Download an SVG of this architecture. There are a couple of reasons for this as described below: Simply put, data refers to raw, unorganized facts. For example, the network functions in the CN domain may use the Ericsson Software Probe to do exposure. For example, extract only once even if there are multiple users of the same data. Learn how AI can secure optimal network performance.Learn more about Ericsson’s work with AI and automation. The current End-to-end SW Pipeline feedback step (step 5 in Figure 1) provides a means to send logs and events back to the vendor. This has always been the case, but it can now be done to a larger extent than before. Where are we going to acquire these resources? In the OAM (Operations, Administration and Maintenance) domain, data may be used as a basis for optimizing network management, customer experience analytics, service assurance, incident management, and so on. Data and information architecture have distinctly different qualities: 1. A data architect models the data in stages (conceptual, logical and physical) and must relate the data to each process that consumes (uses) that data.” Another Sybase white paper , written by Richard Ordowich in 2011, describes IA as the underlying basis of all of an enterprise’s IT operations, and as the first principle in enterprise IT design: ONAP (Open Network Automation Platform) provides a reference architecture as well as a technology source. Seamless data integration. First, technology advancements in compute and networking capacity have made it possible to expose and transport data in unprecedented amounts. And creating information assets is the driving purpose of information architecture. Since we’ve established that data and information are not the same, it stands to reason that they can’t be treated the same way in their architecture platforms. Here comes a brief overview: Exposure of data from network functions builds upon management interfaces and probes. Data Capture: capture of data generated by devices used in various processes in the organisation Contrary to traditional development where an algorithm is coded, in ML a model is trained. Second, technology advancements in Artificial Intelligence (AI) have made it possible to analyse these vast amounts of data in a way that was not possible before. Data, not a functionality, is placed in the center. In perspective, the goal for designing an architecture for data analytics comes down to building a framework for capturing, sorting, and analyzing big data for the purpose of discovering actionable results. Enterprise architect and Microsoft blog contributor, Nick Malik, recognized the inherent confusion when he was part of a group working to clean up the Wikipedia entries on the subjects. Use of this site signifies your acceptance of BMC’s, Mindful AI: 5 Concepts for Mindful Artificial Intelligence. How will distribution in learning and decision-making impact the architecture? The data lifecycle begins with the creation of data at its point of origin through its useful life in the business processes dependent on it, and its eventual retirement, archiving, or destruction. The first experience that an item of data must have is to pass within … The report suggests that when coming up with a new business model, enterprise business leaders ask themselves these questions: But even after a data-driven model has been created, some companies fail because they don’t understand the importance of a workflow that pushes data through the lifecycle and through the process of becoming an information asset. MDAF can be deployed at different levels, including at domain level (for example, RAN or CN) and at end-to-end level (for end-to-end assurance as part of the overall OAM, for example). Enterprise-wide data access and availability will be considered throughout the data and systems lifecycle. Your team must adopt a proactive, lifecycle-based approach … In the following text, we will look at positions that may be necessary for data architecture, information architecture or both. This data can be in many forms e.g. This way, the system can assess when and where there will be no or very little traffic. Example research questions include: How will data-driven architecture evolve the current 3GPP architecture? Well, this basically comes down to three things: In the coming sections, I will explain in a bit more detail some work we are doing on the three bullet points mentioned above. This is the so-called zero-touch vision, and you will find more information on that in our blog post Zero touch is coming. While data architectures may be adjusted within specific functional communities or Air Force components to meet specific needs, architectures will support This could be within a network function, or between network functions within the domain. To summarize, data-driven means that decisions are made based on data. We need to take action to start relevant work on those missing pieces. Data Capture. If not, here’s a quick recap. They have distinctly unique life cycles 4. Curious what that means? One such platform is likely a piece of information architecture, like a CRM, that uses raw customer data to draw meaningful connections about sales and sales processes. While driving, you observe the surroundings: the curve of the road, the brake lights of the car in front of you, pedestrians indicating to cross the road. Combining the building blocks above, we can envision the picture below showing an end-to-end data-driven architecture. In the context of networking, data allows AI algorithms to make better decisions, thereby optimizing the performance and management of the network. Network analytics functions inside the network can provide insights that enhance the network functionality. In the picture above, the data may be used at three different levels. Initiatives are taken in different standardization organizations and alliances, which will affect the evolution towards a data-driven architecture. Hopefully by now, it’s clear why information and data architecture are two different things. We need to detail the data-driven architecture, make it concrete and define what building blocks it is composed of. These insights can, for example, be provided for customer experience, service and application management. In each of the stages, different stakeholders get involved as like in a traditional software development lifecycle. From core to cloud to edge, BMC delivers the software and services that enable nearly 10,000 global customers, including 84% of the Forbes Global 100, to thrive in their ongoing evolution to an Autonomous Digital Enterprise. The system analyzes large amounts of data and finds patterns (that is, it learns). It includes when and where architects interact in the organization, their common tasks by role, any phases of the architecture approach and inputs and outputs to those tasks. Many of the building blocks are already being worked on. Modern Slavery Statement | Privacy | Legal | © Telefonaktiebolaget LM Ericsson 1994-2020, An introduction to data-driven network architecture, Redefine customer experience in real time. At the Ericsson Blog, we provide insight to make complex ideas on technology, innovation and business simple. Workflow Orchestration solutions such as Control-M, help organizations to abstract the complexity involved with the numerous data sources, multiple applications and diverse infrastructure. Just like the vendor’s DataOps, data may be used to produce new insights, to train models and install them, or to optimize the configuration of the system. Essentially, the data model needs to reflect the business model, and the DGT can act as both a translator and a facilitator to ensure this happens. The objective here is to define the major types and sources of data necessary to support the business, in a way that is: 1. The second level where data may be used is indicated by arc number 2. Complete and consistent 3. The objectives of the Data Architecture part of Phase C are to: 1. 3GPP SA5 defines the MDAF as part of OAM. The use cases above are examples of applying AI and Machine Learning (ML). Can we use MR to automate this? Or: I’m almost out of gas, let’s drive a bit more economically. But this is just a glimpse. The work of ITU-T SG 13 is meant to be an overlay to the 3GPP architecture. Data is typically created by an organisation in one of 3 ways: 1. The Salesforce Data Architecture and Management Designer credential is designed for those who assess the architecture environment and requirements and design sound, scalable, and high-performing solutions on the Salesforce Platform as it pertains to enterprise data management. In this post, you will learn some of the key stages/milestones of data science project lifecycle. Architect Journey: Development Lifecycle and Deployment. An example of the latter is a NWDAF analytics service using data from the Access and Mobility Management Function (AMF). Data Architecture provides an understanding of where data exists and how it travels throughout the organization and its systems. These two factors enable numerous use cases where a machine can produce insights from data and do (better) decision-making based on data. Data Flow. For MR to work here, a lot of data and different kinds of data are involved: the observations of the surroundings, the skills, the experience, the reasoning rules. The group focuses on artefacts that allow data exposure and governance and the outcome is an overall framework for multi-domain management that re-uses specifications from other organizations such as 3GPP SA2/SA5. Gone are the days when IT departments were ancillary to process. Bring together all your structured, unstructured and semi-structured data (logs, files, and media) using Azure Data Factory to Azure Data Lake Storage. There may be additional electronic information like maps and notifications on traffic jams and ongoing construction work. The DI architecture also defines data lifecycle management. This arc is based on the End-to-end SW Pipeline (see Figure 1). At Ericsson Research we try to focus on challenges that lie a little further ahead. All these are forms of data. They work with different assets: data assets vs information assets 2. 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