Predictive analytics is a branch of analytics that identifies the likelihood of future outcomes based on historical data. The goal is to provide the best assessment of what will happen in the future. Basically, predictive analytics answers the question “What will happen?” The value of predictive analytics lies in enabling business enterprises to proactively anticipate […]
In today’s data-driven landscape, organizations face the challenge of integrating diverse data sources efficiently. Whether due to mergers and acquisitions (M&A) or the need for advanced insights, a robust data platform with streamlined data operations are essential. Shift in Mindset Data fabric is a design concept for integrating and managing data. Through flexible, reusable, augmented, and […]
The insurance industry is undergoing a revolution, mainly driven by the application of advanced emerging technologies. The application and installation of new technologies enable a better future for our industry, where customers will receive maximum efficiency, security, and flexibility. Here, we address the major technologies and trends that influence this transition, shedding light on their […]
Consider your morning commute–taking your kids to school, your morning coffee run, hurrying to the office–how much time do you spend in the car? And how much money are you spending filling up your tank? Or maybe you’re like me and desperately trying not to think about your carbon footprint every time you drive 20 minutes (each way!) to the grocery store.
Now imagine how it would be if your office was just a block or two down the street, daycare right next door, and your grocery store in-between. Imagine the time savings, cost savings, and reduction in your personal carbon emissions if you could do everything you need, but without having to travel as far. If you could snap your fingers to make it happen, would you?
That’s the question being asked across the world of data processing. Businesses are increasingly seeking efficient and sustainable ways to manage and process their data across the world. End-users are less patient and the sheer volume of data being transferred from one endpoint to the next has massive implications for energy consumption and overall latency.
One solution to this is edge computing, which is the data processing equivalent of reducing your commute from an hour to two minutes. Not only does edge computing use fewer resources and energy, but it’s faster and more efficient, making it a greener choice for managing data.
Understanding Edge Computing
Before delving into the sustainability benefits, it’s crucial to understand what edge computing is. Edge computing is a distributed computing framework where data is processed closer to where it is generated, rather than relying on a centralized data center or the cloud. If you’ve ever used the Target App for shopping, you may notice it’ll give a little warning for items that are low in stock. “Only 2 left at your store!” Retailers like Target use edge enabled sensors to track products on shelves in real-time, automating inventory management for a more reliable picture of what’s available locally.
If not for IoT sensors and edge computing, you likely wouldn’t get a real-time view of inventory– data would be collected via barcode scans, transferred to a centralized data center that could be states away, batch processed, and then synchronized with inventory systems. This could take minutes, hours, or even days depending on the company, and this process is rife with problems like latency, network reliability, bandwidth constraints, and high infrastructure costs. Not to mention that a central server represents a single point of failure–meaning if one store is down, they all are. Not a great experience for shoppers, and a great case for moving to the edge.
The Sustainability Edge
Because it’s 2024, sustainability and environmental, social, and governance (ESG) initiatives are paramount. For example, 90% of S&P 500 companies release ESG reports, and ESG initiatives are considered by 89% of investors when making investment decisions. Sticking with those high numbers, 89% of executives plan to increase their overall technology budget, and 28% say that at least one-fifth of their workforce is involved in emerging tech as part of their primary job function. That’s a huge amount of people who are actively considering both sustainability and emerging technologies in their day-to-day work, in their projections, and in their strategic initiatives.
Edge computing marries these two initiatives beautifully. For instance, 60% of companies are using edge to some degree today, and half of those have deeply integrated edge into their digital core. In fact, Forbes predicts a mass migration from the cloud to the edge in 2024. The sustainability advantages perfectly complement the cost savings and consumer benefits of edge computing as opposed to the traditional cloud.
Here are three primary ways edge computing supports ESG:
Reduced Energy Consumption: Traditional data centers and cloud computing require substantial energy to power and cool the vast arrays of servers. This energy consumption not only translates into high operational costs but also contributes significantly to carbon emissions. Edge computing, on the other hand, decentralizes data processing, distributing it across multiple edge devices that are often located closer to the data source. This decentralization reduces the load on central data centers, leading to lower overall energy consumption.
Optimized Bandwidth Usage: Transmitting large volumes of data to and from centralized data centers or the cloud can be bandwidth-intensive. This not only increases operational costs but also places a strain on network infrastructure. By processing data at the edge, organizations can significantly reduce the amount of data that needs to be transmitted over the network. This not only optimizes bandwidth usage but also reduces the associated energy consumption and emissions.
Decreased Latency and Improved Efficiency: One of the inherent advantages of edge computing is the reduction in latency. By processing data closer to the source, edge computing eliminates the delays associated with transmitting data to distant data centers. This not only enhances the speed and responsiveness of applications but also improves overall system efficiency.
Actian Zen: A Sustainable Edge Solution
Edge computing doesn’t exist in a vacuum, and it takes the right toolkit to take advantage of all the benefits. You need to be sure you have the right database and a database management system (DBMS) that’s edge compatible.
Enter Actian Zen, a high-performance, embedded, and zero-administration DBMS designed for edge computing, IoT applications, and mobile environments. Known for its small footprint, low resource consumption, and ability to operate efficiently on a wide range of devices, Actian Zen provides a versatile and powerful DBMS that meets the needs of modern business across various industries.
Three main benefits Zen delivers include:
Optimizing IT and Cloud Expenditures: Actian Zen is designed to operate efficiently on a wide range of devices, from IoT sensors to industrial gateways. Its compact size means it can be deployed on low-power devices, reducing the need for energy-intensive hardware. Additionally, by processing data locally at the edge, Actian Zen significantly reduces the need for extensive data transmission to central servers or cloud environments. This local processing minimizes bandwidth usage and decreases the load on centralized data centers, leading to lower operational costs associated with data storage and cloud services. Furthermore, the reduced reliance on large, energy-intensive data centers aligns with sustainability goals by lowering overall energy consumption and carbon emissions.
Ensuring Compliance with Internal Policies and External Regulations: By enabling data processing at the edge, Actian Zen reduces the need for data transmission to centralized servers, thus saving bandwidth and energy. This local processing aligns with sustainability initiatives aimed at reducing energy consumption and emissions. Actian Zen also features role-based access, which allows for granular control over who can access and manipulate data, aligning with internal security policies and regulatory standards.
Enabling Scalability and Flexibility to Accommodate Future Growth: With Actian Zen, developers can scale from a core set of libraries capable of single-user client data management to a full-fledged, enterprise-grade server. It’s capable of supporting thousands of users on multicore, VM cloud environments, or in Docker containers with Kubernetes orchestration and Helm chart deployment configuration.
Zen: The Sustainable Database Solution
As the demand for sustainable computing solutions grows, edge computing with Actian Zen emerges as a game-changer. By reducing energy consumption, optimizing bandwidth usage, and decreasing latency, Actian Zen not only enhances operational efficiency but also contributes to a greener future. If you’re looking to balance performance with sustainability, you’ll find Actian Zen’s edge computing capabilities to be a compelling choice. Embrace the power of edge computing with Actian Zen and take a step toward a more sustainable, efficient, and environmentally friendly future.
As organizations across all industries grapple with ever-increasing amounts of data, the traditional on-premises data warehouse is facing intense scrutiny. Data and IT professionals, analysts, and business decision-makers are questioning its viability in our modern data landscape where agility, scalability, and real-time insights are increasingly important.
Data warehouse stakeholders are asking:
How do on-prem costs compare to a cloud-based data warehouse?
Can our on-premises warehouse meet data growth and business demands?
Do we have the flexibility to efficiently integrate new data sources and analytics tools?
What are the ongoing maintenance and management needs for our on-prem warehouse?
Are we able to meet current and future security and compliance requirements?
Can we integrate, access, and store data with a favorable price performance?
Addressing these questions enables more informed decision making about the practicality of the on-premises data warehouse and whether a migration to a cloud-based warehouse would be beneficial. As companies like yours also look to answer the question of whether the on-premises data warehouse is truly a solution of the past, it’s worth looking at various warehouse offerings. Is one model really better for transforming data management and meeting current business and IT needs for business intelligence and analytics?
Challenges of Traditional On-Premises Data Warehouses
Data warehouses that serve as a centralized data repository on-premises, within your physical environment, have long been the cornerstone of enterprise data management. These systems store vast amounts of data, enabling you to integrate and analyze data to extract valuable insights.
Many organizations continue to use these data warehouses to store, query, and analyze their data. This allows them to get a return on their current on-prem warehouse investment, meet security and compliance requirements, and perform advanced analytics. However, the downside is that these warehouses increasingly struggle to meet the demands of modern business environments that need to manage more data from more sources than ever before, while making the data accessible and usable to analysts and business users at all skill levels.
These are critical challenges faced by on-premises data warehouses:
Scalability Issues. A primary drawback of on-premises data warehouses is their limited scalability—at least in a fast and efficient manner. Growing data volumes and increased workloads require you to invest in additional hardware and infrastructure to keep pace. This entails significant costs and also requires substantial time. The rigidity of on-premises systems makes it difficult to quickly scale resources based on fluctuating needs such as seasonal trends, marketing campaigns, or a business acquisition that brings in large volumes of new data.
Limited Flexibility. As new data sources emerge, you need the ability to quickly build data pipelines and integrate the information. On-premises data warehouses often lack the flexibility to efficiently handle data from emerging sources—integrating new data sources is typically a cumbersome, time-consuming process, leading to delays in data analytics and business insights.
High Operational Costs. Maintaining an on-premises data warehouse can involve considerable operational expenses. That means you must allocate a budget for hardware, software licenses, electricity, and cooling the data warehouse environment in addition to providing the physical space. You must also factor in the cost of skilled IT staff to manage the warehouse and troubleshoot problems.
Performance Restrictions. You can certainly have high performance on-premises, yet as data volumes surge, on-prem data warehouses can experience performance bottlenecks. This results in slower query processing times and delayed insights, restricting your ability to make timely decisions and potentially impacting your competitive edge in the market.
These are some of the reasons why cloud migrations are popular—they don’t face these same issues. According to Gartner, worldwide end-user spending on public cloud services is forecast to grow 20.4% to $675.4 billion in 2024, up from $561 billion in 2023, and reach $1 trillion before the end of this decade.
Yet it’s worth noting that on-prem warehouses continue to meet the needs of many modern businesses. They effectively store and query data while offering customization options tailored to specific business needs.
On-Prem is Not Even on Life Support
Despite the drawbacks to on-premises data warehouses, they are alive and doing fine. And despite some analysts predicting their demise for the last decade or so, reality and practicality tell a different story.
Granted, while many organizations have mandates to be cloud-first and have moved workloads to the cloud, the on-prem warehouse continues to deliver the data and analytics capabilities needed to meet the requirements of today’s businesses, especially those with stable workloads. In fact, you can modernize in place, or on-prem, with the right data platform or database.
You also don’t have to take an either-or approach to on-premises data warehouses vs. the cloud. You can have them both with a hybrid data warehouse that offers a modern data architecture combining the benefits of on-premises with cloud-based data warehousing. This model lets you optimize both environments for data storage, processing, and analytics to ensure the best performance, cost, security, and flexibility.
Data Warehouse Options Cut Across Specific Needs
It’s important to remember that your organization’s data needs and strategy can be uniquely different from your peers and from businesses in other industries. For example, you may be heavily invested in your on-prem data warehouse and related tools, and therefore don’t want to move away from these technologies.
Likewise, you may have a preference to keep certain workloads on-prem for security or low latency reasons. At the same time, you may want to take advantage of cloud benefits. A modern warehouse lets you pick your option—solely on-premises, completely in the cloud, or a hybrid that effectively leverages on-prem and cloud.
One reason to take a hybrid approach is that it helps to future-proof your organization. Even if your current strategy calls for being 100% on-premises, you may want to keep your options open to migrate to the cloud later, if or when you’re ready. For instance, you may want a data backup and recovery option that’s cloud based, which is a common use case for the cloud.
Is On-Prem Right For You?
On-premises data warehouses are alive and thriving, even if they don’t receive the amount of press as their cloud counterparts. For many organizations, especially those with stringent regulatory requirements, the on-prem warehouse continues to play an essential role in data and analytics. It allows predictable cost management along with the ability to customize hardware and software configurations to fit specific business demands.
If you’re curious about the best option for your business, Actian can help. Our experts will look at your current environment along with your data needs and business priorities to recommend the most optimal solution for you.
We offer a modern product portfolio, including data warehouse solutions, spanning on-prem, the cloud, and hybrid to help you implement the technology that best suits your needs, goals, and current investments. We’re always here to help to ensure you can trust your data and your buying choices.
The process of choosing the right technology for your specific business and IT needs can be complex, yet making the right decision is critical. So, how do you make an informed choice?
The product landscape changes fast, meaning the products you looked at even a few months ago may have changed significantly. And let’s face it – proof of concepts (POCs) are limited deployments with vendors showcasing their solutions for a brief period of time. You don’t want to find out later, after you’ve invested significant time and money, that a product won’t handle your specific workloads, or give you the security, scalability and price-performance you need.
You need to know upfront how it performs from both a customer and a product experience in essential categories such as performance, reliability, manageability, and validation. Likewise, you want to know that the product has a strong roadmap for your future and peer use cases are available.
The Need for Unbiased Assessments
Independent analyst reports and buying guides can help you make informed decisions. They offer unbiased, critical insights into the advantages and drawbacks of vendors’ products. The information cuts through marketing claims to help you understand how technologies, such as data platforms, truly perform to help you choose a solution with confidence.
These reports are typically based on thorough research and analysis, considering various factors such as product capabilities, customer satisfaction, and market performance. This objectivity can help you avoid the pitfalls of biased or incomplete information.
For example, the 2024 Ventana Research Buyers Guide for Data Platforms evaluated 25 data platform software providers, detailing their strengths and weaknesses. This broad perspective enables you to understand the competitive landscape and identify potential technology partners that align with your strategic goals.
The Buyers Guide is meticulously curated and structured into seven in-depth categories across Product and Customer Experience. A vendor’s overall placement is assessed through a weighted score and is only awarded to companies that meet a strict set of criteria, with the aim to streamline and aid vendor selection.
Ventana’s Market View on Data Platforms
A modern data platform allows businesses to stay competitive and innovative in a data-driven world. They manage the storage, integration, and analysis of data, ensuring a single source of truth.
Data platforms should empower all users, especially non-technical users, with actionable insights. As Ventana Research stated in its 2024 Buyers Guide for Data Platforms, “Data platforms provide an environment for organizing and managing the storage, processing, analysis, and presentation of data across an enterprise. Without data platforms, enterprises would be reliant on a combination of paper records, time-consuming manual processes, and huge libraries of physical files to record, process and store business information.”
Today’s data platforms are typically designed to be scalable and flexible, accommodating the growing and evolving data needs of your business. They support a variety of data from new and emerging sources. This versatility ensures that you can continue to leverage your data as you expand and innovate.
Ventana’s Criteria for Choosing Data Platforms
Ventana notes that buying decisions should be based on research. “We believe it is important to take a comprehensive, research-based approach, since making the wrong choice of data platforms technology can raise the total cost of ownership, lower the return on investment and hamper an enterprise’s ability to reach its full performance potential,” according to Ventana.
Three key evaluation criteria from the 2024 Ventana Buyers Guide for Data Platforms are:
Assess Your Primary Workload Needs and Future-Proof Them for GenAI. Determine whether your primary focus is on operational or analytic workloads, or both. Operational workloads include finance, supply chain, and marketing applications, whereas analytical workloads include business intelligence (BI) and data science. Ventana predicts that by 2027, personalized experiences driven by GenAI will increase the demand for data platforms capable of supporting hybrid operational and analytical processing.
 Evaluate Your Main Data Storage and Management Criteria. Determine the capabilities you need, then evaluate data platforms that align with those requirements. Criteria often includes the core database management system, performance and query functionality, the ability to integrate data and ensure quality, whether the platform offers simple platform usability and manageability, and if it meets cost, price performance, and return on investment requirements.
Consider Support for Data Workers in Multiple Roles. Consider the types of data you need to manage along with the key functionalities required by your users, from database administrators to data engineers to data scientists. According to Ventana, data platforms must support a range of users with different needs – across technology and business teams.
Have Confidence in Your Data Platform
In the rapidly evolving tech landscape, making informed choices is more important than ever. Analyst reports are invaluable resources that provide objective, comprehensive insights to guide those decisions.
Actian is providing complimentary access to the 2024 Ventana Research Data Platforms Buyers Guide. Read the report to learn more about what Ventana has to say about Actian and our positioning as Exemplary.
If you’re in the market for a single, unified data platform that’s recognized by an analyst firm as handling both operational and analytic workloads, let’s talk so you can have confidence in your buying decision.
Companies are investing heavily in AI projects as they see huge potential in generative AI. Consultancies have predicted opportunities to reduce costs and improve revenues through deploying generative AI – for example, McKinsey predicts that generative AI could add $2.6 to $4.4 trillion to global productivity. Yet at the same time, AI and analytics projects have historically […]
Recent years reflect medical device manufacturers shifting value from just hardware, or even hardware and software. Hardware, software, and data are all now being monetized in the era of the Internet of Things. Data-rich IoT medical devices – pumps, monitors, wearables, tablets, and more – span a wide range of functions to support healthcare, diagnostics, […]
The Internet of Things (IoT) is rapidly transforming our world. From smart homes and wearables to industrial automation and connected vehicles, billions of devices are now collecting and generating data. According to a recent analysis, the number of Internet of Things (IoT) devices worldwide is forecasted to almost double from 15.1 billion in 2020 to more than 29 billion IoT devices in 2030. This data deluge presents both challenges and opportunities, and at the heart of it all lies the need for efficient data storage and management – a role increasingly filled by embedded databases.
Traditional Databases vs. Embedded Databases
Traditional databases, designed for large-scale enterprise applications, often struggle in the resource-constrained environment of the IoT. They require significant processing power, memory, and storage, which are luxuries most IoT devices simply don’t have. Additionally, traditional databases are complex to manage and secure, making them unsuitable for the often-unattended nature of IoT deployments.
Embedded databases, on the other hand, are specifically designed for devices with limited resources. They are lightweight, have a small footprint, and require minimal processing power. They are also optimized for real-time data processing, crucial for many IoT applications where decisions need to be made at the edge, without relaying data to a cloud database.
Why Embedded Databases are Perfect for IoT and Edge Computing
Several key factors make embedded databases the ideal choice for IoT and edge computing:
Small Footprint: Embedded databases require minimal storage and memory, making them ideal for devices with limited resources. This allows for smaller form factors and lower costs for IoT devices.
Low Power Consumption: Embedded databases are designed to be energy-efficient, minimizing the power drain on battery-powered devices, a critical concern for many IoT applications.
Fast Performance: Real-time data processing is essential for many IoT applications. Embedded databases are optimized for speed, ensuring timely data storage, retrieval, and analysis at the edge.
Reliability and Durability: IoT devices often operate in harsh environments. Embedded databases are designed to be reliable and durable, ensuring data integrity even in case of power failures or device malfunctions.
Security: Security is paramount in the IoT landscape. Embedded databases incorporate robust security features to protect sensitive data from unauthorized access.
Ease of Use: Unlike traditional databases, embedded databases are designed to be easy to set up and manage. This simplifies development and deployment for resource-constrained IoT projects.
Building complex IoT apps shouldn’t be a headache. Let us show you how our embedded edge database can simplify your next IoT project.
Benefits of Using Embedded Databases in IoT Applications
The advantages of using embedded databases in IoT applications are numerous:
Improved Decision-Making: By storing and analyzing data locally, embedded databases enable real-time decision making at the edge. This reduces reliance on cloud communication and allows for faster, more efficient responses.
Enhanced Functionality: Embedded databases can store device configuration settings, user preferences, and historical data, enabling richer functionality and a more personalized user experience.
Reduced Latency: Processing data locally eliminates the need for constant communication with the cloud, significantly reducing latency and improving responsiveness.
Offline Functionality: Embedded databases allow devices to function even when disconnected from the internet, ensuring uninterrupted operation and data collection.
Cost Savings: By reducing reliance on cloud storage and processing, embedded databases can help lower overall operational costs for IoT deployments.
Use Cases for Embedded Databases in IoT
Embedded databases are finding applications across a wide range of IoT sectors, including:
Smart Homes: Embedded databases can store device settings, energy usage data, and user preferences, enabling intelligent home automation and energy management.
Wearables: Fitness trackers and smartwatches use embedded databases to store health data, activity logs, and user settings.
Industrial Automation: Embedded databases play a crucial role in industrial IoT applications, storing sensor data, equipment settings, and maintenance logs for predictive maintenance and improved operational efficiency.
Connected Vehicles: Embedded databases are essential for connected car applications, storing vehicle diagnostics, driver preferences, and real-time traffic data to enable features like self-driving cars and intelligent navigation systems.
Asset Tracking: Embedded databases can be used to track the location and condition of assets in real-time, optimizing logistics and supply chain management.
The Future of Embedded Databases in the IoT
As the IoT landscape continues to evolve, embedded databases are expected to play an even more critical role. Here are some key trends to watch:
Increased Demand for Scalability: As the number of connected devices explodes, embedded databases will need to be scalable to handle larger data volumes and more complex workloads.
Enhanced Security Features: With growing security concerns in the IoT, embedded databases will need to incorporate even more robust security measures to protect sensitive data.
Cloud Integration: While embedded databases enable edge computing, there will likely be a need for seamless integration with cloud platforms for data analytics, visualization, and long-term storage.
The rise of the IoT has ushered in a new era for embedded databases. Their small footprint, efficiency, and scalability make them the perfect fit for managing data at the edge of the network. As the IoT landscape matures, embedded databases will continue to evolve, offering advanced features, enhanced security, and a seamless integration with cloud platforms.
At Actian, we help organizations run faster, smarter applications on edge devices with our lightweight, embedded database – Actian Zen. And, with the latest release of Zen 16.0, we are committed to helping businesses simplify edge-to-cloud data management, boost developer productivity and build secure, distributed IoT applications.
OpenAI’s ChatGPT release less than two years ago launched generative AI (GenAI) into the mainstream, with both enterprises and consumers discovering new ways to use it every day. For organizations, it’s unlocking opportunities to deliver more exceptional experiences to customers, enabling new types of applications that are adaptive, context-aware, and hyper-personalized. While the possibilities are […]
“Advanced analytics” has been the new buzzword on every organization’s mind for the past several years. Recent advancements in machine learning have promised to optimize every arm of an organization – from marketing and sales to supply-chain operations. For some, investments in advanced analytics have been worth the hype. Those who succeed can gain a […]
Pretectum CMDM integrates customer data systems to provide a holistic view of the customer, this enables data-driven decision-making.
The system analyzes data from multiple touchpoints to gain insights into customer behavior, preferences, and purchasing patterns, allowing businesses to tailor products and services accordingly.
Pretectum CMDM uses Structured and unstructured data modeling. This helps you identify at-risk customers and support the implementation of proactive retention measures.
The platform centralizes customer data, making it accessible across departments and fostering collaboration for a cohesive approach to customer management.
Advanced analytics and real-time data processing enable businesses to swiftly adapt strategies to changing customer preferences and market dynamics.
The system provides real-time insights into customer profiles, allowing organizations to make quick, informed decisions in response to data driven insights.
By collating customer data, Pretectum CMDM helps businesses identify untapped opportunities and innovate products and services. The platform encourages organizations to prioritize the customer and customer preferences. It does this by providing a holistic view of the customer and supporting self service data verification and consent management. Pretectum CMDM enables businesses to treat customers as unique individuals with distinct preferences, moving away from one-size-fits-all approaches.
While offering numerous benefits, the implementation of any CMDM requires addressing data privacy concerns and ensuring robust cybersecurity measures. We think we have it solved!
Read more at https://www.pretectum.com/the-potential-of-pretectum-cmdm-on-customer-lifetime-value-and-retention-strategies/
In today’s data-driven business landscape, the quality of an organization’s data has become a critical determinant of its success. Accurate, complete, and consistent data is the foundation upon which crucial decisions, strategic planning, and operational efficiency are built. However, the reality is that poor data quality is a pervasive issue, with far-reaching implications that often go unnoticed or underestimated.
Defining Poor Data Quality
Before delving into the impacts of poor data quality, it’s essential to understand what constitutes subpar data. Inaccurate, incomplete, duplicated, or inconsistently formatted information can all be considered poor data quality. This can stem from various sources, such as data integration challenges, data capture inconsistencies, data migration pitfalls, data decay, and data duplication.
The Hidden Costs of Poor Data Quality
Loss of Revenue
Poor data quality can directly impact a business’s bottom line. Inaccurate customer information, misleading product details, and incorrect order processing can lead to lost sales, decreased customer satisfaction, and damaged brand reputation. Gartner estimates that poor data quality costs organizations an average of $15 million per year.
Reduced Operational Efficiency
When employees waste time manually correcting data errors or searching for accurate information, it significantly reduces their productivity and the overall efficiency of business processes. This can lead to delayed decision-making, missed deadlines, and increased operational costs.
Flawed Analytics and Decision-Making
Data analysis and predictive models are only as reliable as the data they are based on. Incomplete, duplicated, or inaccurate data can result in skewed insights, leading to poor strategic decisions that can have far-reaching consequences for the organization.
Compliance Risks
Stringent data privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), require organizations to maintain accurate and up-to-date personal data. Failure to comply with these regulations can result in hefty fines and reputational damage.
Missed Opportunities
Poor data quality can prevent organizations from identifying market trends, understanding customer preferences, and capitalizing on new product or service opportunities. This can allow competitors with better data management practices to gain a competitive edge.
Reputational Damage
Customers are increasingly conscious of how organizations handle their personal data. Incidents of data breaches, incorrect product information, or poor customer experiences can quickly erode trust and damage a company’s reputation, which can be challenging to rebuild.
Measuring the Financial Impact of Poor Data Quality
Annual Financial Loss: Organizations face an average annual loss of $15 million due to poor data quality. This includes direct costs like lost revenue and indirect costs such as inefficiencies and missed opportunities​ (Data Ladder)​.
GDP Impact: Poor data quality costs the US economy approximately $3.1 trillion per year. This staggering figure reflects the extensive nature of the issue across various sectors, highlighting the pervasive economic burden​ (Experian Data Quality)​​ (Anodot)​.
Time Wasted: Employees can waste up to 27% of their time dealing with data quality issues. This includes time spent validating, correcting, or searching for accurate data, significantly reducing overall productivity​ (Anodot)​.
Missed Opportunities: Businesses can miss out on 45% of potential leads due to poor data quality, including duplicate data, invalid formatting, and other errors that hinder effective customer relationship management and sales efforts​ (Data Ladder)​.
Audit and Compliance Costs: Companies may need to spend an additional $20,000 annually on staff time to address increased audit demands caused by poor data quality. This highlights the extra operational costs that come with maintaining compliance and accuracy in financial reporting​ (CamSpark)​.
Strategies for Improving Data Quality
Addressing poor data quality requires a multi-faceted approach encompassing organizational culture, data governance, and technological solutions.
Fostering a Data-Driven Culture
Developing a workplace culture that prioritizes data quality is essential. This involves establishing clear data management policies, standardizing data formats, and assigning data ownership responsibilities to ensure accountability.
Implementing Robust Data Governance
Regularly auditing data quality, cleaning and deduplicating datasets, and maintaining data currency are crucial to maintaining high-quality data. Automated data quality monitoring and validation tools can greatly enhance these processes.
Leveraging Data Quality Solutions
Investing in specialized data quality software can automate data profiling, cleansing, matching, and deduplication tasks, significantly reducing the manual effort required to maintain data integrity.
The risks and costs associated with poor data quality are far-reaching and often underestimated. By recognizing the hidden impacts, quantifying the financial implications, and implementing comprehensive data quality strategies, organizations can unlock the true value of their data and position themselves for long-term success in the digital age.
In today’s data-driven business landscape, the quality of an organization’s data has become a critical determinant of its success. Accurate, complete, and consistent data is the foundation upon which crucial decisions, strategic planning, and operational efficiency are built. However, the reality is that poor data quality is a pervasive issue, with far-reaching implications that often go unnoticed or underestimated.
Defining Poor Data Quality
Before delving into the impacts of poor data quality, it’s essential to understand what constitutes subpar data. Inaccurate, incomplete, duplicated, or inconsistently formatted information can all be considered poor data quality. This can stem from various sources, such as data integration challenges, data capture inconsistencies, data migration pitfalls, data decay, and data duplication.
The Hidden Costs of Poor Data Quality
Loss of Revenue
Poor data quality can directly impact a business’s bottom line. Inaccurate customer information, misleading product details, and incorrect order processing can lead to lost sales, decreased customer satisfaction, and damaged brand reputation. Gartner estimates that poor data quality costs organizations an average of $15 million per year.
Reduced Operational Efficiency
When employees waste time manually correcting data errors or searching for accurate information, it significantly reduces their productivity and the overall efficiency of business processes. This can lead to delayed decision-making, missed deadlines, and increased operational costs.
Flawed Analytics and Decision-Making
Data analysis and predictive models are only as reliable as the data they are based on. Incomplete, duplicated, or inaccurate data can result in skewed insights, leading to poor strategic decisions that can have far-reaching consequences for the organization.
Compliance Risks
Stringent data privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), require organizations to maintain accurate and up-to-date personal data. Failure to comply with these regulations can result in hefty fines and reputational damage.
Missed Opportunities
Poor data quality can prevent organizations from identifying market trends, understanding customer preferences, and capitalizing on new product or service opportunities. This can allow competitors with better data management practices to gain a competitive edge.
Reputational Damage
Customers are increasingly conscious of how organizations handle their personal data. Incidents of data breaches, incorrect product information, or poor customer experiences can quickly erode trust and damage a company’s reputation, which can be challenging to rebuild.
Measuring the Financial Impact of Poor Data Quality
Annual Financial Loss: Organizations face an average annual loss of $15 million due to poor data quality. This includes direct costs like lost revenue and indirect costs such as inefficiencies and missed opportunities​ (Data Ladder)​.
GDP Impact: Poor data quality costs the US economy approximately $3.1 trillion per year. This staggering figure reflects the extensive nature of the issue across various sectors, highlighting the pervasive economic burden​ (Experian Data Quality)​​ (Anodot)​.
Time Wasted: Employees can waste up to 27% of their time dealing with data quality issues. This includes time spent validating, correcting, or searching for accurate data, significantly reducing overall productivity​ (Anodot)​.
Missed Opportunities: Businesses can miss out on 45% of potential leads due to poor data quality, including duplicate data, invalid formatting, and other errors that hinder effective customer relationship management and sales efforts​ (Data Ladder)​.
Audit and Compliance Costs: Companies may need to spend an additional $20,000 annually on staff time to address increased audit demands caused by poor data quality. This highlights the extra operational costs that come with maintaining compliance and accuracy in financial reporting​ (CamSpark)​.
Strategies for Improving Data Quality
Addressing poor data quality requires a multi-faceted approach encompassing organizational culture, data governance, and technological solutions.
Fostering a Data-Driven Culture
Developing a workplace culture that prioritizes data quality is essential. This involves establishing clear data management policies, standardizing data formats, and assigning data ownership responsibilities to ensure accountability.
Implementing Robust Data Governance
Regularly auditing data quality, cleaning and deduplicating datasets, and maintaining data currency are crucial to maintaining high-quality data. Automated data quality monitoring and validation tools can greatly enhance these processes.
Leveraging Data Quality Solutions
Investing in specialized data quality software can automate data profiling, cleansing, matching, and deduplication tasks, significantly reducing the manual effort required to maintain data integrity.
The risks and costs associated with poor data quality are far-reaching and often underestimated. By recognizing the hidden impacts, quantifying the financial implications, and implementing comprehensive data quality strategies, organizations can unlock the true value of their data and position themselves for long-term success in the digital age.
Data governance is crucial for businesses aiming to maximize the value of their data, yet several common issues can significantly hinder its effectiveness. Let’s dive straight into these challenges and outline actionable strategies for overcoming them. Silos and Misalignment Data governance often operates in isolation, with dedicated teams having minimal interaction with the end-users of […]
Customer renewal rates are dropping, and your CEO is on the warpath. You need to find out why and fast. At most large companies, that is a pretty tall task. Information about customers is likely scattered across an assortment of applications and devices ranging from your customer relationship management system to logs from customer-facing applications, […]
What are the biggest data strategy challenges facing you and your company? If you are like most, the main reason for developing a data strategy is to be capable of supporting the growth strategy of each type of business in an exclusive way – to offer competitive resilience with balance and maturity to defend and […]
AI governance has become a critical topic in today’s technological landscape, especially with the rise of AI and GenAI. As CEOs express concerns regarding the potential risks with these technologies, it is important to identify and address the biggest risks. Implementing effective guardrails for AI governance has become a major point of discussion, with a […]
I call it a “data crime” when someone is abusing or misusing data. When we understand these stories and their implications, it can help us learn from mistakes and prevent future data crimes. The stories can also be helpful if you have to explain the importance of data management to someone. The Story I met Bob Smith! While that […]
The mere mention of “data governance” can send shivers down the spines of executives and employees alike. The thought of implementing stringent rules and procedures for managing data often conjures images of bureaucratic nightmares and stifled innovation. However, it doesn’t have to be this way. Contrary to popular belief, the implementation of an effective and […]
“What’s a metaphor?” Mr. Biergel posed the question one morning to my high school grammar class. Being typical teenagers, we looked at him with blank-eyed stares. We expected that if we waited long enough, he’d write a paragraph-long definition on the blackboard. “What’s a metaphor?” he repeated. “A place for cows to graze!” We groaned. […]
The use of Artificial Intelligence (AI) in healthcare provides promises, risks, and unintended consequences. This column addresses the evolving AI issues in connection with the following topics: As used in this column, “AI” covers both generative and non-generative AI, with a focus on machine learning as part of non-generative AI. Reducing Administrative Burdens on Physicians […]
The citizen data scientist phenomenon is in full swing and — while the approach has its detractors — the proof is in success, and many organizations are actively succeeding using the citizen data scientist approach.  Gartner has predicted that “by 2025, 95% of decisions that currently use data will be at least partially automated.”  There are […]
We are pleased to announce that data profiling is now available as part of the Actian Data Platform. This is the first of many upcoming enhancements to make it easy for organizations to connect, manage, and analyze data. With the introduction of data profiling, users can load data into the platform and identify focus areas, such as duplicates, missing values, and non-standard formats, to improve data quality before it reaches its target destination.
Why Data Quality Matters
Data quality is the cornerstone of effective data integration and management. High-quality data enhances business intelligence, improves operational efficiency, and fosters better customer relationships. Poor data quality, on the other hand, can result in costly errors, compliance issues, and loss of trust.
Key Features of Actian’s Enhanced Data Quality Solutions
Advanced Data Profiling
Our advanced data profiling tools provide deep insights into your data’s structure, content, and quality. You can quickly identify anomalies, inconsistencies, and errors by analyzing your data sources and leveraging pre-defined rule sets to detect data problems. Users can also create rules based on the use case to ensure data is clean, correct, and ready for use.
Data Cleansing and Enrichment
Actian’s data cleansing and enrichment capabilities ensure your data is accurate, complete, and up-to-date. Our automated processes isolate data that does not meet quality standards so data teams can act before data is moved to its target environment.
Data Quality Monitoring
With real-time data quality monitoring, you can continuously assess the health of your data. Our solution provides ongoing validation, enabling you to monitor deviations from predefined quality standards. This continuous oversight helps you maintain data integrity for operational and analytics use.
Flexible Integration Options
Actian’s data quality solutions seamlessly integrate with various data sources and platforms. Whether you’re working with on-premises databases, cloud-based applications, or hybrid environments, our tools can connect, cleanse, and harmonize your data across all systems.
User-Friendly Interface and Dashboards
Our intuitive interface makes managing data quality tasks easy for users of all skill levels. Detailed reporting and dashboards provide clear visibility into data quality metrics, enabling you to track improvements and demonstrate compliance with data governance policies.
Transform Your Data into a Strategic Asset
Actian’s enhanced Data Quality solutions empower you to transform raw data into a strategic asset. Ensuring your data is accurate, reliable, and actionable can drive better business outcomes and gain a competitive edge.
Get Started Today
Don’t let poor data quality hold your business back. Discover how Actian’s enhanced Data Quality solutions can help you achieve your data management goals. Visit our Data Quality page to learn more and request a demo.
Stay Connected
Follow us on social media and subscribe to our newsletter for the latest updates, tips, and success stories about data quality and other Actian solutions.
Contemporary business demands dynamism and the convergence of customer data and marketing programs is essential for organizations looking to enhance customer engagement and at the same time, drive successful cross-channel marketing campaigns.
As organizations wrestle with challenging integration problems and try to use customer data across their different operations channels, we believe the Pretectum CMDM can offer organizations a transformative alternative solution. Our innovative platform supports federated customer data management, centralizes customer master data and empowers organizational business units in order to make better use of the full potential, of their customer data. They can also do this for more personalized and more effective cross-channel marketing strategies.
Customer Data and Marketing
The relationship between customer data and marketing has never been more pivotal for marketing campaign success. Organizations rarely stay focused on a single channel and need to expand reach, sometimes entering channels previously unexplored, these range from social media, and email to traditional advertising and e-commerce platforms. The need for a unified and accurate view of the customer profile is key to campaign cost-effectiveness, minimizing customer annoyance and intrusiveness. In the absence of a centralized customer master, these are all very real risks. Organizations that do not have a centralized customer master data management program, often find themselves wrestling with departmental or system data silos, inconsistency in customer profiles, and forfeited opportunities for cost-optimized targeted engagement.
Pretectum CMDM is a solution designed not just for customer data management but also as a lever for elevating improved cross-channel marketing and customer engagement.
The CMDM system’s ability to centralize and harmonize customer data sets the stage for a seamless, personalized, and effective marketing approach.
Key Features of Pretectum CMDM that might be compelling for for Cross-Channel Marketing include:
360-Degree Customer Views: Pretectum CMDM aggregates customer data from whichever sources you integrate, as touchpoints and channels of data that, when combined, provide a unified view of the customer profile. This 360-degree customer view is a key for crafting lower friction personalized marketing campaigns serving up the opportunity to personalize messaging that will resonate in alignment with individual preferences and customer tracked behaviors.
Data Enrichment and Cleansing: Ensuring the accuracy and completeness of customer data is imperative for successful marketing initiatives. The CMDM system’s integration with data enrichment and cleansing capabilities helps you to refine and enhance the customer profiles, this in turn enables marketers to work with higher-quality data for better targeted and more effective campaign execution.
Segmentation and Targeting: Pretectum CMDM empowers marketers with the opportunity to undertake advanced data segmentation. By categorizing customers based on attributes, behaviours, and preferences, organizations can tailor marketing messages to data-specific segments, enhancing the relevance and impact of their marketing outreach campaigns.
Personalization at Scale: Achieving personalization at scale is challenging for many organizations. Many try to do this with spreadsheets and small databases but quickly reach the limits of those technologies. Pretectum CMDM addresses this by providing the infrastructure for personalized marketing automation. From personalized email campaigns to targeted social media advertisements, the CMDM system ensures that each customer interaction is able to be made as relevant and meaningful as practically possible.
Cross-Channel Campaign Orchestration: Coordinating marketing efforts across multiple channels is complex when working with differently shaped data that purports to represent the same entities But coordination is essential for an integrated and low-friction customer experience.
The Pretectum CMDM supports cross-channel campaign orchestration, by ensuring consistency and coherence in unified customer data profiles and this can align with the organization’s cost-optimized branding intentions in support of diverse platforms.
The implementation task
Successful integration of any CMDM system into an organization’s cross-channel marketing strategy involves a strategic and phased approach. Some key steps need to be undertaken to implement CMDM to the greatest effect in cross-channel marketing:
Mapping Customer Journeys: Before implementation, you must understand and map how your customer data enters your systems, where it gets used and where it could be used further. This involves identifying data capture points, systems, and triggering events. You will also want to understand the kinds of customer data that you want to capture and the types of preferences, and potential pain points that would be advantageous to know about. Mapping the customer data acquisition and use journey provides insights into where and how CMDM-system-bound initiatives can be most impactful.
CMDM syndicates customer data to the functions that need it
Data Integration and Profiling: The Pretectum CMDM’s data integration capabilities are key to not just integrity but leverage for various customer data holding systems. Organizations that focus on integrating data from different sources and then profile it for quality, ensure that clean and enriched datasets are always in use for marketing activities.
Defining Customer Segments: Using the systems query building features, an organization can define customer segments based on demographics, behaviours, and preferences, in fact any data attribute that makes sense in segmentation These segments serve as the foundation for targeted marketing campaigns, allowing organizations to deliver content that resonates with specific audience segments.
Personalization Strategy: With CMDM in place, organizations can craft a robust personalization strategy. This involves tailoring content, offers, and messages to align with the preferences and behaviors of specific customer segments. Personalization fosters, in the customer, a sense of connection and relevance, with the potential to drive enhanced customer engagement.
Automation and Orchestration: Leveraging CMDM system automation capabilities, organizations can automate data extraction, preparation and syndication. From triggering personalized emails based on customer interactions to synchronizing data exchanges across platforms, automation ensures consistency and efficiency in marketing efforts.
Analytics and Optimization: Your implementation process should include setting up analytics to measure the effectiveness of cross-channel marketing campaigns. CMDM’s analytics tools provide insights into customer engagement, conversion rates, and other key metrics. This data, in turn, informs ongoing optimization efforts for continuous improvement.
Driving toward positive outcomes
A 360-degree view of customers benefits the organization and the customer, with the Pretectum CMDM your organization gets to deliver personalized and cohesive experiences across all enabled channels. This enhances customer satisfaction and loyalty, fostering long-term relationships.
Segmentation and personalization within the platform enhance the effectiveness of tightly targeted campaign management. Your targeted messaging is more likely to resonate with specific audience segments, leading to higher engagement rates and improved conversion outcomes.
Automation and orchestration features streamline the management of the customer data. Teams can save time and resources by having their data load and syndicate tasks automated thereby allowing them to focus more of their efforts on strategy and creativity.
Cross-channel marketing often involves multiple teams and platforms. The platform ensures consistent customer profile management in one place, preventing conflicts or different information across the different channels. This unified experience builds trust and reinforces confidence in your data governance practices both within the organization and
The CMDM system’s querying capabilities mean your teams can make data-driven decisions – this means optimized strategies. Insights derived from customer data can related to behaviour, sentiment, campaign participation or anything the business cares about. The data made us of, could be anything that enables marketers to refine their approaches and adapt to evolving market dynamics.
Pretectum recognizes several implementation styles for Master Data Management (MDM), each suited to different organizational needs and objectives. Here’s a brief overview of the popular styles:
Case Studies
Real-world Success with Customer Master Data Management
Case Study 1: ECommerce Webshop Platform
An e-commerce platform faced challenges in delivering personalized recommendations to its diverse customer base. Implementing a hub and spoke deployment of Customer Master Data Management allowed the platform to serve from a unified customer data master, enabling precise segmentation and personalized recommendations. This resulted in a significant increase in conversion rates and customer satisfaction.
Case Study 2: Global Brand Messaging Campaign Optimization
A global brand sought to streamline its cross-channel marketing efforts, spanning social media, email, and online advertising. The centralised Customer Master Data Management System forced a centralized approach to customer data, ensuring consistent customer profiles and supplementary personalization attributes across all the outbound marketing channels. The brand experienced improved engagement and a notable boost in campaign performance metrics.
As your organization pushes further to handle the ever-shifting business landscape, the role of a robust data management solution needs to be acknowledged. Pretectum’s CMDM platform addresses the challenges posed by disparate data sources and serves as an opportunity to catalyse the elevation of marketing strategies to new heights.
CMDM is not just about technology; it’s about creating meaningful connections with customers. Leveraging CMDM’s capabilities for centralized data management, organizations overcomes the limitations of traditional marketing approaches and tooling, delivering the opportunity for more personalized and impactful campaigns that resonate with hyper-refined audiences.
The journey toward enhanced customer engagement starts with the strategic adoption of Pretectum CMDM—a journey that promises not only marketing success but also lasting customer relationships in an increasingly aggressive and competitive landscape enabled by digital practices. Those who choose to do nothing will be left behind.
