So what are data?
Simply put, data are facts, figures, or information stored in or used by a computer. One example of data is information collected for a research paper, another is an email. In computer science terms, data is information that has been put into a form that can be moved or processed efficiently.
Any data characterized by the 3 Vs can be called Big Data. Volume represents the size of the data. For example, it can be huge in TB. Variety stands for the different forms of data such as structured, semi-structured and unstructured data. Velocity here means the speed at which new data is added. (For example, the speed at which e-commerce companies receive data on Big Billion Day).
Data in itself has no meaning, it is only when it is interpreted that it acquires meaning and becomes information. This is where data analytics comes in. By closely examining data, we can find patterns to perceive information, and then the information can be used to expand knowledge.
Data analytics involves analyzing raw data to identify trends, answer questions, or draw conclusions from large data sets. Key metrics are analyzed by transforming raw data into various forms. Without data analytics, these metrics would likely be buried under a mass of information. This process helps companies increase their overall efficiency.
However, before using data analytics to make decisions, the quality of the data is critical. Data processing is becoming more closely linked to business operations, and companies are making extensive use of data analytics to make business decisions.
A reliable quality data set is critical for analytics and machine learning. You can not expect diamonds if the input to the machine learning algorithm is garbage!
Monitoring data quality helps identify potential issues that impact quality and ensures that shared data can be used appropriately. Customers, employees, and key strategies suffer when data does not meet expectations.Any data science product should focus on the quality of the data. A metrics system is an effective way to assess data quality.
There are real life example of a real estate firm losing over $300 million from its flipping business due to poor data quality. There should be a way to at least partially audit data quality. This could help identify quality issues before they have a major impact.
Autonomous Data Quality Validation: Crafting inspection metrics at the data entry points of your company to lay a robust data & analytics foundation.
Data Polishing: Make readable & organized data from the raw data ingested into your system from various sources.
Transform Information to Intelligence: Help you identify patterns & the unknowns across the metrics in your enterprise using AI & ML methodologies. You might land up launching a game-changing product for your company.
Data Visualization: Convey your complex statistical information using several visualization tools like Power BI, Tableau & simple communication concepts. Interactive dashboard to keep tracking the performance on various metrics.
Data Governance: Effective data governance leads to better data analytics, which in turn leads to better decision-making and improved operational support. We guide you in designing policies around your enterprise data making it consistent and trustworthy.