How to drive trusted decisions without changing your current data infrastructure.
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How Can You Boost Retail Loyalty with Customer Data? DataOS Has the Answer
There’s nothing more important than customer loyalty when it comes to a business’s chance of succeeding. When customers are loyal, they make repeat purchases and advocate for the brand, helping to drive new customer acquisition through word-of-mouth marketing. It’s also cheaper to retain customers than it is to find new ones. So, one really effective way to increase customer loyalty is analyzing customer data to understand their behavior and preferences.
Retailers can’t just collect and store data with no clear plan. They must operationalize data, putting it into action to drive everyday decisions across the organization; all with the singular purpose of delivering incredible customer experiences. How can they do that? Here’s what’s most important to know.
Collecting data is (relatively) easy; knowing what to do with it is the challenge.
Gathering data on customer preferences and behavior isn’t a new idea but deploying it in real-time to address the whole customer is. And there are certain things that companies may not expect when launching a customer data initiative to maximize retail loyalty. Here are three things to keep in mind:
1. Customer data is not a one-time fix
Using customer data to increase retail loyalty is an ongoing process. It requires ongoing monitoring and analysis of customer behavior and the ability to quickly adapt to changing customer needs and preferences. This means that companies must be flexible and responsive and constantly seek new insights to improve the customer experience.
2. Personalization is key
Simply collecting and analyzing customer data is not enough to increase loyalty. Companies must also be able to use that data effectively to personalize the individual shopping experience and meet customer needs. This means that companies need to have a deep understanding of their customers, their preferences, and their behavior and be able to use that information to create personalized interactions.
3. Strong data governance is essential
Risks and challenges associated with using customer data–such as concerns around privacy and security–can derail insights. Companies need to have robust data governance policies in place to ensure that customer data is being used ethically and responsibly while also ensuring that customer privacy is protected.
So how can retailers effectively use customer data to maximize retail loyalty?
Here’s where it gets interesting.
Use data to personalize the shopping experience
By analyzing customer data, companies can gain insights into customer preferences and behavior that can be used to create personalized shopping experiences. For example, using data to make product recommendations, send personalized offers, or create personalized content can help to build a stronger connection with customers and increase loyalty.
Leverage data to improve customer service
Customer service is a critical factor in building customer loyalty. By using customer data to identify common issues or pain points, companies can improve customer service and create a more positive customer experience.
Monitor customer feedback and sentiment
Monitoring customer feedback and sentiment is another effective way to increase loyalty. By using data to track customer reviews, social media mentions, and other feedback, companies can quickly identify and address issues and improve the overall customer experience.
How does DataOS help companies overcome what they don’t expect?
At this point, retailers are saying “Yes, we know data can form the foundation for these actions. But how do we get there?” Retailers need a setup that modernizes their data infrastructure without—and this is crucial—disrupting the entire operation.
To achieve real data operationalization and results that differentiate from the competition, retailers must effectively manage and utilize their customer data to gain valuable insights and personalize the shopping experience. This is where DataOS comes in – a powerful solution for managing and using customer data to maximize retail loyalty.
DataOS is an innovative data operating system that can help companies overcome some of the unexpected challenges associated with using customer data to maximize retail loyalty. Here are a few ways that DataOS can help:
Democratizes access to high-quality, governed, and secure data
DataOS allows retailers to balance democratization with a best-in-class governance framework. It enables users to define clear policies for data usage and authorized access and to meet regulatory compliance requirements such as GDPR. This process encompasses the people, process, and technology required to ensure that data is fit for its intended purpose. By democratizing access to high-quality, governed, and secure data, DataOS can help companies use customer data ethically and responsibly.
Another key feature here is its observability, which allows retailers to monitor the health and performance of their data and enhance data reliability. By ensuring that the data is reliable and trustworthy, retailers can improve customer loyalty by making informed decisions and providing personalized experiences.
Provides a composable and agile data operating system
One thing that companies may not expect when using customer data to increase retail loyalty is the need for speed and agility. DataOS provides a composable and agile data operating system that can be adapted to any data architecture, be it a data fabric, data mesh, lakehouse, or something new.
It democratizes access to high-quality, governed, and secure data in real-time. By connecting all structured, semi-structured, and unstructured data assets across the enterprise, DataOS builds an intelligent semantic layer that enables business and technical users to discover, explore, and collaborate on data products quickly and easily. This unmatched composability lets customers adapt it to any data architecture, which can help companies be flexible and responsive to changing customer needs and preferences.
Streamlines data pipelines and automates data access control
DataOS streamlines data pipelines and automates data access control with granular privacy controls. ABAC governance enables flexible and scalable policies that adapt to changing or new compliance regulations. This can help companies manage data access in a more efficient and effective manner, ensuring that customer data is being used ethically and responsibly.
DataOS also offers a data depot, which enables companies to connect data sources to DataOS without having to move any data. This can help retailers quickly access customer data and gain valuable insights to improve the shopping experience, launch new products and services, and address customer concerns.
Another helpful feature of DataOS is its data software capability, which allows retailers to use and deploy data as software with versioning capabilities. This ensures that retailers always use the most up-to-date and accurate data, which can lead to better decision-making and improved customer satisfaction.
Provides an integrated architecture and enhances existing infrastructure
DataOS enhances existing infrastructure by augmenting the functionality and ROI of current investments. There is no pressure to replace components in use. The integrated architecture drives cost optimizations, which can help companies lower OpEx.
Data sharing features built into DataOS enable seamless, secure, and monitored data collaboration across the business ecosystem to unlock new business models and insights. By sharing data with partners and other stakeholders, retailers can gain valuable insights that can improve the shopping experience.
DataOS is an ideal partner for data-driven insights in retail
DataOS provides a comprehensive solution for managing and utilizing customer data to maximize retail loyalty. By ensuring the reliability, quality, and security of customer data, retailers can gain valuable insights that can be used to personalize the shopping experience and improve customer satisfaction. With its composable and agile data operating system, DataOS offers a powerful solution for retailers looking to leverage the full potential of valuable customer data to drive business success.
Data Mesh + Patient360: A Modern Revolution for Healthcare Data
Data Mesh + Patient360: A Modern Revolution for Healthcare Data
Healthcare organizations are sitting on a treasure trove of customer data. Operationalizing that data makes it actionable and usable, helping improve services, costs, and patient outcomes. However, obstacles like data silos and legacy systems continue to block digital transformation efforts in the industry.
How to Choose the Right Data Management Solution
In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions, we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Remember to read part one if you need a quick refresher.
Companies need more than definitions. In a world where technology evolves, and data assets have exploded in volume, it helps to know the best use cases for each of these solutions and when to avoid them. Here’s a quick guide to get you started.
What factors are most important when building a data management ecosystem?
To choose the most suitable data management solution for your organization, consider the following factors:
- Data types and formats: Do you primarily work with structured, unstructured, or semi-structured data? Consider whether you need a solution that supports one or multiple data formats.
- Data processing and analytics needs: Determine if you require real-time analytics, batch processing, AI/ML capabilities, or a combination.
- Scalability: Assess if the solution can scale to accommodate your organization’s future growth in data volume, variety, and velocity.
- Integration with existing systems: Ensure the solution can integrate seamlessly with your existing tools, applications, and infrastructure.
- Data governance and security: Evaluate the native security, data governance, and data quality management features.
- Cost and complexity: Consider the total cost of ownership, including licensing, infrastructure, maintenance, and support costs, and weigh it against the system’s complexity.
By carefully evaluating these factors and understanding the features and limitations of each solution, you can select the most suitable data management approach for your organization’s needs.
Quick Tips for Choosing a Data Management Solution
Here is a quick guide for determining a solution for a specific use case and when to choose something different.
Data Lake
Choose a data lake if your organization:
- Deals with diverse data types and formats. Data lakes provide the flexibility you need because they can store structured, unstructured, and semi-structured data in their native formats.
- Wants to leverage the power of advanced analytics, AI, and machine learning on large volumes of raw data. Data lakes offer a scalable and cost-effective solution.
Avoid data lakes if your organization:
- Primarily needs fast querying and analytics on structured data. Because data lakes can have performance limitations for these use cases, a data warehouse may be a better fit.
- Does not have the resources to implement robust data governance and management. Data lakes risk becoming data swamps, leading to poor data quality and reduced analytical value.
Data Warehouse
Choose a data warehouse if your organization:
- Primarily works with structured data and requires fast querying and analytics capabilities. Data warehouses offer the optimized performance you need.
- Needs to maintain data consistency and quality. Data warehouses provide robust data governance features and support historical data analysis.
Avoid data warehouses if your organization:
- Deals with diverse data types, including unstructured and semi-structured data. A more flexible solution like a data lake or lakehouse may be better.
- Needs a cost-effective and easily scalable data storage solution, particularly for large volumes of data. In this case, alternatives such as data lakes or data lakehouses would be better.
Date Lakehouse
Choose a data lakehouse if your organization:
- Requires a unified platform to handle diverse analytics workloads and support structured and unstructured data. Data lakehouses combine the best features of data lakes and data warehouses.
- Needs a scalable and cost-effective solution for large-scale data storage that maintains high-performance analytics capabilities.
Avoid data lakehouses if your organization:
- Has limited resources or lacks the expertise to manage complex data integrations and governance. A more straightforward data storage solution, like a data warehouse, may be more appropriate.
- Has minimal data storage and analytics requirements or is primarily focused on a specific data format (e.g., only structured data). A more specialized solution, like a data warehouse or data lake, would better fit.
Data Operating System
Choose a data operating system if your organization:
- Needs a comprehensive end-to-end data solution that unifies data storage, integration, processing, and analytics. Data operating systems simplify overall data management.
- Works with diverse analytics workloads like real-time analytics, machine learning, AI, and batch processing. Data operating systems offer the scalable, flexible, and seamlessly integrated capabilities you need.
Under most circumstances, there is never a reason to avoid data operating systems. Here’s how to choose a data operating system that helps your data strategy evolve.
DataOS is the only end-to-end data operating system, and it works with all other data management and storage solutions.
DataOS helps companies overcome integration challenges and operationalize their data. It connects all tools and data sources — from legacy systems to brand-new technology investments — within a company’s technology ecosystem and provides a flexible and composable way to operationalize data without disrupting business.
Additionally, it removes the need for heavy data expertise, empowering business users to access data insights quickly and easily. While IT can still build complex pipelines and data products using a command line interface, the self-serve capabilities within DataOS allow business users to simply drag and drop the data outcomes they need. DataOS puts organizations on the fastest path from data to insight.
No matter what you have in your toolkit — whether it’s a data lake, warehouse, lake house, or hub —DataOS is the operational layer you need to become a truly data-driven organization.
The Pros and Cons of Leading Data Management and Storage Solutions
Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. Each has unique advantages and drawbacks, and the right choice for your organization depends on its specific requirements and goals. This post will briefly describe each solution and its potential challenges.
Stay tuned for our follow-up post for tips on choosing the data management approach most suitable for your needs.
The Data Lake: A Reservoir of Unstructured Potential
A data lake is a centralized repository that stores vast amounts of raw data. It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs. It enables organizations to perform advanced analytics, AI, and machine learning tasks directly on the raw data, unlocking insights that may not be available in a structured data environment.
Data lakes can handle massive amounts of disparate data, allowing organizations to scale their storage and processing capabilities as data volume, variety, and velocity grow. Unlike traditional storage solutions, data lakes can handle data at scales from terabytes to petabytes and beyond. And by leveraging distributed storage and open-source technologies, they offer a cost-effective solution for handling large data volumes.
One of the most interesting features of a data lake is the “schema-on-read” principle, which means the data schema (the structure and organization of the data) is applied when the data is read or accessed rather than stored. In other words, the data is stored in its raw, unprocessed form, and the structure is imposed when a user or an application queries the data for analysis or processing. This feature allows for a more flexible exploration of data.
Potential downsides of data lakes include governance and integration challenges. Data lakes often lack robust data governance, leading to data quality, consistency, and security issues. Additionally, data lakes can create data silos if not well-integrated with other systems, making it difficult to share data and collaborate across an organization. Without proper data governance and management, a data lake can become a data swamp, where data becomes disorganized, inaccessible, and challenging to analyze. Without adequate integration (and sometimes even with), they can prevent fast querying and limit performance in certain use cases.
The Data Warehouse: Structured Foundations for Efficient Analytics
A data warehouse is a large, structured database optimized for fast querying, reporting, and analysis of structured data. Data is stored in a schema-on-write approach, which means data is cleaned, transformed, and structured before storing. Data warehouses are ideal for organizations that require fast and efficient reporting and analytics on large volumes of specifically structured data.
Data warehouses also support storing historical data, allowing organizations to perform trend analysis, track changes over time, and leverage simplified data modeling for consistency and speed. They perform well for complex queries that require aggregating and analyzing data using multiple tables with built-in functions that ensure efficient query performance. Well-established data modeling techniques — such as star schema and snowflake schema — simplify data organization and improve query performance.
However, data warehouses can experience limitations and scalability challenges. They primarily handle structured data and may struggle to accommodate unstructured or semi-structured data, limiting flexibility for more diverse data needs. Due to conventions like schema-on-write, they can also face scalability limitations when handling huge volumes of data, particularly when compared to distributed storage solutions like data lakes.
Data Lakehouse: Bridging Data Worlds
A data lakehouse combines the best features of data lakes and data warehouses. It stores structured and unstructured data, enables schema-on-read and schema-on-write, and supports real-time data processing and analytics. Data lakehouses provide a unified platform for diverse analytics workloads, including machine learning, AI, and real-time analytics.
Data lakehouses are optimized for fast querying and analytics on large volumes of data, making them suitable for organizations that require efficient reporting and analysis. And thanks to distributed storage and compute technologies, data lakehouses can scale to handle massive amounts of data, providing a future-proof solution for growing data needs.
However, deploying a data lakehouse can be complex as it requires integrating and managing diverse data sources, systems, and analytics workloads within a single platform. While data lakehouses offer improved data governance compared to data lakes, they can still require significant effort to implement robust data governance and quality management processes. Some organizations may also experience vendor lock-ins when using proprietary data lakehouse solutions.
Data Hub: Centralized, Streamlined Connections
A data hub is an integration platform that centralizes data from multiple sources, enabling data sharing, collaboration, and governance. Data hubs allow organizations to centralize and share data from numerous sources, fostering collaboration and simplifying data integration across departments or applications. Data hubs often include data governance and quality management tools, which help ensure data consistency, security, and compliance. They can also accommodate structured and unstructured data, providing a versatile solution for diverse data storage and integration needs.
Data hubs are useful for organizations that need to share data among various departments or applications. By serving as a central point of access for multiple data sources, they streamline the process of locating and retrieving the required data for analytics or processing. They also enable easier maintenance by allowing organizations to maintain and update data in a single location, reducing redundancy and ensuring that users always have access to the most up-to-date information.
One downside to data hubs is that implementation and management can be complex, requiring coordination across multiple data sources, systems, and stakeholders. Users may also face integration challenges when dealing with heterogeneous data sources because the hub must reconcile differences in data formats, structures, and semantics.
Data Operating System: Orchestrating a Unified Data Ecosystem
A data operating system is an advanced data management platform that unifies data storage, integration, processing, and analytics. It provides a flexible, scalable, and secure data infrastructure that can adapt to evolving business needs. It is an end-to-end data solution that includes data ingestion, storage, governance, and analytics.
A data operating system supports diverse analytics workloads that cater to diverse organizational needs, including real-time analytics, machine learning, AI, and batch processing. In addition, data operating systems integrate seamlessly with existing tools, applications, and infrastructure, reducing the complexity of data management and maximizing existing investments.
In some cases, implementing a data operating system can be complex, requiring coordinating and integrating multiple data sources, systems, and processes within a single platform. Most piecemeal solutions on the market require specialized skills and expertise in data management, integration, and analytics to deploy and maintain, which may necessitate additional training or hiring.
So where do companies go from here? There are compelling reasons to choose one solution over another or combine them to create the right tool for a particular use case. In our next installment, we’ll go over how to choose the right combination of solutions to ensure the most value from your data assets.
In the meantime, start exploring the only comprehensive data operating system on the market. See it in action and schedule a demo with one of our data experts today.
DataOS is the Only Real Data Operating System on the Market
What is a data operating system? On the surface, it’s an operating system designed specifically for managing and processing large amounts of data. It typically provides a scalable and flexible infrastructure for storing, processing, and analyzing big data and should also include features that support data management, data protection, and data governance. Its goal is to provide an integrated platform with a single view so businesses can leverage data assets to drive business value.
Many products and services have claimed to offer a data operating system in recent years. However, a peek under the hood will reveal a patchwork of products that address different aspects of a data operating system but nothing that provides one holistic data platform. Much like the horrifying series of tangled wires attached to a power strip behind entertainment systems of the 1990s, these systems create more problems than they solve.
DataOS is a data operating system unlike anything else on the market — it’s so different from other solutions that we’re confident it’s the world’s first true data operating system. Let’s look at what DataOS offers.
A Paradigm Shift in Data
We are changing the way companies interact with their data assets and technologies.
Pre-integrated and Composable
One of Modern’s core pillars is “A modern data ecosystem out of the box.” DataOS makes integration worries obsolete by integrating data from any source, app, tool, or service. Other solutions require a complex set of steps and checkpoints to integrate each tool separately, and many lead only to more troubleshooting as reconfigurations weaken pipelines.
Integration can be a severe problem for many piecemeal approaches because of the following:
- Heterogeneous data sources, particularly if the data sources have different data formats, structures, or protocols
- Data silos make it difficult to consolidate and integrate data from different departments or business units within an organization
- The complexity of big data environments
- Technical limitations, particularly with legacy systems
Data operating systems often overcome these challenges by providing APIs, data connectors, and other tools to facilitate integration with other systems and technologies. Additionally, organizations may adopt a data integration strategy that involves defining data standards and processes to streamline data integration across different systems and sources. However, many of these processes require manual involvement and troubleshooting efforts that aren’t sustainable in the long run.
DataOS is an operational layer that simplifies the data stack. It allows all users access to the data they need for real-time decision making and prioritizes automation to ensure that the highest levels of governance make data safe and trustworthy. It does this as one out-of-the-box solution — not many different solutions pieced together — bypassing integration worries and delivering a transformed data ecosystem in a fraction of the time.
Modern Layer over Legacy Systems
Many data management systems require modernizing legacy systems by decommissioning them entirely or in part. This requirement creates complications for companies that still rely on legacy systems but feel hampered by their lack of integration.
However, many companies can’t simply offload their legacy systems to fix the issue. Replacing them can become prohibitively expensive depending on how much companies must invest in new technical equipment. Legacy systems may also have too many dependencies to risk the switch. Their technical debt would be too high to replace them without creating serious further problems.
At The Modern Data Company, we wondered what would happen if we removed the pressure to replace legacy systems and instead accepted that working with them would be an inherent part of any operating system. So we designed DataOS to integrate cleanly with legacy systems, creating a modern layer that revitalizes their usefulness for the organization without ripping and replacing them.
No Data or Vendor Lock-ins
It’s common for companies selling data products and services to want sole custody of their customers’ data. However, this significantly restricts the customer’s choices to adapt to changing business needs. Additionally, vendor lock-ins can leave companies with less control over vital data and analytical processes, making it difficult to customize or optimize solutions.
In some cases, this leaves companies extremely vulnerable. They’re at risk of increased costs for premium services that they can’t change. They’re also at a loss if their chosen provider experiences a breach, undergoes unplanned downtime, or ceases services altogether.
We strongly believe in open-source data contracts. They provide companies with the agility required for modern operations and allow DataOS to act as a common link rather than a gatekeeper. Data contracts align with another Modern pillar: data-as-software. Companies can leverage flexible APIs, declarative primitives, and in-place automation (all within a secure, well-governed environment) to discover and transform data as necessary with no red tape or workarounds necessary.
Companies ultimately need control of their data and processes to become truly data-driven. No organization should have to rely solely on systems integrators to build their infrastructure, and no company should have to request their own data back from whatever service claims to operationalize it.
A New Era of Self-Service
Data democratization is critical to business operations. Making data accessible, understandable, and usable to a broad range of people within an organization — regardless of their technical expertise or role — leads to better decision-making and greater innovation.
Unfortunately, the complexity of an infrastructure that relies on point solutions puts prohibitive obstacles in the path of the everyday user. In this environment, governance policies are challenging to keep consistent across the board, leading to restrictions and cumbersome access requests. The technical expertise to build appropriate pipelines rests only on IT, bottlenecking business requests and taking up valuable time they could spend elsewhere. It can even lead to the dreaded “shadow IT” as business departments search for their own solutions outside the purview of IT oversight.
DataOS takes an entirely different approach. First, it uses native attribute-based access controls (ABAC) to make data available consistently and safely, regardless of source or user. Company-wide, users can quickly and easily find data available to them. Once they find the data they need, a right-to-left approach to engineering allows users to select the outcomes they want and drag and drop these components in their dashboard while DataOS builds a trustworthy, reliable pipeline behind the scenes.
Think Differently about Your Data with the Only Real Data Operating System, DataOS
DataOS does not rely on piecemeal solutions to operationalize data. Instead, it provides a single connective tissue that modernizes even legacy systems without ripping and replacing and allows access for those who need data in everyday decisions. It is the only data operating system to offer the speed and flexibility required for companies to discover real data value in just days — without the massive disruptions typical migrations cause.
To see it in action, talk to one of our data experts today.