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  • Unleashing the potential of Data Governance

    We all know that organizations are making huge investments in Artificial Intelligence and Machine learning (AI/ML). While that is being done data-driven enterprises ought to know that data is an asset as bad data would drive bad decisions and models. You need some form of Data Governance to drive effective business insights and innovation. Organizations today face several challenges related to data quality and poor data management. Fraud and security breaches are one of their topmost concerns and this is where the data needs to be managed and governed efficiently and Data governance comes into play. An organization meticulously takes care of its inventory, suppliers, finances, and employees. And that is the same way that enterprise data needs to be treated. What is Data Governance? Data Governance is a set of different rules, policies, processes, and standards that manage the availability, security, and quality of data within an enterprise system. Resolving data inconsistencies would be a task if not for data governance. For instance, if a customer’s address is different in person, inventory, and sales systems then it could mess up the data integration efforts. This will not only cause issues in data integrity but will also question the correctness of Business Intelligence (BI). It is said that there has never been an executive who has not received two reports with the same data but different numbers. Utilizing the data is easy if the data is correct and of great quality. For data to benefit the organization, data governance ensures the management of data in the correct way using quality material. You can ethically monetize the data of your organization by utilizing the capabilities of Data Governance. Data Governance and Data Management The accounts of an organization are governed by certain principles and policies that help in auditing and helps in effectively managing the financial assets of a company. Similar to what these principles and policies achieve for financial assets Data governance does for Data, Information or content assets. Now, data management is the data supply chain for a company. Data Governance and Data Management go hand in hand and should not exist without each other. Data management is the actual process or business function to develop and execute the plans and policies that enhance the value of data and information. To relate these two, we have the concept of governance ‘V’. The left side of the V represents governance – providing rules and policies to ensure the correct management of data and content life cycle, and the right represent the ‘hands on’ data management. The V also helps understand the separation of duties and responsibilities for both DG and DM. The DG area develops the rules, policies and procedures and the Information managers adhere to or implement those rules. At the convergence of ‘V’ are the activities that maintain the data life cycle for the organization. Roles and Responsibilities in DG As mentioned earlier Data Governance requires distinct delegation of roles and responsibilities. This is a key factor for Data Governance to survive and flourish. This includes: Data Stewards – Manage and maintain the data assets, and data quality while implementing the data policies. Data Owners – Responsible for the governance and stewardship of specific data domains and sets Data Governance Council – Executive body that sets the data governance policies, processes, and strategies. Data Custodians- Execute and impose data security measures and access controls. Development and Deployment of DG Once data governance is considered in an organization, it means the problem arising with data due to lack of governance is being acknowledged. Data Governance is an essential element of comprehensive Enterprise Information management (EIM). When EIM solutions like Business Intelligence (BI) or Master Data Management (MDM) are implemented then DG is considered. MDM and DG are always implemented together for the expansion of EIM. The delivery framework for Data governance has five key areas of work. Each phase has a set of activities that help enhance the DG Program. Also, it is represented as a cycle below as it is usually iterative. For developing and deploying a data governance framework that is robust the following activities are involved: Engagement: Clear vision of the necessity and scope of the DG initiative. Aligning it with the organizations strategic priorities and engaging all stake holders to support DG Strategy: A set of requirements built to achieve organization goals and initiatives. Architecture & Design: Design and description of new enterprise capabilities and operating models that are embraced by stakeholders Implementation: Plan to deploy and invest in data governance tools and technology. Ensure that data governance is made operational. Operation & Changes: Operational and embedded set of BAU capabilities that enhance any activity using data. Monitor DG activities and measure the KPIs to assess effectiveness of the implemented framework Use Cases of DG There is wide usage of Data governance across industries. This includes: Regulatory compliance assurance: A data governance framework is implemented to comply with regulations such as GDPR, CCPA, and HIPAA. Data Quality Improvement: Data governance processes help improve the reliability, accuracy, and consistency of data. Strengthen decision-making: Leveraging data governance to provide stakeholders with access to high-quality, trusted data for informed decision- making. DG Vendors and Tools Numerous tools are available in the market to support Data Governance, listing a few: Collibra: Data governance workflows and processes can be operationalized to deliver great quality and trusted data across your enterprise Informatica CDGC: Using Cloud Data Governance and Catalog you can discover, understand, trust, and access your data to improve decision-making and govern analytics IBM InfoSphere Information Governance Catalog: A web-based tool that helps deliver trusted and meaningful information through a governed data catalog The first change an organization needs to bring for data monetization success is to get its organization data literate. Data management should be as much a part of an organization as budgets and risk. Data governance and management are both market-driven and to achieve maximum benefit you need to have these capabilities placed effectively.


    In the bustling world of business, seamless data exchange is paramount. Enterprises rely on Electronic Data Interchange (EDI) to facilitate the exchange of business documents in a standardized format. EDI enables companies to exchange documents like purchase orders, invoices, and shipping notices with their trading partners efficiently and reliably. In this blog post, we'll delve into the significance of EDIs, introduce Talend as a powerful tool for EDI integration, and showcase how Pingahla's expertise has led to the development of an EDI accelerator, revolutionizing B2B integration processes. Electronic Data Interchange (EDI) is the electronic exchange of structured business data between different organizations. It replaces traditional paper-based methods with electronic formats, enabling seamless communication between trading partners. EDIs are widely used across various industries such as retail, manufacturing, healthcare, and logistics to automate transactions and streamline processes. By standardizing data formats and communication protocols, EDIs enhance operational efficiency, reduce errors, and accelerate business cycles. While specialized EDI tools exist in the market, many businesses leverage general-purpose integration platforms like Talend for their data integration needs. Talend stands out as a versatile tool due to its robust features, scalability, and ease of use. With Talend, organizations can integrate data from disparate sources, transform it according to business requirements, and load it into target systems seamlessly. Talend's graphical interface, extensive connectors, and built-in data quality features make it a preferred choice for complex integration projects, including EDI implementations. At Pingahla, we understand the challenges businesses face in integrating EDIs into their existing infrastructure. Leveraging our expertise in data architecture and pipeline development, we've created an innovative solution - the Pingahla EDI Accelerator. This accelerator is designed to streamline the integration of EDIs by harnessing the power of tools that organizations already love, such as Talend. With Pingahla's EDI Accelerator, businesses can ingest EDIs in real-time and efficiently process the data into specified databases or template table architectures. Our template tables are meticulously designed to accommodate various use cases for each EDI, ensuring flexibility and scalability. Whether you're dealing with purchase orders, invoices, or shipping notifications, our accelerator simplifies the integration process, allowing you to focus on core business activities. Now, let's take a closer look at the process flow involved in Pingahla's EDI Accelerator. Below is a diagram illustrating the workflow: EDI files are ingested in real time through Pingahla's EDI accelerator on Talend. Talend will detect the type of input EDI (EDI 850 in this example) and send it to a subjob that parses out all of the relevant information from the EDI 850 file into customer tables. The customer tables are example tables for the sake of showing our process. Once the data is in the tables, the customer ERP system is able to modify and use the EDI data or generate new data to be placed into the customer tables. These same tables feed into a second Talend job that generates an EDI 855. The file is sent elsewhere via FTP. The original EDI is conveniently achieved after the ingestion process is complete. By partnering with Pingahla, businesses can leverage our expertise to expedite their B2B integration efforts. Our EDI Accelerator empowers organizations to seamlessly exchange data with trading partners, enabling faster decision-making and improved operational efficiency. Say goodbye to tedious manual processes and embrace automated B2B integration with Pingahla. Ready to streamline your B2B integration processes? Get in touch with Pingahla today to learn more about our EDI Accelerator and how it can transform your business. With our proven track record and dedication to excellence, we're committed to helping you achieve success in the digital age. Unlock the full potential of your data with Pingahla. Connect with us today and embark on a journey towards seamless B2B integration!


    En el mundo de datos, los procesos de extracción, transformación y carga (ETL) y extracción, carga y transformación (ELT) son dos de los métodos más comunes para preparar datos para el análisis. Ambos procesos tienen sus propias ventajas y desventajas, y el método que mejor se adapte a sus necesidades dependerá de los requisitos específicos que la organización requiera. Los términos ELT y ETL son fundamentales, pero a menudo se confunden. Aunque ambos se refieren a procesos de manipulación y transferencia de datos, las diferencias en sus enfoques pueden tener un impacto significativo en cómo las organizaciones almacenan, procesan y utilizan sus datos. En este blog, explicaremos las diferencias clave entre ELT y ETL. ¿Qué es ETL? ETL son las siglas de Extract, Transform, Load (Extraer, Transformar, Cargar). Este proceso ha sido durante mucho tiempo el enfoque estándar para la integración de datos. Se trata de: Extraer (extract) datos de diversas fuentes. Transformar (transform) estos datos, a menudo en un área de almacenamiento intermedio, aplicando una serie de reglas o funciones para convertirlos al formato deseado para su posterior análisis. Cargar (load) los datos transformados en un sistema de destino, como un data warehouse. Ventajas del proceso ETL Control y calidad de datos: Al transformar los datos antes de cargarlos, el ETL permite una limpieza y un control de calidad más exhaustivos. Esto puede ser importante para garantizar que los datos se transformen de manera correcta y consistente. Rendimiento: Al realizar la transformación antes de la carga, el ETL puede reducir la carga en el sistema de destino. El proceso ETL puede proporcionar un mejor rendimiento para cargas masivas de datos. Esto se debe a que las operaciones de transformación se pueden realizar en paralelo con las operaciones de carga. Seguridad: Minimiza los riesgos de seguridad al procesar los datos antes de cargarlos, lo que es crucial cuando se manejan datos sensibles. Desafíos del proceso ETL Flexibilidad: Puede ser menos adaptable a los cambios en las fuentes de datos o en los esquemas de datos. Esto se debe a que las operaciones de transformación se deben realizar antes de que los datos se carguen en el almacén de datos o el sistema de análisis. Velocidad: El proceso puede ser más lento, ya que los datos deben ser transformados antes de ser cargados. Mayor costo: El proceso ETL puede ser más costoso que el proceso ELT. Esto se debe a que se requiere más hardware y software para realizar las operaciones de transformación. ¿Qué es ELT? ELT, por otro lado, implica cargar datos directamente en el sistema de destino y luego transformarlos dentro de este sistema. Este enfoque aprovecha la potencia computacional de los sistemas de almacenamiento modernos y es eficaz para grandes conjuntos de datos, especialmente en entornos basados en la nube. Ventajas del proceso ELT Eficiencia y Escalabilidad: ELT es más eficiente en el manejo de grandes volúmenes de datos, ofreciendo una mayor escalabilidad y velocidad gracias al procesamiento en sistemas modernos de almacenamiento, como los basados en la nube. Flexibilidad: Ofrece una mayor adaptabilidad a diferentes tipos y formatos de datos, lo que es esencial en entornos donde los datos cambian rápidamente o provienen de diversas fuentes. Desafíos del proceso ELT Gestión de la Calidad de Datos: Puede presentar desafíos en la calidad de los datos, ya que la transformación ocurre después de la carga. Dependencia Tecnológica: Requiere sistemas de almacenamiento avanzados con alta capacidad de procesamiento. Diferencias Clave ETL sigue un enfoque más tradicional. En este proceso, los datos se extraen primero de sus fuentes originales. Luego, antes de ser cargados en el almacén de datos, se transforman en un sistema intermedio. Esta transformación puede incluir limpieza, normalización, agregación, y otras operaciones necesarias para garantizar que los datos sean coherentes y de alta calidad. Este método es particularmente valioso en entornos donde la calidad y la precisión de los datos son críticas, como en el sector financiero o en entornos regulados donde se requiere un alto grado de conformidad y seguridad de datos. ELT, por otro lado, representa un cambio en el paradigma impulsado por la tecnología moderna de almacenamiento en la nube. Aquí, los datos se extraen y se cargan directamente en el sistema de destino. La transformación ocurre dentro de este sistema, aprovechando su capacidad de procesamiento robusta. Este enfoque es ideal en escenarios donde se manejan grandes volúmenes de datos, como en big data y análisis en tiempo real, ya que permite una mayor velocidad y flexibilidad en el procesamiento y análisis de los datos. ¿Cuál es el mejor? El mejor método para usted dependerá de sus requisitos específicos. Si necesita un mayor control sobre las transformaciones que se realizan en los datos, o si necesita realizar transformaciones complejas o personalizadas, entonces el proceso ETL puede ser la mejor opción para usted. Sin embargo, si necesita simplificar el proceso, reducir el costo o mejorar la velocidad para cargas masivas de datos, entonces el proceso ELT puede ser una mejor opción. Ejemplos Prácticos ETL en la Industria de la Salud: Para un hospital que integra datos de pacientes de múltiples fuentes, ETL es esencial para garantizar la precisión y la privacidad de los datos antes de que se almacenen en un sistema centralizado. ETL en la Industria Financiera: Utilizado para integrar y transformar datos financieros, asegurando precisión y cumplimiento normativo. ELT en Análisis de Redes Sociales: Una empresa de marketing digital utiliza ELT para procesar y analizar rápidamente grandes volúmenes de datos de comportamiento de usuarios en redes sociales, lo que le permite identificar tendencias en tiempo real. Conclusión La elección entre ELT y ETL debe basarse en factores como el volumen de datos, los requisitos específicos de procesamiento y la infraestructura tecnológica disponible., no es simplemente una cuestión de preferencia, sino que depende de factores como la infraestructura tecnológica, el tipo y volumen de datos, y las necesidades específicas del negocio. Comprender estas diferencias y seleccionar el enfoque adecuado es crucial para maximizar la eficiencia y efectividad de la gestión de datos en su organización. Mientras que ETL se centra en la calidad y el control de los datos antes de la carga, ELT aprovecha la potencia de procesamiento de los sistemas modernos para acelerar la integración y transformación de grandes volúmenes de datos.


    In the world of data, the processes of Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) are two of the most common methods for preparing data for analysis. Both processes have their advantages and disadvantages, and the method that best suits your needs will depend on the specific requirements your organization has. The terms ELT and ETL are fundamental but often confused. Although both refer to data manipulation and transfer processes, the differences in their approaches can have a significant impact on how organizations store, process, and use their data. In this blog, we will explain the key differences between ELT and ETL. What is ETL? ETL stands for Extract, Transform, Load. This process has long been the standard approach to data integration. It involves: Extracting data from various sources. Transforming these data, often in an intermediate storage area, by applying a series of rules or functions to convert them into the desired format for further analysis. Loading the transformed data into a target system, such as a data warehouse Advantages of ETL Data Control and Quality: By transforming the data before loading it, ETL allows for more thorough cleaning and quality control. This can be important to ensure that the data is transformed correctly and consistently. Performance: By performing the transformation before loading, ETL can reduce the load on the target system. The ETL process can provide better performance for large data loads. This is because transformation operations can be carried out in parallel with loading operations. Security: Minimizes security risks by processing the data before loading, which is crucial when handling sensitive data. Challenges of ETL Flexibility: It may be less adaptable to changes in data sources or data schemas. This is because the transformation operations must be performed before the data is loaded into the data warehouse or analysis system. Speed: The process can be slower, as the data must be transformed before being loaded. Higher Cost: The ETL process can be more expensive than the ELT process. This is because more hardware and software are required to perform the transformation operations. What is ELT? ELT, on the other hand, involves loading data directly into the target system and then transforming it within that system. This approach leverages the computational power of modern storage systems and is effective for large datasets, especially in cloud-based environments. Advantages of ELT Efficiency and Scalability: ELT is more efficient in handling large volumes of data, offering greater scalability and speed thanks to processing in modern storage systems, such as those based in the cloud. Flexibility: Offers greater adaptability to different types and formats of data, which is essential in environments where data changes rapidly or comes from diverse sources. Challenges of ELT Data Quality Management: This can present challenges in data quality, as the transformation occurs after loading. Technological Dependence: Requires advanced storage systems with high processing capacity. Key Differences ETL follows a more traditional approach. In this process, data is first extracted from its original sources. Then, before being loaded into the data warehouse, it is transformed in an intermediate system. This transformation can include cleaning, normalization, aggregation, and other operations necessary to ensure that the data is consistent and of high quality. This method is particularly valuable in environments where the quality and accuracy of data are critical, such as in the financial sector or in regulated environments where a high degree of data compliance and security is required. ELT, on the other hand, represents a paradigm shift driven by modern cloud storage technology. Here, data is extracted and loaded directly into the target system. The transformation occurs within this system, leveraging its robust processing capacity. This approach is ideal in scenarios where large volumes of data are handled, such as in big data and real-time analytics, as it allows for greater speed and flexibility in the processing and analysis of data. Which is Better? The best method for you will depend on your specific requirements. If you need more control over the transformations performed on the data, or if you need to perform complex or customized transformations, then the ETL process might be the best option for you. However, if you need to simplify the process, reduce costs, or improve speed for large data loads, then the ELT process might be a better choice. Practical Examples ETL in Healthcare Industry: For a hospital integrating patient data from multiple sources, ETL is essential to ensure the accuracy and privacy of data before it is stored in a centralized system. ETL in the Financial Industry: Used to integrate and transform financial data, ensuring accuracy and regulatory compliance. ELT in Social Media Analysis: A digital marketing company uses ELT to quickly process and analyze large volumes of social media user behavior data, enabling them to identify trends in real-time. Conclusion The choice between ELT and ETL should be based on factors such as data volume, specific processing requirements, and the available technological infrastructure. It is not simply a matter of preference, but depends on factors such as technological infrastructure, type and volume of data, and the specific needs of the business. Understanding these differences and selecting the right approach is crucial for maximizing the efficiency and effectiveness of data management in your organization. While ETL focuses on data quality and control before loading, ELT leverages the processing power of modern systems to accelerate the integration and transformation of large volumes of data.


    It is an application used for data processing. Cloud secure agents allow secure communication through the firewall between the Informatica Cloud and the Organization. HOW TO DOWNLOAD AND INSTALL CLOUD SECURE AGENT FOR WINDOWS Here are the steps to download the Cloud Secure Agent. 1. When you log in to the Informatica Cloud, you will see a window as the image below; select Administrator. 2. Once you select the Administrator option, from the left side menu choose Runtime Environments -> Download Secure Agent. 3. After clicking the Download Secure Agent button, you must select which type of operating system you will work with. In this case, we will install it on a Windows machine. Click on “Copy Install token” and paste it into a Notepad or any text editor; it will be used later. 4. Open the folder where the .exe file to install Secure Agent was saved. Right click and select Run as Administrator. 5. Click on Next. 6. Click Install 7. Once the Secure Agent has been installed, it will open a new window requesting the username and the installation token. Enter the username you used to access Informatica Cloud. Paste the installation token code and click on Register. 8. After clicking on Register, the Secure Agent will display a new window with the status; uploading all services takes a few minutes. 9. If you want to review if all services are running from Informatica Cloud, click on Administrator -> Runtime Environments -> Secure Agent Name (Machine) 10. In the Windows server, you can check if all services for Informatica Cloud are running. 11.If Administrator permissions and privileges are required, right-click on the Informatica Cloud in the Windows services and enter the username and password. 12.       Click Apply, and Ok. Restart the Secure Agent to apply the changes.

  • AI-Based Testing for Data Quality

    Role of AI in Data Quality Data quality is a crucial factor for any data-driven project, especially involving Machine Learning (ML) and Artificial Intelligence (AI). Data quality is referred to as the degree to which the data meets expectations. Poor data quality affects the performance, accuracy, and reliability of AI systems which can lead to inaccurate, unreliable & biased results of AI systems affecting the trustworthiness & value of AI systems. Traditional data quality practices are manual, time-consuming, and error-prone. They cannot handle increasing volume, variety, and velocity of data. Testing data quality is also a complex process. It involves aspects such as data validation, data cleaning, data profiling, etc. which require a lot of human effort and expertise. Therefore, testing data quality is a key challenge for data professionals. This is where AI can help us in testing data quality. Using AI and ML algorithms, it can automate and optimize various aspects of data quality assessment making the testing process smarter, faster, and more efficient. Problems that can be solved Some of the common problems that can be solved using AI-based testing for data quality are: Data validation Data validation is the process of checking whether the data conforms to the predefined rules, standards, and formats such as checking whether the data types, formats, ranges, and values are correct and consistent. AI-based testing can automate data validation by using ML models to learn the rules and patterns from the data and apply them to new or updated data. For example, an AI-based testing tool can automatically detect and flag missing values, duplicates, or invalid values in the data. Data profiling Data profiling is the process of analyzing the structure, content, and quality of the data. Data profiling helps us to understand the characteristics and behavior of the data, as well as identify potential issues or opportunities for improvement. For example, calculating the statistics, distributions, correlations, and dependencies of the data attributes. AI-based testing can automate data profiling by using ML models to extract and summarize relevant information from the data. For example, an AI-based testing tool can automatically generate descriptive statistics, visualizations, or reports on the data quality metrics. Data cleansing Data cleansing is the process of improving the quality of the data by removing or correcting errors, inconsistencies, anomalies, or duplicates in the data. Data cleansing helps us to enhance the accuracy, consistency, reliability, and completeness of the data. AI-based testing can automate data cleansing by using ML models to learn from existing or external data sources and apply appropriate transformations or corrections to the data. For example, an AI-based testing tool can automatically replace missing values based on predefined rules or learned patterns. Data Enrichment Data enrichment is the process of adding value to the data by augmenting or supplementing it with additional or relevant information from other sources. Data enrichment can help increase the richness, relevance, and usefulness of the data. For example, adding geolocation information based on postal codes or product recommendations based on purchase history. AI-based testing can automate data enrichment by using ML models to learn from existing or external data sources to generate or retrieve additional information for the data. For example, an AI-based testing tool can automatically add geolocation information based on postal codes by using a geocoding API or recommend products based on purchase history by using a collaborative filtering algorithm. Advantages of AI-based testing Some advantages of using AI for testing are: Automation AI can help in automating various tasks or processes related to data quality assessment or improvement. AI can help in validating, cleansing, profiling, or enriching the data by using ML models to learn from existing or external data sources and by applying appropriate actions or transformations. Optimization AI can help in optimizing various parameters or aspects related to data quality. AI can help in finding the optimal rules, formats, standards, or constraints by using ML models to learn from the existing or external data sources and apply the most suitable solutions for the data. This can improve the effectiveness, accuracy, and efficiency and enhance the quality of data. Insight AI can help in providing insights and feedback for data quality improvement. AI can help in generating descriptive statistics and visualizations to profile the structure, content, and quality of the data and provide insights on correlations, missing values, duplicates, etc. It can also help in identifying potential issues or scope for improvement in the data quality by providing recommendations for resolving or enhancing them. Drawbacks or Limitations of using AI Despite having its advantages, there are also some drawbacks or limitations that need to be considered. Some of the drawbacks are: Complexity Using AI requires a lot of technical knowledge to design, implement, and maintain the AI and ML models used for testing the data. It also requires a lot of computational resources and infrastructure to run and store the models and the data. Moreover, it may involve various issues such as privacy, security, accountability, and transparency for using AI and ML for testing. It can be a complex and challenging process that requires careful planning, execution, and management. Uncertainty The recommendations, assumptions, or predictions made by the AI and ML models may not always be accurate, reliable, or consistent in their outcomes. They may also not always be able to capture the dynamic or evolving nature of the data or the project requirements. Therefore, using AI for testing can bring some uncertainty or risk in the testing process that needs to be monitored and controlled. Dependency The quality, availability, and accessibility of the existing or external data sources used by the AI and ML models for learning plays a crucial role in testing. However, these data sources may not always be relevant, fair, or representative of the data or the project objectives. Moreover, they may not always be compatible or interoperable with the formats or standards used by the AI and ML models or the tools or platforms used for testing the data. Future of AI Testing Using AI for testing is a promising technique to overcome the challenges and limitations of traditional testing methods. It can automate and optimize various aspects of data quality by using AI and ML algorithms and applying appropriate actions or transformations to the data. It can also provide insights and feedback for data quality improvement by using descriptive statistics and visualizations. When it comes to testing the quality of data using AI, there are different methods and tools available. These include platforms that use AI to offer complete solutions and specific tools that use AI to address specific issues. Depending on the goals and requirements of the project, users can select the most appropriate approach or tool for their testing needs. The use of AI in testing presents a host of challenges and limitations that require careful implementation, evaluation, and maintenance of the AI and ML models. To ensure optimal performance, accuracy, reliability, and fairness, it is crucial to continually monitor and update these models. It should be noted, however, that AI cannot fully replace human judgment and intervention in guaranteeing data quality. Rather, it serves as a valuable tool to augment human efforts through automated assistance and guidance. AI-powered testing for data quality is a rapidly growing field with great potential for innovation. As technology continues to progress, so will the methods and tools for improving data quality through AI. The future of using AI for testing data quality is promising and full of possibilities.

  • Power BI vs Tableau: Who is the leader in 2023?

    Power BI (Microsoft) and Tableau (Salesforce) are both popular business intelligence (BI) tools used for data visualization and analysis. Every year, they are both positioned as leaders in the market by Gartner because of their significant adoption and widespread use across various industries. However, they have some differences in terms of features, functionality, and target user base. Here are some key distinctions between Power BI and Tableau: Ease of use: Power BI is generally considered to be more user-friendly, especially for beginners. It has a simpler interface, it’s easier to navigate and since it’s a Microsoft product, it integrates with many popular tools that are used in most companies, like Teams, Excel and PowerPoint. Tableau, on the other hand, has a steeper learning curve and can be more complex to use. Data connectors: Power BI has a wider range of data connectors and can connect to more data sources than Tableau. For example, it’s a lot easier to connect Power BI to Microsoft Dynamics 365 Business Central, a popular ERP software. On the other hand, since Tableau is part of the Salesforce group, it can access Salesforce data and reports more efficiently because it is not limited by the amount of data it can import from Salesforce. Power BI: Tableau: Pricing: Power BI has a lower entry-level price point, with a free version and a more affordable Pro version at 10$ per month per user or developer. Tableau, on the other hand, is more expensive and has a higher entry-level price point. The monthly Tableau subscription for developers is 70$ per month per developer and the license per viewer is 15$ per month per viewer. Power BI: Tableau: Integration: Both Power BI and Tableau offer integration capabilities with various data sources and other platforms. Power BI can be integrated and embedded in a wide range of applications, including Web Apps. It can also fully integrate with Microsoft Suite, such as Teams, PowerPoint, Excel and soon, Outlook. Tableau also allows users to embed dashboards on the Web and connect to a wide range of data sources, including databases, cloud storage platforms, spreadsheets, and web applications. Tableau and Salesforce seamlessly integrate with each other since Tableau is part of the Salesforce group. That enables productive data analytics that brings benefits to the users of the popular CRM. Tableau also offers many marketing and social media connectors that Power BI don’t, like Facebook Ads Power BI: Tableau: Customization: Tableau is generally considered to be more customizable and flexible than Power BI, because it has more visuals and advanced features for data analysis and visualization. However, Power BI allows you to download custom visuals created from the community of developers on the Power BI visuals marketplace. Power BI visuals marketplace: Power BI core visuals: Tableau: Collaboration: Both tools offer collaboration features, but now that Power BI has released its developer mode, it is considered to be more robust in terms of co-development, source control, Continuous Integration and Continuous Delivery (CI/CD). Ultimately, the choice between Power BI and Tableau depends on your specific needs, preferences and the other tools and software your company is already using. Power BI may be a better choice for businesses with limited budgets and less complex data analysis needs. Tableau may be a better choice for organizations with more complex data needs and a larger budget.

  • Data Observability and its Eminence

    As the importance of data takes more center stage, we have more and more businesses that claim to be data-driven. As companies increase their sources of data, their data storage, pipelines, and usage tend to grow at an enormous speed. With the growth of data, the chances of inaccuracy, errors, and data downtime grow as well. As we are much aware that the decision-making of a company springs from data and the unreliability of data is a pain point for every industry today. It is difficult to make decisions based on capricious data and hence eliminating instances of downtime, bad data, missing data, and the like is going to reach new heights by prioritizing data observability. What is Data Observability? For data engineers, the next crucial step to effectively manage any incident detection within their data pipelines is to establish data observability. In their organizations, data engineers devote half of their time to maintaining these pipelines due to frequent disruptions and breakdowns, which hinder them from effectively constructing data-driven products. This is where Data Observability comes into the picture. Data observability refers to an organization's comprehensive awareness of the well-being and condition of the data present in its systems. Ultimately, it all boils down to the ability to closely track and oversee a pipeline of data that is observed by someone. Let’s walk through the problems that data engineers face: Process quality Data quality or Data integrity Data lineage Process Quality First concern is if the data is moving, or the pipeline is operational. Speed in data processing could be core to the business. Data Integrity Once the functionality of the pipeline has been confirmed, the next step is to examine the activities occurring at the level of the data set. Imagine if data becomes vulnerable, misplaced, or corrupted. As an example, there may be a modification in the schema where we anticipate having 10 columns, but the new schema only has 9 columns. This could pose an issue as the data will have consequences for a downstream process that relies on the data set. Alternatively, if there are any modifications to the data, it will ultimately cause corruption of the subsequent data. Data Lineage This is about how things are connected to dependent pipelines and data sets downstream. The essence of data observability is captured in this statement! To put it simply, Data observability refers to the process of taking action to identify incidents in the original data source, data warehouse, or downstream at the product level. This allows the data engineers team to be promptly notified whenever there is a problem. The team would have the capability to rectify and proactively address the issue, thereby ensuring that it does not affect customers further down the line and, ultimately, avoid significant and expensive consequences for the business. The principles of data observability involve promptly identifying anomalies at their origin, resolving them quickly, understanding their exact location, and predicting their impact on subsequent individuals or processes. To proactively identify, resolve, and prevent irregularities in data, data observability tools utilize automated monitoring, root cause analysis, data lineage, and data health insights. Using this method leads to improved data pipelines, heightened team efficiency, strengthened data management strategies, and ultimately, increased customer contentment. Salient Features of Data Observability The purpose is to understand the essential changes in both organizational and technological perspectives to establish a data observability system that enables flexible data operations. To safeguard the practicality of data observability, it is vital to merge the following actions into its configuration. Monitoring A dashboard that allows a pragmatic viewpoint of your pipeline or system is referred to as monitoring. Alerting Notifications about predictable incidences and anomalies. Alerting permits you to detect complex conditions defined by a rule within the Logs, Infrastructure, Uptime, and APM apps. When a condition is met, the rule tracks it as an alert and responds by triggering one or more actions. Tracking Competence to establish and monitor specific occurrences. Comparison Observations made at different intervals will be compared and any abnormal alterations will be identified through alerts. Analysis Involuntary issue detection that regulates your pipeline and data state, referred to as analysis. Logging maintaining track of an occurrence using a standardized method to enable more rapid resolution. SLA tracking The characteristic of SLA Tracking involves measuring the cohesion of data quality and pipeline metadata to established standards. Data Observability - a future must-have The ability of data teams to be agile and make improvements to their products largely depends on their data observability. If there is no such system, a team's infrastructure or tools cannot be dependable as the identification of errors would take too long. If you do not invest in this important component of the DataOps framework, you will have reduced flexibility in creating new features and enhancements for your customers, resulting in a waste of money. Once Data observability is in place data teams will prevent time consumption in debugging and error fixing and there will be more businesses that will strive to be data driven.

  • Digital Marketers Juggling Act (Data, Solutions & More)

    In today's digital landscape, data has become the driving force behind successful marketing strategies. Digital marketers rely on a wealth of information about their ad campaigns, customers, and more to effectively target new and existing customers. Additionally, the need for advanced solutions and technology has become paramount to capitalize on this data-driven approach. In this blog post, we will delve into why digital marketers require more data and better solutions to achieve superior customer targeting and optimize their campaigns. Understanding Customers: In order to effectively target customers, digital marketers need comprehensive data to understand their target audience better. By gathering and analyzing data on customer demographics, behaviors, interests, and preferences, marketers can gain valuable insights into what motivates their customers. This enables them to tailor their campaigns with precision, delivering relevant and personalized messages that resonate with the audience. The more data available, the more refined and accurate the targeting becomes, leading to higher conversion rates and customer engagement. Refining Customer Segmentation: Data empowers digital marketers to segment their customer base into distinct groups based on various characteristics. With more data points at their disposal, marketers can create more granular and refined segments, resulting in more effective targeting. By identifying different customer personas and understanding their unique needs, preferences, and pain points, marketers can develop highly targeted campaigns that cater to each segment's specific requirements. The result is an increased likelihood of attracting and retaining customers within each segment. Optimizing Ad Campaigns: Accurate data is invaluable in optimizing digital advertising campaigns. By monitoring and analyzing campaign performance metrics such as click-through rates (CTRs), conversion rates, bounce rates, and return on ad spend (ROAS), marketers can gain insights into what works and what doesn't. Armed with this information, they can make data-driven decisions to refine their ad creatives, targeting parameters, and campaign strategies. This iterative process ensures that the marketing efforts are continuously optimized for maximum effectiveness and efficiency. Personalized Customer Experiences: Today's customers crave personalized experiences, and data plays a crucial role in delivering them. By collecting data on customer preferences, purchase history, browsing behavior, and interactions with the brand, digital marketers can create tailored experiences across various touchpoints. From personalized product recommendations to customized email campaigns and dynamic website content, data-driven personalization enhances customer satisfaction, engagement, and loyalty. Enhanced Customer Retention: Data-driven marketing goes beyond acquiring new customers; it also focuses on retaining existing ones. By analyzing customer data, marketers can identify patterns and signals that indicate potential churn or dissatisfaction. With this knowledge, they can implement targeted retention strategies such as personalized offers, loyalty programs, and proactive customer support. By leveraging data to proactively address customer needs and concerns, marketers can increase customer loyalty and lifetime value. Better Solutions and Technology: To harness the power of data effectively, digital marketers require robust solutions and technology. Advanced analytics tools, customer relationship management (CRM) systems, data management platforms (DMPs), and artificial intelligence (AI) technologies enable marketers to collect, analyze, and leverage data at scale. These solutions provide actionable insights, automate processes, and facilitate personalized interactions, empowering marketers to make data-driven decisions with agility and precision. Data has become an indispensable asset for digital marketers seeking to target new customers and optimize their campaigns. By collecting and analyzing data about their customers and ad campaigns, marketers gain valuable insights that inform their strategies and drive superior results. Additionally, access to better solutions and technology allows marketers to harness the full potential of data, delivering personalized experiences and enhancing customer retention. Embracing data-driven approaches and investing in advanced solutions is key to unlocking the true power of digital marketing in today's fast-paced and competitive. In addition, partnering with companies such as Pingahla provide the additional support system a digital marketer now needs to be successful.

  • Harnessing the Power & Benefits of a Multi-Cloud Strategy

    In today's digital landscape, cloud computing has emerged as a game-changer, empowering organizations to scale their operations, improve efficiency, and drive innovation. However, choosing a single cloud platform can be limiting, as each cloud vendor brings its own strengths and weaknesses. This is where a multi-cloud strategy enters the picture, enabling organizations to leverage the best of multiple cloud providers, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). In this blog post, we'll explore the importance of a multi-cloud strategy and its key benefits for organizations. Ensuring High Availability and Failover: One of the primary advantages of a multi-cloud strategy is the ability to achieve high availability and failover capabilities. Organizations can mitigate the risk of service disruptions and downtime by distributing workloads across multiple cloud platforms. Should one cloud provider experience an outage or performance issues, the workload seamlessly transitions to another provider, ensuring uninterrupted service delivery to end-users. AWS, Azure, and GCP all offer robust infrastructure and redundancy features, making them ideal for building a resilient multi-cloud architecture. Optimizing Data Load Latency: Geographical proximity plays a crucial role in determining data load latency. With a multi-cloud approach, organizations can strategically distribute their data across cloud providers' global data centers, placing it closer to end-users or specific regions. This reduces latency and enhances user experience, particularly for latency-sensitive applications. AWS, Azure, and GCP have extensive global footprints, allowing organizations to select data center locations that align with their target audience or compliance requirements. Mitigating Vendor Lock-In Risks: By adopting a multi-cloud strategy, organizations can reduce their dependence on a single cloud vendor, mitigating the risks associated with vendor lock-in. This freedom empowers organizations to negotiate better pricing, leverage competitive advantages, and retain technological flexibility. For instance, AWS offers a wide range of services and strong integration with other Amazon offerings, Azure brings seamless integration with Microsoft technologies, and GCP offers cutting-edge machine-learning capabilities. By leveraging each vendor's strengths, organizations can design a best-of-breed architecture tailored to their specific needs. Enhancing Disaster Recovery Capabilities: Disaster recovery is a critical aspect of business continuity planning. A multi-cloud strategy allows organizations to design robust disaster recovery architectures by utilizing different cloud providers. In the event of a disaster, data, and applications can be replicated and stored across multiple cloud platforms, ensuring rapid recovery and minimal data loss. AWS, Azure, and GCP offer comprehensive disaster recovery services, including backup, replication, and failover mechanisms, making them ideal multi-cloud disaster recovery strategy components. Leveraging Specialized Services and Innovation: Each cloud provider brings unique services, capabilities, and innovation to the table. By adopting a multi-cloud strategy, organizations can tap into a wide array of specialized offerings from AWS, Azure, and GCP. For example, AWS excels in scalable computing and storage services, Azure offers seamless integration with Microsoft's extensive ecosystem, and GCP stands out with its data analytics and machine learning tools. Leveraging these strengths empowers organizations to drive innovation, meet specific business requirements, and gain a competitive edge in their respective industries. In a rapidly evolving digital landscape, a multi-cloud strategy has become essential for organizations seeking to optimize their cloud investments, enhance resilience, and unlock the full potential of cloud computing. By harnessing the strengths of AWS, Azure, and GCP, organizations can ensure high availability, reduce latency, mitigate vendor lock-in risks, enhance disaster recovery capabilities, and leverage specialized services for innovation.

  • Unlocking the Future of Data Management: Migrating from Legacy Informatica to IDMC

    In today's data-driven world, businesses rely heavily on robust data management solutions to extract actionable insights and drive strategic decision-making. Legacy systems, such as Informatica's PowerCenter, Metadata Manager, and others, have served organizations well over the years. However, the time has come to embrace the next generation of data management platforms, such as Informatica's Intelligent Data Management Cloud (IDMC). This blog post explores why current legacy Informatica customers should consider the transition and highlights the benefits of adopting IDMC. Comprehensive Solution Suite: Informatica's IDMC combines a comprehensive suite of data management products under one unified platform. With IDMC, customers gain access to a range of powerful tools, including PowerExchange (PWX), Data Quality (DQ), Data Transformation (B2B DT), Big Data Management (BDM), Data Integration HUB (DIH), and B2B Data Exchange (DX). This integration eliminates the need to purchase these products separately, streamlining the overall data management ecosystem. Enhanced Scalability and Flexibility: As organizations grow and their data management needs evolve, scalability becomes paramount. IDMC offers the scalability required to handle the ever-increasing volumes of data generated by modern businesses. Moreover, its cloud-based architecture allows for elastic scaling, enabling organizations to scale up or down based on demand. This flexibility ensures that businesses can adapt to changing requirements efficiently and cost-effectively. Next-Level Data Quality and Governance: Maintaining data quality and ensuring regulatory compliance is critical for any organization. IDMC leverages Informatica's advanced data quality and governance capabilities, empowering businesses to cleanse, standardize, and enrich their data easily. The platform's comprehensive data governance features, built-in metadata management, and business glossary capabilities enable organizations to establish a solid foundation for accurate and trusted data. Streamlined Operations and Simplified Management: One of the primary advantages of IDMC is its unified and user-friendly interface, allowing for simplified data management and operations. Organizations can leverage a centralized platform to design, develop, deploy, and monitor their data integration workflows and transformations. IDMC's streamlined management and monitoring capabilities lead to increased productivity and operational efficiency. Harnessing the Power of Big Data: In the era of big data, organizations must effectively handle vast amounts of diverse data sources. IDMC's Big Data Management (BDM) capabilities empower businesses to leverage the full potential of big data by seamlessly integrating and processing data from various sources. With IDMC, organizations can unlock insights from structured, semi-structured, and unstructured data, enabling them to make data-driven decisions confidently. Conclusion: In today's rapidly evolving data landscape, legacy data management systems can become a bottleneck for organizations striving to harness the power of their data. By migrating to Informatica's Intelligent Data Management Cloud (IDMC), current legacy Informatica customers can unlock many benefits. With its comprehensive suite of data management products, enhanced scalability, robust data quality and governance features, simplified management interface, and the ability to harness the power of big data, IDMC is poised to drive organizations toward a future of optimized data management and improved business outcomes. So, suppose you are still relying on legacy Informatica solutions. In that case, it's time to consider making the leap to IDMC and embark on a transformative journey toward intelligent and comprehensive data management such as IDMC.

  • How Pingahla implemented Informatica B2B Gateway for EDI at a client

    Implementing Informatica B2B Gateway for EDI requires several steps and specialized skills and expertise with the technology. Here is a general high-level overview of the process: Define the requirements: Determine the business requirements for your EDI implementation. This includes identifying the types of documents you will exchange, the trading partners you will work with, and the specific data elements that must be included in each document. Configure the B2B Gateway: Install and configure the Informatica B2B Gateway software according to the specifications of your environment. This includes configuring the communication protocols, security settings, and other system parameters. Create and map EDI documents: This is where the majority of heavy-lifting in development is done. Use the B2B Gateway tools to create the EDI documents you will be exchanging with your trading partners. You will need to build mappings to map the data elements from your internal systems to the EDI document format and vice versa. Test the EDI transactions: Work with your trading partners to test the EDI transactions. This includes sending and receiving test documents, validating the data, and resolving any issues that arise. Deploy the system: Once testing is complete, you can deploy the B2B Gateway system for production use. Monitor and maintain the system: Monitor the system to ensure that it is operating correctly and resolve any issues that arise. You may need to update the configuration or make changes to the mapping as your business requirements change.

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