Blendata Tackles the 10 Most Common Big Data Questions, Based on Experience

?? Dive into the world of Big Data and discover the top 10 FAQs addressed by Blendata experts to shed light on the world of Big Data and AI! ??

?? 1. Where Do Big Data Sources Come From?

Big Data comprises extensive datasets characterized by three main properties: Volume, Variety, and Velocity. These datasets stem from diverse origins, which fall into three primary categories:

Enterprise Software: Includes ERP (Enterprise Resources Planning), SCM (Supply Chain Management), and CRM (Customer Relationship Management) systems.

In-house Website/Application: Involving data from various mobile applications and data collected from websites interactions, known as In-house Website/Application Data.

External Sources: Includes Social Data from social media, Partner data, and Other Third-Party Data.

For more details about Data Sources, read the article:

Learn More About Data Sources in the Business World

?? 2. How Does Data Lake Technology Differ from Data Warehouse?

Data Lake and Data Warehouse serve as frameworks for managing extensive datasets, yet they consist of distinct characteristics and applications:

Characteristics of How Big Data is Managed:

Data Lake: Developed during the era of massive digital data (Volume), diverse in structure and type (Variety), including data that are generated rapidly and constantly changing (Velocity), which is also known as Big Data. This includes social media data, sensor data, text, images, and videos, in addition to structured data from traditional databases.

Data Warehouse: Primarily manages structured data, which is already being processed and organized (through ETL - Extract, Transform, Load) for analysis and reporting. This data typically comes from organizational sources like transaction databases, ERP systems, or CRM systems.

Pros and Cons:

Data Lake: Efficiently manages large datasets of all types, supports fast processing when being compared to its price and performance, and integrates well with modern analytical tools, especially advanced analytics and AI. However, unlike Data Warehouse, it has limitations in areas like updating data and handling high concurrent workloads (simultaneous large transactions).

Data Warehouse: Has been efficiently managing structured data for a long time, supports report generation (e.g., OLAP - Online Analytical Processing) and integrates well with BI (Business Intelligence) systems. However, it is limited in connecting Big Data with modern analytical tools. Moreover, it has higher costs compared to modern tools.

The Technology of Data Lakehouse emerged to combine the benefits of both, based on Data Lake or Big Data technology, which Blendata Enterprise falls into this category.

Examples of Technologies:

Data Lake: Utilizes efficient storage systems for handling large datasets alongside parallel processing units. Examples encompass the Hadoop Distributed File System (HDFS), Scale-out NAS appliance, Software-defined storage, and cloud storage solutions such as Amazon S3 and Google Cloud Storage. Processing technologies encompass Apache Hive, Impala, and Presto for batch processing, Apache Flink and Storm for real-time processing, or Apache Spark for both, forming the foundation for Blendata Enterprise technology.

Data Warehouse: Typically employs SQL-based structured databases such as MySQL and PostgreSQL. For managing large datasets and executing complex processing tasks, technologies include Teradata, Oracle Exadata, IBM Netezza, and Greenplum..

?? 3. When Should an Organization Initiate a Big Data Project?

Starting a Big Data project within an organization should be prompted by identifiable needs and preparedness, depends on several critical factors:

• Need for Extensive Data Analysis: If the organization requires in-depth analysis of large datasets for purposes like business trend analysis, market demand forecasting, or individual customer behavior analysis, initiating a Big Data project becomes imperative to facilitate informed decision-making.
• Inefficient Processing Tools: If the existing data processing system is sluggish or ineffective, leading to delays in data acquisition and impeding business operations or further data analysis, implementing a Big Data project with advanced technology can vastly enhance processing efficiency.
• Sufficient Data Quality and Quantity: Initiating a Big Data project necessitates evaluating the quality and quantity of available data. Sufficient high-quality data should be accessible for analysis to derive valuable insights and enhance business value.
• Future AI Project Plans: A solid foundation of quality data is essential for AI applications.Launching a Big Data project is critical for the successful integration of AI in the future. Through the consolidation of relevant data and the utilization of suitable technology, AI projects can become more cost-effective and streamlined.

Nevertheless, organizations should thoroughly assess their requirements and preparedness and formulate an appropriate implementation strategy to ensure optimal long-term returns on investment.

?? 4. How Does Big Data Intersect with AI? Is Big Data Necessary for AI Projects?

As artificial intelligence (AI) becomes increasingly valuable across industries, effective integration without errors hinges on two key components: data and skilled personnel. Prior to investing in AI systems, adept data management proves critical. Big Data serves as the foundational cornerstone for AI, fueling the development of intelligent systems. Organizations aiming to harness AI's transformative potential must first strengthen their data management and IT infrastructure, ensuring data quality and readiness. Inadequate data management risks AI's utilization of inaccurate or incomplete data, resulting in skewed outcomes and constrained business advantages. Therefore, organizations must prepare their data management capabilities to unlock the full potential of potent and intelligent AI technology.

?? 5. What Are the Challenges and Difficulties in Implementing Big Data?

Numerous companies in Thailand acknowledge the importance of Big Data and strive to leverage it, yet encounter hurdles in efficiently managing it to optimize effectiveness and return on investment. The challenges include the following:

• Understanding the benefits of Big Data but lacking a clear starting point.
• Dealing with the complexity of infrastructure and data readiness.
• Facing challenges in cost control due to the growing data volumes.
• Addressing organizational culture and personnel issues.

For further details on the challenges and solutions, refer to the article:

Exploring 4 Big Data Management Challenges in Thai Businesses and Overcoming Obstacles to Enhance Competitive Advantage

?? 6. What Are the Steps to Plan a Successful Big Data Project??

Blendata recommends seven best-practice strategies for integrating Big Data and AI into an organization, drawing from experience across various industries.The process involves the following steps:

1. Define clear business objectives.
2. Assess data sources and devise a strategy.
3. Prioritize goals achievable in the short term (Quick Wins).
4. Build a team of data experts.
5. Establish a robust data infrastructure.
6. Promote a culture of data-driven decision-making.
7. Implement strict data governance and rigorously manage data ethics and privacy.

For further insights on planning a Big Data project, please feel free to refer to this article:

7 Strategies to Implement Big Data and AI for a Competitive Business

?? 7. Who Should be Participating in a Big Data Project?

In Big Data projects, complexity often impacts various aspects of a business, necessitating collaboration from multiple teams, including:

• Top Management: Executives and senior managers are responsible for making key decisions and setting policies for the project. They define the project's objectives and assess risks and investments.
• Data Team: This team consists of specialists with three main roles:

The data team is crucial for data collection, analysis, and reporting to support business decisions, utilizing relevant analytics tools and technologies to ensure effective alignment with business objectives.

• IT Team: Manages the technical aspects of the project such as infrastructure for data storage, processing, and security. They play a key role in selecting and implementing appropriate technologies to support the project’s objectives.
• User/Stakeholder Team: DDefines the requirements and provides domain-specific knowledge to ensure project efficiency. For example, the marketing team may be required for a project focused on personalized campaign recommendations using Big Data and AI.
• Legal and Security Team: Ensures compliance with data security policies is in place and monitors adherence to data protection regulations to manage customer and sensitive data appropriately and securely.

Big Data projects require collaborative efforts among these teams to achieve success and deliver maximum benefits. Organizations should foster a data-driven culture and invest in developing comprehensive expertise within their personnel or seek external assistance as needed.

?? 8. What Are The Policies Should Be Considered When Implementing a Big Data Project?

Implementing a Big Data project necessitates careful consideration of data ethics, encompassing aspects such as data privacy, security, transparency, and fairness in data processing and decision-making to prevent biases. Compliance with relevant laws and regulations like PDPA, GDPR, and CCPA is essential.

?? 9. What Are Some Examples of Big Data Use Cases That Can Benefit Businesses in the Digital Era?

In the digital age, Big Data serves as a crucial technology driving organizations towards becoming data-driven entities, offering various positive impacts and enabling agile responses to market dynamics. Here are some interesting use cases:

• Customer Analytics: Analyzing customer behavior data, including internet usage, purchase history, and social media interactions, to evaluate buying behaviors. This insight helps businesses tailor marketing and sales strategies to better align with customer preferences.
• Predictive Analytics: Utilizing historical and relevant data to forecast future events, such as sales forecasts, product demand, or customer complaint predictions. This predictive capability enables proactive planning for favorable outcomes or timely mitigation of potential risks.
• Business Process Improvement: Utilizing Big Data to analyze business operations, identify inefficiencies and enhance processes. Examples include optimizing product logistics to minimize delays and managing inventory based on sales insights to ensure continuous customer satisfaction.
• Risk Analysis and Management: Integrating data from diverse sources, including purchase records, service usage, and financial data, to assess and mitigate business risks effectively. This data-driven approach enhances risk management strategies and facilitates prompt, informed decision-making.
• Product and Service Customization:? Leveraging customer data to personalize products and services according to individual or group preferences. By utilizing past transaction data to suggest future offerings, businesses enhance sales effectiveness and elevate customer satisfaction.
• New Product Development: Harnessing Big Data insights to gain deeper market understanding and inform product development initiatives. This facilitates the creation of innovative products or enhancements to existing offerings that better align with evolving customer needs and market trends.

?? 10. What Are the Future Trends in Big Data Utilization?

With the increasing application of AI due to its capabilities and accessibility, and rising business competition, the future usage of Big Data is expected to grow significantly. Examples include:

As artificial intelligence (AI) continues to advance, driven by its capabilities and accessibility, and with escalating competition in the business landscape, the future of Big Data utilization is set to grow substantially. The anticipated trends include:

• Collecting Big Data for AI Applications: The pursuit of quality data, devoid of inaccuracies or superfluous information, assumes paramount importance as organizations strive to fuel AI algorithms effectively. This entails the accumulation and storage of increasingly vast datasets.
• Utilizing Big Data for Service and Product Enhancement: Analyzing detailed consumer behavior data allows businesses to understand individual or niche group preferences, thereby facilitating targeted service and product enhancement. There is a trend involving the broadening scope of consumer behavior data collection, encompassing aspects such as user identity attribution across websites and applications, coupled with corresponding behavioral analytics.