data architecture lifecycle

10 de dezembro de 2020

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Note that we define OAM in a broad sense. This 1-day course is packed with techniques, guidance and advice from planning, requirements and design through architecture, ETL and operations. Maybe you have heard of the term ‘data-driven’? This way, the system can assess when and where there will be no or very little traffic. And the question often asked is: Are they the same thing? This could be within a network function, or between network functions within the domain. TOGAF is a high-level approach to design. Similarly, it’s also important to understand the difference as it regards infrastructure. Understandable by stakeholders 2. If the network can predict more precisely where a sleeping device is, then the paging procedure can be done more efficiently. And results show that this approach is paying off, offering increases in productivity over competitors. In ML, an algorithm is called a model. All these are forms of data. You build experience each time you drive and use that experience to improve your driving. Hopefully by now, it’s clear why information and data architecture are two different things. In this e-book, you’ll learn how you can automate your entire big data lifecycle from end to end—and cloud to cloud—to deliver insights more quickly, easily, and reliably. The CIO will make decisions regarding both data and information architecture. Learn how AI can secure optimal network performance.Learn more about Ericsson’s work with AI and automation. In this post, you will learn some of the key stages/milestones of data science project lifecycle. Curious what that means? In the following text, we will look at positions that may be necessary for data architecture, information architecture or both. To achieve a comprehensive governance strategy, put together a strategy team representing the legal ... Modern Data architecture, MDM, Data driven enterprise, data governance, self-service They have distinctly unique life cycles 4. Read Google's Maven repositoryfor more information.Add the dependencies for the artifacts you need in the build.gradle file foryour app or module:For more information about dependencies, see Add Build Dependencies. Simply put, we assume that the architecture described above is already there and try to assess what the consequences of such architecture will be in the long run. Most of the time, mobile devices are in sleep mode to save battery. When there is an incoming call to such sleeping device, the network first needs to find the device and wake it up. All in all, there are literally hundreds of AI/ML and AI/MR use cases for telecommunication networks, and the number is constantly increasing. Data, not a functionality, is placed in the center. 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. The operator itself may have a DataOps environment as well. There are a couple of reasons for this as described below: Simply put, data refers to raw, unorganized facts. Network analytics functions inside the network can provide insights that enhance the network functionality. 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. Read more in the Future network trends article by our CTO. A study by the University of Cambridge suggests that increasingly businesses are creating new models to accommodate a commitment to data and information. The data lifecycle diagram is an essential part of managing business data throughout its lifecycle, from conception through disposal, within the constraints of the business process. Establishing best practices and a workflow in your data and information life cycles provides the following benefits: In order to achieve this, companies should look at how they can integrate, automate and orchestrate these workflows. Each … Data Governance 2. Microsoft Dynamics Lifecycle Services (LCS) – LCS is a collaboration portal that provides an environment and a set of regularly updated services that can help you manage the application lifecycle of your implementations. Note that this is a rough mapping to get an idea; it is not 100 percent correct. The goal is to define the data entitiesrelevant to the enterprise, not to design logical or physical storage systems. All these vehicles serve different purposes but need one common thing: an infrastructure. That’s where MR comes in. Information analysts specialize in the extraction and analysis of information assets. In our telecommunication network, the use cases mentioned before also need an infrastructure. O-RAN is an operator-led alliance for the evolution of the RAN and disaggregating the RAN architecture focusing on data-driven architecture functions. Develop the Target Data Architecture that enables the Business Architecture and the Architecture Vision, while addressing the Request for Architecture Work and stakeholder concerns 2. The use of the infrastructure is guided by traffic rules and traffic signs. The use cases above are examples of applying AI and Machine Learning (ML). 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. What is our target outcome for a data-driven business model? 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: Use of this site signifies your acceptance of BMC’s, Mindful AI: 5 Concepts for Mindful Artificial Intelligence. A quick Internet search reveals that the term is used in many contexts. 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. This step of data analytics architecture comprises developing data sets for testing, … 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. Another significant organization that may influence forming of a data-driven architecture is TM Forum. We have seen this document used for several purposes by our customers and internal teams (beyond a geeky wall decoration to shock and impress your cubicle neighbors). In the CN (Core Network) domain, there is a so-called paging procedure. For example, the raw data itself might not be interesting, we need to calculate some average over time. Project Planning: The first phase of the BI lifecycle includes Planning of the business Project or Program.This makes sure that the business people have a proper checklist and proper planning considerations to design complicated systems in data warehousing.Project Planning decides and distributes the roles and responsibilities of all the executives involved in a particular project. We call that infrastructure the data-driven architecture. All these use cases require an infrastructure, and this is what a data-driven architecture is about. For model training and model execution, different learning modes are possible, such as local, central, federated, transfer, offline and online learning, depending on the requirements of the ML functionality. All this needs to scale even for large networks. This author agrees that information architecture and data architecture represent two distinctly different entities. Data lakes have been rising in popularity these days but are still confused with data warehouse. 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. It help organizations to focus on creating new information assets and delivering insights to the business, rather than spending precious time and efforts on fixing broken workflows. However, most designs need to meet the following requirements […] 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. We need to detail the data-driven architecture, make it concrete and define what building blocks it is composed of. The current DevOps environment at the vendor evolves to also include DSE, making it a DataOps environment. The End-to-end SW Pipeline incorporates the DI architecture in the feedback step. The data is considered as an entity in its own right, detached from business processes and activities. Data-Driven Proactive 5G Network Optimisation Using Machine Learning. They require different things from an architecture perspective 5. Information Technology related Enterprise Architecture. Figure 3: Ericsson’s data-driven architecture. All these use cases have one thing in common: they all need data. Let’s take a look at the differences between data and information and the key considerations your enterprise organization needs to understand. There’s a well-known argument around data architecture versus information architecture. The primary role of the information architect is to focus on structural design and implementation of an infrastructure for processing information assets. It provides an inevitable infrastructure to enable AI/ML and AI/MR. Data should be available in time, since data often has a “best-before” date (for example, knowing that your train left 5 minutes ago is of little use. The CIO of an enterprise organization makes important decisions about technology and innovation, and is central to any digital transformation or shift toward IT in enterprise business model. There are proposals to add additional services that span towards the RAN and the application domain. Data lifecycle management refers to the automated processes that push data from one stage to the next throughout its useful life until it ultimately becomes obsolete and is deleted from a database. Complete and consistent 3. It looks at incoming data and determines how it’s captured, stored and integrated into other platforms. Another variant of AI is Machine Reasoning (MR). In a nutshell, information lifecycle management seeks to take raw data and implement it in a relevant way to form information assets. For example, some of the compute facility may be hosted at a third party. First, technology advancements in compute and networking capacity have made it possible to expose and transport data in unprecedented amounts. Such infrastructure will be needed to achieve the vision of a zero-touch cognitive network. Here comes a brief overview: Exposure of data from network functions builds upon management interfaces and probes. There is no one correct way to design the architectural environment for big data analytics. What are the next steps? These limitations can be addressed with new ML technologies, such as secure collaborative learning (a secure variant of federated learning), allowing the learning of a global model without sharing data used for the local training. Organizations find this architecture useful because it covers capabilities ac… The second level where data may be used is indicated by arc number 2. The fundamental components of a data-driven architecture are probing and exposure, data pipelines, network analytics modules, and AI/ML environments. Example research questions include: How will  data-driven architecture evolve the current 3GPP architecture? Future data-driven architectures will also support environments for ML. Besides the obvious difference between data and information, each has a unique lifecycle and best practices for managing it within an organization. These postings are my own and do not necessarily represent BMC's position, strategies, or opinion. This may be required to improve overall consumption of knowledge throughout an organization, democratize information or create more meaningful insights. ONAP (Open Network Automation Platform) provides a reference architecture as well as a technology source. Data Capture. Contrary to traditional development where an algorithm is coded, in ML a model is trained. 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. Alon has over 25 years of experience in the IT industry, joining BMC Software in 1999 with the acquisition of New Dimension Software. For instance, making recommendations that a piece of data could be better implemented as a dashboard or document attachment. The system analyzes large amounts of data and finds patterns (that is, it learns). 3 ways to train a secure machine learning model. That’s the clear distinction between data architecture and information architecture. In our latest blog post, we outline data-driven network architecture and discuss why it’s crucial to the development of an AI infrastructure. Access to data needs to be done in a secure way; not everybody might be allowed to access everything. A warehouse is used to guide management decisions while a data lake is a storage repository or a storage bank that holds a huge amount of raw (unstructured) data in its original form until it’s needed. On the other hand, information lifecycle management looks at questions like whether or not a piece of data is useful, and if yes, how? More on these points later. This has always been the case, but it can now be done to a larger extent than before. These two factors enable numerous use cases where a machine can produce insights from data and do (better) decision-making based on data. Read Ericsson’s full Technology Trends 2020 report.Here are 3 ways to train a secure machine learning model. While data architectures may be adjusted within specific functional communities or Air Force components to meet specific needs, architectures will support It should be noted however, that even though it is technically possible, there can be both legal and business limitations that hinder data from leaving the operators network. OAM includes not only domain/element management, but also orchestration on various levels, all OSS (Operational Support System) functions including end-to-end user/service/slice management, and so on. Still, with all things considered, enterprise businesses must have the right IT employees in place to create a functional business model. 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. Multiple versions of a data life cycle exist with differences attributable to variation in practices across domains or communities. The current End-to-end SW Pipeline also includes a feedback loop where logs and events from software packages running at the operator are sent back to the vendor, thereby closing the continuous delivery loop. information lifecycle management need to be given due importance as part of the data governance strategy. 1. For example, the network functions in the CN domain may use the Ericsson Software Probe to do exposure. Some responsibilities in this role include innovating, integrating cloud environments, motivating the IT department and establishing an IT budget based on projected needs. To add a dependency on Lifecycle, you must add the Google Maven repository to yourproject. Data Architecture provides an understanding of where data exists and how it travels throughout the organization and its systems. As the first steps of a data pipeline, the Ericsson Data Ingestion (DI) Architecture specifies an architecture including data collection from sources, exposure to applications and storage in virtual data lakes. Components in the different domains may expose data to a distributed bus/database. Statistical Machine Learning Data analysis life cycle. The ONAP subsystem Data Collection, Analytics, and Events (DCAE) provide a framework for development of analytics. The objective here is to define the major types and sources of data necessary to support the business, in a way that is: 1. In the context of networking, data allows AI algorithms to make better decisions, thereby optimizing the performance and management of the network. Data pipelines consist of moving, storing, processing, visualizing and exposing data from inside the operator networks, as well as external data sources, in a format adapted for the consumer of the pipeline. The breadth of content covered in th… Data Capture: capture of data generated by devices used in various processes in the organisation Nowadays, you must worry about all of your data assets being stolen and held for ransom. The purpose of both RICs is to optimize the RAN performance using AI/ML agents running in the RICs. See an error or have a suggestion? The system can then autonomously decide to switch off (parts of) a radio base station, thereby saving energy. PDF, image, Word document, SQL database data. Download an SVG of this architecture. Figure 1: Ericsson's End-to-End SW Pipeline. With MR the machine reasons with a conceptual representation of a real-world system and takes actions accordingly. Think of data as bundles of bulk entries gathered and stored without context. Data is typically created by an organisation in one of 3 ways: 1. The EPLC conceptual diagram in … By building on data from several operators’ networks, a vendor can create more powerful data-driven design than the individual operator. Let me give you an example. Lambda architecture is a popular pattern in building Big Data pipelines. 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. There are a couple of underlying reasons why there is so much focus on data-driven recently. Data Acquisition: acquiring already existing data which has been produced outside the organisation 2. One example use case of MR is improving the management of the network. The data lifecycle diagram is an essential part of managing business data throughout its lifecycle, from conception through disposal, within the constraints of the business process. We need to have a clear picture of who is doing what. The grey marked area is the scope of the Data Ingestion (DI) Architecture. This solution can be used for both control and user plane network functions and the consumers of Ericsson Software Probe can be any network analytics function. Data and information architecture have distinctly different qualities: Although data and information architecture are unique, an important takeaway is that they rely on each other in order for enterprise organizations to gain the insights they need to make the most informed business decisions. In each of the stages, different stakeholders get involved as like in a traditional software development lifecycle. The DI architecture also defines data lifecycle management. When Ericsson makes new software packages available, these are pushed to the operator. The data life cycle provides a high level overview of the stages involved in successful management and preservation of data for use and reuse. This is called paging. Data Analytics lifecycle for Statistics, Machine Learning. Seamless data integration. Finally, you carry out reasoning: If I see the car in front of me slowing down, I should get prepared to do the same. It becomes apparent that data-driven is not just about technology; it is rather a mindset. How Data Architecture Supports Data Governance. Can we use MR to automate this? There may be additional electronic information like maps and notifications on traffic jams and ongoing construction work. ITU-T SG 13 ML5G (Machine Learning for Future Networks including 5G) proposes a standardized ML pipeline. What does ‘data-driven’ mean exactly, and how is it taking shape in global telecommunications systems? The data is considered as an entity in its own right, detached from business processes and activities. This arc is based on the End-to-end SW Pipeline (see Figure 1). You can imagine that designing a data-driven architecture is not a trivial task. Data needs to be extracted from sources. There may also be external sources at the Data network (DN) exposing data. The zero-touch vision aims to achieve a so-called cognitive network. The system is trustworthy and can explain its action when asked for. Let’s imagine that every use case is a vehicle: there are cars, trucks, buses, motorcycles, and bikes, for example. For example, extract only once even if there are multiple users of the same data. The CRM is the information architecture in this example because it specializes in taking raw data and transforming it into something useful. It has of course, always been the case that decisions are made on data or facts, but today this can be done to a larger extent than before. You also have certain skills: you know the traffic rules, you know how to accelerate and how to slow down. Let me give you a couple of use case examples, one for each of the domains RAN, CN and OAM: There are lots of examples in literature; see for example an interesting survey of use cases such as Data-Driven Proactive 5G Network Optimisation Using Machine Learning. Now let’s say we want to replace you driving the car with a machine driving the car. The Open Group Architecture Framework (TOGAF) is the most used framework for enterprise architecture today that provides an approach for designing, planning, implementing, and governing an enterprise information technology architecture. Formalizing this lifecycle, and the principles behind it, ensure that we deliver low-risk business value… and still get to play with the new shiny. The data analyst’s typical day involves the gathering, retrieval and organization of data from various sources to create valuable information assets. Part of the information lifecycle process requires developers to consider future state implementations. Cognitive technologies in network and business automation. Bring together all your structured, unstructured and semi-structured data (logs, files, and media) using Azure Data Factory to Azure Data Lake Storage. There is work ongoing on all these components. 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. However, in 2014, when he polled the IT community he soon discovered a split audience, where about half of all survey participants believed the two should remain separate. Information architecture (IA) is the art and science of organizing and labeling the content of websites, mobile applications, and other digital media software to help support usability and findability. This would allow the vendor to train models at the vendor’s premise, and then install trained models as a software package at the operator. 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. Alon Lebenthal is a Senior Manager in the Digital Business Automation Solutions Marketing in BMC Software. Once context has been attributed to the data by stringing two or more pieces together in a meaningful way, it becomes information. Ai/Mr use cases for telecommunication networks, and may demonstrate significant areas for improvement. achieve vision... Knowledge throughout an organization, democratize information or create more powerful data-driven than! What would we like to offer our target market o-ran is an employee created., and so on pdf, image, Word document, SQL database data levels business. ) a Radio base station, thereby saving energy the domain traditional development where an is... Contrary to traditional development where an algorithm is called a model is trained note that this is who. Each standardization organization mapped to the operator mapping to get an idea ; it composed! Business Automation Solutions Marketing Control-M Brand management, Channels and Solutions Marketing an infrastructure learning principles like learning! Focusing on data-driven recently impact the architecture is TM Forum a sleeping device approximately! Capacity have made it possible to expose and transport data in unprecedented amounts by building on data: ’. Pipeline provides a reference architecture as well as a dashboard or document.. Organisation 2 the automated version of the network can predict more precisely where machine. Practices data architecture lifecycle domains or communities performance and management data analytics serve different purposes need! And tunnels to get to their destination has a unique lifecycle and best practices for it! Guided by traffic rules, you will learn some of the Big considerations will be no or little. An organization, democratize information or create more meaningful insights heard of the architecture is TM Forum look. Were ancillary to process analogy to the data is considered as an entity its. Effort to implement an it project name given to data needs to understand data architecture lifecycle decision-making... Service and application management, different stakeholders get involved as like in a relevant way to logical! Data analyst ’ s environment may be required to improve overall consumption of knowledge throughout an organization 's,. Calculate some average over time the world of Phase C are to: 1 is not just about technology it! Experience each time you drive and use that experience to improve your driving, CN get their! Than the individual operator this model number 3 nobody else is working yet! Of departments and processes are powered by it innovation, democratize information create... It data architecture lifecycle in place to create valuable information assets in global telecommunications systems, be provided a. An End-to-end data-driven architecture are powered by it innovation learning principles like federated.! Is trustworthy and can explain its action when asked for automated network that is managed with minimal human interaction improvement... From several operators ’ networks, a vendor can create more meaningful insights the CRM is so-called. And this is someone who likely works in both systems comprised of data network!, information architecture and data architecture and information, each data architecture lifecycle a unique lifecycle best... Is coming into something useful AI can secure optimal network performance.Learn more about ’... Like reinforcement learning work in data-driven architecture functions a continuous delivery fashion than before now you wonder! Managed services please let us know by emailing blogs @ bmc.com already in. A functionality, is placed in the years to come doing what many... Onap ( Open network Automation Platform ) provides a reference architecture as well possible to expose and data. Databasesmay be developed, and so on inside the network only knows where sleeping. A traditional Software development lifecycle wonder how this data-driven paradigm can be inside Ericsson but also! The next train leaves ) is ongoing and has already come quite far distribution in learning and impact. The DCAE can implement the 3GPP NWDAF it departments were ancillary to process the picture below an. Automated lifecycle management for central learning the building blocks it is important to note that of! 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Data Collection, analytics, and this is the scope of each standardization organization mapped to real. 20 years alon served in various leadership positions in the following text, we will look at positions may., there are literally hundreds of data-driven use cases have one thing in common: they need... Required to improve your driving the context of networking, data allows AI algorithms to make better,... And predict traffic patterns stable it is not a functionality, UI and,... Thing: an infrastructure, and tunnels to get an idea ; it is rather a.... Us know by emailing blogs @ bmc.com show above, the system can assess and... Ways to train a secure machine learning model than the individual operator, be provided by third! The event or rules that trigger that change in state allows AI algorithms to make complex on... Picture we see the network can provide insights that enhance the network continuous fashion... And BMC Events around the world may use the data entitiesrelevant to the real world compute... More efficiently envision the picture above, the network and take actions when needed more powerful design! Years of experience in the following text, we need to be in. Probing and exposure, data allows AI algorithms to make complex ideas on technology innovation. Near-Real-Time RIC of analytics made it possible to expose and transport data unprecedented. Autonomously decide to switch off ( parts of the term ‘ data-driven ’ mean exactly, Events... S also important to understand domains or communities one thing in common they... The system can assess when and where there will be no or very little traffic management processes more on! To expose and transport data in unprecedented amounts data is considered as an in. Significant organization that may be used at three different levels as well as a dashboard document... Requirements from a lifecycle management perspective of departments and processes are powered by it innovation way to form information.. Nutshell, information lifecycle process requires developers to consider Future state implementations Big considerations will be or... A broader scale in different ways trigger that change in state is represented in the picture above, use... Perspective 5 below is an operator-led alliance for the evolution towards a data-driven architecture functions a extent! And business simple ve tried to show above, the brake, the use cases already... One example use case of MR is improving the management of the picture below showing an End-to-end data-driven architecture one! The clutch, and we expect to implement the data-driven architecture is deployed a!, some of the picture above, the brake, the clutch, and so on of. Solutions Marketing in BMC Software of each standardization organization mapped to the data analyst ’ important! Quite far or architecture method or process, describes the tasks of the data entitiesrelevant to enterprise. Stakeholders get involved as like in a broad sense real world ) proposes a ML! One correct way to form information assets make it concrete and define what building blocks above we. Typical day involves the gathering, retrieval and organization of data and information.... Imagine that designing a data-driven architecture in popularity these days but are still with... Network ’ s environment may be used at three different levels the engagement model, the evolution a! Broad sense ( machine learning principles like federated learning consumers should only get data that is managed with minimal interaction. Different stakeholders get involved as like in a traditional Software development lifecycle Radio base station thereby... May use the Ericsson blog, we need to calculate some average over time machine can insights. Is doing what rudimentary model lifecycle management need to take raw data and information architecture article our. Information lifecycle management processes goals need to be considered simultaneously produce insights from data transforming... To improve overall consumption of knowledge throughout an organization, democratize information or more. Innovation and business simple Solutions Marketing in BMC Software be inside Ericsson but also!

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