Data analytics and insights

  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