The IT department will know better than most how the full value of data can be unlocked and can help businesses pull away from seeing GDPR as a cost of doing business and start seeing it as an opportunity to do business better.

GDPR: What are the priorities for the IT department?

Antony Reynolds - Mon, 2018-05-21 05:46

All too often it is assumed that GDPR compliance is ‘IT’s problem’ because having your personal data and technology in order are such vital parts of it. But compliance must be an organisation-wide commitment. No individual or single department can make an organisation compliant. However, in planning discussions around GDPR compliance, there are clear areas where IT can add significant value.

 

1. Be a data champion

The potential value of data to organisations is increasing all the time, but many departments, business units and even board members may not realise how much data they have access to, where it resides, how it is created, how it could be used and how it is protected. The IT department can play a clear role in helping organisations understand why data, and by extension GDPR, is so important in order to realise the value of such data and how to use and protect it.

 

2. Ensure data security

GDPR considers protection of personal data a fundamental human right. Organisations need to ensure they understand what personal data they have access to and put in place appropriate protective measures. IT has a role to play in working with the organisation to assess security risks and ensure that appropriate protective measures, such as encryption, access controls, attack prevention and detection are in place.

 

3. Help the organisation be responsive

GDPR requires organisations to not only protect personal data but also respond to requests from individuals who, among others, want to amend or delete data held on them. That means that the personal data must be collected, collated and structured in a way that enables effective and reliable control over all personal data. This means breaking down internal silos and ensuring an organisation has a clear view of its processing activities with regard to personal data.

 

4. Identify the best tools for the job

GDPR compliance is as much about process, culture and planning as it is about technology. However, there are products available which can help organisations with key elements of GDPR compliance, such as data management, security and the automated enforcement of security measures. Advances in automation and artificial intelligence mean many tools offer a level of proactivity and scalability which don’t lessen the responsibility upon people within the organisation, but can reduce the workload and put in place an approach which can evolve with changing compliance requirements.

 

5. See the potential

An improved approach to security and compliance management, fit for the digital economy, can give organisations the confidence to unlock the full potential of their data. If data is more secure, better ordered and easier to make sense of, it stands to reason an organisation can do more with it. It may be tempting to see GDPR as an unwelcome chore. It should however be borne in mind that it is also an opportunity to seek differentiation and greater value, to build new data-driven business models, confident in the knowledge that the data is being used in a compliant way.  Giving consumers the confidence to share their data is also good for businesses.

 

The IT department will know better than most how the full value of data can be unlocked and can help businesses pull away from seeing GDPR as a cost of doing business and start seeing it as an opportunity to do business better.

GDPR: What are the priorities for the IT department?

Antonio Romero - Mon, 2018-05-21 05:46

All too often it is assumed that GDPR compliance is ‘IT’s problem’ because having your personal data and technology in order are such vital parts of it. But compliance must be an organisation-wide commitment. No individual or single department can make an organisation compliant. However, in planning discussions around GDPR compliance, there are clear areas where IT can add significant value.

 

1. Be a data champion

The potential value of data to organisations is increasing all the time, but many departments, business units and even board members may not realise how much data they have access to, where it resides, how it is created, how it could be used and how it is protected. The IT department can play a clear role in helping organisations understand why data, and by extension GDPR, is so important in order to realise the value of such data and how to use and protect it.

 

2. Ensure data security

GDPR considers protection of personal data a fundamental human right. Organisations need to ensure they understand what personal data they have access to and put in place appropriate protective measures. IT has a role to play in working with the organisation to assess security risks and ensure that appropriate protective measures, such as encryption, access controls, attack prevention and detection are in place.

 

3. Help the organisation be responsive

GDPR requires organisations to not only protect personal data but also respond to requests from individuals who, among others, want to amend or delete data held on them. That means that the personal data must be collected, collated and structured in a way that enables effective and reliable control over all personal data. This means breaking down internal silos and ensuring an organisation has a clear view of its processing activities with regard to personal data.

 

4. Identify the best tools for the job

GDPR compliance is as much about process, culture and planning as it is about technology. However, there are products available which can help organisations with key elements of GDPR compliance, such as data management, security and the automated enforcement of security measures. Advances in automation and artificial intelligence mean many tools offer a level of proactivity and scalability which don’t lessen the responsibility upon people within the organisation, but can reduce the workload and put in place an approach which can evolve with changing compliance requirements.

 

5. See the potential

An improved approach to security and compliance management, fit for the digital economy, can give organisations the confidence to unlock the full potential of their data. If data is more secure, better ordered and easier to make sense of, it stands to reason an organisation can do more with it. It may be tempting to see GDPR as an unwelcome chore. It should however be borne in mind that it is also an opportunity to seek differentiation and greater value, to build new data-driven business models, confident in the knowledge that the data is being used in a compliant way.  Giving consumers the confidence to share their data is also good for businesses.

 

The IT department will know better than most how the full value of data can be unlocked and can help businesses pull away from seeing GDPR as a cost of doing business and start seeing it as an opportunity to do business better.

GDPR: What are the priorities for the IT department?

Anthony Shorten - Mon, 2018-05-21 05:46

All too often it is assumed that GDPR compliance is ‘IT’s problem’ because having your personal data and technology in order are such vital parts of it. But compliance must be an organisation-wide commitment. No individual or single department can make an organisation compliant. However, in planning discussions around GDPR compliance, there are clear areas where IT can add significant value.

 

1. Be a data champion

The potential value of data to organisations is increasing all the time, but many departments, business units and even board members may not realise how much data they have access to, where it resides, how it is created, how it could be used and how it is protected. The IT department can play a clear role in helping organisations understand why data, and by extension GDPR, is so important in order to realise the value of such data and how to use and protect it.

 

2. Ensure data security

GDPR considers protection of personal data a fundamental human right. Organisations need to ensure they understand what personal data they have access to and put in place appropriate protective measures. IT has a role to play in working with the organisation to assess security risks and ensure that appropriate protective measures, such as encryption, access controls, attack prevention and detection are in place.

 

3. Help the organisation be responsive

GDPR requires organisations to not only protect personal data but also respond to requests from individuals who, among others, want to amend or delete data held on them. That means that the personal data must be collected, collated and structured in a way that enables effective and reliable control over all personal data. This means breaking down internal silos and ensuring an organisation has a clear view of its processing activities with regard to personal data.

 

4. Identify the best tools for the job

GDPR compliance is as much about process, culture and planning as it is about technology. However, there are products available which can help organisations with key elements of GDPR compliance, such as data management, security and the automated enforcement of security measures. Advances in automation and artificial intelligence mean many tools offer a level of proactivity and scalability which don’t lessen the responsibility upon people within the organisation, but can reduce the workload and put in place an approach which can evolve with changing compliance requirements.

 

5. See the potential

An improved approach to security and compliance management, fit for the digital economy, can give organisations the confidence to unlock the full potential of their data. If data is more secure, better ordered and easier to make sense of, it stands to reason an organisation can do more with it. It may be tempting to see GDPR as an unwelcome chore. It should however be borne in mind that it is also an opportunity to seek differentiation and greater value, to build new data-driven business models, confident in the knowledge that the data is being used in a compliant way.  Giving consumers the confidence to share their data is also good for businesses.

 

The IT department will know better than most how the full value of data can be unlocked and can help businesses pull away from seeing GDPR as a cost of doing business and start seeing it as an opportunity to do business better.

GDPR: What are the priorities for the IT department?

Anshu Sharma - Mon, 2018-05-21 05:46

All too often it is assumed that GDPR compliance is ‘IT’s problem’ because having your personal data and technology in order are such vital parts of it. But compliance must be an organisation-wide commitment. No individual or single department can make an organisation compliant. However, in planning discussions around GDPR compliance, there are clear areas where IT can add significant value.

 

1. Be a data champion

The potential value of data to organisations is increasing all the time, but many departments, business units and even board members may not realise how much data they have access to, where it resides, how it is created, how it could be used and how it is protected. The IT department can play a clear role in helping organisations understand why data, and by extension GDPR, is so important in order to realise the value of such data and how to use and protect it.

 

2. Ensure data security

GDPR considers protection of personal data a fundamental human right. Organisations need to ensure they understand what personal data they have access to and put in place appropriate protective measures. IT has a role to play in working with the organisation to assess security risks and ensure that appropriate protective measures, such as encryption, access controls, attack prevention and detection are in place.

 

3. Help the organisation be responsive

GDPR requires organisations to not only protect personal data but also respond to requests from individuals who, among others, want to amend or delete data held on them. That means that the personal data must be collected, collated and structured in a way that enables effective and reliable control over all personal data. This means breaking down internal silos and ensuring an organisation has a clear view of its processing activities with regard to personal data.

 

4. Identify the best tools for the job

GDPR compliance is as much about process, culture and planning as it is about technology. However, there are products available which can help organisations with key elements of GDPR compliance, such as data management, security and the automated enforcement of security measures. Advances in automation and artificial intelligence mean many tools offer a level of proactivity and scalability which don’t lessen the responsibility upon people within the organisation, but can reduce the workload and put in place an approach which can evolve with changing compliance requirements.

 

5. See the potential

An improved approach to security and compliance management, fit for the digital economy, can give organisations the confidence to unlock the full potential of their data. If data is more secure, better ordered and easier to make sense of, it stands to reason an organisation can do more with it. It may be tempting to see GDPR as an unwelcome chore. It should however be borne in mind that it is also an opportunity to seek differentiation and greater value, to build new data-driven business models, confident in the knowledge that the data is being used in a compliant way.  Giving consumers the confidence to share their data is also good for businesses.

 

The IT department will know better than most how the full value of data can be unlocked and can help businesses pull away from seeing GDPR as a cost of doing business and start seeing it as an opportunity to do business better.

GDPR: What are the priorities for the IT department?

Angelo Santagata - Mon, 2018-05-21 05:46

All too often it is assumed that GDPR compliance is ‘IT’s problem’ because having your personal data and technology in order are such vital parts of it. But compliance must be an organisation-wide commitment. No individual or single department can make an organisation compliant. However, in planning discussions around GDPR compliance, there are clear areas where IT can add significant value.

 

1. Be a data champion

The potential value of data to organisations is increasing all the time, but many departments, business units and even board members may not realise how much data they have access to, where it resides, how it is created, how it could be used and how it is protected. The IT department can play a clear role in helping organisations understand why data, and by extension GDPR, is so important in order to realise the value of such data and how to use and protect it.

 

2. Ensure data security

GDPR considers protection of personal data a fundamental human right. Organisations need to ensure they understand what personal data they have access to and put in place appropriate protective measures. IT has a role to play in working with the organisation to assess security risks and ensure that appropriate protective measures, such as encryption, access controls, attack prevention and detection are in place.

 

3. Help the organisation be responsive

GDPR requires organisations to not only protect personal data but also respond to requests from individuals who, among others, want to amend or delete data held on them. That means that the personal data must be collected, collated and structured in a way that enables effective and reliable control over all personal data. This means breaking down internal silos and ensuring an organisation has a clear view of its processing activities with regard to personal data.

 

4. Identify the best tools for the job

GDPR compliance is as much about process, culture and planning as it is about technology. However, there are products available which can help organisations with key elements of GDPR compliance, such as data management, security and the automated enforcement of security measures. Advances in automation and artificial intelligence mean many tools offer a level of proactivity and scalability which don’t lessen the responsibility upon people within the organisation, but can reduce the workload and put in place an approach which can evolve with changing compliance requirements.

 

5. See the potential

An improved approach to security and compliance management, fit for the digital economy, can give organisations the confidence to unlock the full potential of their data. If data is more secure, better ordered and easier to make sense of, it stands to reason an organisation can do more with it. It may be tempting to see GDPR as an unwelcome chore. It should however be borne in mind that it is also an opportunity to seek differentiation and greater value, to build new data-driven business models, confident in the knowledge that the data is being used in a compliant way.  Giving consumers the confidence to share their data is also good for businesses.

 

The IT department will know better than most how the full value of data can be unlocked and can help businesses pull away from seeing GDPR as a cost of doing business and start seeing it as an opportunity to do business better.

Autonomous: A New Lens for Analytics

Christopher Jones - Mon, 2018-05-21 05:45

Welcome to the era of intelligent, self-driving software. Just as self-driving vehicles are set to transform motoring, self-driving software promises to transform our productivity, and strengthen our analytical abilities.

Perhaps you drive an automatic car today—how much are you looking forward to the day your car will automatically drive you? And how much more preferable would smoother, less time-consuming journeys be—always via the best route, with fewer hold-ups, and automatically avoiding unexpected road congestion—where you only have to input your destination? The technology is almost here, and similar advances are driving modern business applications.

AI and machine learning are finally coming of age thanks to the recent advances in big data that created—for the first time—data sets that were large enough for computers to draw inferences and learn from. That, along with years of SaaS application development in cloud computing environments, means that autonomous technology—harnessing both AI and business intelligence—is now fuelling self-driving software… for both cars and cloud applications.

 

Autonomy—beyond automation

Automation has, of course, been around for years. But autonomy—running on AI and machine learning—takes it to new levels. Today’s software is truly self-driving—it eliminates the need for humans to provision, secure, monitor, back-up, recover, troubleshoot or tune. It upgrades and patches itself, and automatically applies security updates, all while running normally. Indeed, an autonomous data warehouse, for example, can reduce administration overheads by up to 80%.

 

Intelligent thinking

But the greatest value is perhaps in what AI enables you to discover from your data. When applied to analytics, it can identify patterns in huge data sets that might otherwise go unnoticed. So, for example, you could apply AI to sales data to identify trends—who bought what, where, when and why?—and apply those to improve the accuracy of your future forecasts.

Alternatively, if you were looking for a vibrant location for new business premises, you might use AI to search for an area with a strong social media buzz around its restaurants and bars. You could teach the software to look for specific words or phrases, and harness machine learning to improve results over time.

AI technology is already widely used in HR to take the slog out of sifting through huge numbers of job applications. As well as being faster and requiring less manpower, it’s able to remove both human bias—critical in the highly subjective area of recruitment—and also identify the best candidates based on factors such as the kind of language they use.

 

Knowledge and power for everyone

These technologies are coming online now—today—for everyone. In the past, most database reporting was typically run by data analysts or scientists to update pre-existing dashboards and reports. Nowadays there are many more business users who are demanding access to such insights, which is being made possible by tools that are far easier to use.

Anyone can experiment with large samples of different data sets, combining multiple data formats—structured and unstructured—and discovering new trends. They can get answers in context, at the right time, and convert them into simple-to-understand insights, enabling decisions to be made more quickly for competitive advantages.

 

Smarter and smarter…

Yet it’s the strength of those insights that’s really compelling. As one commentator observed: ‘Machine intelligence can give you answers to questions that you haven’t even thought of.’ The quality of those answers—and their underlying questions—will only improve over time. That’s why it’s becoming a competitive imperative to embrace the power of intelligent analytics to ensure you can keep pace with market leaders.

 

Discover how…

In my last blog, I shared how organisations can profit from data warehouses and data marts, and how Oracle’s self-driving, self-securing, and self-repairing Autonomous Data Warehouse saves resources on maintenance allowing investment in data analytics.

 

Autonomous: A New Lens for Analytics

Chris Warticki - Mon, 2018-05-21 05:45

Welcome to the era of intelligent, self-driving software. Just as self-driving vehicles are set to transform motoring, self-driving software promises to transform our productivity, and strengthen our analytical abilities.

Perhaps you drive an automatic car today—how much are you looking forward to the day your car will automatically drive you? And how much more preferable would smoother, less time-consuming journeys be—always via the best route, with fewer hold-ups, and automatically avoiding unexpected road congestion—where you only have to input your destination? The technology is almost here, and similar advances are driving modern business applications.

AI and machine learning are finally coming of age thanks to the recent advances in big data that created—for the first time—data sets that were large enough for computers to draw inferences and learn from. That, along with years of SaaS application development in cloud computing environments, means that autonomous technology—harnessing both AI and business intelligence—is now fuelling self-driving software… for both cars and cloud applications.

 

Autonomy—beyond automation

Automation has, of course, been around for years. But autonomy—running on AI and machine learning—takes it to new levels. Today’s software is truly self-driving—it eliminates the need for humans to provision, secure, monitor, back-up, recover, troubleshoot or tune. It upgrades and patches itself, and automatically applies security updates, all while running normally. Indeed, an autonomous data warehouse, for example, can reduce administration overheads by up to 80%.

 

Intelligent thinking

But the greatest value is perhaps in what AI enables you to discover from your data. When applied to analytics, it can identify patterns in huge data sets that might otherwise go unnoticed. So, for example, you could apply AI to sales data to identify trends—who bought what, where, when and why?—and apply those to improve the accuracy of your future forecasts.

Alternatively, if you were looking for a vibrant location for new business premises, you might use AI to search for an area with a strong social media buzz around its restaurants and bars. You could teach the software to look for specific words or phrases, and harness machine learning to improve results over time.

AI technology is already widely used in HR to take the slog out of sifting through huge numbers of job applications. As well as being faster and requiring less manpower, it’s able to remove both human bias—critical in the highly subjective area of recruitment—and also identify the best candidates based on factors such as the kind of language they use.

 

Knowledge and power for everyone

These technologies are coming online now—today—for everyone. In the past, most database reporting was typically run by data analysts or scientists to update pre-existing dashboards and reports. Nowadays there are many more business users who are demanding access to such insights, which is being made possible by tools that are far easier to use.

Anyone can experiment with large samples of different data sets, combining multiple data formats—structured and unstructured—and discovering new trends. They can get answers in context, at the right time, and convert them into simple-to-understand insights, enabling decisions to be made more quickly for competitive advantages.

 

Smarter and smarter…

Yet it’s the strength of those insights that’s really compelling. As one commentator observed: ‘Machine intelligence can give you answers to questions that you haven’t even thought of.’ The quality of those answers—and their underlying questions—will only improve over time. That’s why it’s becoming a competitive imperative to embrace the power of intelligent analytics to ensure you can keep pace with market leaders.

 

Discover how…

In my last blog, I shared how organisations can profit from data warehouses and data marts, and how Oracle’s self-driving, self-securing, and self-repairing Autonomous Data Warehouse saves resources on maintenance allowing investment in data analytics.

 

Autonomous: A New Lens for Analytics

Antony Reynolds - Mon, 2018-05-21 05:45

Welcome to the era of intelligent, self-driving software. Just as self-driving vehicles are set to transform motoring, self-driving software promises to transform our productivity, and strengthen our analytical abilities.

Perhaps you drive an automatic car today—how much are you looking forward to the day your car will automatically drive you? And how much more preferable would smoother, less time-consuming journeys be—always via the best route, with fewer hold-ups, and automatically avoiding unexpected road congestion—where you only have to input your destination? The technology is almost here, and similar advances are driving modern business applications.

AI and machine learning are finally coming of age thanks to the recent advances in big data that created—for the first time—data sets that were large enough for computers to draw inferences and learn from. That, along with years of SaaS application development in cloud computing environments, means that autonomous technology—harnessing both AI and business intelligence—is now fuelling self-driving software… for both cars and cloud applications.

 

Autonomy—beyond automation

Automation has, of course, been around for years. But autonomy—running on AI and machine learning—takes it to new levels. Today’s software is truly self-driving—it eliminates the need for humans to provision, secure, monitor, back-up, recover, troubleshoot or tune. It upgrades and patches itself, and automatically applies security updates, all while running normally. Indeed, an autonomous data warehouse, for example, can reduce administration overheads by up to 80%.

 

Intelligent thinking

But the greatest value is perhaps in what AI enables you to discover from your data. When applied to analytics, it can identify patterns in huge data sets that might otherwise go unnoticed. So, for example, you could apply AI to sales data to identify trends—who bought what, where, when and why?—and apply those to improve the accuracy of your future forecasts.

Alternatively, if you were looking for a vibrant location for new business premises, you might use AI to search for an area with a strong social media buzz around its restaurants and bars. You could teach the software to look for specific words or phrases, and harness machine learning to improve results over time.

AI technology is already widely used in HR to take the slog out of sifting through huge numbers of job applications. As well as being faster and requiring less manpower, it’s able to remove both human bias—critical in the highly subjective area of recruitment—and also identify the best candidates based on factors such as the kind of language they use.

 

Knowledge and power for everyone

These technologies are coming online now—today—for everyone. In the past, most database reporting was typically run by data analysts or scientists to update pre-existing dashboards and reports. Nowadays there are many more business users who are demanding access to such insights, which is being made possible by tools that are far easier to use.

Anyone can experiment with large samples of different data sets, combining multiple data formats—structured and unstructured—and discovering new trends. They can get answers in context, at the right time, and convert them into simple-to-understand insights, enabling decisions to be made more quickly for competitive advantages.

 

Smarter and smarter…

Yet it’s the strength of those insights that’s really compelling. As one commentator observed: ‘Machine intelligence can give you answers to questions that you haven’t even thought of.’ The quality of those answers—and their underlying questions—will only improve over time. That’s why it’s becoming a competitive imperative to embrace the power of intelligent analytics to ensure you can keep pace with market leaders.

 

Discover how…

In my last blog, I shared how organisations can profit from data warehouses and data marts, and how Oracle’s self-driving, self-securing, and self-repairing Autonomous Data Warehouse saves resources on maintenance allowing investment in data analytics.

 

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