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Sun Life Event: Spark Innovation Series- Cloud and Data Federation, Cloud & Data Federation, Data Solutions Philippines, Exist Data Solutions
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Exist Software Labs discusses Cloud and Data Federation at Sun Life Global Solutions’ Spark Innovation Series
Exist Software Labs discusses Cloud and Data Federation at Sun Life Global Solutions’ Spark Innovation Series 650 486 Exist Software Labs

Cloud adoption has become increasingly essential for businesses looking to enhance their operations, improve scalability, and drive innovation. It allows organizations to leverage the power of cloud computing services to store, process, and analyze large volumes of data with ease.

On Aug 29, 2023, technology experts and IT professionals converged at the Sun Life Centre, in Bonifacio Global City, Philippines. Exist Software Labs graced Sun Life Global Solutions’ Spark Innovation Series, which discussed, for that session, Cloud & Data Federation. The Exist Team shared their insights and experiences as a technology company, focusing on the importance of Cloud and Data Federation in an organization’s Data Maturity Journey.

Cloud Adoption 3.0

In today’s digital age, the adoption of cloud technology has become increasingly important for businesses. Cloud Adoption 3.0, the next phase of cloud adoption, is an innovation where organizations are not only leveraging the power of the cloud, but also embracing data federation.

Sun Life Event: Spark Innovation Series- Cloud & Data Federation, Data Solutions Philippines, Java Developer Philippines, Cloud and Data Federation

Exist Director of Tech Services Dennis De Vera focused his presentation on the implications, benefits, and uses of Cloud Adoption; including increased flexibility, scalability, and cost-effectiveness. Cloud adoption allows companies to store and access data from anywhere in the world, enabling remote work and collaboration.

With the growing amount of data being generated by organizations, managing and analyzing this data becomes a challenge. According to De Vera, “This is where data federation comes into play. Data federation involves integrating and combining data from multiple sources or clouds into a unified view.”

Cloud and Data Federation Strategies

By adopting cloud & data federation strategies, businesses can unlock even more value from their cloud investments. They can gain deeper insights from their data by analyzing it holistically rather than in silos. This enables better decision-making, improved customer experiences, and enhanced operational efficiency.

Furthermore, cloud & data federation also provide organizations with greater control over their data governance and compliance requirements. With centralized control over access permissions and security measures across multiple clouds or sources of data, businesses can ensure regulatory compliance while maintaining high levels of security.

Implement Cloud Adoption and Data Federation to leverage on your cloud investments now

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What part does security play?

Whilst cloud & data federation integrate resources across multiple platforms and servers, it also introduces potential vulnerabilities that must be addressed to maintain a secure environment. Protecting secure assets, such as sensitive customer information or proprietary business data, requires strong access controls, encryption mechanisms, and authentication protocols. 

Regular audits are crucial for maintaining security within a federated environment and identifying potential weaknesses or compliance gaps. By examining security controls across different platforms and servers, organizations can proactively address vulnerabilities before they are exploited by malicious actors.

Security plays an integral part in the Cloud & Data Federation. By implementing robust encryption measures, conducting regular audits, and enforcing strict access controls, organizations can protect their valuable assets while leveraging the benefits of federated environments effectively.

Know more about what cloud adoption and data federation can do, specific to your organization

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Don’t know where to start with cloud adoption? Exist is a multi-awarded technology innovator that provides free technical consultation to help you jumpstart your cloud adoption, cloud federation, and data maturity journey. Discuss with our experts to maximize the benefits of digital transformation today.

Understanding the Importance of Business Intelligence Tools Business Intelligence Tools, Data Ready, Data-Driven Era, Java Philippines, Data Solutions Philippines
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Understanding the Importance of Business Intelligence Tools
Understanding the Importance of Business Intelligence Tools 650 486 Exist Software Labs

In today’s data-driven world, being data-ready is crucial for organizations to gain insights, make informed decisions, and stay ahead of the competition. Business Intelligence tools (BI), such as Tableau and Power BI, play a pivotal role in empowering businesses to harness the full potential of their data. In this blog post, we will explore the importance of being data-ready and delve into the capabilities of these two popular BI tools.

 

Business Intelligence Tools: Understanding the Importance of Being Data Ready

Being data ready means having the necessary mindset, processes, and infrastructure to effectively collect, manage, analyze, and visualize data. It enables organizations to uncover valuable insights, identify trends, and make data-driven decisions. Being data-ready offers several advantages, including:

 

  • Improved Decision-Making: With comprehensive data analysis, organizations can make more informed decisions based on customer behavior and preferences, among others, leading to better product and service design and increased competitiveness.

 

  • Identifying Opportunities and Risks: Being data-ready allows businesses to identify emerging opportunities, potential risks, and market trends, enabling them to adapt and seize competitive advantages.

 

  • Enhanced Efficiency and Productivity: By streamlining data processes and providing easy access to insights, being data-ready boosts operational efficiency and empowers teams to work more productively.

 

  • Customer-Centric Approach: Leveraging data helps organizations understand customer needs, preferences, and behavior, leading to personalized experiences and improved customer satisfaction.

Leverage on Business Intelligence tools to help make intelligent business decisions

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Benefits of Using Business Intelligence Tools 

 

  • Data Consolidation and Integration

One of the primary benefits of BI tools is their ability to consolidate and integrate data from multiple sources. Companies generate data from various platforms such as on-ground sales transactions, customer interactions, marketing campaigns, and supply chain operations. BI tools aggregate and organize these diverse data into a unified view, providing decision-makers with a comprehensive understanding of the business landscape.

 

  • Real-time and Historical Analysis

BI tools offer real-time and historical analysis capabilities, giving businesses the ability to monitor their operations as they unfold and examine past performance trends. This dynamic feature empowers executives to make agile decisions in response to changing market conditions and identify long-term patterns that can shape future strategies.

 

  • Enhanced Data Visualization

Raw data can be overwhelming, making it challenging for decision-makers to grasp crucial insights quickly. BI tools address this challenge by providing advanced data visualization techniques. Interactive dashboards, charts, graphs, and reports transform complex data into intuitive visual representations, making it easier to comprehend and identify key trends, patterns, and outliers.

 

  • Informed Decision-Making

BI tools enable data-driven decision-making, which is critical in today’s fast-paced business environment. When armed with accurate and timely information, executives can confidently make informed choices that align with business goals, leading to more effective strategies and improved outcomes.

 

  • Improved Operational Efficiency:

By streamlining data analysis and reducing manual reporting tasks, BI tools enhance operational efficiency. Employees can focus more on strategic initiatives rather than spending valuable time collecting and collating data. Moreover, with automated reporting features, BI tools facilitate the seamless distribution of insights across departments, fostering collaboration and alignment.

 

The Data-Driven Era

In today’s data-driven era, being data-ready is essential for organizations to thrive. Tableau and Power BI are powerful BI tools that empower businesses to unlock the true potential of their data. By leveraging intuitive data visualization, seamless data integration, advanced analytics, and collaboration features, these tools enable users to make data-driven decisions, enhance operational efficiency, and gain a competitive edge.

Know more about our Data Solutions services here

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Exist offers data solutions services and Business Intelligence tools you can use to make informed decisions, and stay ahead of the competition. Start with your journey to data maturity and become a data-driven organization!

About the Author

Mark Daryll De Venecia is a highly motivated and skilled data engineer with a passion for harnessing the power of data to drive meaningful insights and innovation. He has successfully obtained several certifications that attest to his proficiency in data management, data integration, and data warehousing. These certifications, combined with his problem-solving abilities and meticulous attention to detail, enable him to design and implement robust data solutions that empower businesses to make data-driven decisions efficiently and effectively. Mark continually seeks to expand his knowledge and stay at the forefront of emerging technologies in the dynamic field of data engineering.

DevOps. Java. Java Developer. Data Solutions. Data Solutions Provider Philippines
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Nine (9) Effective Ways DevOps Minimizes Technical Debt
Nine (9) Effective Ways DevOps Minimizes Technical Debt 650 486 Exist Software Labs

DevOps is an amalgamation of philosophies, practices, and tools that addresses numerous developmental and operational challenges an organization faces, one of which is dealing with ‘technical debt’.

Technical Debt is accrued when less-than-ideal coding and design decisions are made – in order to get what the team requires now or if there is a need to go into production sooner.

TEST IN THE EARLY STAGES OF SOFTWARE DEVELOPMENT

This is a key DevOps Practice, to test early in the Software Development Lifecycle (SDLC), testing early is also known as “Shift-Left Testing”. It assists in finding and preventing errors from the early stages of the delivery process. It includes code coverage analysis, static code analysis, unit tests, as well as other code-level practices to catch errors at the earliest time possible where they cost the least to fix.

IDENTIFY AND RESOLVE MAJOR PROBLEMS FIRST

The DevOps Lifecycle has lean, short feedback cycles and faster iterations due to the enhanced level of collaboration between teams. This results in not having to wait until the next feature release to get a fix for bugs, security vulnerabilities, and usability issues, in addition, major complications that can affect users or operations are fixed instantaneously.

The process can be optimized further by defining a ranking for problems from low to high priority to help in deciding which issues should be attended to first. The team should be focus on solving these major problems first and not leave anything for a later time down the line.

CREATE IMPROVED COLLABORATION BETWEEN THE DEVELOPMENT AND OPERATION TEAMS

A possible reason for incurring technical debt or “code debt” is because development teams, regardless of how reluctant they are, may be forced into taking shortcuts to deliver on tight deadlines combined with struggling with constant changes or requirements. However, improving the collaboration between the Development and Operations teams can shorten the SDLC, and enable quicker deployments, in addition to increasing their overall frequency.

Continuous Integration/Continuous Deployment and Continuous Testing can make it easier for teams to navigate and deal with changes. In general, cultivating a collaborative culture inspires code reviews, good coding practices, and robust testing with mutual help.

HIGHLIGHT MORE AUTOMATION

In automating tedious time-consuming tasks, and others that prove to be more prone to errors, your teams will be granted more time to repay technical debt. Additionally, automation that is rooted in CI/CD, in terms of automated testing and building, and Infrastructure as Code (IaC) supports in recognizing debt earlier and facilitates continuous debt repayment. It also enforces code quality standards – hence, automation can reduce existing technical debt while also preventing any future debt.

MANAGE TECHNICAL DEBT

DevOps makes it easier to control and manage technical debt continuously. It empowers constant communication, allowing teams to track debt, incite awareness and resolve it as soon as possible. The appointed team leaders can also include a review of the technical debt into backlog and schedule maintenance sprints to deal with it promptly. DevOps also reduces the likeliness of having incomplete or deferred tasks in the backlog, further helping prevent incurring any additional debt.

CULTIVATE A DEVOPS CULTURE

In managing technical debt over long periods, a proper DevOps Culture will be the key. As we’ve said a number of times over, it encourages strong collaboration between cross-functional teams, provides autonomy and ownership, and practices continuous feedback and improvement. It’s a truly efficient platform in calculating and tracking technical debt whilst communicating it to other teams. A DevOps Culture can also be used as a way to educate and inform developers of the kind of codes that may introduce bugs and raise code quality.

DEFINE YOUR DEVOPS STANDARDS

A well-defined DevOps Standards will allow you to create quality gates for every code check-in before running tests and deployment. It saves your teams from repetitive, prone-to-error tasks while optimizing their development efforts. In enforcing certain DevOps Standards, it can also prevent your teams from cutting corners in the process, which is the major contributor to technical debt, thus, implementing exact DevOps Standards can maintain a high level of productivity and quality while ensuring excellent team morale and indirectly saving money for your organization.

SMOOTHER DEPLOYMENT PROCESS

You should consider utilizing containers to make deployments easier, containers are lightweight and portable and can simplify application development and deployment. A container orchestration tool like Kubernetes, automates container lifecycle in production, allowing your teams to focus on high-value tasks of refactoring apps. or lowering code debt.

APPLICATION PROGRAMMING INTERFACE (API) FIRST MODEL

The way application components communicate with one another is also critical in addressing technical debt, which can also be caused by different systems accessing services and data in ways that are unexpected to the team. If you implement APIs, it can make the interfaces more visible and increases the resilience of communications, due to bad links between different applications, an API-based model permits teams to make changes quickly without affecting the existing structure. In addition, any team that interacts with the service in question at a later date has a clearly defined set of expectations, as a result of this, it’ll be easier to roll out any changes/features with less technical debt.

Are you ready to make the most of your data to set your company up for success? Let Exist Software Labs, Inc. help you start your Data Maturity Journey today!

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.

Data Ingestion, Data Integration, Data Quality,Driven Organization
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The Importance of Data Ingestion, Data Integration, and Data Quality in becoming a Data-Driven Organization.
The Importance of Data Ingestion, Data Integration, and Data Quality in becoming a Data-Driven Organization. 650 486 Exist Software Labs

Data ingestion, integration, and quality are crucial steps in becoming a data-driven organization:

Ingesting, storing, organizing, and maintaining the data generated and gathered by an organization is known as data management. A key component of implementing IT systems that power business applications use to deliver analytical data to support operational decision-making and strategic planning by corporate executives, business managers, and other end users is effective data management.

Data management is a collection of many tasks that aims to guarantee correct, accessible, and available data in business systems. The majority of the work is done by the IT and data management teams, but business users also contribute.

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These are the key steps in transforming a company into a data-driven organization.

What is Data Ingestion, Data Integration, and Data Quality?

  1. Data Ingestion: It is the process of acquiring data from various sources and bringing it into a centralized data repository for analysis and reporting. Without effective data ingestion, data silos can form, making it difficult to access and integrate data across the organization.

 

It involves acquiring data from different sources, such as databases, cloud storage, or even manual input, and ensuring that the data is transformed and formatted in a way that can be easily integrated and analyzed.

 

  1. Data Integration: This process merges data from different sources into a unified view, making it easier to analyze and make informed decisions. Lack of data integration can lead to inconsistencies, duplications, and errors in data analysis.

 

This step requires removing duplicates, resolving conflicts, and transforming data into a consistent format so that the data can be used effectively for analysis and decision-making.

  1. Data Quality (Cleansing): Cleaning data ensures that it is accurate, consistent, and free of errors. Poor data quality can negatively impact decision-making and hinder the effectiveness of data analysis.

 

The data quality process involves validating the data, correcting errors, and removing inconsistencies, to ensure that the data is trustworthy and fit for its intended use. These three steps are crucial for organizations to effectively leverage their data to make informed decisions, drive business growth, and achieve their goals.

By focusing on data ingestion, integration, and quality, organizations can ensure that they have a solid foundation for their data analysis and decision-making processes. It enables organizations to gain valuable insights, make informed decisions, and ultimately drive business growth and success.

Next is How to Operationalize the Data in a data-driven organization:

 

  • Establish a clear data strategy: The first step is to create a clear data strategy that aligns with the overall business strategy. This strategy should define the business problems that data can help solve, the data sources to be used, the tools and technology required, and the KPIs that will be used to measure success.

 

  • Identify data requirements: Determine what data is required to support the business strategy and goals. This involves identifying the types of data needed, the sources of data, and the frequency of data collection and updates.

 

  • Collect and process data: Collect the relevant data and process it in a way that makes it usable for analysis. This may involve data cleaning, normalization, and transformation.

 

  • Analyze data: Use analytics tools and techniques to analyze the data and derive insights that can inform business decisions. This may involve descriptive analytics, predictive analytics, and prescriptive analytics.

 

  • Communicate insights: Communicate the insights to stakeholders in a way that is clear and actionable. This may involve creating dashboards, reports, or visualizations that highlight the key findings and recommendations.

 

  • Integrate insights into operations: Use the insights to inform business operations and decision-making processes. This may involve integrating insights into existing workflows, processes, and systems.

 

  • Monitor and evaluate: Monitor the impact of the data-driven initiatives and evaluate the success against the KPIs identified in the data strategy. Make adjustments as needed to improve performance.

Overall, operationalizing data in a data-driven organization requires a culture that values data-driven decision-making, a commitment to continuous improvement, and the right technology and tools to support data collection, analysis, and communication.

 

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.

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Data Science, Science and Technology
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Data Science 101: What are concepts you need to know before entering the Data Science world?
Data Science 101: What are concepts you need to know before entering the Data Science world? 650 486 Exist Software Labs

I was playing around with data and then I found the Science — Yes, my introduction to the world of Data Science has been a part of my research work.

If you’re like me, starting out with Data Science looking for resources that can give you a jump start or at least a better understanding of it or you have just heard/read the term being coined and want to know what it is, of course, you can find a gazillion materials about it, this is, however, how I started and got familiar with the basic concepts.

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What is ‘Data Science’?

Data Science provides meaningful information based on larger amounts of complex data or big data. Data-Driven Science combines different fields of work in statistics and computation to interpret data for decision-making purposes.

Understanding Data Science

How do we collect data? — Data is drawn from different sectors, channels, and various platforms including cell phones, social media, e-commerce sites, various healthcare surveys, internet searches, and many more. The surge in the amount of data available and collected over a period of time has opened the doors to a new field of study based on big data — the huge and massive data sets that contribute towards the creation of better operational tools in all sectors.

The continuous and never-ending access to data has been made possible due to advancements in technology and various collection techniques. Numerous data patterns and behavior can be monitored and it can make predictions based on the information gathered.

In technical terms, the above-stated process is defined as Machine Learning; in layman’s terms, it may be termed Data Astrology — predictions based on data.

Nevertheless, the ever-increasing data is unstructured in nature and is in constant need of parsing in order to make effective decisions. This process is really complex and very time-consuming for organizations — and hence, the emergence of Data Science.

A Brief History / Background of Data Science

The term ‘Data Science’ has been in existence for about three decades now and was originally used as a substitute for ‘Computer Science’ in the 1960s. Approximately 15–20 years later, the term was used to define the survey of data processing methods used in different applications. 2001 was the year when Data Science was introduced to the world as an independent discipline.

Disciplinary Areas of Data Science

It incorporates tools from multiple disciplines to gather a data set, process and derive insights from the data set and interpret it appropriately for decision-making purposes.

Some of the disciplinary or noteworthy areas that make up the Data Science field include Data Mining, Statistics, Machine Learning, Analytics Programming, and the list goes on. But, we would be doing a brief discussion mainly on the aforesaid topics as the concept of Data Science mainly revolves around these basic concepts, just to keep it simple.

Data Mining applies algorithms to complex data sets to reveal patterns that are then used to extract useful and relevant data from the set.

Statistics or Predictive Analysis uses this extracted data to gauge events that are likely to happen in the future based on what the data shows happened in the past.

Machine Learning can be best described as an Artificial Intelligence tool that processes massive quantities of data that a human is incapable of doing in a lifetime — it perfects the decision model presented under predictive analytics by matching the likelihood of an event happening to what actually happened at a predicted time in the past.

The process of Analytics involves the collection and processing of structured data from the Machine Learning stage using various algorithms. The data analyst interprets, converts, and summarizes the data into a cohesive language that the decision-making team can understand.

Data Scientist

Literally speaking, the job of a Data Scientist is multi-tasking: We collect, analyze and interpret massive amounts of structured and unstructured data, and in a maximum number of cases, to improve an organization’s operations. Data Science professionals develop statistical models that analyze data and detect patterns, trends, and various relationships in data sets.

This vital information can be used to predict consumer behavior or to identify business and operational risks. Hence, the job of a Data Scientist can be described as a story-teller that uses data insights in telling a story to the decision-makers in a way that is understandable. The role of a Data Scientist is becoming increasingly important as businesses rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies.

Present & Future of Data Science

Data Science has become the real thing now and there are potentially hundreds and thousands of people running around with that job title. And, we too have started seeing these Data Scientists making large contributions to their organizations. There are certainly challenges to overcome, but the value of data science from a business point of view is pretty clear at this point.

Now, thinking about the future, certain questions definitely arise — “How will the practice of data science be changing over the next five years? What will be the new research areas of data science?”

“Will the fundamental skills remain the same?”

These are certainly debatable questions, but one thing is for sure — inventions have happened and will continue to happen when there arises any demand for the betterment of the future. And, the world would keep benefiting from data science through its upcoming innovations.

The possibilities of how to utilize Data Science in real-world scenarios are endless! Our Data Solutions team would be happy to help you capitalize on this technology for your enterprise.

Exist Software Labs Inc, Informatica Data Governance Pocket Session
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Exist Software Labs Inc and Informatica Pocket Session 2022: Realizing Data Governance Benefits in a Cloud-Hybrid World
Exist Software Labs Inc and Informatica Pocket Session 2022: Realizing Data Governance Benefits in a Cloud-Hybrid World 650 486 Exist Software Labs

Exist Software Labs Inc and Informatica Pocket Session: Realizing Data Governance Benefits in a Cloud-Hybrid World

On September 15, Exist Software Labs, in a joint effort with Informatica, gathered various market leaders from various verticals to conduct another pocket session on Data Governance and its benefits in a Cloud-Hybrid World.

Exist and Informatica: Realizing Data Governance Benefits in a Cloud-Hybrid World

Jon Teo, Data Governance and Privacy Expert at APJ spoke at the event about its benefits. He demonstrates how it helped various industries such as healthcare, automotive, insurance, manufacturing, power, and others around the world by leveraging its risk and compliance to protect the enterprise, as well as data intelligence that unlocks more value and data opportunities for businesses.

According to him, rapid cloud adaptation and a hybrid ecosystem generate more volume from more sources, making it difficult to discover, manage, and control data, requiring the urgent need for an agile governance approach.

Kingsley Dsouza, a Technical Data Governance Privacy Domain Expert, was one of the speakers who also demonstrated Informatica’s services. According to him, “Data Governance platform helps users in finding information that will assist them in solving their day-to-day business problems, which most organizations struggle with and take a long time to process.”

It’s no secret that the Asia-Pacific region lags behind the rest of the world in data management, with less than 50% of organizations having standardized data management capabilities. As the amount of data generated in the region continues to grow at an exponential rate, organizations are scrambling to find effective ways to manage and store all of this information, which is where the agile governance approach comes into play.

Mitigate security risks and ensure compliance with data privacy laws by standardizing your data management! Get in touch with our team to know more.

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    Exist Software Labs Inc. and Informatica
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    Exist Software Labs Inc. and Informatica held a joint Pocket Session on Intelligent Data Management Cloud at the Shangri-La Fort Hotel in BGC!
    Exist Software Labs Inc. and Informatica held a joint Pocket Session on Intelligent Data Management Cloud at the Shangri-La Fort Hotel in BGC! 650 486 Exist Software Labs

    Exist Software Labs Inc. and Informatica held a joint Pocket Session on Intelligent Data Management Cloud at the Shangri-La Fort Hotel in BGC!

    ‘Data is the new oil. Like oil, data is valuable, but if unrefined it cannot really be used. It has to be managed/processed (integrated, mapped, transformed) to create a valuable entity which provides insights that drives profitable activities.’ – Informatica

    A collaboration with Informatica


    Exist Software Labs inc collaborated with Informatica for an exclusive face-to-face event last July 28, 2022, at the Shangri-La Fort Hotel in BGC. The guests were able to meet with data management expert and Informatica’s Head of Cloud Product Specialist, Daniel Hein, who shared how companies can bridge the gap between technology and business through automation, integration, and data governance, unlocking true business value from data.

     

    The world is changing, and so are your business’s needs. You must be able to adapt quickly to keep up with the changes. “In the last two years, a lot has changed. We are faced with new ways of doing business; the world is moving to a data-driven digital economy… However, there are CONSTRAINTS that you must overcome.” says Daniel Hein, Head of Cloud Product Specialists, APAC and Japan.

    That is why businesses must change their approach. The new Intelligent Data Management Cloud intends to help clients with that! The first and most comprehensive AI-powered data management solution in the industry. A single cloud platform. Every cloud-native service you’ll ever need for next-generation data management.

    IDMC

    Meet the new Intelligent Data Management Cloud of Informatica!

    IDMC platform cuts through red tape and provides accurate AI models across your organization so you can make timely decisions based on the most up-to-date information.

    It also gives you 360-degree views of your data across all areas of your business—so you can see who has access and what they’re doing with it—and allows easy workflow management.

    It is built on top of an enterprise cloud platform; and is equipped with a powerful security model that helps keep sensitive information secure from hackers.

    If you’re looking for a way to help your company prepare for this transition and stay competitive in an ever-changing marketplace, look no further! We specialize in helping companies not only to keep pace but also to improve their bottom line through digital transformation.

    Download our FREE DATASHEET!

    Begin your journey toward data maturity.
    and transform into a data-driven organization today!

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    Download FREE Datasheet!

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