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Create a Digital Twin of your Business with Enterprise Architecture

Digital Twin and Enterprise Architecture.jpg
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To create a digital twin of your organization, let’s outline a three-step approach:

  1. Map your organization to create a digital twin

Enterprise Architecture helps you create a digital twin of your organization. It provides you with a detailed understanding of your organization by modelling each piece of it: strategy, business capabilities, processes, customer experience, data, applications, and infrastructure. All these elements are tied to one another in a single platform, so that you can easily perform impact analysis and help transform your business. It also allows you to ensure that strategy is well executed at a minimum cost while improving time-to-market. But all these models are not alive! To make them alive, you need to infuse some blood into them with real-life data. Real-life data will help you optimize your models by giving visibility into what really happens. But to do so, you’ll have to define KPIs first, so that you can reduce the scope of what you need to measure.

  1. Identify KPIs to narrow down the scope

In this step, define measurements based on your business objectives or other factors. By doing so, you can limit the amount of collected data and only focus on the ones that are of interest to you. For example, you can monitor the lifecycles of the software technologies that underlie your business applications, putting potentially at risk business capabilities that use these applications. You can measure customer satisfaction on the various touchpoints of a customer journey. For a specific process, you may also want to measure the time to execute a task, or check if an order has not been paid twice in SAP for example. KPIs can also be defined more broadly at an enterprise level such as revenue growth, customer satisfaction or EBITDA.

  1. Perform and analyze data mining

So far, you have created models and identified KPIs for your organization, now you can improve its efficiency by incorporating real-life data. This requires analyzing event logs stemming from information systems such as ERPs or CRMs to identify trends and patterns. Use APIs to connect to these systems and import the data relevant to your analysis. This analysis is performed on a continuous basis to discover inefficiencies, to check whether real-life data conforms with what have been modeled, or simply to discover undocumented processes in the organization.

For example, by analyzing real-life data, you can realize that the same task can take twice as long in one branch as in another. You can then correct your processes and ensure there won’t be any further deviations to the process model that was initially defined. As another example, by examining patient records in a hospital, you can identify new processes that have not yet been documented and hence, improve patient management. You can also fuel satisfaction ratings at the various touchpoints of a customer journey based on customer feedback systems and provide recommendations on how underlying processes can be improved.

In summary, to create a digital twin of your organization, create business and IT models as a starting point. Then, define KPIs to narrow down the scope of measurements. Finally, based on a continuous analysis of real-life data, optimize existing processes and EA models. By performing these steps, you get a clearer view of your organization so that you can efficiently tackle business transformation challenges with more accurate models.

3390
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Comment

To create a digital twin of your organization, let’s outline a three-step approach:

  1. Map your organization to create a digital twin

Enterprise Architecture helps you create a digital twin of your organization. It provides you with a detailed understanding of your organization by modelling each piece of it: strategy, business capabilities, processes, customer experience, data, applications, and infrastructure. All these elements are tied to one another in a single platform, so that you can easily perform impact analysis and help transform your business. It also allows you to ensure that strategy is well executed at a minimum cost while improving time-to-market. But all these models are not alive! To make them alive, you need to infuse some blood into them with real-life data. Real-life data will help you optimize your models by giving visibility into what really happens. But to do so, you’ll have to define KPIs first, so that you can reduce the scope of what you need to measure.

  1. Identify KPIs to narrow down the scope

In this step, define measurements based on your business objectives or other factors. By doing so, you can limit the amount of collected data and only focus on the ones that are of interest to you. For example, you can monitor the lifecycles of the software technologies that underlie your business applications, putting potentially at risk business capabilities that use these applications. You can measure customer satisfaction on the various touchpoints of a customer journey. For a specific process, you may also want to measure the time to execute a task, or check if an order has not been paid twice in SAP for example. KPIs can also be defined more broadly at an enterprise level such as revenue growth, customer satisfaction or EBITDA.

  1. Perform and analyze data mining

So far, you have created models and identified KPIs for your organization, now you can improve its efficiency by incorporating real-life data. This requires analyzing event logs stemming from information systems such as ERPs or CRMs to identify trends and patterns. Use APIs to connect to these systems and import the data relevant to your analysis. This analysis is performed on a continuous basis to discover inefficiencies, to check whether real-life data conforms with what have been modeled, or simply to discover undocumented processes in the organization.

For example, by analyzing real-life data, you can realize that the same task can take twice as long in one branch as in another. You can then correct your processes and ensure there won’t be any further deviations to the process model that was initially defined. As another example, by examining patient records in a hospital, you can identify new processes that have not yet been documented and hence, improve patient management. You can also fuel satisfaction ratings at the various touchpoints of a customer journey based on customer feedback systems and provide recommendations on how underlying processes can be improved.

In summary, to create a digital twin of your organization, create business and IT models as a starting point. Then, define KPIs to narrow down the scope of measurements. Finally, based on a continuous analysis of real-life data, optimize existing processes and EA models. By performing these steps, you get a clearer view of your organization so that you can efficiently tackle business transformation challenges with more accurate models.