The aim of big data management is to ensure a high level of data quality and accessibility for business intelligence and help you to make important financial decisions based on analysis of big data analytics applications.


Why You Need Big Data Management?
The aim of big data management is to ensure a high level of data quality and accessibility for business intelligence and help you to make important financial decisions based on analysis of big data analytics applications.

Big data management is almost related to the concept of data lift cycle management (DLM). It manages data as a valuable resource for determining what kinds of information could be stored and scheduling the data when can safely be deleted.

It surrounds the policies and technology used for collection, storage, governance, organization, administration, governance and delivery of many of both structured and unstructured data. Various processes involved in big data management that include data cleansing, migration, integration and preparation for data reporting (data virtualization) and analytics.

8 Benefits Entitled to You Include the Following:

  • Increased revenue
  • Improved customer service
  • Enhanced marketing
  • Increased efficiency
  • Cost savings
  • Enabling new applications
  • Improved accuracy for analytics
  • Competitive advantage

Here’s What You Can Expect from Us…!

  • Provide professional consultation to understand what you need
  • Help you to integrate the big data system
  • Help you to design all-rounded interactive visual analysis
  • Assist your business to more competitive in your industry



Data integration complicates joining data from distinct sourcesinto a single, unified and providing user with a significant and beneficial view of them.

This process becomes the meaningful logical concept in a variety of situations(aligning, combining, and presenting) to encourage collaboration and deliver trusted data between organizational departments as well as external remote sources, which include both commercial and scientific domains. The unified view is usually stored in a central data repository to satisfy integrator goals.

Data integration appears with the needs to share existing data explodes and increasing frequency as the Big Data. This process is regularly a requisite to other processes including analysis, reporting, and forecasting.

3 Main Reasons for the Demand of Data Integration Strategy

  1. Costs and Budgets
    To enhance the value of business data, you might need the data integration solutions. Data Integration tools can deduct the skill set requirements for developers, making it possible to deduct staffing costs over time. With hand coded solutions, costs typically will develop linear with new requirements. Advanced tools and solutions can prove to be a smart investment in a solid basis for low-cost and high-speed implementation of new solutions and applications.

  2. Reduce Data Complexity
    To deduct the workload, data integration can help you to increase operational efficiency.  The modern IT organization has to support multiple platforms and various types of data stored in disconnected silo’s. For better collaboration, it is vital to connect all various data sources with each other to handle the value of insights.

    Data integration relates to manage complexity and streamline connections to deliver data to any system easily. This might involve creating a data hub that’s easy to publish to and subscribe to.The automation of unified views can reduce the needs for manually gathering data, and no longer to build connections for running a report or building an application.

  3. More Insights Increase the Value of Data Through Unified System
    To be more competitive, you might wish to access reliable data fast for the right decision making. A data integration strategy can help you to achieve your goal. It can support you from the fast and reliable reports and analysis based on the available information and straightforwardly improve your processes.


Data transformation is the process of changing the data format, structure, values or arrangement to another particular one. Data transformation is vital toseveral kinds of activities such as data management and data integration.

Methods to Transform Data

  • Extraction and parsing
  • Translation and mapping
  • Filtering, aggregation, and summarization
  • Enrichment and imputation
  • Indexing and ordering
  • Anonymization and encryption
  • Modeling, typecasting, formatting, and renaming

Processes such as data integration, data migration, data warehousing, and data wrangling all may involve data transformation.

Data transformation may be defined as these types:

Constructive Add, copy, and replicating data
Destructive Deleting fields and records
Aesthetic Standardizing salutations or street names
Structural Rename, move, and combine columns in a database

2 stages of data pipeline for data analytics projects:

  1. On-premises data warehouses: use an ETL (extract, transform, load) process, in which data transformation is the middle step

  2. Use cloud-based data warehouses: can scale compute and storage resources with latency measured in seconds or minutes.

The scalability of the cloud platform lets organizations skip preload transformations and load raw data into the data warehouse, then transform it at query time — a model called ELT (extract, load, transform).

An enterprise can choose among a variety of ETL tools that automate the process of data transformation. Data analysts, data engineers, and data scientists also transform data using scripting languages such as Python or domain-specific languages like SQL.

Benefits of Data Transformation

Transforming data yields several benefits:

  • Better-organized: easier for both humans and computers to use.
  • Improve data quality and protects applications from potential landmines
    (null values / unexpected duplicates / incorrect indexing / incompatible formats)
  • Data transformation facilitates compatibility for multiple purposes


Data Modeling is the process of creating a data model for storing data in a database. Data model is used to visual representation of data regulatory compliances and to enforce business rules / regulatory compliances / government policies on the data. It can also ensure the quality of the data with consistence in naming conventions, default values, semantics and security.

Purposes for Different Data Modeling

It is vital to define the purpose of the data model to start data modeling. There are two types of data models:

  1. Relational Data Modeling
    – Design for building transactional and operational systems
    – Designed to get data into database that maintains integrity and stores every piece of non-key data only once

  2. Dimensional Data Modeling
    – Design for building reporting and analytical systems
    – Designed to get data into data warehouse and into the hands of business users
    – Easy to query across many different tables

Data modeling is the process of creating a data model. It occurs at the following levels:

  • Physical Model
    – Scheme / framework for how data stored in a database

  • Conceptual Model
    – High-level / user view of data

  • Logical Data Model
    – Middle level: between the physical and conceptual levels
    – Separate the logical representation of data and its physical storage


Nowadays, many organizations are focussing on data quality is even better than it was before. The reason is they know that good data quality is always good for business.

Data Quality is the ability of a given data set to serve an intended purpose which must be consistent and unambiguous. In order to assure the data fit for consumption and meet the needs of data consumers, you might have to plan, implement, and control on quality management techniques.

When improving data quality, the aim will be to measure and improve a range of data quality dimensions. The basic dimensions of data quality are as below:

  • Accuracy
  • Completeness
  • Consistency
  • Integrity
  • Reasonability
  • Timeliness
  • Uniqueness / Deduplication
  • Validity
  • Accessibility

Data Quality Management

To prevent future data quality issues and fulfil the data quality Key Performance Indicators (KPIs), the aim for data quality management is to employ a balanced set of remedies for achieve the business objectives.

The data quality KPIs must relate to the KPIs used to measure the business performance in general.The categories of themfor core business data assets are as follows:

  • Uniqueness
  • Completeness
  • Consistency
  • Conformity
  • Precision
  • Relevance
  • Timeliness
  • Accuracy
  • Validity
  • Integrity

The remedies used to prevent data quality issues and eventual data cleansing includes these disciplines:

  • Data Governance
  • Data Profiling
  • Data Matching
  • Data Quality Reporting
  • Master Data Management (MDM)
  • Customer Data Integration (CDI)
  • Product Information Management (PIM)
  • Digital Asset Management (DAM)


Advanced analytics is the part of data science that is a comprehensive set of analytical techniques and methods. By using advanced modeling techniques (sophisticated algorithms, analytical techniques and tools), it can forecast businesses trends, events or discover patterns to allow more refined answers and decisions.

Particularly predictive analytics can help you to perform high-level statistical models or calculation, as well asproject the future, drive change using data-driven and fact-based information.

Advanced analytics can answer questions including:

  • Why is this happening?
  • What if these trends continue?
  • What will happen next? (prediction)
  • What is the best that can happen? (optimization)

Why is Advanced Analytics so Important?

It is the true potential of data to use and operationalize business intelligence applications within business processes to leverage their data assets. The benefits are shown as below.

  • Reduction in day-to-day requests
  • Ability to focus on strategic projects
  • Focus on projects that require 100% accuracy
  • Ability to achieve mature modeling goals


What is Artificial intelligence all about?

Artificial intelligence is a technology about the ability of a digital computer to perform tasks associated with intelligent beings. The intellectual processes characteristic of humans (plan, perceive,process natural language, the ability to reason, discover meaning, generalize, or learn from experience) are applied to the project of developing system frequently.

Artificial intelligence is further defined as “narrow AI” or “general AI”.

  • Narrow AI: Design to perform specific tasks within a domain (e.g. language translation).
  • General AI:Hypothetical and not domain specific, but can learn and perform tasks anywhere.

Current Uses of AI:

  • Email filtering:
    – Email services use artificial intelligence to filter incoming emails.
    – Users can mark emails as “spam” to train their spam filters.

  • Personalization:
    – Applied AI to personalize your experience services.
    – Learn from your previous actions in order to recommend relevant content for you.

  • Fraud detection:
    – Determine if there is strange or unexpected activity that could be flagged by the algorithm.

  • Speech recognition:
    – Optimize speech recognition functions

AI can carry out very complex data handling and tasks, such as mathematical theorems or playing chess. It is already impacting how users interact with, and are affected by the Internet. In reality reflection, AI has potential to extremely change the way that humans interact through their works or socioeconomic institutions.

In these recent years, the perfect environment for AI application and services has been made for Big Data project and Internet of Things (IoT). Some applications related to AI are already appeared in a lot of fields like healthcare diagnostics and targeted treatment, etc.On the other hand, AI is applied to the internet that also has the potential to be a new engine for economic growth.


Machine learning is a division of artificial intelligence(AI) that concentrate on the development of computer programs. It based on the idea that systems can learn and develop from access data, observations or experience without being expressly programmed.

The primary aim for machine learning is tooffer systems the ability to automate data-driven model building and make decisions with minimal human intervention.

Machine Learning – Algorithms That Generate Algorithms

Algorithms are a series of command used to instruct computers in new tasks to solve problems. Computer algorithms based on particular instructions and rules to organize tremendous amounts of data into intelligence and services.

Provide training data to a learning algorithm is the basic practice of machine learning. Based on the implication from the data, the learning algorithm can generate a rule set.It is fundamentally generating a new algorithm as a machine learning model.

By using different training material (data or experience) with same learning algorithm, it could be used to generate different data models.

  • Supervised Machine Learning Algorithms:
    – The training material is about the analysis of a known training dataset
    – Produces an advanced function to predict the output values
    – Use labelled examples to apply learning outcomefor predicting future events
    – Able to provide targets for any new input after adequate training
    – Compare its output with the intended output
    – Find errors to modify the model accordingly

  • Unsupervised Machine Learning Algorithms:
    – The training material is unclassified or unlabelled data
    – Infer a function to describe a hidden structure from unlabelled data by unsupervised learning studies
    – Cannot figure out the right output
    – Analyse the data and make conclusion from datasets to define hidden structures from unlabelled data

  • Semi-Supervised Machine Learning Algorithms:
    – Between supervised and unsupervised learning
    – The training material is labelled and unlabelled data
    – Provide relevant training and resources for acquired labelled data

  • Reinforcement machine learning algorithms:
    – Produce actions and discover error / reward to interact with its environment
    – The reinforcement learning is to have trial and error search and delayed reward
    – To maximize the performance, determining the best behaviour within unidentified context automatically is necessary
    – The reinforcement signal is to learn which action is the best by provided simple reward feedback or punishments as signals to agent

Machine learning requires extra time and resources to train it properly to enable analysis of massive quantities of data. After the learning processes, it can identify profitable opportunities or dangerous risks from more accurate results rapidly.To be more effective on processing enormous volumes of information, machine learning with AI and cognitive computing should combine together.

How Machines Learn

Machine learning methods can be categorized as three general types:

  • Supervised Learning:
    – The learning algorithm is given labelled data and the desired output.

  • Unsupervised Learning:
    – The unlabelled data given to the learning algorithm is to identify data patterns.

  • Reinforcement Learning:
    – Dynamic environment provides rewards and punishments to interact with algorithm.

Key Considerations

To address AI to the context of people’s trust in the internet, there are several specific factors that must be considered:

  • Socio-economic impacts
  • Transparency, bias and accountability
  • New uses for data
  • Security and safety
  • Ethics
  • New ecosystems


Data governance is the main component of data management strategy. To be more efficiency, it is important to centralized control mechanisms within the bounds of policy and regulation. It composes the collection of general data management that includes these processes:

  • Collecting
  • Validating
  • Storing
  • Protecting (Data security)
  • Efficiently
  • Cost-effectively

These processes and technologies can protect the organization’s data assets to ensure the data can be understandable, correct, complete, trustworthy, secure and discoverable.

The topics covered by data governance are:

  • Data architecture
  • Data modeling& design data storage & operations
  • Data security
  • Data integration & interoperability
  • Documents & content
  • Reference & master data
  • Data warehousing & business intelligence
  • Meta-data
  • Data quality

To cover the topics of data governance, it is necessary to establish methods with clear responsibilities and processes to standardize, integrate, protect and store corporate data. The targets are to:

  • Minimize risks
  • Establish internal rules for data use
  • Implement compliance requirements
  • Improve internal and external communication
  • Increase the value of data
  • Facilitate the administration of the above reduce costs
  • Reduce costs
  • Ensure the sustainable of the company through risk management and optimization

The programs can affect the strategic, tactical and operational levels in organizations. Data governance is a prerequisite for tasks or projects and has many benefits:

  • Offer better and more comprehensive decision support
  • Increase the scalability of the IT landscape
  • Offer potential to optimize the cost of data management
  • Increased process efficiency through the use of synergies
  • Higher confidence in data through quality-assured and certified data
  • Security for internal and external data
  • Clear and transparent communication through standardization