Leading business enterprises generate volumes of data and often look for tools and technologies to survive in the data-driven economy. An enterprise data management framework paves the way for strategic storing and managing data for actionable decision-making. This data management strategy aims to eliminate data duplication, remove data silos, and ensure regulatory compliance, which ultimately improves operational efficiency. This whitepaper outlines the Data lifecycle management framework best practices, proven methodologies, and actionable steps to help business leaders, IT teams, and data professionals transform raw data into a trusted strategic asset.

In modern analytics consulting, client dashboards are far more than visual aids. They are daily decision systems that guide marketing spend, inventory levels, and multimillion‑dollar investments. Expectations are unforgiving: a single mishandled null, silent field rename, or overlooked row‑count mismatch can undo months of relationship‑building and trigger costly escalations. Across the industry, leaders report the same pattern: frontline teams rush to meet aggressive timelines, quality checks become person‑dependent, and senior managers burn the midnight oil in crisis huddles, tracing data errors that should never have left development. This is a common risk across data visualization and dashboards, regardless of the BI platform used.

There are several ways to combine data depending on our requirements and the structure of the data.

Power BI is fast emerging as the go-to BI tool for many companies. Backed by Microsoft, Power BI provides companies a low cost but effective BI solution to companies for their reporting needs.

Customer Lifetime Value (CLV) is an expectation of all the esteem a business will get from their whole association with a client.

There is ssh enabled On-premise MySql(MariaDB) db, we are trying to copy data to SQL server using Azure Data Factory.

Warehouse and employee management expenses as well as time of the employees get wasted in this process.

In inferential statistics, we use a randomly selected sample data to describe and make inferences about the population.

Automation can be defined as the formation of technology and its use for the purpose of control and monitor the production and supply of various types of goods and services with the minimal use of human beings or without the use of help of human beings.

In the era of internet and communication, the cloud is filled with data that is further converted into useful information.

In the previous article about inferential statistics, we considered a hypothesis that predictions of sample data can infer those of the actual population, only if the sample data represents the actual population (assumption).

Engaging one-on-one with current and future customers in real-time can be highly beneficial to the business.