Data Analytics

data analytics

Data Collection

We use professional software for data collection and gather and acquire data from various sources that can be used for data analysis in different business. In data analytics, data collection is a crucial step as the quality and relevance of data directly affect the insights and outcomes of data analysis.

The process involves various methods, including surveys, experiments, interviews, observations, and web scraping and other data analytics tools. The type of data collected can range from structured data, such as numerical data and categorical data, to unstructured data, such as text and multimedia.

Once the data is collected, it is usually stored in a database or data warehouse, where it can be preprocessed, cleaned, and transformed before being analysed. Our data analyst can use techniques such as descriptive statistics, data visualization, predictive modelling, and machine learning. The insights gained from data analysis can then be used to make informed decisions, improve processes, and drive business outcomes.

We use various methods depending on the type of data , data sources from different industries, research goals to conduct data collection process:

Unlike other data analytics companies, we create surveys using tools for data analytics such as Google Forms, SurveyMonkey, or Qualtrics, and distribute them to target audience via email, social media, or website to collect data in lots of data analytics projects.

We conduct in-person or online interviews with individuals or groups to gather data on their experiences, opinions, and behaviours.

We collect data by observing people, processes, or events in real-time, either in-person or remotely using video or audio recordings.

We use web scraping tools for data analytics to collect data from websites and online databases. This method is useful when we need to collect large amounts of structured or unstructured data, such as data analytics platform.

We collect data from existing sources such as government databases, industry reports, academic journals, or social media platforms, like tiktok data collection

Once we have collected data, we need to preprocess it by cleaning, transforming, and structuring it so that it is ready for business data analytics. This step is important because the quality and accuracy of data can directly affect the insights and outcomes of big data analytics or marketing data analytics.

Data Preprocessing

We provide data preprocessing which cleaning, transforming, and structuring the data to prepare it for analysis. The goal of data preprocessing is to improve the quality and accuracy of the data and to make it easier to analyse. There are some common steps involved in data preprocessing

We identify and correct errors, missing values, and inconsistencies in the data. We include tasks such as removing duplicates, filling in missing values, and correcting typographical errors.

We rescale the data so that it has a similar range or distribution, using techniques such as Z-score normalization or min-max normalization to adjust the data so that it has a similar shape or distribution, dividing continuous variables into discrete intervals or categories, converting categorical variables into numerical values, encoding can be done using techniques such as one-hot encoding, ordinal encoding, or binary encoding.

We combine data from two or more sources based on a common key or identifier, summarize data from multiple sources into a single dataset, use techniques such as fuzzy matching or record linkage to remove duplicate records from the dataset, convert data from different sources into a common format, enrich the dataset with additional information from external sources.

We select a subset of the original features or variables that are most relevant to the analysis, and use techniques such as PCA, linear discriminant analysis (LDA), or independent component analysis (ICA) to create new features or variables from the original ones that capture the essential information, use  techniques such as PCA, LDA, t-distributed stochastic neighbor embedding (t-SNE), or autoencoder neural networks to reduce the number of dimensions or variables in the dataset while retaining as much information as possible, use techniques such as random sampling, stratified sampling, or cluster sampling to select a subset of the original data points to analyze instead of analyzing the entire dataset.

We convert data from one type to another type. For example, we may need to convert string data to numerical data, or date data to a specific date format, convert data into a common format or range. For example, we may need to normalize data to a specific scale or range, such as converting all data to a range of 0 to 1, remove or correct errors or inconsistencies in the data. For example, you may need to remove duplicate or missing values, correct misspelled or inaccurate data, or standardize inconsistent data.

Proper data formatting is important for accurate and efficient data analysis. We help to ensure the data is in a format that can be easily understood and analysed by the data analysis tools or software. The specific formatting techniques will depend on the nature of big data, the analysis you want to perform, and the requirements of the data analysis tools.

data analytics, data processing
data analytics, data science

Data Analytics

We conduct data analytics during business analytics among different industries by using different big data technologies and big data tools:

We analyse historical data to understand what has happened in the past, which focuses on summarizing and visualizing data to provide insights into past trends, patterns, and relationships. It  is useful for understanding historical performance, identifying areas for improvement, and providing context for future analysis.

We use statistical and machine learning techniques to analyse historical data and make predictions about future outcomes. It focuses on identifying patterns and relationships in the data that can be used to make informed predictions about future events or trends. Predictive analytics is useful for forecasting future trends, identifying potential risks and opportunities, and making data-driven decisions.

We use optimization and simulation techniques to identify the best course of action for a given scenario. It focuses on using data and mathematical models to make recommendations for future actions or decisions. Prescriptive analytics is useful for identifying the optimal solution to a problem, optimizing business processes, and making complex decisions based on multiple criteria.

Each type of data analytics is useful for different purposes and can provide different insights and big data solutions into the data. In practice, many data analytics projects will involve a combination of descriptive, predictive, and prescriptive analytics to provide a comprehensive understanding of the data and inform decision-making.

Data Modelling and Prediction

We create a conceptual representation of data, which helps to define the structure, relationships, and constraints of the data. It involves using a formal model, such as an entity-relationship (ER) diagram or a data flow diagram, to create a visual representation of the data and its relationships. We use different types of data models that can be used for different purpose

We focus on identifying the high-level concepts, entities, and relationships in the data, which is used in the early stages of a project to define the scope and requirements of the data. Conceptual data models are typically represented using an entity-relationship (ER) diagram, which uses symbols to represent entities, attributes, and relationships.

We focus on defining the structure and relationships of the data in a more detailed and formal way. Logical data models provide a more detailed representation of the data than conceptual data models and are often used to guide the design of databases or data warehouses. Logical data models are typically represented using a data modelling language, such as UML (Unified Modelling Language) or IDEF1X (Integrated Definition for Information Modelling).

We focus on the physical storage and organization of the data, including tables, columns, indexes, and other database objects. Physical data models are used to guide the implementation of databases or data warehouses and are often specific to a particular database management system (DBMS) or platform. Physical data models are typically represented using a data definition language, such as SQL (Structured Query Language).

The different type of data modelling used will depend on the specific needs and requirements of the project, as well as the stage of the data analytics process. In many cases, all three types of decision modelling may be used to provide a comprehensive understanding of the data and to guide the development of databases, data warehouses, or other data storage and management systems.

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We use data and statistical or machine learning techniques to make informed guesses about future events or outcomes. It involves analysing historical data to identify patterns and relationships, and using that information to make predictions about what is likely to happen in the future, including forecasting future trends, identifying potential risks and opportunities, and making data-driven decisions.
It’s important to note that predictions are not guarantees and can be influenced by a variety of factors. Therefore, it’s important to use caution and scepticism when interpreting predictions and to consider other sources of information when making decisions.