The Most Important Metric for Sales Leaders in Subscription Businesses to Track and Use to Drive Revenue Growth

Subscription businesses that charge customers monthly for using their offerings are expanding rapidly in response to consumers’ and b-to-b buyers’ desire to try a product and walk away easily if they’re not achieving the expected value. However, the subscription-based business model puts tremendous pressure on sales organizations to facilitate a seamless transition between the buyer and customer journey and ensure customers are seeing the value they were promised during their evaluation. Many sales leaders are drowning in data and struggling to figure out what key metrics they should track to reveal their progress in growing subscription revenue. One powerful, simple metric enables sales leaders to pull the right levers to grow revenue: the ratio of customer lifetime value (LTV) to customer acquisition cost (CAC).

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Tescom

Tescom was one of the world's largest independent Software Quality Assurance companies and a pioneer in the industry. Founded in 1990, the company grew from an Israeli start-up into a global enterprise with 6 offices in 6 countries and 600 employees.

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Leading Data Warehouse Software Vendors That Enhance Analytics

Article | February 20, 2024

From amplified scalability to data transparency, top data warehouse software vendors boost businesses’ analysis capabilities. Learn how companies gain data agility with cloud data warehouse software. Contents 1. Understanding the Fundamentals of a Cloud Data Warehouse 2. Key Advantages of Data Warehousing for Businesses 3. Data Warehouse Strategies to Integrate and Analyze Data 4. Top Data Warehouse Software Vendors Offering Analytics Prowess 4.1 Dremio 4.2 Starburst 4.3 Firebolt 4.4 Lyftrondata 4.5 CData 4.6 Zap 4.7 Druid 4.8 TimeXtender 4.9 dbt Labs 4.10 Redwood Software 5. Final Thoughts 1. Understanding the Fundamentals of a Cloud Data Warehouse Representing a robust solution for handling and storing vast datasets, a cloud data warehouse offers businesses high convenience, quick data access, and remarkable flexibility. The data warehouse is essentially hosted in a cloud computing environment that empowers organizations to modify storage and processing as and when required without needing hardware upgrades. The growing prominence of the advanced setup today can notably be ascribed to its unparalleled accessibility, which allows companies to manage their data from any part of the world via internet access. Likewise, the warehouse’s core components include a centralized repository with distinct computing abilities, which enable scalable data retention and optimized resource allocation. Moreover, the data warehouse software boasts phenomenal data integration and handling tools, allowing businesses to link to diverse data sources, build datasets, set permissions, and run queries. It further boosts companies’ performances through parallel query processing, system backups, data encryption, etc. Moreover, supporting innovative analytics and insights, the cloud environment enables businesses to make data-backed decisions, augmenting their analytics prowess and driving high ROIs. 2. Key Advantages of Data Warehousing for Businesses From enhanced financial efficiencies to unmatched data access, data warehousing practices offer multiple benefits to businesses that allow sound decision-making and operational success. The chief merits of a data warehouse software include: Scalable Infrastructure for Unhindered Productivity With a cloud data warehouse, businesses can effortlessly adjust resources to handle diverse data processing requirements and scale up or down without bearing upgrade costs ascribed to traditional on-premises systems. By simply modifying payment schemes, companies can increase or decrease their computing power or storage, paying only for what they use. The unparalleled scalability further enables businesses to ensure undisturbed operations, faster innovations, and speed-to-market, benefiting from high agility in operations and augmenting business productivity. Quick Data Access Cloud data warehouses enhance data availability exponentially. They unify diverse data into a centralized repository, enabling users to access data quickly from anywhere in the world via web access. This flexible access, in turn, encourages remote work and collaboration endeavors, promoting real-time decision-making and augmenting operational responsiveness. Enhanced Visual Data Analysis With leading-edge business intelligence tools, advanced data warehouses support swift data processing, empowering businesses to extract crucial insights and analyze data in real time. The boosted analytics and data visualization abilities help companies make sound decisions and stay ahead of the curve. Cost-effectiveness A cloud data warehouse is a more economical substitute for on-premises systems. It works on a pay-as-you-go model, eliminating the need for physical hardware. Therefore, companies pay only for the computing resources and storage they use. Most data warehouse providers offer flexible pricing models, enabling businesses to maintain cost-efficient systems for data warehousing. Robust Security and Disaster Recovery A significant advantage of the cloud data warehouse is that it implements notable security measures like access controls, data encryption, and periodic security audits to protect sensitive business data and information from unauthorized breaches. It offers all-inclusive disaster recovery solutions, including scheduled backups and data replication across various geographic locations. Such features secure unhindered functions and data resilience in times of unanticipated interruptions. Performance Excellence Boasting features like distributed computing and parallel processing, cloud data warehouses guarantee elevated business performance as they quicken data analysis and query execution. From optimizing operations through automated tasks to eliminating the need for costly hardware investments, data warehouse setups on the cloud promise boosted productivity and efficiency. Impressive Integrations Cloud data platforms offer impressive integration capabilities, helping businesses connect with various data origins and third-party applications. Such incorporations expedite effective data reporting and analysis and augment businesses’ ability to extract valuable insights, showcasing the advantage of data warehouse integration and leading to better performance. Reliable Support and Ease-of-Use Established cloud data warehouse providers render round-the-clock support to businesses, ensuring uninterrupted operations and prompt problem resolutions. The warehouse, which comprises intuitive user interfaces, simplifies data access for non-technical teams, reducing dependency on IT staff and enabling the autonomous performance of data tasks, such as data reading, editing, and writing, through low-code or codeless platforms. 3. Data Warehouse Strategies to Integrate and Analyze Data Businesses employ various data warehousing techniques and strategies, like ETL or incremental loading, to maintain excellent quality in business processes and secure optimized performance. Here’s a breakdown of the prominent data warehouse strategies that companies leverage to derive crucial insights and gain remarkable scalability: Effective Metadata Categorization With strong metadata categorization, businesses can enhance data extraction and organization, simplifying data queries and augmenting accessibility. The centralized metadata management further documents data usage, lineage, and definitions, facilitating efficient data governance. The strategy helps stakeholders easily find and understand the required data, ensures compliance with data use regulations, and promotes data transparency and availability within the company. ETL Technique for Data Integration Through the ETL processes, i.e., extract, transform, load, data can be retrieved from diverse sources, transformed to match the warehouse schema, and loaded into the target system or data warehouse. This strategy is pivotal for incorporating data into the setup. It effectively streamlines data flows and allows businesses to conduct impactful data analysis. Data Segmentation and Indexing Also called ‘partitioning’, data segmentation involves dividing vast tables into easy-to-manage segments, maximizing data extraction, storage, and management. Each of these segments or ‘partitions’, therefore, holds a subset of the large table’s data, divided according to established criteria, like hash keys or value ranges. Indexing, likewise, allows speedy data retrieval based on specific criteria, as it creates systematized lists of the table’s fundamental values. Collectively, these promising strategies optimize query processing and database management, ensuring responsiveness with increasing data volumes. Data Modeling Strategy Data can be organized effectively for optimum querying by leveraging techniques like star or snowflake schemas. For instance, measurable data like sales can be stored in fact tables, while attributes like time or location are stored in dimension tables. Hence, the data modeling strategy makes it simpler to ask intricate questions about data, boosting performance and enabling easy-to-understand data analysis and exploration. Incremental Loading for Quicker Data Processing Another prominent data warehousing strategy is incremental loading. This strategy involves speeding up information handling processes by only making changes since the last data update. While the previous data remains unchanged, only new data gets added since the last load. Therefore, the approach reduces the time and resources needed for updating large datasets, promptly making the latest data available. It further strengthens real-time data analytics, where information changes regularly, allowing analysts to access the most recent information. Data Protection Implementing strong data security and governance is a critical data warehousing strategy. This strategy includes measures like role-based access control (RBAC), which limits who views the data. Such measures ensure high data integrity and safeguard it against unauthorized breaches or access. Furthermore, compliance with data laws and rules ensures that sensitive information is protected and best practices are adhered to. Query Enhancement The query optimization technique includes effectively organizing queries or questions so that maximum benefits may be derived from partitions, indexes, and materialized views that store precomputed results. The strategy benefits businesses by expediting query processing, allowing users to promptly get their answers, enhancing user experience, and facilitating complex analytical processes. Data Quality Assurance A prominent data warehousing strategy, data quality assurance relates to maintaining data accuracy, reliability, and conciseness. This involves various crucial measures, such as data examination, validation, and cleaning, which aim to mitigate errors that can influence analysis and decision-making. Such measures not only promise dependable findings but also result in optimal decisions. Data Presentation and Reporting A cloud data warehouse helps users make data-backed decisions and build their own dashboards and reports by converting complex data into valuable insights. Therefore, data presentation and reporting as a strategy help companies leverage business intelligence to ensure sound decisions and goal attainment. 4. Top Data Warehouse Software Vendors Offering Analytics Prowess Empowering businesses with spectacular analytics capabilities, the leading data warehouse software vendors ensure smarter decisions for companies. Here’s a list of the leading data warehouse software vendors promising robust analytics capabilities to businesses: 4.1 Dremio Dremio is an open, self-service SQL analytics platform provider that expedites time to insight, incorporating data lake adaptability with data warehouse performance. Relied on by leading organizations, the company empowers businesses by rendering seamless BI capabilities and allowing unified analytics across varied environments, like on-premises, hybrid, and cloud, for reduced costs. Furthermore, Dremio significantly boosts data management and ensures optimized data functions by streamlining data integration processes. The company facilitates the transformation of Hadoop workloads for sub-second queries by offering a Unified Lakehouse Platform. Dremio’s impressive features include its support for Apache Iceberg, which enables effective data handling with version control. It also can establish distributed data architectures, securing relevant data delivery across hybrid and cloud environments. From reducing ETL complexity to supporting AI endeavors with speedier data access, the company’s striking platform promises simplified data management and supports frictionless self-service analytics directly on data lakes via SQL Query Engine. 4.2 Starburst Starburst is a pioneering data warehouse software vendor that offers fast and adjustable data access solutions, leveraging the power of Trino, a premier SQL analytics engine. Renowned for its enterprise-grade reliability and promising a high-performance data lakehouse solution, the company aims to help customers overcome expensive, rigid, and slow data access constraints. The company’s platform effectively overcomes data silos problems, supports near real-time analytics, and adds to Trino’s capabilities by including tools to connect with diverse data sources, providing 24/7 support, and ensuring robust security. Starburst optimizes data management across traditional (on-premises) and cloud environments through its comprehensive products and features. For instance, the Starburst Galaxy multi-cloud platform allows users to leverage data warehouse-like performance and flexibility. Providing high-efficiency SQL queries directly on the data lake and built-in components for cluster management, data governance, etc., it allows businesses to optimize data analytics processes. Moreover, the Starburst Enterprise platform empowers organizations to link to any data source, employing advanced analytics tools for effective data analysis without data movement. The company notably supports AI workloads at vast scales and provides resilient query processing across multiple data sources, enhancing functional productivity. 4.3 Firebolt Founded in 2019, Firebolt has emerged as a global leader in the cloud data warehouse field and aims to broaden access to robust data analysis tools for more users. Promoting a culture of transparency, responsibility, and customer obsession, the company delivers a leading-edge cloud-based data warehouse platform that incorporates the swiftness of a query accelerator and the ability to handle large volumes of information like a traditional data warehouse. Imperatively, the company strives to empower businesses by streamlining data engineering, amplifying performance, and reducing costs. The company renders spectacular features, transforming cloud data warehousing with low-latency analytics for high concurrency and more agile query responses. With components like distributed multi-threading, data processing in arrays or batches, and an innovative optimizer for more effective execution plans, Firebolt facilitates agile and accurate data operations. The company further ensures ACID compliance, securing data integrity and reliability in database transactions. Additionally, it allows scaling both horizontally and vertically, enabling businesses to handle varying data volumes. Furthermore, the company’s platform encourages active collaborations and remains prepared for continuous integration and continuous deployment practices, supporting automated testing and frictionless updates to data pipelines. 4.4 Lyftrondata Providing a leading-edge data fabric platform, Lyftrondata empowers companies to convert data silos into valuable insights with promptness. Its self-service, agile data-delivery platform includes prominent offerings, such as Data Virtualization, a Managed Warehouse supported by Snowflake, a Data Pipeline, an API Data Hub, a Data Catalog, etc. The company facilitates effortless data integration across varied sources to expedite business insights, leveraging more than 300 connectors and further ensuring HIPAA compliance. Lyftrondata provides a unified environment, allowing users of different technical abilities to manage ETL processes seamlessly. From automating data retrieval to transforming complex data into actionable insights, the company’s forward-thinking platform eliminates manual coding. It employs flexible technologies, such as Kafka streaming and Apache Spark, to ensure easy automation for data integration and processing. The company boasts one of the best data warehouse solutions, empowering users to carry out large-scale analytics. It further leverages data virtualization to speed up live analytics and optimizes data governance, securing data privacy and adhering to enterprise-level security standards. 4.5 CData Standing at the forefront of data access and connectivity solutions, CData aims to simplify data connection processes for organizations and applications in today’s business world, where data is spread out in multiple locations. With real-time connectivity solutions, the company, hence, breaks down and converts data silos, allowing easy access, integration, and sharing of data. The company’s platform boosts businesses’ efficiency by standardizing interactions for operational consistency and optimizes performance at the socket level. It further supports two modes of integration, replicated and live data, and renders real-time data access to users across varied enterprise applications like ERP or CRM systems. CData’s remarkable offerings include CData Virtuality, which enables companies to get immediate access to over 200 data sources and leverage enterprise data virtualization through a consolidated interface to utilize data from varied origins. It further optimizes query performance with caching mechanisms. Another prominent tool, CData Sync, simplifies the creation and implementation of ETL or ELT data pipelines, facilitating the multi-environment deployment of data integration techniques and strategies, offering a cost-effective approach to data replication with its connector-based pricing model, and supporting a broad selection of data origins. Through secure, centralized data management, CData reduces data breaches, backs extensive integrations, and offers self-service connectivity, enhancing decision-making. 4.6 Zap Streamlining data handling for businesses, Zap efficiently integrates data from diverse sources, enriches it, and ensures that it is prepared for analysis. With its innovative Power BI analytic tool or patented Data Hub Analytics, Zap empowers companies to handle their functional and financial analytics requirements effortlessly. The company further leverages pre-defined data models enhanced with specialized knowledge to expedite the data preparation processes and attain actionable insights. The platform’s users can choose between Power BI or the proprietary Zap Data Hub Analytics for all-inclusive business and financial analysis, while Zap augments their capabilities through extensive training. Among the chief features of the company’s platform remain the advanced data modeling options, which include pre-built solutions for ERP systems and a codeless UI with SQL abilities. From boosting analytics with self-detected measures through semantic layer modeling to offering user-friendly dashboards and printable reports, Zap ensures exponential improvement in data and decision-making processes for businesses. 4.7 Druid Catalyzing powerful digital transformation, Druid is an ingenious technology company that specializes in data and analytics. The company employs advanced technologies like artificial intelligence, machine learning, and IoT to help businesses streamline and monitor their operational processes. The company’s services include IoT analytics, serverless frameworks, digital acceleration, and complex integrations. In addition to providing thorough data analysis, Druid further facilitates cloud migration for businesses, amplifying their agility, resource management, and operational prowess. It effectively supports incorporating and managing IoT solutions in the cloud and boasts commendable expertise in designing big data architecture. The company’s promising platform renders intelligent insights to businesses by integrating AI and ML technologies, ensuring boosted productivity and strategic growth. Importantly, Druid’s platform promises agile solutions for businesses, integrating via APIs, optimizing IT project lifecycles, and emphasizing digital advancement. 4.8 TimeXtender TimeXtender embraces innovation and sustainability to streamline data integration techniques and strategies for businesses. Leveraging metadata-driven solutions, it empowers companies to make strategically sound decisions, establishes benchmarks, and keeps a focus on both social and environmental impact. With its powerful and responsive data infrastructure, the company optimizes AI and analytics endeavors of businesses, enabling them to maximize the utmost potential of data. The core functionalities offered by TimeXtender include Master Data Management, which automates data correction, creates hierarchical relationships among critical data entities, and handles reference data, augmenting data governance capabilities. Its platform further ensures excellent data integration, supporting more than 250 pre-configured connectors, entailing incremental loading, and reducing processing times. Furthermore, with its premier Unified Metadata Framework, it facilitates automated code generation for consistent data workflows and supports a shared semantic layer for congruous understanding and usage of data across different departments. Not to mention, the company amplifies data accuracy with its striking data quality management tools for continual evaluation and easy troubleshooting. 4.9 dbt Labs A leader in the data development field, dbt Labs empowers data analysts to amplify data product delivery, ensuring preciseness and reliability in results. Trusted by industry leaders, the company strives to limit the use of dated tools to elevate data confidence and faster operations. From encouraging self-serve data analysis to superlative data oversight, the company stays resolute in its commitment to limit costs and optimize data models. With its robust end-to-end platform, dbt Cloud, it adopts an SQL-first workflow and empowers teams to handle intricate data tasks effortlessly, following best practices in data development and securing high-quality data delivery. From enabling the creation of modular analytics code to automated code testing, the advanced platform ensures result accuracy. It further boasts impressive documentation capabilities, promotes uniform metrics with dbt Semantic Layer, incorporates Git-enabled version control for uniformity, and provides robust security with advanced features. Moreover, offering dbt Explorer, it lets users visualize data transformation lineage, fostering trust, transparency, and reliability in data-backed decisions. 4.10 Redwood Software Redwood Software is a renowned automation platform provider that empowers businesses to automate their processes securely across on-premises and cloud environments. Focused on augmenting enterprise productivity, the company provides impressive cloud-native solutions, allowing integrations for ERP systems and incorporating REST web services via user-friendly API wizards. Serving as a cost-effective choice for businesses’ automation requirements, it provides a consumption-based or pay-as-you-go pricing model to mitigate financial risks and support budget management. The company’s leading-edge platform, RunMyJobs, offers comprehensive features for data warehouse management, allowing businesses to extract valuable insights from big data across departments. The platform ensures proactive data management by enabling automated data extraction from any database and enhancing live data pipeline monitoring via a unified interface. The platform further emphasizes security and optimizes resource management with dynamic load balancing. Such an approach allows strong data governance and promises precision and agility in data processes. 5. Final Thoughts Cloud data warehouses have transformed data processes, strengthening businesses with prompt data access, spectacular analytics capabilities, cost-effectiveness, and unmatched scalability. By partnering with the leading data warehouse software providers, companies today leverage advanced tools to attain crucial insights, drive intelligent decision-making, and achieve operational excellence. With the continual adoption of these advanced technologies, the future of cloud data warehouses is poised to witness continued incorporation of machine learning and artificial intelligence in cloud data warehousing processes. This would effectively automate data-handling tasks and augment predictive analytics for business implementation. The advancement would further witness an inclination toward containerized implementation, exhaustive data governance features, and deployment of microservices architecture. Such changes would equip companies with leading-edge tools to retrieve deeper data insights, enhance innovation, and maximize profitability.

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Cloud Security, Cloud App Management, Cloud Infrastructure Management

What Is Cloud-Native and Why Does it Matter for CI

Article | August 8, 2023

Continuous intelligence (CI) relies on the real-time analysis of streaming data to produce actionable insights in milliseconds to seconds. Such capabilities have applications throughout a business. In today’s dynamic marketplace, new CI applications that use data from various sources at any given time might be needed on very short notice.The challenge is how to have the flexibility to rapidly develop and deploy new CI applications to meet fast-changing business requirements. A common approach employed today is to use a dynamic architecture that delivers access to data, processing power, and analytics capabilities on demand. In the future, solutions also will likely incorporate artificial intelligence applications to complement the benefits of traditional analytics. Increasingly, cloud-native is the architecture of choice to build and deploy AI-embedded CI applications. A cloud-native approach offers benefits to both the business and developers. Cloud-native applications or services are loosely coupled with explicitly described dependencies.

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Cloud Infrastructure Management

Why Microsoft Should Spinoff Its Cloud Business

Article | February 29, 2024

Microsoft currently features old-school solutions that are growing relatively slowly (Office and Windows) and new cloud solutions that are growing tremendously (Dynamics 365 and Azure). If the company stays in its current form, Microsoft stock will keep steadily advancing. But because the company’s total top and bottom lines are never going to increase much more than 30% or 35% per year, the shares are never going to deliver truly huge returns. But that would change if the company was to spin off its rapidly growing cloud businesses. In such a scenario, the current owners of Microsoft stock would receive shares in a cutting edge cloud services company (let’s call it Azure), and shares in a company focused on providing old, mostly PC-based software to businesses and consumers.

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Intelligence Giant Upgrading its Cloud Technology

Article | February 10, 2020

With the huge amounts of data in all fields, a future in the cloud is imperative to help deal with this explosion of data, especially in the field of intelligence technology. This is the reason why the US Central Intelligence Agency is updating its cloud technology. The agency has recently released a draft request for proposal for its Commercial Cloud Enterprise contract.The C2E tens of billions contract will be a multi-award commercial cloud computing contract with a five-year base period and two five-year options for a period of performance of up to 15 years, according to nextgov.com.In a March 2019 presentation by the Directorate of Digital Innovation, a division of the CIA, the department outlined its vision for C2E. It would be broad and include infrastructure, platform and software cloud services supporting a broad range of users, with a variety of security clearances and a worldwide presence, as reported by techcrunch.com. The principal C2E Program objective is to acquire cloud computing services directly from commercial cloud service providers with established records for innovation and operational excellence in cloud service delivery for a large customer base,the department stated in the presentation.Apparently, the agency prefers to avoid all the attention that the Pentagon’s JEDI cloud procurement process got, and quietly go about its business.

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Spotlight

Tescom

Tescom was one of the world's largest independent Software Quality Assurance companies and a pioneer in the industry. Founded in 1990, the company grew from an Israeli start-up into a global enterprise with 6 offices in 6 countries and 600 employees.

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