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Gawad Kalasag Management and Information Systems Hand Over
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Strengthening Disaster Resilience: Catholic Relief Services hands over Gawad Kalasag Management and Information System to Office of Civil Defense, Powered by Exist Software Labs
Strengthening Disaster Resilience: Catholic Relief Services hands over Gawad Kalasag Management and Information System to Office of Civil Defense, Powered by Exist Software Labs 1300 972 Exist Software Labs

Exist present at the Office of Civil Defense last Nov. 21, 2023

Gawad Kalasag Managemement and Information Systems Hand Over

 In photo (L-R): Exist Director, Tech Services Dennis de Vera, Exist VP-Operations Jonas Lim, OCD Asec. Bernardo Rafaelito, OCD OIC-PDPS Dir. Harry Barber, USAID-BHA Humanitarian Assistance Officer Rachelle Gallagher, CRS Country Head Jonas Tetangco and CRS Marcelle Rubis.

QUEZON CITY – The Catholic Relief Services together with USAID formally handed over the Gawad Kalasag Management and Information System (GKMIS) – Web Application Development to the Office of Civil Defense (OCD) last Tuesday, 21 November 2023. 

The GKMIS aims to help the OCD consolidate data from LGUs into one platform to help implement their disaster risk reduction programs, projects and concerns efficiently. The platform enables the OCD, in particular the National GK Secretariat, the Validation and Selection Committee, and the National GK Committee to encode evaluation scores and generate assessment results in a data visualization dashboard. The National GK Secretariat is expected to use the system for database-related tasks. 

The GKMIS web application will be hosted on a cloud platform, utilizing Amazon Web Services (AWS) as the designated service provider and boasts of high availability and scalability. It features a public page and navigation to different modules and has a preview map of the Philippines with data from Local Disaster Risk Reduction and Management Office (LDRRMO) Assessment and tables and charts from the LDRRMF and LDRRMP data. This groundbreaking project has been made possible in collaboration with Exist Software Labs and marks a significant stride towards enhancing public services. 


Ceremonial Handover

The handover took place at the Office of Civil Defense in Quezon City, Philippines. 

CRS Country Representative, Mr. Jonas Tetangco together with USAID’s Humanitarian Assistance Officer, Ms. Rachelle Gallagher and OCD’s Asec. Bernardo Rafaelito and Dir. Jose Harry Barber, together with Exist Software Labs Vice President for Technology, Mr. Jonas Lim and Tech Services Director, Mr. Dennis De Vera, came together to symbolize the transfer of responsibilities and expertise. The handover featured speeches from key stakeholders, expressing gratitude for partnership and emphasizing the shared commitment to strengthening the nation’s resilience against natural disasters.

Gawad Kalasag Managemement and Information Systems Hand Over

About Gawad KALASAG (GK) Search for Excellence in Disaster Risk Reduction and Management and Humanitarian Assistance

GK is the nation’s highest honor for excellence in disaster risk reduction and management. It has played a pivotal role in highlighting and elevating the standard of disaster resilience programs. This prestigious prize gains a technological edge from the use of a customized management system, which streamlines the collection, processing and distribution of information.

Role of Exist Software Labs

Gawad Kalasag Managemement and Information Systems Hand Over The Office of Civil Defense chose Exist Software Labs Inc for its capability to deliver all features in terms of their reference and cost efficiency.  Exist will implement a comprehensive management information system solution designed to streamline various aspects of governance, from citizen services to internal operations. The project encompasses a range of innovative technologies that are poised to elevate the overall efficiency and effectiveness of the local administration.

Moving Forward

The event closed with a significant handshake from representatives as a new era in disaster resilience technology is being brought in with the GKMIS. This collaboration of technology innovation, political control, and humanitarian commitment paves the way for a time when communities are better equipped to deal with natural calamities.

The GKMIS Web App Database System powered by Exist Software Labs will provide the infrastructure needed to store, manage, organize and utilize data effectively in the context of disaster risk reduction and management. Start your data management journey with us and become a data-driven organization.

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A Complete Guide to Data Management– Best Practices and Strategies in 2023

Data warehouse, big data and analytics, big data management, data management, data management philippines, data solutions philippines, Java, Java developer Philippines, master data management philippines
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Reasons why your business needs a Data Warehouse
Reasons why your business needs a Data Warehouse 650 486 Exist Software Labs

Data Warehouse and E-commerce

Imagine a large e-commerce company that has been operating for several years. Over time, the company has accumulated vast amounts of data from various sources such as customer transactions, website interactions, inventory management systems, and marketing campaigns. These data sources are spread across multiple databases, applications, and departments within the company.

As the company grows, the management team realizes that they need a unified and centralized view of their data to gain meaningful insights and make data-driven decisions. They also face challenges in extracting, transforming, and loading (ETL) data from different sources, which are crucial for performing complex analytics and generating accurate reports.

Furthermore, their current systems lack the scalability and performance required to handle the increasing volume and complexity of data. Queries on their operational databases are becoming slower and affecting the overall user experience. The company recognizes the need for a solution that can handle large data volumes, support complex queries, and provide fast response times.

At what stage should you be considering a Data Warehouse?  As per described in our data maturity analysis.


In this scenario, the organization may already be considering a Data Warehouse or realizing the need for one. A Data Warehouse can act as a central repository for all their disparate data sources, enabling them to integrate, consolidate, and organize the data in a structured and optimized manner. With a Data Warehouse, they can design efficient ETL processes, transform and cleanse data, and store it in a format suitable for analytics and reporting. 

So if you are at this stage you may already be considering or need a Data Warehouse.  

There are several compelling reasons why a business can benefit from implementing a data warehouse, particularly with Microsoft Azure. Here are some key reasons:

  1. Centralized Data Storage: A data warehouse provides a centralized repository for storing large volumes of structured and unstructured data from various sources. It enables organizations to consolidate data from disparate systems, databases, and applications into a single location, making it easier to manage and analyze data.
  2. Improved Data Accessibility: By using Microsoft Azure, a data warehouse can be hosted in the cloud, offering accessibility from anywhere at any time. This enables employees to access and analyze data using familiar tools and interfaces, fostering collaboration and data-driven decision-making across departments and locations.
  3. Scalability and Performance: Azure provides scalability features, allowing the data warehouse to grow and handle increasing data volumes effortlessly. With Azure’s elastic scaling capabilities, businesses can adjust the computing resources allocated to the data warehouse, ensuring optimal performance and response times, even with large datasets and complex queries.
  4. Advanced Analytics and Reporting: A data warehouse provides a solid foundation for advanced analytics and reporting. By integrating Azure services like Azure Synapse Analytics, Power BI, and Azure Machine Learning, businesses can gain powerful insights from their data. They can perform complex data transformations, run sophisticated analytics, build interactive dashboards, and develop machine learning models to drive data-based decision-making.
  5. Data Integration and Transformation: A data warehouse offers robust data integration and transformation capabilities. With Azure Data Factory, businesses can efficiently extract data from various sources, transform it into a consistent format, and load it into the data warehouse. This enables organizations to combine data from different systems, ensuring data consistency and integrity for analysis and reporting purposes.
  6. Data Security and Compliance: Azure provides robust security measures to protect data in transit and at rest. It offers encryption, identity, and access management, and compliance certifications to meet industry-specific regulations. Implementing a data warehouse on Azure ensures data security and compliance with privacy laws, enhancing trust and mitigating potential risks.
  7. Cost Optimization: Azure’s pay-as-you-go model allows businesses to optimize costs by scaling resources based on demand. Data warehousing on Azure eliminates the need for upfront hardware investments, reduces maintenance costs, and enables organizations to pay only for the storage and computing resources they use.
  8. Real-time Data Insights: Azure provides real-time data processing capabilities through services like Azure Stream Analytics and Azure Event Hubs. By integrating these services with the data warehouse, businesses can gain timely insights from streaming data, enabling real-time decision-making and enhancing operational efficiency.

In summary, implementing a data warehouse with Microsoft Azure offers centralized data storage, improved accessibility, scalability, advanced analytics capabilities, data integration, security, cost optimization, and real-time insights. These benefits empower businesses to unlock the full potential of their data, make informed decisions, and gain a competitive edge in today’s data-driven world. 

Like one of our clients, the Universities Project. By leveraging Microsoft Azure Synapse, Universities can build a powerful student information system that centralizes data storage, improves accessibility, enables scalability, offers advanced analytics capabilities, integrates data from various sources, ensures security, optimizes costs and provides real-time insights. This comprehensive solution enhances administrative efficiency, supports data-driven decision-making, and fosters student success initiatives.

Exist Data Solutions offers custom project-based development services, tailored fit data solutions, and consulting services. Exist can assist you with all your data management needs. Click here to learn more about Exist Data Solutions.

A Complete Guide to Data Management: Best Practices and Strategies in 2023. Java, Java Developer Philippines
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A Complete Guide to Data Management: Best Practices and Strategies in 2023
A Complete Guide to Data Management: Best Practices and Strategies in 2023 650 486 Exist Software Labs

Data management is a critical aspect of modern businesses and organizations. With the exponential growth of data in today’s digital world, effectively managing and utilizing data has become a crucial factor for success.

However, DM can be complex, involving various processes and strategies to ensure data accuracy, integrity, security, and usability.

Need help with Data Management? Click here to talk to our specialist.

In this comprehensive guide, we will delve into the world of data management, covering best practices, strategies, and tools to help you harness the power of data and make informed decisions.

In today’s digital world, data has become one of the most valuable assets for businesses and organizations. Proper DM is essential for ensuring data accuracy, integrity, confidentiality, and availability, while also enabling organizations to make informed decisions and gain insights from their data.

We will cover the fundamentals of DM, including the key concepts, best practices, and challenges involved in handling data effectively.

Whether you’re a business owner, data professional, or simply interested in learning more about data management, this guide will provide you with a solid foundation to understand the importance of data management and how to implement it in your organization.

Key Concepts of Data Management: 

Data management encompasses a wide range of activities, from data collection and storage to data analysis and interpretation. Here are some key concepts that form the foundation of data management:

  1. Data Governance: Data governance involves defining policies, standards, and procedures for managing data across an organization. It includes establishing roles and responsibilities for data management, ensuring data quality, and complying with regulatory requirements.
  2. Data Lifecycle: The data lifecycle consists of different stages, including data creation, data capture, data storage, data processing, data analysis, and data archiving or deletion. Understanding the data lifecycle is critical for effectively managing data at each stage of its life.
  3. Data Quality: Data quality refers to the accuracy, completeness, consistency, and reliability of data. Ensuring data quality is crucial for making informed decisions based on accurate and reliable data.
  4. Data Security: Data security involves protecting data from unauthorized access, alteration, or destruction. Data breaches can have severe consequences, including financial loss, damage to reputation, and legal liabilities. Implementing proper data security measures is essential to safeguard sensitive data.

Best Practices for Effective Data Management

Implementing best practices can help organizations ensure that their data is managed effectively. Here are some key best practices for DM:

  1. Define Data Management Policies: Establishing clear DM policies, including data governance policies, data quality policies, and data security policies, is critical for guiding data-related activities in an organization. Policies should be documented, communicated, and enforced consistently. 
  2. Create a Data Inventory: Creating a data inventory helps organizations identify and catalog their data assets, including data sources, data types, data owners, and data usage. This helps in understanding the scope of DM and enables effective data governance. 
  3. Implement Data Quality Controls: Implementing data quality controls, such as data validation, data profiling, and data cleansing, helps ensure that data is accurate, complete, and consistent. Data quality controls should be applied at different stages of the data lifecycle to maintain data integrity. 
  4. Secure Data Access: Implementing proper data access controls, such as role-based access controls (RBAC) and data encryption, helps ensure that only authorized users have access to data. Regularly review and audit data access permissions to prevent unauthorized access. 
  5. Backup and Disaster Recovery: Implementing regular data backup and disaster recovery procedures is essential to protect data from loss due to hardware failure, software malfunction, natural disasters, or other unforeseen events. Test and validate backup and disaster recovery procedures to ensure data recoverability.

Challenges in Data Management

Data management is not without its challenges. Some of the common challenges in DM include:

  1. Data Complexity: Data comes in various formats, structures, and volumes, making it challenging to manage and analyze effectively. Organizations must deal with different data sources, data integration, and data transformation to ensure data consistency and accuracy. 
  2. Data Privacy and Compliance: Data privacy regulations, such as GDPR and CCPA, impose strict requirements on organizations to protect personal data and comply.

As we reach the middle of 2023, DM continues to be a critical aspect of any organization’s success. With the increasing importance of data in decision-making, it is essential to have proper data management practices and strategies in place.

Furthermore, organizations should develop a DM strategy that aligns with their business goals and objectives. This strategy should include data storage, data access, data sharing, and data retention policies.

In conclusion, with the increasing importance of data, organizations must prioritize data management best practices and strategies to derive value from their data and gain a competitive advantage in their industry.

Big Data, Data Solutions, Healthcare, Retail
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Trends and Industries: How Data Solutions upend existing sectors to new heights in 2023?
Trends and Industries: How Data Solutions upend existing sectors to new heights in 2023? 650 486 Exist Software Labs

The defining era of data is currently upon us. Business model threats and economic shocks are common. Power is changing wherever you look, including in the market, our technological infrastructure, and the interactions between companies and customers. Change and disruption have become the norm. Data Solutions have been useful in innovating the industry.

Data-savvy businesses are well-positioned to triumph in a winner-take-all market. In the past two years, the distance between analytics leaders and laggards has increased. Higher revenues and profitability can be found in companies that have undergone digital transformation, embraced innovation and agility, and developed a data-fluent culture. Those who were late to the game and who still adhere to antiquated tech stacks are struggling, if they are even still in operation.

So, when you create your data and analytics goals for 2023, these are the key trends to help you stay one step ahead of your competitors.

Healthcare

Data Analytics and Data Solutions can be used to improve patient outcomes, streamline clinical trial processes, and reduce healthcare costs. 

Some specific examples of how Analytics is being used in healthcare include:

  1. Improving patient outcomes: Analytics can be used to identify patterns and trends in patient data that can help healthcare providers make more informed decisions about treatment plans. For example, data from electronic health records (EHRs) can be analyzed to identify risk factors for certain conditions, such as heart disease or diabetes, and to determine the most effective treatments for those conditions.
  2. Streamlining clinical trial processes: Data Analytics can be used to improve the efficiency of clinical trials by allowing researchers to identify suitable candidates more quickly and by helping them to track the progress of trials more closely.
  3. Reducing healthcare costs: Analytics can be used to identify inefficiencies in healthcare systems and to help providers implement cost-saving measures. For example, data analysis can be used to identify patterns of overutilization or unnecessary testing, and to develop strategies for reducing these costs.

Financial services

Data Analytics can be used to detect fraud, assess risk, and personalized financial products and services. 

Some specific examples of how Data Analytics is being used in the financial industry include:

  1. Fraud Detection: Data Analytics can be used to identify patterns and anomalies in financial transactions that may indicate fraudulent activity. This can help financial institutions to prevent losses due to fraud and to protect their customers.
  2. Risk Assessment: Analytics can be used to assess the risk associated with various financial products and services. For example, data analysis can be used to assess the creditworthiness of borrowers or to identify potential risks in investment portfolios.
  3. Personalizing financial products and services: Analytics can be used to gain a deeper understanding of individual customers and to personalize financial products and services accordingly. For example, data analysis can be used to identify the financial needs and preferences of individual customers, and to offer customized financial products and services that are tailored to those needs.

Retail

Retail companies can use Data Analytics to optimize pricing, understand customer behavior, and personalize marketing efforts. 

Some specific examples of how Data Analytics is being used in the retail industry include:

  1. Prizing Optimization: Retail companies can use Data Analytics to identify patterns in customer behavior and to optimize their pricing strategies accordingly. For example, data analysis can determine the most effective price points for different products and identify opportunities for dynamic pricing (i.e., adjusting prices in real time based on demand).
  2. Understanding customer behavior: Analytics can be used to gain a deeper understanding of customer behavior and preferences. This can help retailers to make more informed decisions about the products and services they offer, and to identify opportunities for cross-selling and upselling.
  3. Personalizing marketing efforts: Analytics can be used to deliver more personalized and targeted marketing efforts to customers. For example, data analysis can be used to identify customer segments with similar characteristics and to develop customized marketing campaigns for each segment.
  4. Cost Reduction: Being able to have a JIT (Just in Time) procurement and storage of items which in turn increases/optimizes warehouse capacity and reduces spoilage, and improves logistics.

Manufacturing

Data Analytics can be used to optimize supply chain management, improve production efficiency, and reduce costs. 

Some specific examples of how Data Analytics is being used in the manufacturing industry include:

  1. Optimizing supply chain management: Analytics can be used to improve the efficiency of the supply chain by identifying bottlenecks and inefficiencies, and by developing strategies to address these issues.
  2. Reducing fuel consumption: Analytics can be used to identify patterns in fuel consumption and to identify opportunities for fuel savings. For example, data analysis can be used to identify the most fuel-efficient routes or to identify vehicles that are consuming more fuel than expected.
  3. Improving fleet management: Analytics can be used to improve the efficiency of fleet management by identifying patterns in vehicle maintenance and repair data, and by helping fleet managers to develop strategies to optimize vehicle utilization and reduce downtime.
  4. Forecast roadworthiness of vehicles: This can help set trends on when a vehicle would break down or need repairs based on utilization, road conditions, climate, and driving patterns.

Energy

Data Analytics can be used to optimize the production and distribution of energy, as well as to improve the efficiency of energy-consuming devices.

Some specific examples of how Analytics is being used in the energy industry include:

  1. Optimizing the production and distribution of energy: Analytics can be used to optimize the production and distribution of energy by identifying patterns in energy demand and by developing strategies to match supply with demand. For example, data analysis can be used to predict when energy demand is likely to be highest and to adjust energy production accordingly.
  2. Improving the efficiency of energy-consuming devices: Analytics can be used to identify patterns in energy consumption and to identify opportunities for energy savings. For example, data analysis can be used to identify devices that are consuming more energy than expected and to develop strategies to optimize their energy use.
  3. Monitoring and optimizing energy systems: Analytics can be used to monitor and optimize the performance of energy systems, such as power plants and transmission grids. Data analysis can be used to identify potential problems or inefficiencies and to develop strategies to address them.

Agriculture

Analytics can be used to optimize crop yields, improve the efficiency of agricultural processes, and reduce waste.

Some specific examples of how Data Analytics is being used in agriculture include:

  1. Optimizing crop yields: Analytics can be used to identify patterns in crop growth and to develop strategies to optimize crop yields. For example, data analysis can be used to identify the most suitable locations for growing different crops and to develop customized fertilization and irrigation plans.
  2. Improving the efficiency of agricultural processes: Data Analytics can be used to identify patterns in agricultural data and to develop strategies to optimize processes such as planting, fertilizing, and harvesting.
  3. Waste Reduction: Analytics can be used to identify patterns in food waste and to develop strategies to reduce waste. For example, data analysis can be used to identify the most common causes of food waste on farms and to develop strategies to address those issues.

These are just a few examples of the many industries that are likely to adopt Data Analytics technologies as part of their digital transformation efforts in the coming years. 

Other industries that are also likely to adopt Analytics Technologies include Government, Education, and Media, among others. In general, Data Analytics Technologies are being adopted across a wide range of industries because they can help organizations to gain insights from their data, make more informed decisions, and improve their operations. 

As more and more organizations recognize the value of Analytics, it’s likely that we’ll see even greater adoption of these technologies in the coming years.

To learn more about our Data Solutions Services, click here.

Befriending Your Data in 2021, Java, Java Philippines
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Befriending Your eye-opening Data in 2021
Befriending Your eye-opening Data in 2021 768 487 Exist Software Labs

It’s the new year and everybody is still living in the wake of the COVID-19 pandemic. We all need a friend in times of trouble and this is no different in the case of business organizations.

This year, 2021, the friend that your company needs more than ever, especially in these trying times, is data.

Given the disruption that this virus caused in the preceding year, enterprises need to start (if they haven’t already) befriending their own internal data, and perhaps external data as well if they are to at least stay viable and at most grow.

The following are some insights from respected data management leaders on how to make friends with your data this year:

  • Eran Vanounou, CEO at Varada

    “Data warehouses are not going to disappear. Data warehouses will continue to be an important legacy technology that organizations will use for mission-critical business applications well into the future.

    With the transition to the cloud, data warehouses got a fresh new look and offer some modern attractive capabilities including self-service and serverless.

    With the rise of the cloud, data lakes are the new kid on the block. Data lakes are becoming a commodity, a legacy technology in their own right. Their rapid emergence from the innovation stage means two things going forward.

    First, organizations will demand simpler, easier to manage, and more cost-effective means of extracting usable business intelligence from their data lakes, using as many data sources as possible.

    Second, those same organizations will want the above benefit to be delivered via tools that do not lock them into proprietary data management platforms.

    In short, 2021 will begin to see the rapid introduction and evolution of tools that allow users to keep their data lakes in one place and under their control while driving performance up and cost down.”

  • Paige Roberts, Open-Source Relations Manager at Vertica

    “Distributed analytical databases and affordable scalable storage are merging into a single new thing called either a unified analytics warehouse or a data lake house depending on who you’re talking to.

    Data lake vendors are scrambling to add ACID capabilities, improve SQL performance, add governance, resource management, security, lineage, and all the things that data warehouse vendors have been perfecting for the last three or four decades.

    During the ten years, while data lake software has been coalescing, analytical databases have seen their benefits and added them to their existing stacks: unlimited scale, support for widely varied data types, fast ingestion of streaming data, schema-on-read, and machine learning capabilities.

    Just like a lot of things used to claim to be cloudy before they really were, some vendors will claim to be a unified analytics warehouse when they’ve just jammed the two architectures together into a complicated mess, but everyone is racing to make it happen for real.

    I think the data warehouse vendors have an unbeatable head start because building a solid, dependable analytical database like Vertica can take ten years or more alone.

    The data lake vendors have only been around about ten years, and are scrambling to play catch-up.”

  • Raj Verma, CEO at SingleStore

    “One single SQL query for all data workloads

    The way forward is based not only on automation but also on how quickly and widely you can make your analytics accessible and shareable.

    Analytics gives you a clear direction of what your next steps should be to keep customers and employees happy, and even save lives. Managing your data is no longer a luxury, but a necessity–and determines how successful you or your company will be.

    If you can remove the complexity or cost of managing data, you’ll be very effective.

    Ultimately, the winner of the space will take the complexity and cost out of data management, and workloads will be unified so you can write one single SQL query to manage and access all workloads across multiple data residencies.”

  • Prat Moghe, CEO at Cazena

    “Expect more enterprises to declare the battle between data lakes and data warehouses over in 2021 – and focus on driving outcomes and modernizing.

    Data warehouses can continue to support reporting and business intelligence, while modern cloud data lakes support all analytics, AI and ML enablement far more flexibly, scalably, and inexpensively than ever – so enterprises can go transform quickly.

    Cloud migrations and related cloud data lake implementations will get demonstrably faster and easier as DIY approaches are replaced by turnkey SaaS platforms.

    Such solutions will slash production cloud data lake deployment times from months to minutes while controlling costs and providing the continuous operations, security and compliance, AI and ML enablement, and self-service access required for modern analytics initiatives.

    That means that migrations that used to take 9-12+ months are complete in a fraction of the time.”

  • Adi Paz, CEO at GigaSpaces Technologies

    “Co-locating analytics and operational data results in faster data processing to accelerate actionable insights and response times for time-sensitive applications such as dynamic pricing, hyper-personalized recommendations, real-time fraud and risk analysis, business process optimization, predictive maintenance, and more.

    To successfully deploy analytics and ML in production, a more efficient Data Architecture will be deployed, combining OLTP (CRM, ERP, billing, etc.) with OLAP (data lake, data warehouse, BI, etc.) systems with the ability to build the feature vector more quickly, and with more data for accurate, timely results.”

To summarize the various points made by these industry pundits:

1

SQL-driven data warehouses are here to stay and will continue to be the data analytics platform of choice for enterprises in the current year.

2

Data management platforms that integrate well with existing data lakes will dominate as opposed to platforms that focus on one or the other.

3

Data management platforms that have built-in AI/ML functionalities will dominate as well, as this eliminates the cost and complexity of separate AI/ML analytics platforms.

4

Data management platforms that are cloud-ready will also have an edge over those that are not.

Is there a data management platform that possesses all these qualities and has a proven track record in Fortune 500 companies?

Yes, there is. It’s called Greenplum. Read about it here.

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