The first experience that an item of data must have is to pass within … Seamless data integration. They require roles with different specialties to be part of an enterprise organization Although data and information archite… And creating information assets is the driving purpose of information architecture. Note that we define OAM in a broad sense. Maybe you have heard of the term ‘data-driven’? 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. In a nutshell, information lifecycle management seeks to take raw data and implement it in a relevant way to form information assets. That’s where MR comes in. Please sign up for email updates on your favorite topics. However, it’s important to realize that these two have unique differences and are used in different ways. Hopefully by now, it’s clear why information and data architecture are two different things. 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 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. With MR the machine reasons with a conceptual representation of a real-world system and takes actions accordingly. How can this be monetized to support a revenue model? For instance, making recommendations that a piece of data could be better implemented as a dashboard or document attachment. It becomes apparent that data-driven is not just about technology; it is rather a mindset. Future data-driven architectures will also support environments for ML. There are a couple of reasons for this as described below: Simply put, data refers to raw, unorganized facts. There are hundreds of data-driven use cases defined, and we expect many more to come. At the lower part of the picture we see the network’s domains OAM, RAN, CN. 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. They yield different results 3. All these vehicles serve different purposes but need one common thing: an infrastructure. In this post, we take a look at the different phases of data architecture development: Plan, PoC, Prototype, Pilot, and Production. This data can be in many forms e.g. Modern Slavery Statement | Privacy | Legal | © Telefonaktiebolaget LM Ericsson 1994-2020, An introduction to data-driven network architecture, Redefine customer experience in real time. Like what you’re reading? There is no one correct way to design the architectural environment for big data analytics. All this needs to scale even for large networks. How do we do model lifecycle management? Alon is a regular speaker in Big Data conferences and BMC events around the world. It looks at incoming data and determines how it’s captured, stored and integrated into other platforms. Within the engagement model, the lifecycle or architecture method or process, describes the tasks of the architecture team. Use of this site signifies your acceptance of BMC’s, Mindful AI: 5 Concepts for Mindful Artificial Intelligence. These postings are my own and do not necessarily represent BMC's position, strategies, or opinion. Network Data Analytics Function (NWDAF) and Management Data Analytics Function (MDAF) are examples of such analytics functions. ITU-T SG 13 ML5G (Machine Learning for Future Networks including 5G) proposes a standardized ML pipeline. 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. 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. It is typically modeled at four levels: Business, Application, Data, and Technology. The vendor’s environment not only includes a DataOps part. Architect Journey: Development Lifecycle and Deployment. 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. What would we like to offer our target market? 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. Data architecture defines the collection, storage and movement of data across an organization while information architecture interprets the individual data points into meaningful, useable information. Download an SVG of this architecture. The data is considered as an entity in its own right, detached from business processes and activities. On the other hand, information lifecycle management looks at questions like whether or not a piece of data is useful, and if yes, how? how AI can secure optimal network performance. 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. The third level where data may be used is within the domains as indicated by the arcs with number 3. The ONAP subsystem Data Collection, Analytics, and Events (DCAE) provide a framework for development of analytics. 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. Contrary to traditional development where an algorithm is coded, in ML a model is trained. In this post, you will learn some of the key stages/milestones of data science project lifecycle. In the context of networking, data allows AI algorithms to make better decisions, thereby optimizing the performance and management of the network. Understandable by stakeholders 2. We need to extract data efficiently. The purpose of both RICs is to optimize the RAN performance using AI/ML agents running in the RICs. 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 needs to be transported to the consumer. 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. We need to detail the data-driven architecture, make it concrete and define what building blocks it is composed of. A quick Internet search reveals that the term is used in many contexts. For example, the network functions in the CN domain may use the Ericsson Software Probe to do exposure. If not, here’s a quick recap. Below is an employee snapshot created for both information architecture and data architecture. Data Architecture for Data Governance 1. The second level where data may be used is indicated by arc number 2. Initiatives are taken in different standardization organizations and alliances, which will affect the evolution towards a data-driven architecture. First, technology advancements in compute and networking capacity have made it possible to expose and transport data in unprecedented amounts. Note that this is a rough mapping to get an idea; it is not 100 percent correct. The grey marked area is the scope of the Data Ingestion (DI) Architecture. Now you may wonder how this data-driven paradigm can be used in telecommunication networks. 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. Such infrastructure will be needed to achieve the vision of a zero-touch cognitive network. We call that infrastructure the data-driven architecture. The objective here is to define the major types and sources of data necessary to support the business, in a way that is: 1. 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 The challenge of the paging procedure is that the network only knows where a device is approximately. How would new AI technologies like reinforcement learning work in data-driven architecture? 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. 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 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. 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. 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 vendor may also use the data for managed services. What challenges will we face in accomplishing these goals? There may also be external sources at the Data network (DN) exposing data. IT Project Management & Life Cycle. 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. 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. They require different things from an architecture perspective 5. 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. We need to identify the building blocks that nobody else is working on yet. The DI architecture also defines data lifecycle management. Information analysts specialize in the extraction and analysis of information assets. 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. 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. In the picture above, the data may be used at three different levels. For example, the DCAE can implement the 3GPP NWDAF. Data science projects need to go through different project lifecycle stages in order to become successful. Stable It is important to note that this effort is notconcerned with database design. You want to know when the next train leaves). You build experience each time you drive and use that experience to improve your driving. Statistical Machine Learning Data analysis life cycle. Workflow Orchestration solutions such as Control-M, help organizations to abstract the complexity involved with the numerous data sources, multiple applications and diverse infrastructure. This is someone who likely works in both systems comprised of data architecture and information architecture. 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). Information technology (IT) project management involves managing the total effort to implement an IT project. The operator itself may have a DataOps environment as well. Hopefully by now, it’s clear why information and data architecture are two different things. If not, here’s a quick recap. The fundamental components of a data-driven architecture are probing and exposure, data pipelines, network analytics modules, and AI/ML environments. 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. Example research questions include: How will data-driven architecture evolve the current 3GPP architecture? As I’ve tried to show above, the evolution towards a data-driven architecture is ongoing and has already come quite far. In the following text, we will look at positions that may be necessary for data architecture, information architecture or both. 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. Let’s take a look at the differences between data and information and the key considerations your enterprise organization needs to understand. Data-Driven Proactive 5G Network Optimisation Using Machine Learning. While data architectures may be adjusted within specific functional communities or Air Force components to meet specific needs, architectures will support This is the so-called zero-touch vision, and you will find more information on that in our blog post Zero touch is coming. 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). O-RAN has specified the logical functions called non-real-time RAN Intelligent Controller (RIC) and near-real-time RIC. Now let’s say we want to replace you driving the car with a machine driving the car. The breadth of content covered in th… We may need to pre-process extracted data. 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. The system analyzes large amounts of data and finds patterns (that is, it learns). 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. Lambda architecture is a popular pattern in building Big Data pipelines. This way, the system can assess when and where there will be no or very little traffic. Each change in state is represented in the diagram, which may include the event or rules that trigger that change in state. 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? 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. At Ericsson Research we try to focus on challenges that lie a little further ahead. The primary role of the information architect is to focus on structural design and implementation of an infrastructure for processing information assets. We need to have a clear picture of who is doing what. Another variant of AI is Machine Reasoning (MR). 3 ways to train a secure machine learning model. By building on data from several operators’ networks, a vendor can create more powerful data-driven design than the individual operator. 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. Data lakes have been rising in popularity these days but are still confused with data warehouse. Figure 1: Ericsson's End-to-End SW Pipeline. As it regards data architecture, one of the big considerations will be deciding between a data lake and a data warehouse. Plan 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. This has always been the case, but it can now be done to a larger extent than before. In the RAN (Radio Access Network) domain, an AI algorithm could monitor the traffic of mobile devices and predict traffic patterns. It provides an inevitable infrastructure to enable AI/ML and AI/MR. There may be additional domains like transport or cloud infrastructure, but these are not shown here. information lifecycle management need to be given due importance as part of the data governance strategy. There’s a well-known argument around data architecture versus information architecture. 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. The work of ITU-T SG 13 is meant to be an overlay to the 3GPP architecture. 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: Cognitive technologies in network and business automation. Your team must adopt a proactive, lifecycle-based approach … 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. The use of the infrastructure is guided by traffic rules and traffic signs. The first level where data may be used is indicated by arc number 1. 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). Can we use MR to automate this? MR is simply the automated version of the car driving example. Model Building. Another significant organization that may influence forming of a data-driven architecture is TM Forum. They have distinctly unique life cycles 4. 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. Multiple versions of a data life cycle exist with differences attributable to variation in practices across domains or communities. Data Acquisition: acquiring already existing data which has been produced outside the organisation 2. How will distribution in learning and decision-making impact the architecture? 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. ©Copyright 2005-2020 BMC Software, Inc. This arc is based on the End-to-end SW Pipeline (see Figure 1). 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. Figure 3: Ericsson’s data-driven architecture. We split the telecommunications network often in administrative domains. In ML, an algorithm is called a model. 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 step of data analytics architecture comprises developing data sets for testing, … For example, extract only once even if there are multiple users of the same data. Data Flow. One example use case of MR is improving the management of the network. If you want to know more about MR in telecommunication networks, take a look at the article, Cognitive technologies in network and business automation. See an error or have a suggestion? You can imagine that designing a data-driven architecture is not a trivial task. 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. These two factors enable numerous use cases where a machine can produce insights from data and do (better) decision-making based on data. 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. 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. Some of these use cases are already implemented in our products, and we expect to implement many more in the years to come. Here comes a brief overview: Exposure of data from network functions builds upon management interfaces and probes. 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