In a nutshell, information lifecycle management seeks to take raw data and implement it in a relevant way to form information assets. Access to data needs to be done in a secure way; not everybody might be allowed to access everything. 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. We need to detail the data-driven architecture, make it concrete and define what building blocks it is composed of. There may be additional electronic information like maps and notifications on traffic jams and ongoing construction work. What does ‘data-driven’ mean exactly, and how is it taking shape in global telecommunications systems? DCAE is designed for scalability and to be deployed hierarchically which may support distributed machine learning principles like federated learning. These postings are my own and do not necessarily represent BMC's position, strategies, or opinion. That’s the clear distinction between data architecture and information architecture. Many of the building blocks are already being worked on. 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. When Ericsson makes new software packages available, these are pushed to the operator. Starting template for a security architecture – The most common use case we see is that organizations use the document to help define a target state for cybersecurity capabilities. However, it’s important to realize that these two have unique differences and are used in different ways. Download an SVG of this architecture. Let me give you an example. The first level where data may be used is indicated by arc number 1. There is data-driven marketing, data-driven programming, there are data-driven businesses, and so on. 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. The more influential alliances we have identified are the following: 3GPP SA2 defines the NWDAF, a network function which is part of the CN which provides insights that enhance the CN functionality. All this needs to scale even for large networks. ITU-T SG 13 ML5G (Machine Learning for Future Networks including 5G) proposes a standardized ML pipeline. Content should be treated as a living, breathing thing with a lifecycle, behaviors, and … This could be within a network function, or between network functions within the domain. Data Flow. However, most designs need to meet the following requirements […] An example of the latter is a NWDAF analytics service using data from the Access and Mobility Management Function (AMF). All these are forms of data. At the heart of a well-functioning enterprise business is an IT department with the right people in place to manage their information and data architectures. This architecture allows you to combine any data at any scale, and to build and deploy custom machine learning models at scale. Also note that parts of the vendor’s environment may be provided by a third party. We need to have a clear picture of who is doing what. In the past 20 years Alon served in various leadership positions in the Control-M Brand Management, Channels and Solutions Marketing. Within the engagement model, the lifecycle or architecture method or process, describes the tasks of the architecture team. (However, linkages to existing files and databasesmay be developed, and may demonstrate significant areas for improvement.) A study by the University of Cambridge suggests that increasingly businesses are creating new models to accommodate a commitment to data and information. We need to extract data efficiently. The End-to-end SW Pipeline incorporates the DI architecture in the feedback step. Combining the building blocks above, we can envision the picture below showing an end-to-end data-driven architecture. TOGAF is a high-level approach to design. We need to identify the building blocks that nobody else is working on yet. In the CN (Core Network) domain, there is a so-called paging procedure. The use of the infrastructure is guided by traffic rules and traffic signs. 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. All in all, there are literally hundreds of AI/ML and AI/MR use cases for telecommunication networks, and the number is constantly increasing. 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. At Ericsson Research we try to focus on challenges that lie a little further ahead. Data Governance 2. PDF, image, Word document, SQL database data. 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. Seamless data integration. And creating information assets is the driving purpose of information architecture. Let’s make an analogy to the real world. This may be required to improve overall consumption of knowledge throughout an organization, democratize information or create more meaningful insights. The grey marked area is the scope of the Data Ingestion (DI) Architecture. 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. 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. And results show that this approach is paying off, offering increases in productivity over competitors. A quick Internet search reveals that the term is used in many contexts. Gone are the days when IT departments were ancillary to process. Now let’s say we want to replace you driving the car with a machine driving the car. 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. 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. The DI architecture also defines data lifecycle management. The data is considered as an entity in its own right, detached from business processes and activities. In the context of networking, data allows AI algorithms to make better decisions, thereby optimizing the performance and management of the network. You want to know when the next train leaves). 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. Understandable by stakeholders 2. You can easily see that reasoning can become quite complex, especially when multiple goals need to be considered simultaneously. Still, with all things considered, enterprise businesses must have the right IT employees in place to create a functional business model. There are a couple of reasons for this as described below: Simply put, data refers to raw, unorganized facts. Alon has over 25 years of experience in the IT industry, joining BMC Software in 1999 with the acquisition of New Dimension Software. We need to take action to start relevant work on those missing pieces. O-RAN is an operator-led alliance for the evolution of the RAN and disaggregating the RAN architecture focusing on data-driven architecture functions. All these vehicles serve different purposes but need one common thing: an infrastructure. Information technology (IT) project management involves managing the total effort to implement an IT project. Cognitive technologies in network and business automation. The system can then autonomously decide to switch off (parts of) a radio base station, thereby saving energy. 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. The ONAP subsystem Data Collection, Analytics, and Events (DCAE) provide a framework for development of analytics. Read Ericsson’s full Technology Trends 2020 report.Here are 3 ways to train a secure machine learning model. NWDAF services include statistics/predictions of user mobility patterns, user communication patterns, user service experience, slice or network function load, and so on (3GPP TS 23.288). Contrary to traditional development where an algorithm is coded, in ML a model is trained. More on these points later. The DI architecture also defines data lifecycle management. That’s where MR comes in. The first phase of the data lifecycle is the creation/capture of data. Model lifecycle management can be divided into two phases: 1) data preparation, modelling and validation; and 2) deployment and execution of the models. The work of ITU-T SG 13 is meant to be an overlay to the 3GPP architecture. The system is trustworthy and can explain its action when asked for. Enterprise-wide data access and availability will be considered throughout the data and systems lifecycle. Data, not a functionality, is placed in the center. If not, here’s a quick recap. Each change in state is represented in the diagram, which may include the event or rules that trigger that change in state. In our telecommunication network, the use cases mentioned before also need an infrastructure. This 1-day course is packed with techniques, guidance and advice from planning, requirements and design through architecture, ETL and operations. Data Acquisition: acquiring already existing data which has been produced outside the organisation 2. Think of data as bundles of bulk entries gathered and stored without context. Figure 1: Ericsson's End-to-End SW Pipeline. His team believed the entries should be combined. Architecture. There is no one correct way to design the architectural environment for big data analytics. Stable It is important to note that this effort is notconcerned with database design. In addition, information assets have their own lifecycle and value, which are determined by the quality and usefulness of data involved as well as the type of asset as described above. This course prepares you to successfully implement your data warehouse/business intelligence program by presenting the essential elements of the popular Kimball Approach as described in the bestselling book, The Data Warehouse Lifecycle Toolkit (Second Edition). The data life cycle provides a high level overview of the stages involved in successful management and preservation of data for use and reuse. There are proposals to add additional services that span towards the RAN and the application domain. Or: I’m almost out of gas, let’s drive a bit more economically. Figure The Engagement Model Components Transportation may be across a large geographic area, and might pass through organisational borders. The EPLC conceptual diagram in … 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. They require different things from an architecture perspective 5. IT Project Management & Life Cycle. It provides an inevitable infrastructure to enable AI/ML and AI/MR. Moreover, you also learn. 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. Data needs to be transported to the consumer. The data is considered as an entity in its own right, detached from business processes and activities. How do we scale when the architecture is deployed over a large geographic area? The current DevOps environment at the vendor evolves to also include DSE, making it a DataOps environment. If you want to know more about MR in telecommunication networks, take a look at the article, Cognitive technologies in network and business automation. All these use cases require an infrastructure, and this is what a data-driven architecture is about. Data Architecture for Data Governance 1. More and more, IT departments are becoming an integral part of the enterprise business model. ETSI ZSM (Zero Touch Network and Service Management) specifies an architecture for zero-touch operations at the end-to-end level by connecting different domains (for example, RAN, CN, transport, edge cloud, etc.). Once context has been attributed to the data by stringing two or more pieces together in a meaningful way, it becomes information. Network Data Analytics Function (NWDAF) and Management Data Analytics Function (MDAF) are examples of such analytics functions. The breadth of content covered in th… Like an information architect, data architects work on the structural design of an infrastructure but in this case it’s specific to collecting data, pulling it through a lifecycle and pushing it into other meaningful systems. Data architecture is foundational. 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. Such infrastructure will be needed to achieve the vision of a zero-touch cognitive network. Let’s take a look at the differences between data and information and the key considerations your enterprise organization needs to understand. The second level where data may be used is indicated by arc number 2. See an error or have a suggestion? 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. 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. For example, the DCAE can implement the 3GPP NWDAF. The operator itself may have a DataOps environment as well. Information Technology related Enterprise Architecture. More and more, some functions of the data analyst are being automated, but even with automation, analysts remain important to the creation of future information states. So what is Ericsson Research doing to implement the data-driven architecture in our telecommunication networks? In this post, you will learn some of the key stages/milestones of data science project lifecycle. In our latest blog post, we outline data-driven network architecture and discuss why it’s crucial to the development of an AI infrastructure. These insights can, for example, be provided for customer experience, service and application management. 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. information lifecycle management need to be given due importance as part of the data governance strategy. 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. You can imagine that designing a data-driven architecture is not a trivial task. There are a couple of underlying reasons why there is so much focus on data-driven recently. There’s a well-known argument around data architecture versus information architecture. Bring together all your structured, unstructured and semi-structured data (logs, files, and media) using Azure Data Factory to Azure Data Lake Storage. The goal is to define the data entitiesrelevant to the enterprise, not to design logical or physical storage systems. O-RAN has specified the logical functions called non-real-time RAN Intelligent Controller (RIC) and near-real-time RIC. They yield different results 3. ©Copyright 2005-2020 BMC Software, Inc. Model Building. I have presented a couple of examples on use cases above. In the RAN (Radio Access Network) domain, an AI algorithm could monitor the traffic of mobile devices and predict traffic patterns. There is work ongoing on all these components. Example research questions include: How will data-driven architecture evolve the current 3GPP architecture? With MR the machine reasons with a conceptual representation of a real-world system and takes actions accordingly. Data Analytics lifecycle for Statistics, Machine Learning. There are hundreds of data-driven use cases defined, and we expect many more to come. Network analytics products have broad capabilities such as measuring and predicting perceived customer experience, ingesting, auditing and contextualizing data for service assurance and network operations, detecting incidents, performing root cause analysis and recommending solutions. This is the so-called zero-touch vision, and you will find more information on that in our blog post Zero touch is coming. Some of these use cases are already implemented in our products, and we expect to implement many more in the years to come. 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. Data-driven simply means that decisions are made based on data. The CIO will make decisions regarding both data and information architecture. Please sign up for email updates on your favorite topics. All these use cases have one thing in common: they all need data. 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. As I’ve tried to show above, the evolution towards a data-driven architecture is ongoing and has already come quite far. The DI architecture defines how to collect, route and distribute data. While data architectures may be adjusted within specific functional communities or Air Force components to meet specific needs, architectures will support Maybe you have heard of the term ‘data-driven’? The zero-touch vision aims to achieve a so-called cognitive network. In other words, the End-to-end SW Pipeline can use DI such that the combination gives a rudimentary model lifecycle management for central learning. In this post, we take a look at the different phases of data architecture development: Plan, PoC, Prototype, Pilot, and Production. Another variant of AI is Machine Reasoning (MR). Another significant organization that may influence forming of a data-driven architecture is TM Forum. Building a data warehouse is complex and challenging. Part of the information lifecycle process requires developers to consider future state implementations. 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. 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. Lambda architecture is a popular pattern in building Big Data pipelines. Data Architecture provides an understanding of where data exists and how it travels throughout the organization and its systems. Besides the obvious difference between data and information, each has a unique lifecycle and best practices for managing it within an organization. Now, the vast majority of departments and processes are powered by IT innovation. Components in the different domains may expose data to a distributed bus/database. Greatly reduces the complexity between all cloud environments, They work with different assets: data assets vs information assets, They require different things from an architecture perspective, They require roles with different specialties to be part of an enterprise organization. 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