What Is a Data Warehouse?

A data warehouse is a centralized repository designed to store structured data that is cleaned, transformed, and organized for business intelligence and reporting. Data warehouses follow a schema-on-write approach, meaning data must fit a predefined schema before being loaded.

Key characteristics of a data warehouse:

• Stores structured data in relational tables
• Optimized for SQL queries and business reporting
• High-performance for analytics at scale
• Requires ETL (Extract, Transform, Load) before storage

Typical use cases:

• Business intelligence dashboards
• Historical trend analysis
• Regulatory and compliance reporting

Examples: Snowflake, Amazon Redshift, Google BigQuery, Microsoft Azure Synapse Analytics

What Is a Data Lake?

A data lake is a centralized repository that stores raw, unprocessed data in its native format. Unlike warehouses, data lakes use a schema-on-read approach, which means data can be ingested first and structured later when it is queried.

Key characteristics of a data lake:

• Stores structured, semi-structured, and unstructured data (text, video, IoT streams, logs)
• Highly scalable and cost-efficient
• Works well with big data analytics and machine learning
• Supports advanced analytics but requires careful governance to avoid becoming a “data swamp”

Typical use cases:

• Machine learning model training
• Predictive analytics
• Large-scale data ingestion from multiple sources

Examples: Amazon S3 with AWS Lake Formation, Azure Data Lake Storage, Hadoop HDFS

What Is a Data Lakehouse?

The data lakehouse is a newer architecture that merges the reliability and governance of a warehouse with the flexibility of a lake. It enables organizations to store both structured and unstructured data in a single platform, while supporting BI workloads as well as AI and ML use cases.

Key characteristics of a data lakehouse:

• Combines schema-on-write and schema-on-read capabilities
• Supports SQL queries and BI reporting alongside ML workloads
• Handles governance, metadata management, and ACID transactions
• Reduces the need for separate systems, lowering complexity and cost

Typical use cases:

• Real-time data analytics
• Unified platform for BI and data science teams
• Hybrid workloads requiring both governance and scalability

Examples: Databricks Lakehouse Platform, Delta Lake, Apache Iceberg, Snowflake’s Unistore capabilities

Data Warehouse vs. Data Lake: What Are the Key Differences?

In short, warehouses excel at structured analytics, while lakes are ideal for raw, large-scale data storage and exploration.

Data Lakehouse vs. Data Warehouse: How Do They Compare?

A data warehouse is purpose-built for structured, relational data, while a data lakehouse is designed to handle both structured and unstructured data in one system. This distinction gives lakehouses a broader range of use cases, especially for businesses adopting AI and real-time analytics.

• Flexibility: Warehouses are rigid because data must be pre-structured before loading. Lakehouses can ingest raw, semi-structured, and unstructured data, giving organizations more agility in how they use information.
• Performance: Warehouses deliver excellent performance for historical analysis and predefined queries. Lakehouses, however, are optimized for real-time analytics and support workloads where instant insights are critical.
• Cost: Warehouses can be expensive at scale since they require structured data storage and processing. Lakehouses reduce costs by separating compute from storage and using cloud-native architectures.
• Use Case Fit: Warehouses are ideal for BI dashboards and compliance-driven reporting. Lakehouses are better suited for businesses looking to unify BI, advanced analytics, and machine learning in a single, future-proof platform.

Data Lakehouse vs. Data Lake: What Sets Them Apart?

While data lakes provide cost-effective, large-scale raw data storage, they often struggle with governance and query performance. A data lakehouse addresses these gaps by adding structure, reliability, and integration without losing the flexibility of a lake.

• Governance: Data lakes lack strong governance, which can lead to quality issues and “data swamps.” Lakehouses introduce ACID transactions, metadata management, and auditability, making them enterprise-ready.
• Query Speed: Queries in a raw data lake often require additional layers or processing engines to achieve acceptable performance. Lakehouses use indexing, caching, and schema enforcement, resulting in faster, more consistent query responses.
• Integration: Data lakes usually need third-party tools or complex pipelines for BI connectivity. Lakehouses are designed to integrate seamlessly with BI platforms while still supporting ML frameworks, creating a single environment for all teams.
• Future Scalability: While lakes provide storage scalability, lakehouses deliver both scalability and usability. They serve as a single source of truth for enterprises that want long-term flexibility without data silos.

What Are the Benefits of a Data Warehouse?

1. Mature technology: Proven systems with decades of reliability
   Data warehouses have been around for more than three decades, which makes them one of the most stable and trusted technologies for enterprise analytics. Their maturity ensures robust query optimization, standardized SQL support, and compatibility with virtually every business intelligence tool on the market. Enterprises in finance, healthcare, and government sectors often choose warehouses for mission-critical reporting because of their long-standing reliability.
2. Optimized for BI: Perfect for dashboards, KPIs, and structured reporting
   A data warehouse is purpose-built for analytics and reporting. Since the data is pre-structured and organized using ETL processes, it becomes easy to generate dashboards, performance scorecards, and KPI reports with speed and accuracy. For decision-makers who need clear insights without technical complexity, warehouses provide a consistent and fast way to transform raw data into actionable intelligence.
3. High security: Strong compliance and governance support
   Data warehouses come with mature security frameworks, offering role-based access control, data masking, encryption, and compliance certifications (HIPAA, GDPR, SOX). This makes them ideal for industries that operate under strict regulations. Built-in governance ensures that sensitive data is handled properly, which reduces risk and builds trust across the organization.
4. Vendor ecosystem: Wide variety of integrations and managed services
   Leading vendors like Snowflake, Amazon Redshift, and Google BigQuery provide extensive ecosystems of managed services, integrations, and partner solutions. Businesses can easily connect warehouses with visualization tools, CRM systems, and ERP applications. This flexibility reduces implementation complexity and ensures seamless adoption across departments.

What Are the Benefits of a Data Lake?

1. Scalability: Can handle petabytes of data cost-effectively
   Data lakes are designed to scale almost without limit. Whether you are storing terabytes of structured customer transactions or petabytes of unstructured IoT sensor data, a data lake can expand as needed. Using cloud-based object storage, organizations can ingest massive volumes of data without worrying about storage capacity bottlenecks.
2. Flexibility: Supports any data type from logs to multimedia
   Unlike warehouses that handle only structured data, data lakes can store virtually any format including JSON logs, clickstream data, videos, images, audio, or social media feeds. This flexibility makes them ideal for organizations that collect diverse data streams and need a single repository to manage them.
3. AI and ML ready: A natural fit for data science workloads
   Since data lakes can store raw, unprocessed data, they are well-suited for advanced analytics and machine learning projects. Data scientists can experiment with large datasets, run predictive models, and extract insights without needing to restructure everything upfront. This accelerates innovation in fields like fraud detection, customer personalization, and predictive maintenance.
4. Cost efficiency: Cheaper to store raw data than in a warehouse
   Data lakes leverage inexpensive storage options, especially in cloud environments, where object storage is far cheaper than structured relational storage. This makes it feasible to retain historical or rarely accessed datasets at minimal cost. For businesses with big data ambitions, data lakes provide an affordable foundation without the upfront expense of warehouses.

What Are the Benefits of a Data Lakehouse?

1. Unified architecture: Eliminates the need for separate warehouses and lakes
   A data lakehouse bridges the gap between structured and unstructured data by combining both capabilities into a single architecture. Instead of maintaining two separate systems (a warehouse for BI and a lake for AI/ML), businesses can consolidate workloads into one platform. This reduces duplication, ensures consistency, and simplifies management.
2. Governance and reliability: Provides structured governance like a warehouse
   Lakehouses overcome one of the biggest limitations of traditional data lakes: lack of governance. With built-in support for ACID transactions, metadata management, and quality controls, a lakehouse enforces the same standards of trust and reliability as a warehouse. This ensures that data remains accurate, auditable, and compliant.
3. Advanced analytics: Supports both SQL reporting and ML/AI models
   A data lakehouse is designed to serve multiple teams at once. Business analysts can use familiar SQL queries for dashboards and reporting, while data scientists can run complex ML models on unstructured or semi-structured data. This convergence of use cases accelerates decision-making and eliminates the silos between BI and data science functions.
4. Reduced complexity: Lowers costs by removing redundant data systems
   By consolidating capabilities, lakehouses significantly reduce the overhead of maintaining two separate infrastructures. Instead of paying for a data warehouse license and a large-scale data lake, organizations can invest in a single system that provides both. This reduces operational complexity, simplifies data pipelines, and ultimately lowers total cost of ownership.

Which Is Better for Your Business: Data Warehouse, Data Lake, or Data Lakehouse?

When it comes to choosing the right data architecture, there is no universal solution. The decision depends on several factors, including your organization’s goals, level of data maturity, industry requirements, and budget. Each option, data warehouse, data lake, and data lakehouse, has its strengths and limitations, and the right choice lies in aligning these with your business priorities.

When a Data Warehouse Is the Better Choice

A data warehouse is most suitable for organizations that prioritize structured reporting and compliance-driven analytics. If your business relies on dashboards, KPIs, and recurring reports that demand accuracy and speed, a warehouse provides the best foundation.

• Best fit for: Financial services, government agencies, healthcare providers, and enterprises that must meet strict compliance standards.
• Advantages: Ensures data quality, governance, and fast SQL-based queries. Business leaders can trust the accuracy of insights delivered.
• Limitations: Less flexible with unstructured data and costly at very large scales. Not ideal if you want to experiment with raw or streaming data.

Example scenario: A regional bank tracking loan performance and regulatory compliance would benefit from a data warehouse because of its ability to deliver consistent, audited reports.

When a Data Lake Is the Better Choice

A data lake is the right option for businesses that generate massive volumes of diverse data types and need flexibility in how that data is used. If your organization is experimenting with machine learning, IoT data, or advanced analytics, a data lake offers the scalability and cost efficiency required.

• Best fit for: Technology companies, research institutions, and organizations with significant unstructured data such as clickstreams, IoT feeds, or video logs.
• Advantages: Extremely cost-effective at scale, can store virtually any type of data, and provides the foundation for AI/ML innovation.
• Limitations: Without strong governance, a lake can quickly become a disorganized “data swamp,” making it harder to extract reliable insights.

Example scenario: An e-commerce company storing billions of clickstream events and product images can use a data lake to run machine learning models for personalized recommendations.

When a Data Lakehouse Is the Better Choice

A data lakehouse is an evolution of both warehouses and lakes, combining governance, scalability, and flexibility in one platform. For businesses that need to unify analytics, BI reporting, and AI/ML workloads without juggling multiple systems, a lakehouse is often the most future-ready solution.

• Best fit for: Enterprises in fast-moving, data-intensive industries such as finance, healthcare, retail, and logistics.
• Advantages: Provides real-time analytics, supports both SQL queries and advanced AI/ML models, and reduces operational complexity by consolidating infrastructure.
• Limitations: As a relatively new approach, it may require investment in training and adoption to fully realize its benefits.

Example scenario: A healthcare provider integrating structured patient records, unstructured medical imaging, and predictive AI models for disease detection could achieve all objectives through a lakehouse.

Decision-Making Framework

To help guide your choice, here is a simplified framework based on organizational size, industry, and data strategy:

1. Small and Mid-Sized Enterprises (SMEs):
   • If your needs are primarily BI dashboards and reporting, start with a data warehouse.
   • Warehouses are easier to implement and maintain, and they provide a strong entry point into data-driven decision-making without the overhead of managing large, complex systems.
2. Data-Driven Enterprises in Finance, Healthcare, and E-commerce:
   • These industries often deal with both structured and unstructured data while requiring strong governance.
   • A data lakehouse is the most suitable choice because it ensures compliance, enables advanced analytics, and supports large-scale innovation.
3. Organizations Investing in AI and Machine Learning:
   • If your core focus is leveraging data for predictive analytics, recommendation systems, or other AI-driven initiatives, prioritize a data lakehouse.
   • It combines the raw data flexibility of a lake with the governance and query optimization of a warehouse, making it ideal for innovation at scale.

Final Recommendation

• Go with a Data Warehouse if: Accuracy, compliance, and structured reporting are your top priorities.
• Go with a Data Lake if: You want to store massive, diverse datasets and experiment with ML or big data analytics.
• Go with a Data Lakehouse if: You are looking for a long-term, unified architecture that can handle both traditional BI and modern AI use cases without maintaining multiple systems.

Conclusion

The debate between data warehouse vs. data lake vs. data lakehouse is not about which technology is universally superior, but which best fits your business objectives. Warehouses remain reliable for structured analytics, lakes are powerful for unstructured data and machine learning, while lakehouses combine the two into a unified, future-proof architecture.

For organizations seeking long-term scalability, real-time insights, and AI-driven innovation, the data lakehouse is emerging as the strongest option. Now is the time for businesses to evaluate their data strategy and invest in an architecture that will sustain growth and competitiveness.

At Charter Global, we help enterprises modernize their data landscape with tailored solutions in data warehousing, cloud data lakes, and advanced lakehouse architectures. Our experts ensure seamless implementation, governance, and optimization so your business can leverage the full value of data.

The post Data Warehouse vs. Data Lake vs. Data Lakehouse: Which Is Better for Your Business? appeared first on Charter Global.

Big Data in Retail

Big Data refers to the massive volume of structured and unstructured data that businesses collect from various sources, including customer transactions, website activity, and social media interactions. Retailers are leveraging Big Data to gain actionable insights that drive better decision-making.

Applications of Big Data in Retail:

• Predictive Analytics for Demand Forecasting:
  Big Data enables retailers to analyze past sales trends, seasonal demand, and external factors like weather and holidays to accurately predict future demand. This ensures optimal inventory levels and minimizes stockouts or overstocking.
• Personalization and Customer Segmentation:
  Retailers use data to understand customer preferences and behaviors, creating highly personalized shopping experiences. For instance, Amazon’s recommendation engine, powered by Big Data, accounts for nearly 35% of its total sales.
• Inventory Optimization and Supply Chain Efficiency:
  Real-time data analysis helps retailers track inventory, reduce waste, and streamline supply chains. Walmart, for example, uses Big Data analytics to optimize its supply chain and reduce delivery times.

By leveraging Big Data, retailers can respond proactively to market trends and customer needs, ensuring they stay ahead of the competition.

AI Revolutionizing Retail

Artificial Intelligence is another game-changing technology that is revolutionizing the retail industry. With its ability to process vast amounts of data and make intelligent decisions, AI is reshaping how retailers engage with customers and optimize operations.

Applications of AI in Retail:

• Chatbots for Customer Service:
  AI-powered chatbots provide 24/7 assistance, answering customer queries, suggesting products, and even resolving complaints. Sephora’s chatbot, for example, offers beauty advice and product recommendations, enhancing customer satisfaction.
• AI-Driven Product Recommendations:
  By analyzing customer browsing and purchase history, AI algorithms suggest products that align with individual preferences. This personalization boosts sales and customer loyalty.
• Dynamic Pricing Strategies:
  AI enables retailers to implement dynamic pricing by analyzing competitor prices, demand, and market conditions in real-time. This ensures competitive pricing without sacrificing profitability.
• Visual Search and Virtual Try-Ons:
  AI-driven visual search tools allow customers to upload images of desired products and find similar items instantly. Additionally, virtual try-on features, such as those offered by L’Oréal, enable customers to test products virtually, bridging the gap between online and in-store shopping.

AI not only enhances the customer experience but also empowers retailers to operate more efficiently and profitably.

IoT Transforming Retail Experiences

The Internet of Things (IoT) connects physical devices to the internet, enabling seamless communication and data sharing. In the retail sector, IoT is creating smarter stores and more engaging customer experiences.

Applications of IoT in Retail:

• Smart Shelves and Automated Inventory Management:
  IoT-enabled smart shelves use sensors to track inventory levels in real-time, automatically notifying staff when restocking is needed. This reduces manual errors and ensures shelves are always stocked.
• Beacons for Personalized In-Store Experiences:
  Beacons are small IoT devices that send personalized offers and notifications to customers’ smartphones as they move through a store. For instance, Macy’s uses beacons to deliver location-based promotions, enhancing in-store engagement.
• Connected Devices for Seamless Checkout:
  IoT-powered systems like Amazon Go’s “Just Walk Out” technology allow customers to shop without waiting in checkout lines. Sensors track items picked by customers and automatically charge their accounts, making the process hassle-free.

IoT not only streamlines operations but also bridges the gap between digital and physical retail experiences, ensuring a unified journey for customers.

Benefits of Integrating Big Data, AI, and IoT

When combined, Big Data, AI, and IoT create a powerful ecosystem that drives significant benefits for retailers:

• Enhanced Customer Experience: Personalization, seamless shopping, and real-time engagement foster customer loyalty and satisfaction.
• Improved Operational Efficiency: Automation reduces costs, minimizes errors, and optimizes resource utilization.
• Real-Time Insights: Retailers can make data-driven decisions quickly, staying agile in a competitive market.
• Competitive Advantage: Early adopters of these technologies can differentiate themselves and capture greater market share.

Challenges in Adopting These Technologies

While the potential benefits are immense, the integration of Big Data, AI, and IoT comes with its own set of challenges:

• Data Privacy and Security Concerns: The collection and use of vast amounts of customer data raise privacy and security issues that must be addressed with robust safeguards.
• Integration with Existing Systems: Many retailers face difficulties in integrating new technologies with legacy systems.
• High Implementation Costs: Deploying these advanced technologies requires significant investment, which may be a barrier for smaller retailers.
• Employee Training and Expertise: Retail staff need training to effectively use these technologies, creating an additional layer of complexity.

The Future of Retail Technology

The next wave of retail technology is poised to take the industry even further:

• Metaverse Shopping: Virtual stores within the metaverse will provide immersive shopping experiences, allowing customers to browse and purchase products in a virtual world.
• Augmented Reality (AR): AR will enhance both online and in-store shopping by allowing customers to visualize products in real-world settings.
• 5G Technology: The widespread adoption of 5G will enable faster and more reliable IoT connectivity, enhancing real-time applications.
• Sustainability Solutions: Technology will also play a crucial role in creating sustainable retail practices, from energy-efficient stores to AI-optimized supply chains that reduce waste.

The future of retail is a blend of physical and digital, powered by advanced technologies that cater to evolving consumer demands.

Conclusion

The convergence of Big Data, AI, and IoT is driving a retail revolution, creating smarter, more efficient, and customer-centric experiences. Retailers who embrace these technologies will not only thrive in this competitive landscape but also set new standards for the industry.

At Charter Global, we specialize in delivering innovative technology solutions tailored to the retail sector. From implementing AI-driven analytics to designing IoT-enabled smart stores, our expertise helps retailers discover their full potential.

Contact us today to explore how our solutions can transform your retail business and position you as a leader in the future of retail.

The post The Future of Retail: How Big Data, AI, and IoT are Creating Change appeared first on Charter Global.

Big data encompasses extensive and intricate data sets that traditional data processing systems struggle to handle. However, with the advent of sophisticated data analytics tools, businesses are now equipped to analyze big data, uncovering patterns, understanding customer behaviours, refining operations and boosting overall business performance.

Let’s delve into the significance of big data in the realm of digital transformation and its impact on business decision-making.

The Critical Role of Big Data in Digital Transformation

For businesses to thrive in the digital era, adopting new technologies, processes and strategies is essential. Big data stands as a key component in this evolution, enabling companies to make decisions grounded in data analysis. The ability to collect and analyze copious amounts of data offers invaluable insights into customer preferences, market dynamics and operational effectiveness. These insights empower businesses to pinpoint areas needing enhancement and strategically allocate resources.

Advantages of Utilizing Big Data Analytics in Business

Big data analytics, the process of examining large and varied data sets, stands at the forefront of this transformation. It enables businesses to uncover hidden patterns, market trends, and customer preferences, leading to smarter business decisions. The advantages of integrating big data analytics into business operations are numerous and significant. Here are some of the key benefits that businesses can reap:

• Enhanced Decision-Making: Big data analytics significantly enhances decision-making in businesses. By providing critical insights, it enables companies to make more informed decisions, thus gaining a competitive edge. This technology offers a deeper understanding of market trends, customer behaviors and other key factors. As a result, businesses can strategize more effectively, respond to market changes with agility, and anticipate future challenges and opportunities. By leveraging big data, organizations can optimize their operations, tailor their offerings to meet customer needs better, and ultimately surpass their competitors in performance and innovation.
• Boost in Operational Efficiency: Businesses can leverage the analysis of operational data to identify areas ripe for improvement. By scrutinizing this data, they gain insights into how to streamline their processes, which can lead to significant cost reductions. This streamlined approach not only reduces unnecessary expenses but also optimizes various business operations. As a result, organizations experience a marked increase in overall efficiency. This methodical analysis and subsequent implementation of data-driven strategies ensure that businesses operate more effectively, thereby enhancing their operational efficiency and achieving better outcomes in their day-to-day operations.
• Advancement in Product Development: By analyzing various data points such as customer feedback, usage patterns and additional relevant information, companies gain a deeper understanding of their products’ real-world performance. This comprehensive analysis enables businesses to identify strengths and weaknesses in their current offerings. Armed with this knowledge, they can strategically refine and innovate, leading to the development of more sophisticated and customer-aligned products. Big data not only informs but also transforms the process of product development, ensuring that new products are more attuned to market needs and customer expectations.
• Elevated Customer Satisfaction: By leveraging big data, companies can tailor their offerings to better match what customers want, resulting in increased satisfaction. This approach involves customizing products and services based on detailed customer preference data, ensuring a closer alignment with consumer desires and needs, thereby significantly elevating the overall customer experience.
• Strengthened Risk Management: Utilizing big data analytics, companies can improve their risk management techniques. By analyzing information from various sources, they can detect potential risks at an early stage. This allows for the implementation of preemptive measures to mitigate these risks before they become significant issues.

Creating a Comprehensive Big Data Strategy

To maximize the benefits of big data, companies should create an all-encompassing strategy that encompasses these key steps:

1. Establishing Business Objectives:
   Clarify the specific business objectives that the big data initiative will bolster.
2. Evaluating Data Requirements:
   Identify the specific types of data required to fulfill these business objectives.
3. Implementing Data Governance Frameworks:
   Formulate guidelines and protocols for the effective management and safeguarding of data.
4. Selecting Appropriate Technologies:
   Opt for the most suitable technologies and tools for gathering, storing, and analyzing data.
5. Forming a Data Analytics Unit:
   Recruit and train a team of data analysts and related professionals to implement and maintain the big data strategy.

Security and Privacy in the Realm of Big Data

The advent of big data introduces distinct challenges in terms of security and privacy for organizations. To safeguard data integrity and maintain confidentiality, companies must adopt the following strategies:

1. Restrictive Access Management: Implement a stringent access management system to guarantee that sensitive data is accessible only to those with explicit authorization. This involves establishing robust verification protocols to verify the identity and authorization level of personnel seeking access. The system should be designed to restrict data access to individuals whose roles necessitate it, thereby minimizing the risk of unauthorized data exposure. Regular audits and updates of access permissions are essential to maintain the integrity of this system, ensuring that only currently authorized personnel have access and that any changes in roles or responsibilities are promptly reflected in access rights.
2. Robust Data Encryption: This method ensures the security of sensitive data, both during its transfer and while it is stored. It effectively prevents unauthorized access and breaches, safeguarding the information from potential threats. This robust encryption approach is essential in maintaining data confidentiality and integrity, particularly in environments where data security is a top priority. By implementing strong encryption standards, it provides a reliable shield against unauthorized intrusions, ensuring that only authorized parties can access the protected data.
3. Effective Data Masking: This practice aims to prevent unauthorized individuals from accessing sensitive data. It involves various techniques that anonymize or alter data, ensuring it remains secure and private. Data Masking is a critical aspect of data protection, as it helps maintain the confidentiality and integrity of information. It plays a pivotal role in safeguarding against data breaches and unauthorized access, by making sensitive data unreadable or unrecognizable to those without proper authorization. Implementing effective data masking is essential in any organization’s data security measures.
4. Comprehensive Data Retention Policies: Develop comprehensive data retention policies to manage the storage and deletion of information. These policies should be rigorously enforced to ensure compliance with legal and regulatory standards. The focus should be on creating a balanced approach that maintains necessary records for appropriate periods while also ensuring timely data deletion to comply with privacy and legal requirements. By systematically managing data, organizations can protect sensitive information, uphold data privacy laws, and mitigate risks associated with data breaches or non-compliance. These policies must be regularly reviewed and updated to stay aligned with evolving legal and industry standards.

The Evolution of Big Data

Looking ahead, big data is poised for exponential growth, fuelled by an increase in data quantity and intricacy from both corporate and individual sources. The rise of the Internet of Things (IoT) and advancements in machine learning are anticipated to further amplify data generation. This growth offers companies unprecedented chances for insightful analysis and data-driven decision-making.

Nevertheless, with the escalating scale and complexity of data, organizations must be prepared to invest in sophisticated analytics tools and technologies to stay abreast of these changes. Furthermore, cloud computing is likely to be instrumental in the evolution of big data, offering an efficient, scalable solution for storing and processing extensive data sets.

Big data plays a pivotal role in driving digital transformation for enterprises. By gathering and scrutinizing extensive datasets, companies can unlock critical insights about consumer behavior, market dynamics, and process efficiency. To effectively utilize big data, organizations must craft a thorough strategy. This involves setting clear business objectives, evaluating data requirements, establishing data management policies, selecting appropriate technologies, and forming a skilled data analytics team.

Are you ready to harness the power of big data for your enterprise? Reach out to us today to discover how we can assist you in achieving digital transformation and enhance decision-making through cutting-edge technologies and expert talent. Join us in elevating your business to new heights!

The post How Big Data is Driving Business Digital Transformation appeared first on Charter Global.

How do we overcome this obstacle, you ask? Start with acquiring a C-Level Sponsor who leads, commits and holds organizations accountable to a Big Data strategy. The three goals of the C-Level Sponsor are:

• First, you have to have a plan. It takes a strategy to get you there.
• Second, the plan must put together a proven set of formulas, approaches, and methods to win. There is no way to luck into success. As my high school football coach used to say, “Luck is when preparation meets opportunity”.
• Third, is to establish the strategy outcomes by answering the question, “Why are we doing this?” This answer should be quantified in business value creation dollars and remain the focus and measurement of the success of the strategy.

CEOs are being asked by the investor community and/or industry analysts to share their organization’s Big Data strategy. The investment community is wise to the fact that organizations that deploy and adopt Big Data and Predictive Analytics provide for long term stability and value gain to the shareholders. With the failure rate at approximately 70% for Big Data projects, it is important for the C-level Sponsor to mitigate risk and dollars in establishing a successful winning strategy. A winning strategy aligns executive, operational and IT goals into a single focus for significant value gain to the organization, shared responsibility and transparent accountability.

Seek outside help with an IT Consulting Partner who earns the trust of IT, Business & C-Level to help guide your organization through the strategy, process, methods and multiple POCs that are needed. There are many advantages for partnering with an outside trusted advisor:

• They accelerate your organization’s adoption of proven methods and approaches that guarantee success.
• There are politics in everything these days and Big Data has Big Politics attached internally with careers made and loss on such decisions; leverage an outsider to help manage through the status quo successfully because they have done it many times before.
• Having a trusted voice that is vendor neutral and is concentrating specifically on your success. Assisting you in managing vendors and proving out their claims before investing millions of dollars.
• Your team gets immediate access to critical key talent with a proven track record that you currently do not possess internally or cannot find.
• A trusted advisor guides the building of a cross-functional team and helps create a common set of semantics for communication. Big Data problems are modeling problems, and the models you are trying to create are those of the entities on which you gather data. The dynamics of the data and the products that derive from them are so dynamic that the business and IT folks need to be part of the same discussion and accountable as a team.
• Making the project successful, will require that the cross-functional team avoids any folks that are part of the old way of thinking; otherwise, the gap will persist and widen. A trusted advisor keeps a keen eye on the team and engages the C-level sponsor as needed to guide focus and outcome.
• Most importantly, they keep the focus of Big Data directed on outcomes that support the business and valuable outcome.

The post Big Data Strategy: More than a Numbers Game appeared first on Charter Global.

As a result, companies are struggling to find and hire top talent from the job market. With that said, the talent-hunting market has become increasingly dynamic. According to research, a number of job candidates use online resources exclusively to look for suitable positions.

Through online platforms, potential recruits are offered a window into a company’s values to gain perspective on the culture, salary ranges, career advancement opportunities, and work-life balance. Similarly, the quality of an organization’s management has become a deciding factor in whether or not employees will continue to stay at their current place of work.

Using Employer Data to Understand Valuable Workforce Trends

Employers are already in possession of a wealth of data. And, when leveraged correctly, this powerful possession can be a great tool in luring, hiring, and most importantly – retaining – the best candidates.

Companies and Bigdata Recruitment Strategy can take advantage of the trends and patterns within their own historical data to achieve precision in the course of hiring — they simply need to source through better-fitting candidates to hire and retain.

For example, a high turnover rate can be assessed by examining trends in the data associated with the employees who chose to leave their positions. By understanding why these candidates are coming and going so frequently, it is possible to develop and implement a comprehensive solution.

What’s more, reducing a high turnover rate is beneficial to an employer’s reputation, which, these days, is paramount in securing young, fresh talent. A little good reputation goes a long way. During the course of the Bigdata Recruitment Strategy, the ability to present a stable, yet dynamic workforce is enticing and alluring, and a surefire way to help turn candidates into recruits.

Employers Should Source for the Best Talents from the Onset

In today’s hiring niche, transparency is becoming highly prioritized. It is important to determine whether the qualified candidates are interested in the company — and if they are truly eligible for the position in question; along with ensuring they won’t turn elsewhere too soon.

On the other hand, job candidates want to determine whether or not the position in question matches their career path as well as skill sets, including the culture practiced in the workplace as well as their management policies.

By engaging data, Bigdata Recruitment Strategy can shift gears towards the era of transparency, sourcing eligible applicants who are best suited for the positions at hand. In addition to engaging data, automation will also help employers to achieve this feat faster and better than before.

Engaging Automation and Data in Recruiting

Before now, employers do not have a solid guide, or handbook gauging whether or not a prospective employee has a higher or lower flight risk — they only engage their intuition.

Thankfully, employers can now engage tools featuring artificial intelligence as well as in-depth analytic capabilities to parse data on their current workers — to truly determine their skills, previous work experiences, and most recent achievements.

Bigdata Recruitment Strategy should engage these tools and even social recruiting methods to hunt for and hire the best talents. After they get in the door, it’s even more important to find ways to keep and retain this talent for as long as possible.

The post Why Big Data Should be Part of Your Recruitment Strategy appeared first on Charter Global.

Blockchain Basics

Blockchain was developed in 2008 by Satoshi Nakamoto, a pseudonym used as a core component of the digital currency, “bitcoin.” Blockchain is simply a distributed and a write-once-read-only record of digital events in a chronological order that is shared in a peer-to-peer network.

Because it’s a simple technology, experts are eager to use it to replace more complex technologies in different sectors. It is more than a trend in 2018. Fortune 500 high tech companies Microsoft, Oracle, and IBM introduced cloud-based blockchain services, and they have collaborated with providers to seek more business opportunities in the blockchain arena.

Top blockchain trends to watch in 2018: 

Blockchain and the Internet of Things (IoT)

Blockchain technology can be applied to more than finance applications. The introduction of Ethereum has completely changed that perception due to the creation of a smart contact framework. Many experts believe that blockchain technology holds the key to the successful implementation of IoT.

The Internet of Things (IoT) is all about inter-connecting devices, vehicles, and even buildings (M2M). Stakeholders in the IoT development sphere are exploring potential applications of blockchain to the IoT model. Current enterprise security protocols can’t handle the demands of IoT due to issues stemming from the scale and the distributed nature of data points.

Blockchain and Artificial Intelligence (AI)

Artificial Intelligence (AI) emphasizes the creation of intelligent machines that work and react like humans. When you introduce blockchain to the AI equation, you know it’s going to be something to watch. Because blockchain technology can be integrated with AI implementations, the issues in theexchange of big data that currently exists in AI can be resolved cost effectively. Furthermore, blockchain provides a more secure platform that connects big data owners and AI developers. As a result, smart devices can take advantage of the system and apply AI innovations.

Blockchain in healthcare

In healthcare, critical information is scattered across multiple systems and often challenging to access. The current healthcare infrastructure is inadequate to the challenge of handling information exchange. Blockchain provides the “innovation trigger” and is just at the “peak of inflated expectations,” according to a recent Gartner Hype Cycle, and it could be integral in transforming how we view healthcare and data together. Blockchain’s potential for healthcare, however, depends on the willingness and capabilities of healthcare organizations in creating the required technical infrastructure.

Because blockchain is costly, there are some concerns regarding its integration with existing technology platforms, and there certainly is speculation about its cultural adoption. Blockchain requires significant investments, but with equally significant benefits. It is no surprise that blockchain is poised to be one of the key pillars in transforming the big data landscape.

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Having this large amount of raw data doesn’t offer much value out of the box. However, when applying the correct analytics, we can extrapolate powerful insights.

Some of the common questions that are asked are:

• Where do I begin?
• Are there different types of analytics?
• Which is most impactful for my environment?

Understanding analytics and the associated tools can help answer these and many other questions. I will describe how these tools differ, as well as the value they provide.

In its raw state, the data that we capture is indecipherable. However, the data we can conclude using analytics is indispensable. As you explore your data, you will begin to identify patterns at which point you can start to build your templates and analyze your data.

Descriptive Analytics:

Called the “simplest class of analytics”, descriptive analytics allows you to condense big data into smaller, more useful bits of information or a summary of what happened.

It has been estimated that more than 80% of business analytics (e.g. social analytics) are descriptive. Some social data could include the number of posts, fans, followers, page views, check-ins,pins, etc. It would appear to be an endless list if we tried to list them all.

Predictive Analytics:

Predictive analytics is the next step up in data reduction. It utilizes a variety of statistical, modeling, data mining and machine learning techniques to study recent and historical data, thereby allowing analysts to make predictions about the future.

Think of an actuary for an insurance company. They take the raw data and provide the percentage or chance that an event (e.g. crash) can occur. Although we use the name predictive, predictive analytics can only forecast what might happen in the future; it cannot predict the future.

In the most general cases of predictive analytics, “you basically take data that you have to predict data that you don’t have”.

Prescriptive Analytics:

Prescriptive analytics goes above and beyond the previous tools by predicting not one possible future, but rather “multiple futures” based on the decision-maker’s potential actions. By showing the likely outcome of each decision, the decision-maker can take this information and execute. Additionally, it can recommend the best course of action for any pre-specified outcome.

Prescriptive analytics requires two additional components: 1) actionable data and 2) a feedback system that tracks the outcome produced by the action taken. This ultimately aids in capturing more data to analyze and adjust the action to provide more accurate results.

This is a very high level explanation of Big Data Analytics as I understand them.I welcome any feedback or perspective

If you have a question for our experts, please leave us a comment below, visit our site https://charterglobal.com/ or email our team directly at marketing@charterglobal.com.

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Remember Kristanna Loken in Terminator 3 – Rise of the Machines? She was sent back to eliminate her target John Connor. Early on, she dials up Connor’s old high school to get an address. The phone on the other end rings, it gets answered, and the next thing you hear, is that harsh fax machine sound on the other end we dread getting because we dialed one number off. But do you remember what Cyborg Loken did? Without hesitation, she started speaking ‘fax’ over the phone. It’s how machines talk, it is what they sound like. Click on play button to hear the fax at 10KB/SEC.

Truly, machine language in the cloud exists. Humans are the endpoints, but machines are the intermediaries. Humans use dialect at the endpoints, but what about machine dialect? And, why should we care? Because, we are becoming a well-connected worldwide system, and machines embody sentiment, at different levels, at different frequency, i.e., the Striatal Beats of Neuroscience.

Below, one operator is sending a webpage and another is receiving the web-page. The wire-in-the- cloud transmits the machine language. Click on play button to hear the machines talkback and forth at 10MB/SEC; that’s FAX on steroids!

Now the picture in the middle with all the blue and red dots, represents the written language of what the machines are saying. Like human language, the computers have their own set of phonemes to go with their particular brand of dialect. In any event, what we are hearing and seeing is an audio/visual evaluation of Big Data via machine to machine IP communication. The catch acronym for this is AVIPE.

Now the natural question is can this AVIPE technique be applied to all signal types? The answer is that pretty much, yes. The trick is to find the type of phoneme transform that makes sense. Returning to the first blog, recall that the raw Data Source was pictured as a pure blue rectangle and next to it a rectangle with obvious vertical segmentation strips (see figure below). In terms of audio the former rectangle would be monotone in contrast to latter rectangle sounding off with something much less trivial, as you can imagine.

So this is the end of this three parts series on the Big Data Eco-System from the perspectives of ‘The Formal Script’, ‘The Round-table discussion’, and ‘The Comp’s sense of A/V’. We hope that as you read though the three blogs you picked up on the somewhat unorthodox way in which the data was visualized; not your standard dashboard displays. Dashboards are great tools for displaying the after-processing, while most of our visualization focused on pre-processing/investigation stage where are all the pieces are put on display to enable the round-table discussion. The techniques employed are testament to yet another aspect of the ‘Data Scientist’ and that is, the ‘Signal Processor’. A future blog will compare and contrast the roles of Data Scientist and Signal Processor. (Note: Reddit tackled the difference between data scientist and statistician which might serve as a good starting point.

Have a question for our experts? Leave us a comment below, check out our Big Data page or contact our team directly at at info@charterglobal.com.

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This week’s example, “The Round-table Big Data Eco-system”, will use the same technology domains shown and discussed last week, but this time the BD/BI directives will be using a circular Eco-system framework. The round-table approach incorporates high level visualizations of the evolution of discrete and asynchronous data to business solution. We explore how even the stakeholders sitting at the table contribute to the business-technology derivatives at the center of the eco-system.

The figure below emphasizes the visualization aspects, it begins with the business development task initiated at step 1. We will track the solution in a CCW loop to the solution given in step 6. In doing so the roles of the solution stakeholders (BD, System Architecture, ETL, Modeling, and BI) are openly portrayed. An “Eco-System” box is placed in the center and acts as the place for stakeholder contributions and the place where discussions are initiated. The entries to this box may range from Quality Assurance issues to unforeseen gleanings for the BI/BD group.

The task is to develop a schedule reflecting a customers’ home activity. Sensors within the home were used to populate a spreadsheet of activity times in step 2. At this point, in a legacy system, where the activity variable is well known, a simple model would have sufficed to directly provide the answer in step 6. However, we believe filling in the details, which we describe below, enriches the whole process and places the stakeholders on a stage with empowerment and responsibility. We ask one thing as we continue this journey. Patience. It is not easy at first sight but soon it will be the only way you want to go.

With Big Data, CGI knows the multiple sources and behaviors are not, in general, consistent. For this example we took the spreadsheet information and engineered a visualization in step 3 that allowed us to see at a very high and yet intelligible level. To do this we actually increased the number of data points by an order of magnitude. We aligned 1,000 customers in a matrix that included 2,500 time stamps running down the side. The term “matrix” shouldn’t scare anyone away, instead, it should be embraced. The organization a matrix can offer with 2.5 million numbers in one image is astounding.

We would like to note that these matrices are drawn with Matlab, an engineering/visualization tool, but recognize there are other visualization tools that do a good job too.

Activity from the spreadsheet is indicated in the matrix display by a yellow dot. A blue dot means no activity. A subtle point to catch is that the spreadsheet only shows “what is” whereas our matrix shows “what is and what is not”. Already, we begin to incur deeper perspective through diversity.

With these two simple on/off indicators, the round-table can begin to get a feel for the information. In step 3 there are vertical columns entirely coded in blue, meaning no activity was listed at all. In step 4 we reorganize the columns according to amount of activity. We find out that in fact there are approximately 400 columns (customers) without activity, as indicated with the red box. The green and yellow boxes indicate gaps in activity that too would be difficult to see with a spreadsheet approach.

The plot in step 4 is very important. We see this as a pivotal point in the solution where the pieces of the solution are exposed in a very raw form. It is the main feeder point into the Eco-System box directly above it. At this point all of the round-table stakeholders should be engaged with each other and forming discussion points.

The solution continues with taking a well-defined segment of information from step 4 and feeding it to the modelers in step 5. Now it is apparent that the modeling is based on approximately 200 home sand not the 1,000 homes listed in the spreadsheet.The plot shown in step 5 exhibits the several stages of the modeling process used to produce a solution. These stages are the impetus for feeding the BI/BD portion in the Eco-System since it contains information about customer activity beyond the BD work schedule task. A solution is passed on to step 6 in the form of a plain text work schedule and one of the plots from step 5.

In closing, with the loop from (1) to (6) we hope to have convinced you that with the help of a good visualization tool there comes novel visualization techniques that can express information and encourage cross-stakeholder conversation in ways that are complimentary to the approach shown in last week’s blog. We really do need both approaches.

Check back next week for the final blog in this series, an audio/visual computer perspective on Big Data.

Have a question for our experts? Leave us a comment below, check out our Big Data page or contact our team directly at at info@charterglobal.com.

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Today, video cameras are widely used for traffic monitoring and data collection. The combination of space and time is a defining feature of digital video. However, considering the large space and expensive cost in traffic video storage, traffic video data are usually saved into video segments, scenes, shots, or frames. In addition, it is very difficult to extract key spatio-temporal data, such as individual vehicle trajectories and traffic aggregate data, from the physical storage medium. Ideally, this would be done automatically, but typically this information is obtained using manual methods. Therefore, it is difficult for current video database systems to quickly scan traffic video data and find the desired transportation-related spatio-temporal query results.

There are two main ideas for storing video data. First, once the video cameras collect discrete vehicular trajectory data (some number of frames per second), the trajectory data is interpolated into continuous traffic data. The continuous traffic data is described by some functions of a temporal parameter t and the spatial parameters. Second, the continuous traffic data is stored in spatio-temporal databases.

Development Method

The transportation spatio-temporal system can optimize traffic data completeness and offer high-level spatio-temporal queries of transportation data. The design and development of the transportation spatio-temporal system consists of the following four main parts

• Data extraction: Through video-capture methods, traffic video data extraction provides vehicular trajectory data (i.e., vehicular instantaneous location, time, and speed data points).
• Data interpolation: Discrete individual and average vehicular velocity points with different time intervals are interpolated into continuous velocity by the linear and nonlinear data models.
• Data integration: Both of the highway spatial data of GIS shape-files and the continuous vehicular trajectory data are transformed and integrated into spatio-temporal databases.
• Data retrieval: A high level traffic information query interface guides the users in performing spatio-temporal queries of the integrated dynamic transportation information.

Advantages

Data completeness

Data completeness requires that data sources in databases should cover all information (i.e., all data types and the complete information of each data type) to meet the current and future demands of various data users. Traffic stream is observed at each spatial point within some distance interval over time, not just at one spatial point.

Existing transportation software systems store discrete traffic aggregate data, such as volume, density, headway, queue length, spacing etc., in relational databases. Aggregate data incompleteness in space and time causes the insufficient performance of traffic engineering models in transportation software systems. For example, due to the lack of volume over continuous time and space, not all travelers can gain desired travel time query information from volume-based travel time estimation models in advanced traveler information systems (ATIS).

The transportation spatio-temporal system can offer complete individual vehicle trajectory and traffic aggregate data over continuous space and time. Complete traffic data sources are useful for the description of traffic flow phenomena and for the calculation of various transportation engineering models. Such spatio-temporal system can be particularly advantageous in understanding highway flow breakdown (i.e., incident detection), and dynamical traffic congestion, because a detailed picture of traffic parameters over both time and space is better than these parameters in time alone.

Besides individual vehicular and traffic aggregate parameters, the transportation spatio-temporal system can offer other traffic data over continuous time and space. These specific traffic data involve the speed difference among moving cars at an intersection at any time, the identification of cars that drive in excess of the speed limit during any period, the number of trips during any period in a city, and so on.

Data redundancy

Discrete traffic aggregate data archives cause not only the loss of a large amount of aggregate traffic data, but also the increase of data redundancy in databases. Aggregate data are a typical data redundancy in databases, and it means that some data are stored for multiple times. Efficient data operations require data consistency and data synchronization in databases by minimizing or avoiding data redundancy.

In relational databases traffic data redundancy often causes data anomalies, data corruption, and data retrieval errors. It is difficult for existing transportation management systems to keep data synchronization between volume values and the above four traffic parameters. The frequent operations of traffic data in databases easily cause data inconsistency or anomalies and data retrieval errors.

By using new traffic data models, spatio-temporal databases just request the collection and storage of individual vehicular time, location, and instantaneous velocity. Traffic aggregate data can be retrieved from the transportation spatio-temporal system by database query designs. Therefore, the transportation spatio-temporal system provides traffic data archiving methods that can solve the above problems concerning traffic data redundancy. In addition, traffic devices for aggregate data collection could be removed from highways.

Data operation

In contrast to existing static traffic data sources, the transportation spatio-temporal system offers an adjustable dynamic transportation information environment. It means that the data collection of individual vehicular trajectory would be more important than traffic aggregate data for data collection and storage for transportation applications. The integration of highway spatial data and vehicular trajectory data create the spatio-temporal logical relationships among the entire transportation motion data.

Based on the above data integration, vehicular trajectory data not only control the accuracy of all aggregate data, but also determine data synchronous operations among traffic aggregate data. This synchronicity of adjustable traffic information is an immense advantage for the analysis and verification of dynamic traffic phenomena. In addition, the dynamic information environment provides potential opportunities for the development of dynamic or multi-dimensional transportation engineering theory and the optimization of transportation software systems.

Conclusions

Video cameras can easily collect traffic information, but storing the raw video data generally requires a huge storage space. More importantly, it is difficult to retrieve the values of traffic parameters from video data for the calculations of transportation engineering models or the development of transportation software, not to mention traffic data operation or adjustability. The transportation spatio-temporal system is recommended to overcome the storage problem by converting traffic videos into a spatio-temporal database.

The transportation spatio-temporal system converts traffic video data into vehicular motion information in spatio-temporal databases. The transportation spatio-temporal system interpolates the vehicular trajectory data (time, location, and velocity), which are extracted from video, and integrates them with spatial road information for the storage of dynamic transportation environments. The transportation spatio-temporal system can avoid data storage and retrieval issues caused by traffic videos. Moreover, users can manage and operate multiform and multidimensional traffic data in a spatio-temporal transportation environment.

If you have a question for our experts, please leave us a comment below, or check out www.charterglobal.com or email our team directly at marketing@charterglobal.com.

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