How to drive trusted decisions without changing your current data infrastructure.
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In the modern data-driven landscape, organizations are constantly seeking ways to extract valuable insights from their data assets. While individual data products provide significant value, the true potential lies in harnessing the power of interconnected data products. By chaining data products together, organizations can unlock new levels of data-driven decision-making and drive impactful use cases. In this article, we will explore the concept of chaining data products and delve into how it enables the delivery of compelling use cases.
Chaining data products refers to the process of integrating and interconnecting multiple data products to create a data ecosystem that supports complex use cases. Instead of treating data products as isolated entities, organizations can leverage their complementary nature and create a network of interconnected data products. This network allows data to flow seamlessly across different products, enabling a holistic view of information and facilitating more advanced analytics and insights.
Enhanced Data Integration: Chaining data products enables the integration of diverse data sources and types, breaking down data silos and fostering a unified data environment. This integration allows for a comprehensive understanding of relationships and patterns within the data, leading to more accurate and insightful analysis.
Advanced Analytics and Insights: By chaining together data products, organizations can leverage the combined capabilities of each product to perform more sophisticated analytics and derive deeper insights. The interconnected nature of data products enables the utilization of enriched datasets and facilitates complex data transformations, empowering organizations to uncover hidden patterns and make data-driven decisions with confidence.
Seamless Data Flow: Chaining data products establishes a seamless flow of data, eliminating bottlenecks and ensuring the timely availability of information. This uninterrupted data flow enables real-time or near-real-time analytics, enabling organizations to respond quickly to changing circumstances and make informed decisions in dynamic environments.
Scalability and Flexibility: The modular nature of data products allows organizations to scale their data ecosystems and adapt them to evolving business needs. As new data products are introduced or existing ones are updated, they can be seamlessly integrated into the existing chain, expanding the organization’s analytical capabilities and accommodating changing requirements.
Predictive Maintenance: Chaining data products such as IoT sensors, data lakes, and predictive analytics models can enable organizations to implement predictive maintenance use cases. By continuously monitoring sensor data, feeding it into data lakes, applying advanced analytics models, and triggering automated maintenance actions, organizations can proactively identify potential equipment failures and optimize maintenance schedules, minimizing downtime and maximizing operational efficiency.
Customer 360° View: Chaining data products encompassing customer relationship management (CRM) systems, transactional databases, and data visualization tools can provide organizations with a comprehensive 360° view of their customers. By integrating and analyzing customer data from various sources, organizations can gain valuable insights into customer behavior, preferences, and purchasing patterns, enabling personalized marketing campaigns, targeted cross-selling, and improved customer experiences.
Fraud Detection: Chaining data products involving transactional data, machine learning models, and anomaly detection algorithms can empower organizations to combat fraud effectively. By analyzing transactional patterns, identifying deviations from normal behavior, and applying machine learning algorithms, organizations can detect and prevent fraudulent activities in real time, safeguarding their financial assets and ensuring trust and security for their customers.
Supply Chain Optimization: Chaining data products such as inventory management systems, logistics data platforms, and predictive analytics tools can optimize supply chain operations. By integrating data from various supply chain touchpoints, organizations can gain visibility into inventory levels, transportation routes, and demand forecasts. This enables them to optimize inventory management, streamline logistics operations, minimize delays, and improve overall supply chain efficiency.
Chaining data products opens a world of possibilities for organizations seeking to maximize the value of their data assets. By integrating and interconnecting data products, organizations can leverage enhanced data integration, advanced analytics, seamless data flow, scalability, and flexibility. Furthermore, the delivery of compelling use cases becomes attainable, enabling organizations to drive innovation, improve operational efficiency, and gain a competitive edge in today’s data-driven landscape. Embracing the power of chained data products is a key step toward unleashing the true potential of data-driven decision-making.
Data products encompass several key aspects that contribute to their effectiveness and value in addressing data challenges and delivering actionable insights. These aspects ensure that data products are well-designed, user-centric, and aligned with business goals. Let’s explore the key aspects of a data product:
A data product must have a clear purpose and well-defined goals that align with the organization’s objectives. It should address specific data challenges, such as improving operational efficiency, enhancing customer experience, or driving data-driven decision-making.
Ensuring data quality is crucial for any data product. High-quality data, free from errors, inconsistencies, or biases, forms the foundation for accurate analysis and reliable insights. Data products should incorporate mechanisms for data validation, cleansing, and ongoing monitoring to maintain data integrity.
A data product should be designed with the end-users in mind. Understanding user needs, roles, and workflows is essential for creating an intuitive and user-friendly interface. The design should enable users to easily access, explore, analyze, and visualize data, empowering them to derive insights without extensive technical expertise.
Data products should provide seamless access to data for authorized users. This involves ensuring appropriate data permissions, security measures, and user-friendly interfaces for data exploration and retrieval. Facilitating data usability through intuitive search, filtering, and visualization capabilities enhances user productivity and adoption.
The primary goal of a data product is to deliver actionable insights that drive informed decision-making. It should provide relevant, timely, and contextualized insights that enable users to identify patterns, trends, correlations, or anomalies in the data. Actionable insights empower users to take meaningful actions based on the data product’s outputs.
As data volumes grow, data products must be capable of handling large datasets and processing complex analytical queries efficiently. Scalability ensures that the data product can accommodate increased data loads and user demands without compromising performance or responsiveness.
Data products should embrace a culture of continuous improvement and iteration. This involves soliciting user feedback, monitoring usage patterns, and incorporating enhancements based on evolving user needs and emerging technologies. Regular updates, feature additions, and optimizations ensure that data products remain relevant and valuable over time.
Data products should adhere to data governance principles and comply with applicable regulations and privacy requirements. Implementing proper data governance frameworks ensures data privacy, security, ethical data handling practices, and regulatory compliance. It fosters trust in the data product and safeguards sensitive information.
Data products should enable collaboration and integration with existing systems, tools, and workflows within the organization. Integration capabilities allow seamless data sharing and interoperability with other applications or platforms, enabling data product users to leverage data across multiple contexts and processes.
Monitoring the performance and usage of data products is critical to assess their effectiveness and identify areas for improvement. Analytics capabilities enable tracking key performance indicators, user engagement metrics, and data product usage patterns. This data-driven approach helps optimize the data product’s performance and enhance user experiences.
Proper documentation and training resources are essential for effective utilization of data products. Clear documentation, including user guides, data dictionaries, and technical specifications, aids users in understanding the data product’s functionalities, features, and usage. Training programs and resources facilitate skill development and empower users to leverage the full potential of the data product.
By considering and implementing these key aspects, organizations can develop data products that effectively address data challenges, unlock the value of their data assets, and empower users to make data-driven decisions with confidence.
Technical debt is something that many companies are aware of and are attempting to address. It is a big enough issue that several of our recent blog posts (Lessons in Technical Debt from Southwest Airlines, Start Paying Down Your Technical Debt Today, and A Better Way to Plan the Payoff of Technical Debt) discussed it at length. What about data debt?
Whether you consider it a subset of technical debt or a close cousin, data debt isn’t typically understood or planned for to the extent of the broader technical debt. That’s a problem, and organizations need to wake up to their data debt today and start planning to address it. This blog post will explain what data debt is, the impacts it can have, and how to handle it.
Like technical debt, data debt represents the liability accrued over time by inefficient and outdated methods and technologies for handling corporate data. It is important to note that data debt has methodological and technological components. Data management technologies grow old and out of date. Using poor data management methods on top of whatever data technologies are in place is just as common and often equally costly.
Even a fully modernized data infrastructure won’t operate well today or scale in the future if it is implemented and managed poorly. The longer an organization ignores its data debt, the worse it will become. Further, the downstream impacts of data debt can be more significant than technical debt related to a single critical corporate application. An application focuses on only one area of the business. In contrast, data is often accessed and utilized by a wide range of applications that a broader swath of the company relies on. When data debt is high, the impacts spread company-wide.
Let’s get more specific about how data debt can drive up costs, reduce productivity, and hurt morale across an organization.
Nobody sets out to cause these problems, but inattention toward data debt will undoubtedly result in their appearance. The best way to avoid them is to lower your data debt. Luckily, there are ways to do that!
One way to start retiring today’s data debt while minimizing the extent to which it will reappear in the future is to implement a data operating system, such as DataOS from The Modern Data Company. A data operating system acts as a connective tissue that ties all your data assets together, including the ability to incorporate legacy systems as-is while enabling functionality to be added to their data.
A data operating system provides a single place where all corporate data is cataloged and operationalized — with an enterprise level of governance and security atop it all. By allowing more to be done with data, data debt starts to melt away. Over time, as the underlying systems are updated, access and processing speeds and the breadth of data usage will only increase. A data operating system can achieve these feats by using the most modern technologies and bringing them together to form today’s best-in-class approach to data management.
To learn more about how a data operating system like DataOS can help your organization modernize its systems and end user functionality, download our white paper DataOS® – A Paradigm Shift in Data Management.
Technical debt is an ongoing issue no one should expect to square away because as technology advances, even today’s top systems will eventually achieve full “legacy” status. However, if you don’t keep on top of it, technical debt will eventually cause significant damage to your pocketbook and reputation. If you think that sounds like an exaggeration, get up to speed on Southwest Airlines’ meltdown during the 2022 holiday season. It was a technical debt-driven debacle of the most public nature.
Instead of pretending technical debt doesn’t exist or fooling yourself into thinking that you can solve it once and for all, consider addressing technical debt as a recurring cost related to maintaining your technology investments. If you’re ready to get a handle on your organization’s technical debt, let’s talk about some ways to smooth the process through better planning and using a data operating system.
The first step in any planning process related to system replacement is to understand what is in place and how the necessary future state looks. Unfortunately, many legacy systems are difficult to navigate and hard to query. As a result, it can be quite a task for any team, technical or not, to dive into legacy systems and validate precisely what data each system contains, its structure, what the replacement system will need to create, and which subsets of that data you need to extract and migrate before retiring the system.
A data operating system, such as DataOS from The Modern Data Company, can immensely benefit this situation. A data operating system can be laid on top of any legacy system and tied into its data. It then provides a modern interface layer that easily interacts with common reporting and analysis tools. These tools can request data from the data operating system, which in turn handles extracting the data from the legacy system. This seamless connectivity lets people use the tools they are comfortable with, even when working with data from a legacy system. As a result, inventorying and profiling the data is much easier and faster than using the tools embedded within a legacy system. This is especially true given that few people will be knowledgeable about and comfortable with those legacy toolsets.
Looking into a specific system is a start. However, to unlock the maximum power of corporate data, it is necessary to mix data from different systems and allow each data source to enhance the others. Various architectures, from data warehouses to data lakes, have attempted to help solve this problem over the years. However, those solutions entail extracting data from the source systems and copying it into a more analysis-friendly environment. Even if enterprise-level data repositories of this nature are in place, it is unlikely that they contain 100% of the information contained within source legacy systems. This discrepancy is because only the most important and widely used data is typically extracted and loaded into an environment such as a data warehouse or data lake.
Before retiring a legacy system, however, it is a good idea to ensure that there aren’t other data worth preserving that never made it out in the past. A data operating system makes this exploration seamless because any system already mapped can be joined and mixed with any other system. The data operating system will handle the access and movement of data from each underlying system to facilitate any query. Just putting a layer over unchanged legacy systems isn’t the long-term solution. But in the short term, it allows for a thorough exploration of an organization’s data to document and plan what data will be migrated and kept and what data a replacement system will need to generate.
Once the data from the first two phases is understood and documented, the next step is determining what functionality users will need and how to deliver them. There is no better way to help users decide their requirements than to allow them to access prototypes that can be created and updated quickly. Using prototypes to define and validate requirements mitigates risk and streamlines the development of a final solution.
Laying an application on top of a data operating system’s map of corporate data enables application prototypes to be developed quickly and easily. Interfacing with the data operating system can happen through fully modern tools and protocols while the data operating system handles the messy details of dealing with the legacy systems. Over time, prototypes will evolve into production applications that can sit on top of the same operating system as the initial prototypes. Modernizing applications and access to data is a crucial component of retiring technical debt, and this phase gets an organization a good part of the way there as far as the front-end applications go.
While bolting modern applications on top of legacy systems is a start, it has some shortcomings. First, the legacy systems are still in place, even if users have better access to their data and more functionality. Next, performance won’t be optimal because there is still a dependency on the legacy platforms to serve the data. Finally, as legacy systems age, they become more prone to failure and to having underlying code that contains now-unsupported functions and components.
The final step in retiring technical debt is to replace each legacy system over time. The beauty of doing this after a data operating system is in place is that the end-user interfaces don’t need to change. As a new system comes online, the data operating system can be repointed to map to the new system instead of the legacy system. While technical work is still necessary to make that remapping occur, it will be seamless for the end users and won’t require application changes. Instead, those applications will make the same data requests as always, but the data operating system will execute them differently by making the most of the modernized replacement systems. At this point, there are updated applications sitting on top of updated systems connected by a modern layer of technology that, when combined, retires your technical debt! When systems eventually need to be replaced again, repeat the process. It’s a winning approach.
Following the steps outlined, you can modernize your handling of data as you retire technical debt. While it will still require time, effort, and money to retire your technical debt, it will require much less than it would have using approaches of the past. Following the steps outlined here will also help you avoid having technical debt blow up in your face, as happened so publicly to Southwest Airlines.
A data operating system, such as DataOS from The Modern Data Company, can be used to assess and diagnose where debt exists up front, how to best mitigate that debt, and can also be a part of the final solution. Once an architecture based on a data operating system is in place, it will make future technical debt much more manageable. Just make sure to budget the costs as part of ongoing maintenance efforts so that your technical debt doesn’t again grow to a dangerous level in the future.
To learn more about how a data operating system like DataOS can help your organization modernize its systems and end-user functionality, download our e-book “Modernize Your Data Architecture Without Ripping and Replacing.”
Technical debt is an issue that often isn’t given the attention it deserves. Companies can even get away with ignoring it for quite a while. However, once it rears its ugly head, technical debt can be incredibly costly both in terms of money and reputation. Look no further than Southwest Airlines’ meltdown during the 2022 holiday season for an example of technical debt causing massive problems that hurt a company’s reputation as much as its balance sheet. Luckily, there are some steps that organizations can take to start addressing technical debt without breaking the bank.
As with anything, technical debt can’t be solved all at once. Resolving it takes concerted effort over time. At a minimum, the following stages must be completed before technical debt can be considered paid off:
Many companies struggle to even get past the first stage. It’s not that the technical debt isn’t recognized within pockets of an organization, such as IT or the users of a given system. It’s usually that the senior executives holding the budget don’t truly understand the magnitude of the problem. This could be due to employees not feeling comfortable with telling the executives how bad things are or due to a non-technical executive simply not understanding how to quantify the risk that exists.
Let’s assume you and your organization can get past the first stage. Congratulations! As you navigate the remaining stages, making use of a new technology called a data operating system, such as DataOS from The Modern Data Company, can make the process of removing technical debt go faster and more smoothly.
One challenge in this stage is that there can be multiple legacy systems in place that are not well (if at all) integrated. This makes it particularly hard to analyze information across those systems. A data operating system lays on top of existing systems, even legacy systems, and provides an inventory of the data assets within each system. This requires no changes to the underlying platforms outside of allowing the data operating system to have access. Once the corporate data has been mapped, the data operating system creates a single, central entry point that allows users to query and explore data across the enterprise. It also adds a cross-system security and governance layer that ensures that corporate policies are followed.
Once this layer is in place, it is much easier to perform analysis to understand the extent of the technical debt. Data can be compared to see if it matches across different systems. New ways of combining the data from different systems can also be explored to help develop the requirements of the future. In effect, a data operating system enables an organization to explore data as though it wasn’t locked inside a mix of outdated and insufficiently functional systems.
Being able to see all of the data together helps validate accuracy, identify problems, and solidify new requirements. That’s a winning combination. Using that information, it is then possible to attack stages 3 and 4 to come up with a modernization plan that you can have confidence in.
The good news is that as you begin to upgrade and replace your current systems in stage five, the data operating system that was put in place to help with the scoping and planning work in stages two through four can stay right where it is. The operating system will maintain its cross-system security and governance layer that ensures compliance while work is done to modernize the underlying systems.
The data operating system also adds a layer of abstraction on top of the other corporate systems so that it serves as a centralized service. Downstream processes and applications can be redirected to access the data operating system layer. In turn, the data operating system layer can be adjusted to make use of new systems and functionality as they become available. A single redirection of the operating system layer will flow automatically to all the other downstream processes and applications.
While a data operating system might at first sound too good to be true, it is really a natural extension of the evolution of APIs, services, and system interconnectivity. It can help you isolate and minimize interaction with outdated systems by centralizing that access within the operating system. Having enterprise-ready security and governance adds more power since levels of sophistication that may not be available within any given system can still be laid on top of them.
If your organization has a lot of technical debt that needs addressed today, then start by learning about DataOS from The Modern Data Company. The first and most robust data operating system, DataOS is helping companies modernize their data and analytics functionality. DataOS can be used to assess and diagnose where debt exists up front, how to best mitigate that debt, and DataOS can also be a big part of the final state. Consider making use of DataOS today to — hopefully — avoid your own Southwest-style meltdown caused by pent up and unaddressed technical debt.
To learn more about how a data operating system like DataOS can help your organization modernize its systems and end user functionality, download our e-book Maximize Your Data Transformation Investments.