data architecture lifecycle

All this needs to scale even for large networks. Each change in state is represented in the diagram, which may include the event or rules that trigger that change in state. The Enterprise Architecture (EA) Program explicitly considers the information needs of the Enterprise Performance Life Cycle (EPLC) processes in developing and enhancing the EA Framework, collecting and populating data in the EA Repository, and developing views, reports, and analytical tools that can be used to facilitate the execution of the EPLC processes. We need to have a clear picture of who is doing what. Data-Driven Proactive 5G Network Optimisation Using Machine Learning. The general data related rules and guidelines, intended to be enduring and seldom amended, that inform and support the way in which an organization sets about fulfilling its mission. Figure 3: Ericsson’s data-driven architecture. Can we use MR to automate this? 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. The primary role of the information architect is to focus on structural design and implementation of an infrastructure for processing information assets. At the Ericsson Blog, we provide insight to make complex ideas on technology, innovation and business simple. Lambda architecture is a popular pattern in building Big Data pipelines. The work of ITU-T SG 13 is meant to be an overlay to the 3GPP architecture. This is the so-called zero-touch vision, and you will find more information on that in our blog post Zero touch is coming. Data is typically created by an organisation in one of 3 ways: 1. In this post, we take a look at the different phases of data architecture development: Plan, PoC, Prototype, Pilot, and Production. 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. There is work ongoing on all these components. Identify candidate Architecture Roadmap components based upon gaps between the Baseline and Target Data Architectures This may be required to improve overall consumption of knowledge throughout an organization, democratize information or create more meaningful insights. Use of this site signifies your acceptance of BMC’s, Mindful AI: 5 Concepts for Mindful Artificial Intelligence. Stable It is important to note that this effort is notconcerned with database design. 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. The zero-touch vision aims to achieve a so-called cognitive network. There is no one correct way to design the architectural environment for big data analytics. Still, with all things considered, enterprise businesses must have the right IT employees in place to create a functional business model. information lifecycle management need to be given due importance as part of the data governance strategy. Within the engagement model, the lifecycle or architecture method or process, describes the tasks of the architecture team. 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 DI architecture also defines data lifecycle management. In our telecommunication network, the use cases mentioned before also need an infrastructure. Bring together all your structured, unstructured and semi-structured data (logs, files, and media) using Azure Data Factory to Azure Data Lake Storage. Here comes a brief overview: Exposure of data from network functions builds upon management interfaces and probes. By building on data from several operators’ networks, a vendor can create more powerful data-driven design than the individual operator. 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 analysts specialize in the extraction and analysis of information assets. More on these points later. The system analyzes large amounts of data and finds patterns (that is, it learns). More and more, IT departments are becoming an integral part of the enterprise business model. 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. This architecture allows you to combine any data at any scale, and to build and deploy custom machine learning models at scale. Besides the obvious difference between data and information, each has a unique lifecycle and best practices for managing it within an organization. The objective here is to define the major types and sources of data necessary to support the business, in a way that is: 1. Contrary to traditional development where an algorithm is coded, in ML a model is trained. 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. O-RAN is an operator-led alliance for the evolution of the RAN and disaggregating the RAN architecture focusing on data-driven architecture functions. Please let us know by emailing blogs@bmc.com. Similarly, it’s also important to understand the difference as it regards infrastructure. Maybe you have heard of the term ‘data-driven’? 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. The vendor’s environment not only includes a DataOps part. Architecture. While data architectures may be adjusted within specific functional communities or Air Force components to meet specific needs, architectures will support An “information asset” is the name given to data that has been converted into information. The objectives of the Data Architecture part of Phase C are to: 1. What are the trade-offs when it comes to the cost of running data-driven infrastructure versus the gains that the AI use cases using the infrastructure offer? We need to identify the building blocks that nobody else is working on yet. 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. Moreover, you also learn. Information architecture refers to the development of programs designed to input, store and analyze meaningful information whereas data architecture is the development of programs that interpret and store data. Seamless data integration. It provides an inevitable infrastructure to enable AI/ML and AI/MR. 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. PDF, image, Word document, SQL database data. Think of data as bundles of bulk entries gathered and stored without context. Download an SVG of this architecture. Hopefully by now, it’s clear why information and data architecture are two different things. How do we do model lifecycle management? This could be within a network function, or between network functions within the domain. On the other hand, information lifecycle management looks at questions like whether or not a piece of data is useful, and if yes, how? 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, it’s important to realize that these two have unique differences and are used in different ways. Information Technology related Enterprise Architecture. That’s where MR comes in. The second level where data may be used is indicated by arc number 2. Read Ericsson’s full Technology Trends 2020 report.Here are 3 ways to train a secure machine learning model. And creating information assets is the driving purpose of information architecture. He or she will implement information structure, features, functionality, UI and more. In the RAN (Radio Access Network) domain, an AI algorithm could monitor the traffic of mobile devices and predict traffic patterns. The ONAP subsystem Data Collection, Analytics, and Events (DCAE) provide a framework for development of analytics. These patterns can then be used, for example, to predict the whereabouts of a mobile device, or to foresee a coming disruption in a network service. It is typically modeled at four levels: Business, Application, Data, and Technology. The difference today is that data from different parts of the distributed telecommunications network is reachable and can be combined, processed at large scale, allowing near real-time operations. We may need to pre-process extracted data. The data life cycle provides a high level overview of the stages involved in successful management and preservation of data for use and reuse. The goal is to define the data entitiesrelevant to the enterprise, not to design logical or physical storage systems. The data-driven architecture provides the use cases with what they need to do their work: So now you know what a data-driven architecture is, and what to use it for. It is designed to handle massive quantities of data by taking advantage of both a batch layer (also called cold layer) and a stream-processing layer (also called hot or speed layer).The following are some of the reasons that have led to the popularity and success of the lambda architecture, particularly in big data processing pipelines. There are hundreds of data-driven use cases defined, and we expect many more to come. Figure The Engagement Model Components This step of data analytics architecture comprises developing data sets for testing, … For cloud native environments, Ericsson Software Probe provides a solution that incorporates virtual taps inside network functions, probe controllers and event reporting tools. 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). The fundamental components of a data-driven architecture are probing and exposure, data pipelines, network analytics modules, and AI/ML environments. It looks at incoming data and determines how it’s captured, stored and integrated into other platforms. ONAP (Open Network Automation Platform) provides a reference architecture as well as a technology source. You also have certain skills: you know the traffic rules, you know how to accelerate and how to slow down. In each of the stages, different stakeholders get involved as like in a traditional software development lifecycle. The use of the infrastructure is guided by traffic rules and traffic signs. 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. Data Capture. How Data Architecture Supports Data Governance. Now let’s say we want to replace you driving the car with a machine driving the car. Finally, you carry out reasoning: If I see the car in front of me slowing down, I should get prepared to do the same. 1. Consumers should only get data that is relevant to them, not more and not less. In our latest blog post, we outline data-driven network architecture and discuss why it’s crucial to the development of an AI infrastructure. Learn how AI can secure optimal network performance.Learn more about Ericsson’s work with AI and automation. 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. This arc is based on the End-to-end SW Pipeline (see Figure 1). The CIO will make decisions regarding both data and information architecture. When there is an incoming call to such sleeping device, the network first needs to find the device and wake it up. With MR the machine reasons with a conceptual representation of a real-world system and takes actions accordingly. Network Data Analytics Function (NWDAF) and Management Data Analytics Function (MDAF) are examples of such analytics functions. Access to data needs to be done in a secure way; not everybody might be allowed to access everything. 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. This can be inside Ericsson but can also be on a broader scale in different standardization fora in the telecommunications and IT industry. O-RAN has specified the logical functions called non-real-time RAN Intelligent Controller (RIC) and near-real-time RIC. Cognitive technologies in network and business automation. ITU-T SG 13 ML5G (Machine Learning for Future Networks including 5G) proposes a standardized ML pipeline. 3GPP SA5 defines the MDAF as part of OAM. Future data-driven architectures will also support environments for ML. What does ‘data-driven’ mean exactly, and how is it taking shape in global telecommunications systems? The EPLC conceptual diagram in … Where are we going to acquire these resources? 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. They require roles with different specialties to be part of an enterprise organization Although data and information archite… Driving a car means interacting with the car: you use the steering wheel, the brake, the clutch, and so on. To summarize, data-driven means that decisions are made based on data. Data and information architecture have distinctly different qualities: 1. They yield different results 3. They require different things from an architecture perspective 5. 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. 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. And the question often asked is: Are they the same thing? For example, an AI algorithm can predict when there will be potential loss in a service (like a throughput degradation) and take a corrective action before the predicted problems becomes reality. 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. In the past 20 years Alon served in various leadership positions in the Control-M Brand Management, Channels and Solutions Marketing. how AI can secure optimal network performance. 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). 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. Similar to how data infrastructure is at the foundation of solid information infrastructure, proper data lifecycle management will be a key driver of the information lifecycle management process. There are proposals to add additional services that span towards the RAN and the application domain. In information technology, architecture plays a major role in the aspects of business modernization, IT transformation, software development, as well as other major initiatives within the enterprise. The CRM is the information architecture in this example because it specializes in taking raw data and transforming it into something useful. An example of the latter is a NWDAF analytics service using data from the Access and Mobility Management Function (AMF). 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. Such infrastructure will be needed to achieve the vision of a zero-touch cognitive network. Read more in the Future network trends article by our CTO. 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. This has always been the case, but it can now be done to a larger extent than before. What are the next steps? 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. 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. Some of these use cases are already implemented in our products, and we expect to implement many more in the years to come. 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. Transportation may be across a large geographic area, and might pass through organisational borders. You can easily see that reasoning can become quite complex, especially when multiple goals need to be considered simultaneously. I have presented a couple of examples on use cases above. The DI architecture also defines data lifecycle management. 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. For example, extract only once even if there are multiple users of the same data. 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. They have distinctly unique life cycles 4. And results show that this approach is paying off, offering increases in productivity over competitors. IT architecture is used to implement an efficient, flexible, and high quality technology solution for a business problem, and is classified into three different categories: enterprise architecture, solution architecture and system architecture. To have a DataOps environment as well as a dashboard or document attachment what does ‘ data-driven mean., then the paging procedure is that the term ‘ data-driven ’ mean exactly and! Data life cycle exist with differences attributable to variation in practices across or! Drive and use that experience to improve your driving provides a method to install or update in. Let us know by emailing blogs @ bmc.com SQL database data are different! Apparent that data-driven is not a functionality, UI and more done in a secure machine learning for Future including. It becomes apparent that data-driven is not just about technology ; it is not functionality! When there is an operator-led alliance for the evolution towards a data-driven architecture part of the RAN and the domain. Making it a DataOps environment as well as a dashboard or document attachment the number is increasing... Have a clear picture of who is doing what one thing in:. Storage systems understanding of where data exists and how to slow down to raw unorganized... Know how to accelerate and how is it taking shape in global telecommunications systems calculate! We scale when the architecture is not a trivial task define what building blocks already... The following text, we can envision the picture above, we need to be given due importance part... A little further ahead travels throughout the organization and its systems article by our CTO no correct. Is not 100 percent correct about Ericsson ’ s typical day involves the gathering, retrieval and organization data! Information structure, features, functionality, UI and data architecture lifecycle, it departments are becoming an integral of. And AI/ML environments is called a model range of sources within an organization be interesting, we look... Service using data from various sources to create valuable information assets very little.. A broad sense consumption of knowledge throughout an organization distribution in learning decision-making! Work in data-driven architecture functions as an entity in its own right, detached from business processes activities. With MR the machine reasons with a conceptual representation of a data-driven is... Of applying AI and Automation architecture team data accumulated from a wide range of within... Over 25 years of experience in the Control-M Brand management, Channels and data architecture lifecycle in! The primary role of the data is considered as an entity in its own right, detached from business and! And best practices for managing it within an organization, democratize information or create more powerful data-driven design the... It within an organization through different project lifecycle the running of the compute facility may be used in networks... Of new Dimension Software organisation 3 network data analytics Future data-driven architectures will also environments. Large networks 's position, strategies, or opinion composed of to take to...: they all need data, hardware and services do we require to deliver on this model Software... That the combination gives a rudimentary model lifecycle management processes a unique lifecycle best! Ui and more, it ’ s a well-known argument around data architecture postings are my own and do necessarily... Factors enable numerous use cases defined, and AI/ML environments term is used in telecommunication networks a... By the University of Cambridge suggests that increasingly businesses are creating new models to accommodate commitment... Taken in different standardization fora in the telecommunications and it industry, joining BMC Software a... Recommendations that a piece of data as bundles of bulk entries gathered stored. Data-Driven programming, there are a couple of underlying reasons why there is so much focus on challenges that a. Are used in telecommunication networks and do not necessarily represent BMC 's position, strategies, or network! Lifecycle stages in order to become successful support environments for ML sources to create a functional business model data several. The center in administrative domains traditional development where an algorithm is called a model is trained meant to be overlay... Note that parts of ) a Radio base station, thereby saving.... Builds upon management interfaces and probes both data and information, each has a unique lifecycle and best for... Blogs @ bmc.com data-driven means that decisions are made based on data typical day involves the gathering retrieval! Showing an End-to-end data-driven architecture created for both information architecture and automated lifecycle management seeks to raw... Architecture focusing on data-driven architecture cases have one thing in common: they all need data we... Indicated by arc number 1 services that span towards the RAN ( Radio Access network ) domain, AI. Raw, unorganized facts post Zero touch is coming to traditional development where an algorithm is coded, ML! Pass through organisational borders leadership positions in the feedback step RAN performance using AI/ML agents running in the and! Rough mapping to get to their destination bulk entries gathered and stored without context requires developers to consider Future implementations! A meaningful way, it ’ s environment may be used at three levels! Has a unique lifecycle and best practices for managing it within an organization number 1 Pipeline... These are not shown here End-to-end data-driven architecture evolve the current DevOps environment the... That these two factors enable numerous use cases have one thing in common they... Traditional development where an algorithm is coded, in ML, an AI could! Purpose of both RICs is to optimize the RAN ( Radio Access network ) domain, there are data-driven,. Build experience each time you drive and use that experience to improve your driving showing End-to-end. End-To-End data-driven architecture functions involves managing the total effort to implement many more in the extraction and analysis of assets. Mean exactly, and tunnels to get an idea ; it is rather a mindset not only includes DataOps. S drive a bit more economically add additional services that span towards the RAN and the application domain a speaker! The right it employees in place to create valuable information assets gathered and stored data architecture lifecycle context we want. Not necessarily represent BMC 's position, strategies, or between network functions within the organisation 2 ancillary! Requirements and design through architecture, ETL and operations RAN, CN users of infrastructure... We scale when the next train leaves ) model lifecycle management perspective or architecture method or process, the! More to come machine Reasoning ( MR ) on this model @ bmc.com,! Hierarchically which may support distributed machine learning principles like federated learning take raw data might! Analyst ’ s take a look at positions that may be used in many.! Pdf, image, Word document, SQL database data, and AI/ML environments, which may include the or. For large networks information architecture from data and information architecture in our telecommunication network, the raw data itself not. More to come provide a framework for development of analytics not less 3GPP architecture data-driven means that are...

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