big data and analytics

Data Management, AI, Java Developer, Java. Developer in the Philippines

How to Maximize AI potential through Data Maturity for Innovation and Growth this 2024

How to Maximize AI potential through Data Maturity for Innovation and Growth this 2024 1200 630 Exist Software Labs

In today’s data-driven world, organizations are increasingly turning to artificial intelligence (AI) to unlock valuable insights and drive innovation. However, the success of AI initiatives heavily depends on the quality and maturity of the underlying data. In this blog post, we’ll explore how data maturity plays a crucial role in preparing your data for AI applications.

Understanding Data Maturity

Data maturity refers to the level of readiness of an organization’s data management processes. It encompasses various aspects such as data quality, accessibility, governance, and integration. A high level of data maturity indicates that an organization has well-defined processes in place to manage its data effectively. The value of data maturity for AI lies in its ability to enhance model accuracy, reliability, and performance, leading to better insights, decision-making, and ultimately, business outcomes. Essentially, the better the quality and maturity of the data, the more effective and impactful the AI applications can be.

Your organizations data maturity

The Importance of Data Maturity for AI

  1. Data Quality: High-quality data is a pre-requisite for AI. It ensures that your data is accurate, consistent, and reliable, which is essential for training AI models and making accurate predictions. The better quality of the data, the more effectively you can leverage your AI for improved decision-making and gain valuable insights.
  2. Data Accessibility: AI algorithms require access to a wide range of data sources. A mature data environment ensures that data is accessible across the organization, enabling AI applications to leverage diverse datasets for analysis.
  3. Data Governance: Data governance frameworks ensure that data is managed in a transparent, compliant, and ethical manner. This is critical for AI applications, as they often deal with sensitive data and require strict controls to protect privacy and ensure regulatory compliance.
  4. Data Integration: AI models perform best when they have access to comprehensive and integrated datasets. Data maturity enables organizations to break down data silos and integrate disparate data sources, providing a more holistic view of their data landscape.

Start Your Data Maturity Assesment Here:

Steps to Achieve Data Maturity for AI Readiness

  1. Assess Current Data Practices: Conduct a thorough assessment of your organization’s current data management practices, identifying areas for improvement and opportunities for optimization.
  2. Implement Data Quality Controls: Invest in tools and processes to monitor and improve data quality, including data cleansing, deduplication, and validation techniques.
  3. Establish Data Governance Policies: Develop robust data governance policies and procedures to ensure data integrity, security, and compliance with relevant regulations.
  4. Invest in Data Integration: Implement data integration solutions to consolidate and harmonize data from different sources, enabling seamless access and analysis for AI applications.
Data Maturity, Data, Data Analytics, AI

Brad Edwards explained in his article the importance of Data Maturity to build solid ground for AI. According to his article, a company’s data maturity level is assessed based on its proficiency in utilizing data for analytics, machine learning, and decision-making. Companies with a higher data maturity tend to possess advanced AI capabilities and services, which play a crucial role in the effectiveness and achievements of their machine learning endeavors. With their high data maturity, A company can deploy AI models to predict consumer behavior, optimize inventory management, and personalize marketing campaigns. For instance, their AI-driven recommendation system analyzes historical purchase data, online browsing behavior, and customer feedback to suggest products tailored to individual preferences, leading to increased sales and customer satisfaction. You can read more about it here.

Data Management, AI, Java Developer, Java. Developer in the Philippines

Before AI, Data Maturituy (Successful AI Projects are Built on Solid Ground)

Conclusion

In the age of AI, data maturity is a prerequisite for success. By investing in data quality, accessibility, governance, and integration, organizations can ensure that their data is AI-ready and capable of unlocking valuable insights to drive business growth and innovation. Start your journey towards AI readiness today by prioritizing data maturity within your organization.

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      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

      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

      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

      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.

      Want to learn more about Data Solutions and Services? Click here.

      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

      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.

      Enterprise Technology Solutions Leader, Exist Reveals Top 5 IT Trends for 2019. Java, Java Philippines

      Exist Software Labs, Inc Reveals Top 5 IT Trends for 2019

      Exist Software Labs, Inc Reveals Top 5 IT Trends for 2019 768 487 Exist Software Labs

      IT Trends in 2019

      The demand for business companies to go digital continues as they face the new year with new expectations, competitors, channels, threats, and opportunities.

      Digitalization has created a new breed of market that companies of all sizes- small, medium-sized, or even large corporations cannot ignore. Traditional businesses have now accepted using digital transformation as a business strategy— to deliver products and services through the web, reap data from every market interaction then gain insights to rapidly optimize their value chain and help them increase competitive advantage.

      By transforming digitally, businesses are able to build a connection with their customers, speed up the pace of innovation, and, as a result, claim a greater share of profit.

      Today, companies that invest in digital transformation are building an edge over those that don’t, which will enable them to succeed in reaching the expanding digital lives of consumers that encompass the rules of engagement that strongly influence customer loyalty.

      With this profound effect on business organizations, allow us to share what are the IT Trends our top executives think would make an impact by the year 2019:

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      Top 5 IT Trends

      Blockchain

      By allowing digital information to be distributed but with a highly secured transaction, blockchain technology created the backbone of the new type of Internet. What do we really mean when we say blockchain? According to Don and Alex Tapscott, authors of the book Blockchain Revolution (2016), “the blockchain is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value”. The growing list of records found in blockchain is called blocks which are linked using cryptography.

      In an interview with Mr. Mike Lim, President & CEO of Exist, he stated, “Blockchain has been really gaining quick traction not only because of bitcoin or cryptocurrency but because of the promise of more transparent but secured communication between B2B companies or even B2C depending on what vertical you are.” This IT trend gives internet users the ability to create value and authenticates digital information. By storing data across its peer-to-peer network, blockchain eliminates a number of risks that come with data being held centrally. Every network participant validates the transaction so that the data stored is immutable and cannot be forged.

      Real-world applications of blockchain technology are becoming more mainstream resulting in the amount of transactional data becoming huge.

      Combining blockchain and big data sparks a new level of analytics. Executives believe that the blockchain promise of secure, traceable transactions and improved transparency of information can streamline supply chain management. Thus, continuing to make a disruptive change in technology by the year 2019.

       

      Big Data

      With today’s digital technologies, it’s possible to analyze your data and turn it into insights rapidly. This IT Trend enables enterprises to make better decisions.

      According to Gartner, big data is high-volume, high-velocity, and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision-making, and process automation. As the head of the healthcare services at Exist and the VP for Sales and Marketing, Mr. Willex Perez shared with us his thoughts: “Imagine the possibilities of what big data can do for predicting illnesses. If you collect enough clinical information you’ll be able to compare your status or your clinical values with others. After which, you can search available research or studies to check your risk rating as an individual.”

      Emphasizing on healthcare, Mr. Willex added that among the growth  IT trends the use of big data in healthcare will be essential. “With analytics, enterprises will be able to drive innovation and come up with intelligent business decisions. While organizations collect data for analytics purposes combined with IoT as another major source for data,” Mr. Willex concludes, “it is inevitable to use big data analytics to complete the picture.”

       

      Internet of Things (IoT)

      Countless business opportunities are in the firehose of IoT data as products and services have become more connected. The Internet of Things refers to the network of devices such as home appliances, and mechanical and digital devices that contain electronics, software, and connectivity which allows them to interact and exchange data.

      Considered as one of his top IT trends, Mr. Christopher Silerio, the VP for Operations at Exist, believes IoT sensors provide us a valuable real-time update of the exchange of data with the sources. He shares, “there will be a time when there will be more data exchanges happening between sensors even without human interaction.  From smart appliances or smart meters, devices will continuously send data signals to a certain component or machine, providing information in real-time. It’s reasonable to say that IoT has begun to transform the business landscape and is expected to continue in 2019.”

       

      Cloud

      While IoT generates huge amounts of data, the cloud ensures that these are captured and stored properly. The simplicity and accessibility of cloud computing to manage vast amounts of data remain a catalyst enabling the rapid expansion of IoT. Cloud computing provides small to medium enterprises the ability to enjoy low implementation costs for their total IT infrastructure and software systems.

      Utilizing the abilities of cloud computing, enterprises of all sizes can deploy applications a lot quicker and cheaper compared to the cost of setting up whole IT Infrastructure and service by themselves. According to Forrester’s predictions for 2018, the total global public cloud market will be $178B in 2018, up from $146B in 2017, and will continue to grow at a 22% compound annual growth rate. From this perspective, the cloud seems to be a key driver of digital transformation and economic growth.

      “Cloud makes it easier for organizations to worry more about their business process rather than infrastructures. It makes it easier for startups to build their business quickly,” reveals Mr. Jonas Lim, the VP of Technical Services at Exist. “As early as almost a decade ago, we believed that cloud computing is a real game-changer and it has proven to be true as the future continues to bring us into a world of unlimited connectivity empowered by the cloud,” he further adds.

       

      Artificial Intelligence

      Artificial intelligence or AI doesn’t only apply to robotics. As a branch of computer science, AI involves the development of computer programs to complete tasks which would otherwise require human intelligence. As evidence of its spread, AI is even available for use along with other cloud solutions to which businesses can just subscribe to.

      Internet technology companies also make use of  AI to optimize their IT infrastructures. In fact, according to Wikibon: “AI-optimized application infrastructure is one of today’s hottest IT trends in the IT business. More vendors are introducing IT platforms that accelerate and automate AI workloads through pre-built combinations of storage, compute, and interconnect resources.”

      Mr. Jonas Lim pointed out the increasing use of chatbots in business services. Chatbots are programs built to automatically engage with received messages simulating actual human interaction. In addition, artificial intelligence might just be ready to explode with its use, particularly inside the healthcare industry.

      What started with manufacturing has now spread to knock and open the doors to a greater digital business scale but now with analytics and computing intelligence as an IT Trend at the forefront of cutting-edge changes in the upcoming years. “[Like] growing population of robotics is bound to happen,” Mr. Willex added, “and although we don’t know the future, it is quite evident that interacting with AI will soon be part of our everyday lives.”