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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 But if you’re relying on outdated architecture where your data is trapped in silos or lost in a data lake, access to the functional data you need is seriously limited.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 >>

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 >>

Speed to value delivery: Optimizing with data virtualization and data fabric

Jun 6, 2022

This article originally appeared in CDO magazine. Data and analytics have long held promise in helping organizations deliver greater value across the entire stakeholder landscape, including customers, associates, and partners. However, since the beginning of the data warehousing and BI movement, achieving business value rapidly — in alignment with windows of opportunity — has proven elusive. For an organization to be competitive in the era of digital transformation, data must be front and center — and accessible in near real-time. But many organizations are struggling with data that is deeply buried, complex to access, difficult to integrate, and inaccessible to business users. Problems like these diminish the value of your data and its ability to inform decision-making at all levels. For most organizations, it’s hard to produce value from data quickly The main challenge has been the distributive and siloed nature of the data subjects that need to be integrated to achieve business-relevant insights. Data subjects — customers, products, orders, warehouses, etc. — typically reside in different systems/databases, requiring extraction, transformation, and loading into a common database where analytics can be mounted. Often, data delivery solutions like data warehouses, self-service BI, and data lakes are used to try and unlock these data silos; however, each of these solutions presents drawbacks in terms of effort, complexity, cost, and time-to-market. That is where data virtualization comes in and delivers a holistic view of information to business users across all source systems. So what exactly is data virtualization? In its simplest form, data virtualization allows an organization to attach to its data subjects where they reside in real-time. It presents disparate data subjects through a semantic layer that enables them to be integrated on the fly to support query and analytic use cases. By eliminating the need to design and build complex routines that move data from multiple source locations into a single integrated data warehouse, products like Denodo enable organizations to compress weeks to months of data preparation time out of the idea-to-execution value stream. As a result, value delivery is significantly accelerated. Learn more about how Fusion & Denodo can help you streamline data access to support your most critical business needs >> Optimization with data fabric. While data virtualization integrates data from different sources into one layer to provide real-time access, data fabric is an approach with end-to-end architecture that allows organizations to manage massive amounts of data in different places and automates the integration process. The thing about data fabric is that it has a huge job to do and must have a robust integration backbone to do it. A data fabric must support many data sources, be compatible with several data pipeline workflows, support automated data orchestration, empower various kinds of data consumers, and more. To do this successfully, a data fabric requires powerful technologies and a solid data integration layer to access all data assets. Many in the data community believe that you must choose data virtualization OR data fabric, but that is not the case — and that solid data integration layer is an example of why. The reality is that data fabric can be operationalized through data virtualization and optimizes your modern data architecture, allowing you to move with the speed of your business. By building a model that utilizes both concepts, businesses make finding, interpreting, and using their data near seamless. Technology by itself isn’t the answer. Even with the proven results of this class of technologies, many organizations continue to struggle with traditional data management and analytic architectures and solutions. This inability to adopt new approaches for data management and analytics only serves to deprive decision makers of rapid access to insights that are necessary to support agility in the pandemic-induced, rapidly transforming digital/global economy. The solution is not just found in technology. Instead, it is found in the minds of the humans responsible for delivering data management and analytic capabilities. It is a human change management problem we face. Remember the adage, people/process/data/technology? The next frontier to be conquered is optimizing the thinking and innovation risk tolerance of stewards of data management and analytics solutions within organizations. What do you think? Is your organization facing any of these issues or trying to tackle how to deliver significant value — better, faster, cheaper, smarter? I’m happy to chat about where you’re at and how to get where you would like to be. If you want to talk, send me a note.

Level up your data management

May 31, 2022

On the journey from data to analysis to insight, companies are shifting from a traditional approach and leaping forward into new ways of delivering actionable business intelligence. While the core goals remain the same — enabling data-driven decisions, optimizing cost efficiencies, and driving revenue growth — new tactics demand new skills. The pivot to a use-case model and good governance throughout the data lifecycle meets those challenges while also delivering faster time to insight. Connecting the business to fit-for-purpose data The new data mindset is purpose-driven. Based on specific use-cases generated by the business, today’s data teams build, deploy, and configure purpose-built data assets that meet the organization’s needs fast. This streamlined process represents a significant shift from the status quo for traditional data teams, but the streamlined workflow pays off. To generate fit-for-purpose data, start here: Solicit use cases from the business Understand and analyze the characteristics and dynamics of the use case Assess your existing data portfolio and identify information that might meet the need Consider the appropriate technology to synthesize data sets and deliver actionable insights Establishing a data asset creation workflow pays off in efficiency and value for IT and the business units involved. Learn more about how to develop data as an asset >> Speeding time to insight Traditional data warehousing models impose a high cost for integrating disparate data sets. A legacy workflow might include: Amending data architecture Creating a semantic model Time-consuming extract, transform, and load (ETL) processes for all data sets involved Preparing the data Making the data available for analysis Today’s businesses don’t have that long to wait for insights. Modern data technologies like Hadoop make it possible to stage data in a platform for immediate access. To structure your data and technology architecture toward a use-case driven model that fosters speed to insight key considerations include: A prioritized list of problems that need solutions Any characteristics or constraints that might impact time to value Available data assets and technologies, like data virtualization, that would enable you to access and analyze data in place Once you adopt methods for analyzing data in place, your team can deliver value on a much shorter timeline. Learn more about data architecture and integration >> Improving data literacy The demand for data-driven insights continues to accelerate. Companies at the forefront of the shift from volume to velocity use analytics pervasively throughout their organization and have the technology and agility to act on insights quickly. To become a competitive, speed-driven organization, your business must excel throughout the analytics lifecycle: Acquire: Harvest data quickly by exploring evolving big data technologies and optimizing first-party data strategies Analyze: Identify the most impactful insights Act: Implement the insights iteratively and strategically That final step involves your organization’s data literacy. Providing insights is one thing, but training your people to take the next right action on the data they see might require new skills. Upskilling the workforce to better understand and use data pays off richly in transformative accuracy, speed, and confidence. Learn more about building data literacy within your organization >> Implementing harvest-to-delivery data governance As the volume of available data continues to increase, businesses are building complementary abilities to understand and use it. But implementing tools and technology to harvest, integrate, and analyze data without robust governance frameworks opens companies up to significant risk. Building strong governance into your data asset creation and management workflows from the start can help. Learn more about how to implement good data governance >> Elevating data leaders As the world becomes more digital, and more customer behaviors move to a mobile context, businesses are changing to meet and match digital footprints with geospatial dimensions. Leadership must keep pace The need for a Chief Data Officer (CDO) at the table isn’t really a question anymore. Today, leading companies are asking where analytics and digital belong in the leadership playbook. To get the most value out of your data management, the right team members — with the right support and authority in place — could not be more important. Learn more about the importance of empowering your CDO >> Data management can be complex. A strategic viewpoint can help. Find out more about Fusion’s approach to strategic data management, or ask us your questions. Wherever you are on your data journey, we can help you keep moving forward.

Fuse Highlights: Coconuts, horses, and beer, oh my!

May 26, 2022

Every few weeks, we share insights with our Fuse subscribers along with news and trends we’re following across the web, including book recommendations. Here’s a compilation of some of our key insights from last six weeks. If you want content like this delivered directly to your inbox, we’ve got you covered. Subscribe to the Fuse here. Data is the Holy Grail In the classic film Monty Python and the Holy Grail, viewers hear King Arthur and his trusty servant Patsy approaching with a trademark “clip-clop, clip-clop” sound. When the duo emerges from the primordial mist, you see (spoiler alert) that the source of all this noise is not, as might be supposed, a horse. Rather, Patsy is banging two coconut shells together as the king trots about on his own two legs. The duo is getting from point A to point B in their quest, but not in the most efficient or effective way possible. Many companies follow that script. Equipped with buzzword mandates like process optimization and data-driven decision making, it’s all too easy to make small adjustments that sound like you’re headed in the right direction but aren’t necessarily getting you there any faster. How do you drop the coconuts and get on the horse (metaphorically speaking)? What does it look like to use data to drive optimization in real terms? We’ve got our eye on digital twins. Before you run away (how’s that for a deep cut Monty Python reference?) from yet another data buzzword, it’s worth another look at this practical application of machine learning and data analytics. Digital twins are most often used to optimize physical assets and processes like manufacturing, warehousing, and logistics. Using sensors to collect data on a product, machine, or physical process, the digital twin feeds real-time data to a machine learning algorithm to test variables and scenarios faster — ultimately leading to actionable process improvement insights. These days, we’re starting to see more businesses use digital twin frameworks to optimize and innovate non-physical business processes like accounting, HR, and marketing as well. A digital twin simulation can help you surface interdependencies and inefficiencies that might otherwise be blind spots, especially if they’re baked into your business culture as “the way we’ve always done it.” In the quest for digital transformation, don’t settle for coconuts. Instead, let’s talk about the ways your data can carry more of the weight for you. Get smart: If all this talk of Monty Python and the Holy Grail puts you in the mood for an old-school movie night, good news: it’s available on Netflix. And if you’re looking for a more literary scratch for your Middle Ages (ish) itch, we’re reading Cathedral by Ben Hopkins. It’s a fascinating look at the complex processes involved in constructing architectural marvels in the days before edge computing. We may handle optimization differently now, but human nature stays the same. Read the full Fuse: Data for April here. A horse is a horse, and other martech myths Martech is a crowded field, and a lot of the voices weighing in on your options have a horse in the race.* No one is out to skew the odds on purpose, but your organization is unique. Just because one solution is a front-runner doesn’t necessarily mean it’s a great fit for your business. So how do you sort the facts from the hype and decide where to place your bets? We rounded up a few martech myths as a starting point. Myth: One CDP is as good as another. Fact Check: Finding the right CDP (or CRM, or DMP, or any other solution you can think of) isn’t a simple box to check. And, once you make your decision, integrating and customizing your platform will also take time and attention. Myth: Everyone needs a CDP. Fact Check: Depending on your use cases, you might be able to do everything you need to do within your current tech stack. Myth: You should pick a platform and go all in. Fact Check: It probably goes without saying, but when it comes to technology there are no one-size-fits-all solutions. One product might be a great fit for your needs, but that doesn’t mean that vendor should supply your entire tech stack. Myth: Data and tech silos are just the way business works. Fact Check: Regardless of size, scope, or industry, today’s businesses can’t afford to be siloed. When you’re evaluating a tech solution or rethinking your entire customer data strategy, prioritizing integration is always a safe bet. On your mark. Ready to put your martech through the paces? Read on to find resources to help you optimize your stack and get your customer data strategy across the finish line. *Full disclosure: one of our team members won $100 when Rich Strike won the Kentucky Derby, but for the most part we are platform- and livestock-agnostic. Get Smart: We try not to be too on the nose with our book recommendations but couldn’t help ourselves this time. In Data Strategy, Bernard Marr collects a solid primer on the data landscape and how your organization can use it (legally and ethically) to advance your goals. Spoiler alert, though probably not surprising given the title, strategy turns out to be the foundational driver for effective data use. Whether you read the book, our Ultimate Guide to Customer Data Strategy, or just want to get a sense of potential next steps, we’d love to chat about customer data. Grab a time that works for you. Read the full Fuse: Marketing for May here. Three technology strategies walk into a bar If you’ve got a monolithic legacy system on your hands, sticking with the status quo isn’t a fun choice. But going nuclear and building back from scratch probably isn’t realistic. Wouldn’t it be great to find a middle ground? Meet the composable enterprise. It’s an iterative path toward digital transformation, with applications repackaged into components that can be used to build new solutions across the business. Piece by modular piece, you rebuild your technology ecosystem — becoming more efficient, effective, and scalable as you go. As the glue that holds those components together, APIs are key to building a composable business. And developing secure API solutions that accommodate shifting capacity demands and amplify your technology takes a hefty dose of strategy and expertise. That’s what we love about it! If APIs are your jam, too, or if you’re wondering if a composable system makes sense for your business, let’s talk. We’re talking APIs over IPAs in Cincinnati on June 16 and you’re invited. It’ll be fun! Get smart: You’ve probably spent the day wondering how speculative/sci-fi/literary fiction relates to API strategy and microservices (or maybe that’s just us). But, we’d guess, the same type of mind that enjoys transforming legacy monoliths into composable enterprises would also really track with a book like How High We Go in the Dark by Sequoia Nagamatsu. Modular pieces linked together by strong bonds leading to an intricate and ever-expanding whole? We’re here for it (the book and the technology strategy). Read the full Fuse: Technology for May here.

How to choose a CDP

May 13, 2022

Organizations collect data from a wide range of sources and store it in any number of solutions, which can be spread across the business. For marketing departments trying to deliver personalized customer experiences, those silos present a problem, and a customer data platform (CDP) can seem like an easy answer. That could be the right conclusion, but it also might be premature. If you’re wondering how to choose a CDP, it makes sense to start with the basics. How to choose a CDP: 1. Understand the benefits and limitations of CDP solutions 2. Get internal alignment around the need and timing for a CDP 3. Surface your requirements, and prioritize your top 3-5 4. Map your core requirements to functionality you need in a CDP 5. Get stakeholder buy-in and secure a budget 6. Create a CDP selection matrix 7. Get external help if you need it 1. Understand the benefits and limitations of CDP solutions The CDP Institute defines a customer data platform as “packaged software that creates a persistent, unified customer database that is accessible to other systems.” This simple definition goes a long way toward unpacking the pros and cons of CDPs. A CDP is packaged software. PRO: A CDP is prebuilt and doesn’t require as much help from IT to implement and maintain as other solutions like custom platforms and data warehouses. CON: Because it’s packaged software and not a platform, a CDP will be less customizable and less efficient than adding customized functionality to an existing architecture. A CDP must create a persistent, unified customer database. PRO: A CDP ingests data from multiple sources across the business, standardizes it, and uses it to build an identity graph to enable real-time customer identification that fuels targeted multi-channel marketing efforts. CON: A CDP overcomes some of the difficulty of identity resolution but obscures the methodology. You get a unified customer view, but not a lot of certainty that it’s accurate. A CDP must be accessible to other systems. PRO: You can use the aggregated data and outputs from a CDP with your downstream systems. CON: The CDP you select must either come with the API connections you need for the rest of your martech and enterprise tech stacks or you’ll need to factor in costs for custom APIs. 2. Get internal alignment around the need and timing for a CDP Today’s martech stacks are growing fast, and it’s not always clear whether the functionality your marketing team needs already exists in the broader enterprise architecture. Overlapping features and blindspots are a problem — but also an opportunity. Gathering a cross-functional group that includes marketing, IT, data, and product teams can help you figure out how to get the most from your technology investments across the business. If you’re wondering how to choose a CDP, you might be surprised to find that many of the core functions you’re looking for in a CDP are already available in your CRM, MDM, data warehouse, analytics, and BI solutions. For example: Some analytical CRMs can track real-time online events like website browsing, adding to cart, and the like, much like CDPs. Your data warehouse may allow for an identity graph overlay and machine learning algorithms that can play a key role in enterprise-wide customer identity resolution. Your IT and data teams may already have identity and access management processes in place, and that single source of customer data truth could be integrated with your existing martech stack. So, how do you know if your organization needs a CDP? Your cross-functional group can explore potential use cases in light of departmental needs, budget, and existing functionality. While every organization is different, here are some ways to frame the conversation. You might need a CDP if: Your organization has a large volume of customer data stored in multiple places, and you either can’t or haven’t been able to integrate it into a single, real-time view Your marketing team can’t access customer data or perform data tasks without help from data and IT teams You can’t unify your online metrics, CRM data, and offline touchpoint and transactional data, making it hard to build a 360-degree view of your customer You’re moving to a first-party data strategy, but you don’t have systems in place to use your data to inform audience segmentation and personalized campaigns You have functionality gaps in your current martech stack that match up to CDP features You might NOT need a CDP if: You have a minimal and well architected martech stack Your customer data is simple or straightforward enough to analyze easily without additional tools Your marketing plan doesn’t require a lot of personalization, either because your products and services don’t require it, or because your roadmap doesn’t call for it in the short term Your customer data strategy has already mapped your needs to existing solutions and your roadmap doesn’t include a CDP Your budget doesn’t allow for duplicating storage costs, building and operating data ingestion processes, or keeping up with the steep total cost of CDP ownership Your security requirements don’t allow for third-party customer data storage Your internal IT and data teams find more affordable and secure means to implement identity and access management, democratize data access, and connect data storage with martech tools 3. Surface requirements, and prioritize your top 3-5 As you meet with your cross-functional team and discuss your need for a CDP, you can also surface requirements and use cases for a CDP solution. From your larger list, choose 3-5 top priorities to help you choose a CDP. Some common examples of CDP use cases include: Streamlining identity resolution, and making those outputs more accessible and actionable Combining online and offline data Creating more personalized content experiences on your website Using more strategic targeting in your multichannel campaigns Integrating and standardizing data across systems and making it easier to use those outputs in omnichannel marketing efforts 4. Map your core requirements to features you need in a CDP After you identify your top 3-5 use cases, map those core requirements to features you need in a CDP. For example, if one of your core requirements is enabling more targeted multichannel marketing campaigns in the EU, you might need to look for a CDP that offers GDPR-compliant identity resolution processes. Be sure to note which systems and solutions you’ll need to connect to your CDP both in terms of data ingestion and output sharing with downstream systems. Your CDP will need integrations and APIs to enable those connections. Common CDP integrations include CRMs, analytics tools and dashboards, advertising platforms, BI tools, data warehouses, and data lakes. Meet with your cross-functional group again to confirm your conclusions and assumptions. This meeting is a good time to clarify any security and data governance implications for your CDP selection as well. 5. Get stakeholder buy-in and secure a budget Your next step is to get stakeholders on board for the CDP acquisition and to establish funding to make the purchase. Leadership needs to know the full report from your cross-functional team, but they’ll also have questions about what kind of return the organization can expect from the investment As you frame up the value story, consider thinking about what not having a CDP costs your company: Is your marketing team using a significant amount of IT or data team hours looking for information? If you were to solve your data connectivity issues with custom-built APIs, how much would that cost? How much are you losing in customer value by not providing a customized experience? How much time is your marketing team spending manually moving data from one system to another? 6. Create a CDP selection matrix The vendor selection process doesn’t have to be painful. Your team can feel more confident about choosing a CDP because you’re going into the process with defined use cases and requirements, as well as support from the organization. That said, with an ever-growing CDP vendor landscape, it helps to have a process for narrowing down your choices. To accomplish that task, many companies use an internally framed selection matrix or decision tree, customized to fit CDP-specific needs and requirements. These tools can help you think through solution options using your own criteria. However you handle the vendor review, plan to bring a short list of possibilities back to your cross-functional team for a final discussion before making a purchasing decision. 7. Get external help if you need it. Figuring out how to choose a CDP and where the solution fits into your larger customer data strategy isn’t easy. Fusion Alliance helps companies navigate changing customer data environments with a unique methodology that fosters collaboration, transparency, and shared ownership of digital transformations.   Whether you’re just getting started or stuck in the messy middle of a CDP selection process, we help you unpack your processes and partnerships to identify risks and opportunities so you can take the right next steps toward a future-focused customer data strategy.   Let’s talk. Keep reading: The Ultimate Guide to Creating Your Customer Data Strategy

CDP vs CRM: 5 key questions to inform your decision

Apr 22, 2022

The difference between a customer data platform (CDP) and customer relationship management (CRM) solution may be difficult to determine at first, because both options collect, store, and put customer data to use in support of business goals. While their functions may overlap, the CDP vs CRM debate becomes easier when you get clarity about the people, processes, and use cases for each option. How to make the CRM vs CDP decision 1. What is a CDP? 2. What is a CRM? 3. What data is collected by a CRM vs CDP? 4. Who uses a CDP vs CRM and for what purpose? 5. What do we need: a CDP, CRM, or both? 1. What is a CDP? A CDP unifies and standardizes large and detailed data sets from a wide variety of sources, resulting in robust customer profiles that enable real-time personalization. The CDP Institute defines a CDP as “packaged software that creates a persistent, unified customer database that is accessible to other systems.” Additionally, a CDP must have the following capabilities: Ingest data from any source Capture full detail of ingested data Store ingested data indefinitely (subject to privacy constraints) Create unified profiles of identified individuals Share data with any system that needs it Through the process of identity resolution, the CDP can match, merge, and deduplicate data into a single customer view that can be segmented and analyzed — by human analysts or with the assistance of machine learning. 2. What is a CRM? A CRM and a CDP are both software solutions that handle customer data, but they differ in how, why, and who for. The difference came about organically, as organizations adopted different use cases for their customer data over time. “CRM solutions were often proposed to tackle customer data management problems. The idea was that you could get ‘all of your data in one place’ to use for sales, marketing, and customer service. The promise was they’d break down silos in enterprises and design a view of the customer that wasn’t specific to sales or marketing or customer service. That sounds familiar to the promise of CDPs, doesn’t it?” — Lizzy Foo Kune, senior director analyst at Gartner A CRM helps organizations manage customer relationships by consolidating what is known about customers from one-to-one touchpoints and transactional details into a single database, giving sales and service teams personal and actionable insights. According to the Microsoft Dynamics 365 website “CRM systems help you manage and maintain customer relationships, track sales leads, marketing, and pipeline, and deliver actionable data.” Sound similar to a CDP? There’s a key difference: CRMs only apply to known customers and contacts. Moreover, they don’t cleanse, combine, standardize, or deduplicate the customer records, so they can’t give a business a “single customer view” across channels. 3. What data is collected in a CRM vs CDP? That key difference reflects the two business silos that CRMs were developed to unite: marketing and sales. Marketing needs a high volume of customer data across touchpoints in a single, unified view to understand your customers and their behavior. CDPs collect digital data automatically using integrations and code snippets embedded in digital touchpoints, gathering customer data from websites, laptops, mobile devices, apps, and even CRMs into one place. The CDP then cleans it, and produces consolidated customer. Sales needs customer data to help manage the customer relationship. CRMs store historical data about customer interactions in order to inform future interactions. The data CRMs collect is usually entered manually and its purpose is tightly focused on logging an interpersonal or transactional interaction — for example, notes from the latest sales call – to inform future interactions. The data inputs are simple, although difficult to standardize or automate, and are usually done manually by sales (and service) people to track the progress of the relationship. 4. Who uses a CDP vs CRM and for what purpose? Your organization’s CDP vs CRM discussions may come down to who needs to use the system to accomplish critical business tasks. As we’ve said above, marketers need a unified view of the customer’s entire experience of the brand over time. A CDP’s ability to ingest, cleanse, manage, and analyze large volumes of data from many digital sources makes that task easier. But for sales and support teams, the key driver is managing customer relationships. In these customer-facing roles, contact management is critical, so a CRM’s ability to capture notes and manual inputs about one-to-one interactions facilitates that function. 5. What do we need: a CRM, CDP, or both? While choosing between solutions isn’t easy, it’s not necessarily an either/or decision. You might find a both/and solution serves your business better. How do you make the call? If your business primarily needs to manage customer relationships in a more detailed, efficient, and personalized way, you might choose a CRM. In fact, over the last few years, CRMs have been innovating and evolving to function more and more like CDPs so it might be prudent to wait and/or choose vendors carefully. Gartner predicts that 70% of independent CDP vendors will be acquired by larger technology vendors or will diversify by 2023.  “CRM systems have seen the competitive threat that CDPs brought to the table,” Gartner’s Foo Kune said. “As CRM technologies recognize that they need to update their aging databases to meet the needs of modern business functions, including marketing, augmenting your CRM with a CDP may be unnecessary.” If your business primarily needs to have a broad view of who your customers are and how they engage with your business, you may opt for a CDP. “Companies seeking a new strategy to form personalized customer experiences through data will need a CDP as it offers the resources to create a comprehensive view of the customer across each platform they interact with in real-time — whether it’s social media, apps or mobile,” says Heidi Bullock of Tealium, a CDP provider. “CRMs, on the other hand, help manage sales-focused customer data rather than collecting data across different channels.” And, if your business is broad you can choose both a CDP and a CRM.  While CDPs and CRMs offer two different marketing and sales data management solutions with differing strengths, you don’t necessarily have to choose between them. “CDPs and CRMs can actually operate simultaneously, as they work to fulfill different business goals,” Tealium’s Bullock notes. It’s possible to use a CRM as an input and output channel to a CDP, and, in turn, use a CDP to provide a 360° customer view data set within the CRM. Choosing both a CDP and a CRM can deliver both an amazing customer experience and tremendous business value: achieving high marks in customer satisfaction and providing integrated tracking and engagement. The CDP vs CRM choice depends on your roadmap. Fusion works with clients to define a customer data strategy that fits each organization’s unique strategic objectives, operational needs, and timeline. From there, our team creates a tactical roadmap to define actionable steps toward those goals. Whether you’re just getting started or trying to get your digital transformation back on track, we can help. Ask a question >> Book a workshop >> Learn more about customer data strategy >>

Data executives empowered: The importance of the data community and elevating the CDO

Jul 22, 2021

Almost 20 years ago, Capital One recognized the need for one person to oversee their data security, quality, and privacy, and the role of the Chief Data Officer was born. Now reports show that 68% of organizations have a CDO (Harvard Business Review, 2020). And while the role has become more common and has significantly evolved, many data executives are still struggling to get a seat at the table or bring data to the forefront of their organization. In fact, in a recent survey, only 28% of respondents agreed that the role was successful and established. Company leaders agree that there needs to be a single point of accountability to manage the various dimensions of data inside and outside of the enterprise, including the quality and availability of that data. But now we are at a crossroads — what is the best way to align the work that the CDO does with the strategy of the business as a whole? The reality is that CDOs often struggle to find the internal and external support and resources needed to educate others to align with the organization’s goals. Implementing enterprise data governance, data architecture, data asset development, data science, and advanced analytics capabilities — such as machine learning and video analytics — at scale, is not an easy task. To be successful, data executives need support, resources, and communities focused on the elevation of data. We are proud to continue to help these communities come to life for the benefit of our colleagues and clients, establishing local resources here in the Midwest with global scale, reach, and impact. Read on as Mark Johnson, our Executive Leader for Data Management and Analytics, provides insight on the current state of data and the CDO, and provides details on multiple opportunities for data leaders of different levels to get more involved in the data community. Q: How has the role of data changed/evolved for organizations? The reality is that information is everything. This global pandemic proved that to many organizations. For some, it showed that their digital network was ready, and they were aptly prepared to take on COVID. For others, it has forced them to recognize their own immaturity with data and analytics. On its own, managing data is not exciting — the information just sort of exists. To give data value, you have to put it to use. And so, I think we are going to see the Chief Data Officer and/or Chief Data Analytics Officer really find their own in the coming years. It’s time for their seat at the table. The C-suite is now asking questions that can only be answered with data, and now they truly understand both the value and consequences of the data game. Q: What do you think are the biggest challenges facing CDOs/data leaders today? I think that the biggest challenge for data executives today is the acquisition of talent that is seasoned and experienced where you need them to be for your organization. Higher education hasn’t necessarily kept up with the data world, and often times it takes additional training to reach the right levels. The reality is that right now the talent is manufactured in the real world. Data executives have to be connected and equipped to mentor, train, and keep the right people. Q: You’ve mentioned that data leaders need to connect with each other. What value can people expect from these data communities? I think there is tremendous value. As we are seeing the power of data evolve in organizations, and the role of data leaders evolve as well, I think coming together to collaborate and share elevates the leader, the organization, and the view of data as a whole. In these communities, it gives people a safe space to talk about how they are doing, what they are doing, what their biggest challenges are, and what solutions are working for them. These communities have truly become both a learning laboratory and an accelerator for data. Q: As a big proponent of connecting data leaders, you have been involved in creating different opportunities for people to get together. What groups/events would you recommend, and how can people get involved? I personally have been involved with the MIT Chief Data Officer and Information Quality Symposium (MIT CDOIQ), which is such a great opportunity to start with for connection. It has developed into additional opportunities for data leaders at all levels to get involved and create the kind of community we need to truly elevate the value of data. Organizations like the CDO Magazine, the creation of CDO roundtables across the nation, and the International Society of Chief Data Officers (isCDO) all evolved from connecting data leaders and identifying common challenges. MIT CDOIQ: The International MIT Chief Data Officer and Information Quality Symposium (MIT CDOIQ) is one of the key events for sharing and exchanging cutting-edge ideas and creating a space for discussion between data executives across industries. While resolving data issues at the Department of Defense, the symposium founder, Dr. Wang, recognized the need to bring data people together. Now in its 15th year, MIT CDOIQ is a premier event designed to advance knowledge, accelerate the adoption of the role of the Chief Data Officer, and change how data is leveraged in organizations across industries and geographies. Fusion has been a sponsor of this symposium for seven years now, and we are so excited to see how the event has grown. Designed for the CDO or top data executive in your organization, this is a space to really connect with other top industry leaders. CDO Roundtables Fusion has always been focused on building community and connecting people. And when one of our clients, a Fortune 500 retailer, mentioned wanting to talk with other data leaders from similar corporations, we realized that there was a big gap here — there was no space that existed where data leaders could informally come together, without sales pitches and vendor influence, and simply talk. That’s how the CDO roundtables were born — a place that allows data leaders to get to know each other, collaborate, accelerate knowledge growth, and problem solve. We just started two years ago in Cincinnati, but now, now we’ve expanded to multiple markets including Indianapolis, Columbus, Cleveland, Chicago, and Miami. These groups are designed for your CDO/CDAO and truly create an environment for unfiltered peer-to-peer discussion that helps solves data leadership challenges across industries. If you’re interested in joining one of these roundtables or starting one in your market, email me or message me on LinkedIn. I’m here and ready to get these roundtables started with executives in as many communities as I can. The more communities we have, the more data leaders and organizations we can serve. International Society of Chief Data Officers (isCDO) Launched out of the MIT CDOIQ symposium, the isCDO is a vendor-neutral organization designed to promote data leadership. I am excited to be a founding member of this organization, along with our Vice President of Strategy, David Levine. Our ultimate goal is to create a space that serves as a peer-advisory resource and enables enterprises to truly realize the value of data-driven decision making. With multiple membership options available, isCDO is the perfect opportunity for data leaders looking to connect with their peers and gain a competitive advantage by focusing on high-quality data and analytics. CDO Magazine I am really proud to be a founder of the CDO magazine, as it really is a resource for all business leaders, not just the CDO. We designed the magazine to be a resource for C-suite leaders — to educate and inform on the value proposition, strategies, and best practices that optimize long-term business value from investments in enterprise data management and analytics capabilities. Check out the publication here. And if you’re interested in contributing content or being interviewed, let me know at mjohnson@fusionalliance.com. Closing: The role of the CDO is integral to organizations, but it’s still evolving. Now more than ever, it is important that data leaders come together to collaborate and problem-solve. Fusion is excited to be a part of each of these initiatives, and we are committed to being an agent of change in the communities we serve and beyond. By connecting global thought leaders we believe that organizations will realize the value of data to power their digital transformation. If you’re interested in joining any of these data communities or just have questions, feel free to reach out to Mark via email or on LinkedIn.

The rise of big data technologies and why it matters

Mar 24, 2021

Getting the right data to the right people at the right time is the name of the game in today’s demanding marketplace. Every company has to find a way to harness big data and use it to drive growth. And if your organization isn’t talking big data, you are at a competitive disadvantage. This article covers a top-level view of big data’s evolution and key components. It can help you understand the importance of big data and technologies that are essential to its discussion. With this foundation, you can proceed to the next step — addressing what to do with your data and how. Just how much data exists? Every passing moment, the pace of data creation continues to compound. In the time it takes you to read these few paragraphs there will be: more than 200 million emails sent millions of dollars in e-commerce transacted 50 hours of YouTube videos uploaded millions of Google searches launched tens of millions of photos shared Every few minutes, this cycle repeats and grows. In 2019, 90% of the world’s digital data had been created in the prior two years alone. By 2025, the global datasphere will grow to 175 zettabytes (up from 45 zettabytes in 2019). And nearly 30% of the world’s data will need real-time processing. Over the last decade, an entire ecosystem of technologies has emerged to meet the business demand for processing an unprecedented amount of consumer data. What is big data? Big data happens when there is more input than can be processed using current data management systems. The arrival of smartphones and tablets was the tipping point that led to big data. With the internet as the catalyst, data creation exploded with the ability to have music, documents, books, movies, conversations, images, text messages, announcements, and alerts readily accessible. Digital channels (websites, applications, social media) exist to entertain, inform, and add convenience to our lives. But their role goes beyond the consumer audience — accumulating invaluable data to inform business strategies. Digital technology that logs, aggregates, and integrates with open data sources enables organizations to get the most out of their data, and methodically improves bottom lines. Big data can be categorized into structured, unstructured, and semi-structured formats. The development of modern data architecture Until recently, businesses relied on basic technologies from select vendors. In the 1980s, Windows and the Mac OS debuted with integrated data management technology, and early versions of relational database engines began to become commercially viable. Then Linux came onto the scene in 1991, releasing a free operating system kernel. This paved the way for big data management. What is big data technology? Big data technologies refer to the software specifically designed to analyze, process, and extract information from complex data sets. There are different programs and systems that can do this. Distributed file systems In the early 2000s, Google proposed the Google file system, a technology for indexing and managing mounting data. A key tenet to the idea was using more low-cost machines to accomplish big tasks more efficiently and inexpensively than the hardware on a central server. Before the Information Age, data was transactional and structured. Today’s data is assorted and needs a file system that can ingest and sort massive influxes of unstructured data. Open-source and commercial software tools automate the necessary actions to enable the new varieties of data, and its attendant metadata, to be readily available for analysis. Hadoop Inspired by the promise of distributing the processing load for the increasing volumes of data, Doug Cutting and Mike Cafarella created Hadoop in 2005. The Apache Software Foundation took the value of data to the next level with the release of Hadoop in Dec. 2011. Today, this open-source software technology is packaged with services and support from new vendors to manage companies’ most valuable asset: data. The Hadoop architecture relies on distributing workloads across numerous low-cost commodity servers. Each of these “pizza boxes” (so called because they are an inch high and less than 20 inches wide and deep) has a CPU, memory, and disk storage. They are simple servers with the ability to process immense amounts of various, unstructured data when running as nodes in a Hadoop cluster. A more powerful machine called the “name node” manages the distribution of incoming data across the nodes. By default, data is written to at least three nodes and might not exist in its entirety as a single file in any one node. Below is a simple diagram that illustrates the Hadoop architecture at work. Open source software The majority of enterprises today use open source software (OSS). From operating systems to utilities to data management software, OSS has become the standard fare for corporate software development groups. Serving as a progressive OSS organization, Apache Software Foundation is a non-profit group of thousands of volunteers who contribute their time and skills to building useful software tools. As the creators, Apache continuously works to enhance Hadoop code — including its distributed file system called Hadoop Distributed File System (HDFS) — as well as the code distribution and execution features known as MapReduce. Within the past few years, Apache released nearly 50 related software systems and components for the Hadoop ecosystem. Several of these systems have counterparts in the commercial software industry. Vendors have packaged Apache’s Hadoop with user interfaces and extensions, while offering enterprise-class support for a service fee. In this segment of the OSS industry, Cloudera, Hortonworks, and Pivotal are leading firms serving big data environments. Now software systems are so tightly developed to the core Hadoop environment that no commercial vendor has attempted to assimilate the functionality. The range of OSS systems, tools, products, and extensions to Hadoop include capabilities to import, query, secure, schedule, manage, and analyze data from various sources. Storage Corporate NAS and SAN technologies, cloud storage, and on-demand programmatic requests returning JSON, XML, or other structures are often secure repositories of ancillary data. The same applies to public datasets — freely available datasets, in many cases for economic activity by industry classification, weather, demographics, location data, and thousands more topics. Data of this measure demands storage. Distributed file systems greatly reduce storage costs while providing redundancy and high availability. Each node has its local storage. These drives don’t require speed or solid-state drives, commonly called SSDs. They are inexpensive, high-capacity pedestrian drives. Upon ingestion, each file is written to three drives by default. Hadoop’s management tools and the Name Node monitor each node’s activity and health so that poorly performing nodes can be bypassed or taken out of the distributed file system index for maintenance. The term “data lake” describes the vast storage of different types of data. These vastly different data sources are in a minimum of a dozen different file formats. Some are compressed or zipped. Some have associated machine data, as found in photos taken with any phone or digital camera. The date, camera settings, and often the location are available for analysis. For example, a query to the lake for text messages that included an image taken between 9 p.m. and 2 a.m. on Friday or Saturday nights in Orlando on an iPhone would probably show fireworks at Disney World in at least 25% of the images. Administration of big data initiatives The enterprise administration of applications — their storage requirements, security granularity, compliance, and dependencies — required Hadoop distributions to mature these capabilities in the course of becoming a managed service to an enterprise (like those from Cloudera and Hortonworks). In the graphic above, you can see a view of Hadoop’s place among other software ecosystems. Note that popular analysis tools (below) are valuable in developing big data solutions: Excel and Tableau databases such as SQL Server and Oracle development platforms such as Java and Informatica Data Quality Administration through Cisco and HP tools is common. Further development of big data Commercial software companies have begun connecting to Hadoop, offering functionality such as: data integration quality assessment context management visualization and analysis from companies such as IBM, Microsoft, Informatica, SAP, Tableau, Experian, and other standard carriers. Analytics and data science Analytics is the endgame for developing a big data environment. The rise of big data has given credence to a new resource classification, the data scientist — a person who embodies an analyst, technologist, and statistician all in one. Using several approaches, a data scientist might perform exploratory queries using Spark or Impala, or might use a programming language such as R or Python. As a free language, R is rapidly growing in popularity. It is approachable by anyone who is comfortable with macro languages such as those found in Excel. R and its libraries implement statistical and graphical techniques. Moving to the cloud Cloud computing is very different from server-class hardware and software. It involves cloud storage, multi-tenant shared hosts, and managed virtual servers that are not housed on a company’s premises. In cloud environments, an organization does not own equipment, nor does it employ the network and security technologists to manage the systems. Cloud computing provides a hosted experience, where services are fully remote and accessed with a browser. The investment to build a 10- or 20-node HDFS cluster in the cloud is relatively small compared to the cost of implementing a large-scale server cluster with conventional technologies. The initial build-out of redundant centers by Amazon, Microsoft, Google, IBM, Rackspace, and others has passed. We now have systems available at prices below the cost of a single technician. Today, cloud computing fees change rapidly with pricing measured by various usage patterns. Conclusion: The rise of big data is evident Big data is not a fad or soon-to-fade trending hashtag. Since you began reading this article, more than 100 million photos have been created, with a sizeable portion having a first-degree relationship to your industry. And the pace of data creation continues to increase. The distribution of computing processes can help organizations to gain a 360-degree view of their customers through big data collection and analysis. And companies that embrace big data technologies and solutions will rise ahead of their competitors. Big data technologies are becoming an industry standard in finance, commerce, insurance, healthcare, and distribution. Welcoming big data technologies and solutions is key in the optimization and continued growth going forward. Companies that embrace data solutions can continue to improve management and operational processes and create a competitive advantage to withstand an ever-evolving marketplace.

Digital transformation: Our predictions for 2021 and beyond

Mar 16, 2021

What will the future hold when it comes to digital transformation? We don’t have a Magic-8 ball or special spidey sense, but our team does anticipate sizeable change. We asked a few of our team members what they thought based on their work and personal experiences. Here’s what they’re envisioning for 2021 and beyond. The cloud gains more ground I expect cloud-based business application platforms such as Dynamics 365, Salesforce, ServiceNow, and Workday to drive significant digital transformation within modern workplaces in the next year. Following on the heels of many core infrastructure services moving to the cloud — such as email, servers, files, and data — the next major lift for many organizations will be to modernize and automate their core business processes. I anticipate areas like finance, HR, production, and other critical business operations and workflows will be the next major shift to using cloud-based business application platforms. Moving away from legacy, on-premise solutions is not always a simple task, but in doing so, employees can then work remotely without being tethered to an office environment. Greg Deckler, Vice President, Cloud Services Connect with Greg on LinkedIn Remote workers collaborate differently The work-from-anywhere model has been proven to work and it will continue. However, right now, a Zoom meeting is about the extent of what most people see as remote teamwork — and we all know those can be exhausting. I predict greater adoption of tools like Miro and Mural. These online workspaces allow for active collaborating and co-creating in real time. The need to move quickly and keep pace with digital transformation will require these types of tools, and those who know how to leverage them, to make the most of a remote team’s time together. Doug Scamahorn, Solution Director, UX Design & Innovation Connect with Doug on LinkedIn Cookie compensation I think 2021 will be the year when businesses and marketers confront the pending deprecation of the third-party cookie. Google is driving the industry towards new solutions for retargeting and attribution following the announcement that Chrome will cease to support third-party cookies in 2022. While industry players debate over a long-term replacement, expect to see a scramble to shore up first-party data in the meantime. At a tactical level, this will look like increased pushes for “registered” online experiences where users must explicitly identify themselves, as well as the integrations that power these points of data collection. In the background, businesses will be pushing to connect the dots between online and offline touchpoints using a variety of identifiers, from email to devices to data from “walled gardens” like Amazon, Facebook, and even Walmart and Target. Companies may opt for a CDP (consumer data platform) solution on top of their existing data stack to manage data points specifically for targeted marketing campaigns. When reporting on campaign success and attribution, analysts may need to adopt new tools and strategies for managing “fuzzier” readouts on customer behavior and journey identification. Amy Brown, Solutions Director Connect with Amy on LinkedIn Augmented reality becomes actual reality As mobile processing and bandwidth progresses and matures, we can expect more augmented reality (AR) apps to provide visual assistance in a huge range of applications. I fully expect we will see vehicles with heads-up displays, smart glasses (remember Google Glass?), and other clear displays to be adopted by more companies and thus, individuals. Visual processing in itself is gaining in popularity. Retailers like IKEA are already using AR with their IKEA Place app to enable customers to “see” furniture in their spaces. Microsoft’s recent HoloLens release is a good example of where we’re headed. Jeremy Keiper, Competency Lead Connect with Jeremy on LinkedIn B2B marketers will get more creative There’s always been an understanding that marketing is both an art and a science. Over the last decade, marketers have leaned into the science. Data provided marketers with information about customer behavior that was never available before. Even before the pandemic, B2B marketers were relying heavily on digital channels to engage customers. But pandemic office closures caused marketers to rely on channels like email, webinars, social media, and search engine marketing (SEM), in an attempt to reach prospective buyers who were now working from home. And they had to get creative. Marketers had to be willing to test new ideas and try things that haven’t been “proven,” and to think creatively about how we connect with and engage prospects and customers. I anticipate this to continue, and marketers will use customer data to make sure they understand consumer goals and motivations, then get creative about how to reach out and connect. Kristin Raikes, Sr. Director of Digital Strategy Connect with Kristin on LinkedIn Looking ahead Thinking about the year ahead, we do know that even after offices reopen and things get back to “normal,” the new “normal” will look different than it did before. If people continue to work from home or prefer to engage with brands virtually versus physically, then technology will have to adapt. Are there any major trends not listed above that you think will be a key to digital transformation for this year? If you have questions about specific trends, you can also connect with our team via their LinkedIn profiles above. Our consultants and team members work with clients to improve, streamline, and create actionable change. We create exceptional customer experiences by leveraging data insights, experience design, and technology to transform the way you connect with your customers. Interested in learning more? Let us know, or sign up for our newsletter to get to know us.

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