Data analytics and insights

What Is Data Profiling? Steps and Types of Data Profiling Data Profiling is the analysis of source data to determine how it is structured, what it contains, and how it interacts with other data, as well as the identification of projects that could benefit from it. Data Profiling Procedures Step 1: Conduct data profiling at the beginning of a project to establish if the data is suitable for analysis and if the project should continue. Step 2: Before putting the source data into the target database, identify and resolve any quality concerns with the source data. Step 3: As the data moves from source to destination, look for data quality issues that can be fixed using Extract-Transform-Load (ETL). Profiling data can tell whether additional manual processing is necessary. Step 4: Use unexpected business rules, hierarchical structures, and relationships between foreign keys and private keys to refine the ETL process in step four. Data Profiling Types Content Discovery: An individual

Optimizing Your Data for Success: Data Cleaning Steps & Process Whatever type of data analytics you perform, your analysis and any subsequent processes are only as good as the data you start with. Most raw data, whether text, images, or data stored in spreadsheets, is incorrectly formatted, imperfect, or downright dirty, and must be cleaned and structured before you begin your analysis. To ensure that your data is properly prepared for analysis, you can use a variety of data cleaning, "data cleansing," or "data scrubbing" techniques. Data cleaning is the process of repairing or erasing inaccuracies, corruptions, improperly formatted, duplicate, or incomplete data from a dataset. When different data sources are combined, numerous potential for data duplication or mislabeling exist. Cleaning your data is as simple as following this six-step guide: Get Rid Of Any Information That Isn't Relevant: The first step is to find out what analyses you'll be doing

Data Extraction Data analysis starts with a company's raw transactional and operational data. Data Sources are single or many data storage locations like Cloud, ERP, Datawarehouse, flat files, etc. Collecting and extracting data is the initial stage in an ETL process (extract, transform, and load). The goal of ETL is to prepare data for analysis or business intelligence (BI). There are 2 types of data extraction methods: Data transformation process ·          Logical Extraction:.  It is the first step in creating a physical data extraction plan.  o     Full Extraction :  Data copied from the source system in its entirety, even if untimestamped. o     Incremental Extraction :  This method extracts data in increments. Timestamps can monitor new or changed data. ·          Physical Extraction  Extract data from the source system logically or physically. o     Online Extraction :  The staging area processes data directly from the source system. o     Offline Extraction

  Data Consolidation After operational data is created and gathered from various sources and formats, it must be combined, cleaned, and checked for faults before being stored in a data warehouse or data lake. Business executives prefer “Hand coding” by data engineers for small datasets from few sources or “ETL Tools” for huge datasets from many sources. In both cases, data tables from different sources are connected by a schema, which helps standardise and address meta data problems of data consolidation like: data may not have all the required columns. data has more columns than required data types of columns may not match across datasets. columns may not be in the same order across datasets. data rows to be removed as its not relevant to the data analysis. Data enrichment companies | data transformation process | data transformation services Even data consolidation is a lengthy, time-consuming task with resources like limited, data spread across multiple locations, and challenges l

Large and small businesses are increasingly looking to capitalise on and reap the benefits of easy-to-use technology. However, as the amount of data created by businesses increases, it becomes increasingly difficult to extract valuable and usable information. This is where data science comes in. This is no longer limited to IT companies; it has spread to manufacturing, retail, and healthcare. Data science is quickly adopted in many fields. Many data scientists need to build the architecture from scratch and deploy it for startups, but large industries may not need to create products due to their prior knowledge and wealth of expertise and services.   In a startup, data scientists must recognise important business KPIs to measure and anticipate, create predictive models of consumer behaviour, conduct experiments to test product improvements, and design data products that allow new product features. 1.        A brief explanation of how statistics work Using   Data enrichment , co

  T he humongous amount of data changed the game for international business forever. This is why brands are beginning to ramp up their digital modifications. The consequences have been a massive spike in requests for information Analytics. The contemporary digital world is seeing more movements are given birth due to the advancement of data.   Information analytical resolution-making has become one of the most commonly used plans for achievements these days.     Data analytics and insights provide small brands more chances through the utilisation of Analytics. Machine learning and artificial intelligence are trouble-making technologies that are shaking up the landscape. More and more businesses in recent years are adopting big data. Business owners are experiencing increasing requests from 15 per cent to 60 per cent in less than three years.   As outcome brands that utilised the information analytics enjoy a revenue growth that touched as high as 15 per cent.   Even