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5 Imperatives of a modern data platform

Nov 21, 2022

Accessing data at the speed of business is critical to remaining competitive in a digital-first world. But if you’re relying on outdated architecture where your data is trapped in silos or lost in a data lake, access to the functional data you need is seriously limited. When your existing framework is no longer serving your business, it makes sense to transition to a modern data platform, but you may have hesitations about whether it can help you succeed. To help you better understand this solution and what you need to gain from it, we are looking at data platform capabilities and sharing five modern data platform imperatives that will help achieve a more logical data management system. What is a modern data platform? With so many emerging data solutions, we understand that data is a very complicated environment, so we want to start by clearly defining what a modern data platform is and its capabilities. A modern data platform is a flexible, cloud-based, end-to-end data architecture that supports collecting, processing, analyzing, and delivering data to the end user in a way that is aligned and responsive to the needs of the business. On the surface, aside from it being cloud-based rather than on-premise, modern data platform capabilities aren’t different from traditional data architecture. The difference is in how new technologies have expanded their capabilities. Here are some of the ways modern data platforms can deliver more for your organization: Data ingestion Bringing new data into the environment is the first step to managing data, and in a legacy architecture, that is mainly done through batch processing. Batching collects and processes data at specific time periods or intervals. By leveraging the higher computing capacity of a cloud-based architecture, data can be streamed in real time to data storage units, eliminating bottlenecks and delays to keep data moving through the system in a more fluid manner. Quality and governance With AI integrated into the architecture, data quality and governance tools can be automated, speeding up how new data sources are analyzed, categorized, and assessed for security concerns. Security Security measures can be integrated at the base level for new data products, providing inherent encryption whether it’s at rest or in transit. Within a modern data platform, security measures are implemented to dynamically filter and obscure data as needed to support your organization’s security policies. Storage Cloud-based architecture offers the potential for nearly unlimited storage and offers a pay-as-you-go model, so you only need to invest in the volume of storage you need today. As your data storage needs increase in the future, you can add and seamlessly integrate additional space without creating silos for new data. Transformation In legacy architecture, transformations such as quality adjustments and business logic need to be applied in the early stages of data flow during large batch processing. While this ensures that the downstream usage of the data is more performant, it also locks the business rules in place which removes flexibility in how the business looks at and interacts with the data. The expanded computing power and advanced tools in a modern data platform offer a more flexible timeline to add transformations to the data. Business rules and logic can be applied later in the data flow and adapted to suit changing needs. Discovery Data discovery is streamlined through integrated tools within a modern data platform that can automatically scan, categorize metadata, and organize it so the most appropriate data is accessed more easily and quickly. Delivery In a legacy architecture, data delivery visualization tools required the data to be specifically structured prior to business usage, whether for reporting, data extracts, or API access. Now, visualization tools have advanced features that support access to semi-structured and unstructured data without the need for intensive (and expensive) data processing. Integrated tools simplify both data extraction and data sharing and have built-in security and monetization features. DevOps and DataOps In a modern data platform, DevOps/DataOps are cross-platform and cross-language supportive, which makes it easier and faster to coordinate development and release implementation tasks when architectures are built using multiple tools. 5 modern data platform imperatives The overall framework, capabilities, and patterns of managing data are universal within a modern data platform. However, no two platforms are the same. Each one is highly customized to support the data and data needs of the organization and require different combinations of tools or features to achieve specific functionalities and cover the needed capabilities. You still need to ensure your platform manages the data in a way that aligns to your organization’s unique needs, and this means that five modern data platform imperatives must be met. 1. Greater flexibility The greatest challenge of legacy data architecture is the lack of flexibility. The physical servers can’t be added to or modified easily to meet the changing data needs of your organization, so they need to be built with the capacity for future data needs. This is easier said than done given the rapidly changing landscape and the sheer volume of data you’re taking in. A modern data platform is incredibly flexible. It allows you to consider your data needs today and budget accordingly rather than trying to predict your data needs in the future which requires a significantly larger investment. As you need to increase data storage, adopt automation, or pivot in your data needs, these updates can be integrated seamlessly into the platform. 2. Improved access The people and applications accessing data need it in real time and in the proper format, but the needs of your data science team vary greatly from the needs of your business intelligence team. A modern data platform must support a faster time to market for data assets, and one way it does this is through a medallion architecture. A medallion architecture creates a multi-layered framework within the platform to move data through a pipeline to the end user. Bronze layer: Raw data is collected directly from the source systems with little to no transformation and stored here to provide a base layer of full history for additional processing. Silver layer: Data from multiple sources are curated, enriched, integrated, and organized in a structure that reflects the data domains of the organization. Gold layer: Data needed to support specific business drivers is aggregated and organized so it can be used for dashboard creation and self-service analysis of current states and trends. This architecture allows a diverse user base to access the data in the form that best suits their needs. Data scientists can access raw data from the bronze layer to identify new and emerging patterns, business applications can access data in the silver layer to produce data products, and business users can access the gold layer to perform analytics and create dashboards. 3. Incremental implementation Rather than transitioning to a modern data platform in a single, giant step, we recommend an incremental move. This makes it significantly easier and faster to focus on the current data products your organization needs, like reports and dashboards, while you are starting to build out the initial infrastructure. An incremental implementation lets you take a clear, informed look at the data you need, how you need it, and how it aligns with your business drivers. You can then choose to add, adjust, or stop processing certain data to put more focus on the data that will answer pivotal business questions. At the same time, building only what you need when it’s needed, an incremental implementation saves money and avoids bringing over old data that no longer serves your business. 4. Better communication between IT and business users A modern data platform needs to support improved communication between your IT or data engineers and your business users. As data flows through the framework and reaches the end user in the language they speak, the end-user has greater clarity. For business users, this may mean seeing gaps in how the existing data is not directly answering their questions and needs to find a different way to utilize the data. For the data engineers, this may mean seeing opportunities in how to filter out aberrations in the data to improve the aggregated data. This clarity allows the teams to work together to target solutions that will cover existing or emerging needs. 5. Re-focus valuable resources Once the initial data set is built, we apply repeatable patterns to the mechanics controlling data ingestion, storage, and delivery. Having a proven framework that can be repeated to unlimited data sets saves time and reduces the cost of building, operating, and maintaining the platform. Your data team can refocus their time on higher-level tasks, including improving data quality and speeding up delivery. Whether you have questions about data platform capabilities and functionalities or you’re ready to make the shift to a modern data platform, we’re here to help! Set up a call to talk to an expert or visit our modern data platform hub to learn more. Ask us your questions >> Learn more about modern data platforms >>

Is data virtualization the future of data integration and management?

Nov 17, 2022

In a perfect world, all your data would be stored in an updated, organized database or data warehouse where your business intelligence and analytics teams could keep your company ahead of the competition by accessing the precise data they need in real time. In reality, as your organization has grown, your data has probably been stretched across multiple locations, including outdated databases, localized spreadsheets, cloud-based platforms, and business apps like Salesforce. This not only causes costly delays in accessing information, but also impacts your teams’ ability to make informed, data-driven decisions related to both day-to-day operations as well as the long-term future of your organization. So, how do you improve access to your data when it’s siloed in multiple areas? Data virtualization, while still fairly new, is an efficient, effective data delivery solution that offers real-time access to the data your teams need, and it is rapidly growing in popularity among large to enterprise-level organizations. While the market was estimated at $1.84 billion in 2020, a 20.9 percent CAGR has the data virtualization market projected to go beyond $8 billion by 2028 according to a 2022 Verified Market Research report. To help you determine if data virtualization solutions are the best option for your company, we’ll take a look at what data virtualization is, how it can solve your greatest data challenges, and how it stacks up to other data integration solutions. Understanding data virtualization First, what is data virtualization? When you have data housed across multiple locations and in various states and forms, data virtualization integrates these sources into a layer of information, regardless of location or format, without having to replicate your information into new locations. While this layer of data is highly secure and easily managed within governance best practices, it allows the data consumers within your organization to access the information they need in real time, bypassing the need to sift and search through a variety of disparate sources. Data virtualization supports your existing architecture Data virtualization does not replace your existing data architecture. Instead, it’s a single component in a larger data strategy, but it is often essential in executing the strategy successfully and meeting the goals of your organization. Think of your current data architecture as an old library where your data is kept on a variety of shelves, over multiple floors, and some of it is even stored in boxes in the basement. When you are looking for specific information, you have to go on an exhaustive, lengthy search, and you may not even find what you need. Data virtualization acts as the librarian who understands the organizational system, knows exactly where everything is located, and can provide you with the information you need immediately. Choosing data virtualization vs an ETL solution When reporting is delayed, analytics are inaccurate, and strategic planning is compromised due to bottlenecks, it’s essential that your organization prioritizes how data is integrated and accessed. Traditionally, organizations would only choose Extract, Transform, and Load (ETL). ETL is an intensive process in which all your data is duplicated from the original sources and moved into a data warehouse, database, or other storage. While ETL can bring your data together, there are two key problems with this method. The cost of moving and relocating data is often the chief concern most organizations have. And while it does improve your collection of data by keeping it siloed in one location, it doesn’t improve your connection to analyzable data that is needed to improve day-to-day operations. On the other hand, data virtualization solutions streamline how you access and connect to your data. Your business users submit a query, and the Denodo data virtualization platform pulls the data from across locations, extracts the relevant information and delivers it in real time in the needed format so it’s ready to analyze and use. The result? Increased productivity, reduced operational costs, and improved agility among business users while your architects and IT teams have greater control on governance and security. Take a deeper dive into data virtualization solutions Ready to dig deeper into data virtualization? We partnered with data management leader Denodo Technologies to put together Modernizing Integration with Data Virtualization, a highly informative webinar to help you learn how data virtualization helps your company save time, reduce costs, and gain better insight into your greatest asset. To learn how Fusion Alliance can create custom data virtualization solutions to scale your data management and improve access, reach out to our team. Ask us any questions or set up a quick call to explore your options. Learn more about modern data platforms >>

Data fabric vs data mesh: Choosing the best data architecture for your organization

Nov 14, 2022

Whether your data is housed in a monolithic data architecture or across multiple, disparate sources such as databases, cloud platforms, and business applications, accessing the specific information you need when you need it probably presents a huge challenge. The length of time it takes to find data may have you or your analytics teams constantly relying on outdated information to run reports, develop strategies, and make decisions for your organization. If you’re exploring data solutions that will improve time-to-market while simplifying governance and increasing security, you’ve probably come across the terms “data fabric” and “data mesh,” but you may not know how to apply them to your business. To help you better understand these emerging trends in data architecture, we’re digging into what a data fabric and data mesh are and the specific benefits they bring to large and enterprise-level organizations. This will give you the foundational knowledge to determine how to choose data fabric vs data mesh or how both may be able to serve your organization. What is data fabric? When you think of every bit of data in your organization as an individual thread, it makes sense that it takes so long to access specific information. If thousands of individual threads are stored together in a bin, like in a monolithic architecture, or separated across hundreds of individual boxes with little to no organizational method, like in a distributed architecture, how long would it take to find the single thread you’re looking for and get it untangled so you can use it? A logical data fabric solves this problem by weaving all the threads of data together into an integrated, holistic layer that sits above the disparate sources in an end-to-end solution. Within the layer are multiple technologies working together to catalog and organize the data while machine learning and artificial intelligence are implemented to improve how new and existing data are integrated into the fabric as well as how data consumers access it. Are data virtualization and data fabric the same? A common misconception is that data virtualization and data fabric are the same. On the surface, they both support data management through the creation of a single, integrated layer of processed data atop distributed or unstructured data. Data virtualization is an integrated abstraction layer that speeds up access to data and provides real-time data returns, and this technology is a key component within the data fabric. However, data virtualization is still only one of the multiple technologies comprising the entity, which is a more comprehensive data management architecture. Benefits of data fabric Now that you have a better understanding of what data fabric is, let’s consider the problems it solves and why it may be right for your organization. Access your data faster When your data is in multiple formats and housed in a variety of locations, gaining access to the specific details you need can take hours, days, or even weeks, depending on your architecture. A logical data fabric leverages metadata, semantics, and machine learning to quickly return the needed data from across multiple sources, whether it’s a large amount of historic information or highly specific data used to drill down into a report. Democratize your data Data fabric uses advanced semantics, so the data is accessible in the language of business users, such as BI and analytics teams. Data consumers within the organization can access what they need without having to go through data engineers or the IT department, eliminating bottlenecks and sharing ownership of data. Improve governance Because of the automation capabilities of data fabric, you can implement a governance layer within the fabric. This applies global policies and regulations to data while allowing local metadata management to reduce risk and ensure compliance. What is data mesh? Monolithic data architecture keeps data in one centralized location. On paper, this seems like a more cost-effective, efficient option compared to a distributed architecture, but it still brings several challenges. Consider that in many large organizations relying on a monolithic architecture, massive volumes of unstructured data are stored in a data lake. For information to get into the hands of data consumers or before productization can occur, the data must be accessed and processed through the IT department, creating significant bottlenecks and bringing time to market to a crawl. A data mesh can solve this challenge. This is a new type of data architecture, only proposed in 2019 by Zhamak Dehghani of Thoughtworks, in which a framework shifts data from a monolithic architecture to a decentralized architecture. More specifically, the data is distributed across autonomous business domains where the data consumers own, manage, and share their own data where they see fit. While the domains are given a separate virtual schema and server so they can have full ownership over data productization, governance, security, and compliance are still unified within the monolith. Benefits of data mesh The challenges of centralized data ownership include latency, added costs of storage, software, replication, and lack of practical access for consumers, but implementing a data mesh can solve these. Eliminate IT bottlenecks When all data is forced to go through the IT department before being distributed to the individuals or teams requesting it, bottlenecks occur and slow down the flow of data. A data mesh allows data to bypass the IT department, allowing data to flow freely to the needed source. Improve flexibility and agility Finding specific information within the massive volume of unstructured, undefined data stored in a data lake requires increasingly complicated queries to get the needed information. However, a data mesh gives ownership of datasets to individual teams or business owners, simplifying access and offering real-time results through scalable, automated analytics. Increase connection to data By transferring data ownership to the data consumers, those who use it directly have a greater connection to it. The data is available in the language of business, and it can be shared across teams with greater ease and transparency. Choosing data fabric vs data mesh Data fabric and data mesh both support data democratization, improve access, eliminate bottlenecks, and simplify governance. While data fabric is built on a technology-agnostic framework to connect data across multiple sources, data mesh is an API-driven, organizational framework that puts data ownership back in the hands of specific domains. So, which is better in the debate between data fabric vs data mesh? The simple answer is neither one is better than the other, and the right option is determined by the use case. If the goal of your organization is to streamline data and metadata to improve connection and get real-time results across multiple teams, a data fabric built on a data virtualization platform can help you meet your goals. On the other hand, if you need to improve the process of data productization and decentralizing your data, a data mesh may be the best option. But the real answer is that contrary to popular belief, the two are not mutually exclusive and most businesses succeed by implementing both options. Data fabric and data mesh are complementary solutions that can work together to solve the challenges of your existing architecture. Learn more about data fabric and data mesh Want to gain further insight into choosing data fabric or data mesh? We partnered with data management leader Denodo Technologies for a recorded webinar. In Logical Data Fabric vs Data Mesh: Does It Matter? we provide an in-depth look at monolithic and distributed data architecture, the challenges they bring, and how both data fabric and data mesh can improve agility, reduce costs, and elevate the quality of your data. To ask additional questions or learn how Fusion Alliance can help you create and implement a successful data strategy to meet your unique challenges and goals, connect with our team today. Learn more about modern data platforms >>

Accelerating time to value by implementing logical data fabric

Oct 12, 2022

Today’s businesses collect more data than ever before, but many don’t have the architecture in place to store, process, and recall the data in real time. Whether an enterprise-level organization stores all its data in a single data lake or relies on multiple, disparate sources, both options cause significant delays in finding the specific information you’re looking for. Traditionally, if your organization wanted to update and upgrade the existing architecture, the only option was extract, transfer, and load (ETL) the data to a new framework but implementing a logical data fabric offers a better alternative — giving companies a cost-effective, efficient way to collect and integrate data while building a stronger framework across the organization. At a recent CDO Data Summit, Mark Johnson, Fusion Alliance Executive Vice President and editorial board chair for CDO magazine, sat down with thought leaders in the data industry to discuss why logical data fabric is essential in accelerating time to value. What is a logical data fabric? When you have multiple disparate data sources, a data fabric acts like a net cast over the top, pulling individual information sets together in an end-to-end solution. Data fabric is a technology-driven framework that lies within the existing architecture, unlike a data mesh, which is a methodology regarding how data should be distributed among data owners and consumers. In a logical data fabric, multiple technologies are implemented to catalog and organize existing data and integrate new data into the fabric. Data virtualization is the central technology deployed within this framework, creating an abstracted layer of unified data that is more secure and easily accessible. What challenges are solved by a data fabric architecture? Logical data fabric architecture offers a solution to the challenges organizations relying on numerous data storage solutions or repositories of structured and unstructured data face: Overcome slow data delivery By consolidating data into an integrated semantic layer, common business applications can process, analyze, and return the data in real time, in the language of the data consumer. This improves accessibility and significantly reduces latency that comes from applications having to search across multiple sources to return information. Simplify governance If every data warehouse, database, and cloud-based platform within your organization relies on separate governance, you are dealing with significant inconsistencies. By stitching the data together in a logical data fabric, centralized governance can be applied across all data and automated to maintain and streamline the process. Reduce IT bottlenecks Data fabric automates how data is processed, integrated, governed, and utilized, enabling real-time analytics and reporting. This puts data in the hands of your BI and analytics teams more quickly while removing bottlenecks from your IT department. With a logical data fabric architecture, your business can respond to trends and changes within your industry more quickly, helping you to evolve both short and long-term strategies to reflect what your data is telling you in real time. Is a logical data fabric the right solution for your organization? Learn more about data fabric architecture from the CDO Data Summit’s round table discussion. Mark Johnson is joined by: Baz Khauti, President at Modak USA Richie Bachala, Principal, Data Engineering at Yugabyte Ravi Shankar, SVP and Chief Marketing Officer at Denodo Saj Patel, VP of Data Solutions at Fusion Alliance This panel addresses critical questions about data in today’s business to help you solve your unique data challenges, including: Is the fabric of data virtual, physical, or both? How do we get value out of our data? Do we take a connect or collect approach? How comprehensive do we need our data approach to be? Are we optimizing for agility or for flexibility? How do we deliver unified data? Is the organization in agreement with what we are looking for out of their data? What AI/ML techniques do we want to employ, if any? If you have specific questions or are ready to take the next step and learn how we can help you create custom data solutions for your organization, reach out to us today for a quick chat! Learn more about modern data platforms >>

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