We know that the main and most valuable resource of modern business is data. However, is all data equally valuable?
Data loses its value within a few days – it becomes irrelevant and provides little benefit to the company. The goal of every company is to find a solution that can extract useful information and insights from data before it becomes outdated.
Data quality and algorithm efficiency can greatly complicate the challenge of delivering data in a timely manner. So, it is worth paying attention to the data warehouse. Making the right storage decisions is essential to delivering insights quickly.
At the very beginning of the digital revolution, companies stored data to comply with regulations or evaluate past performance. The purpose of data storage has changed over the past 10 years. Now historical data can help to make forecasts and determine future trends. This has led to the opening of new opportunities in the business decision-making process. At the same time, the volume of generated data has sharply increased, and many technologies have emerged for collecting and analyzing it.
Data storage is often not a priority. Huge amounts of data are still stored on disks. This significantly complicates access to data, increasing financial costs and energy consumption. A quality data storage solution frees up time and shifts the focus from high-cost issues to analytics, optimization, and AI solutions. For example, in pharmaceuticals, the time it takes to obtain information is essential in fighting pandemics, developing new, more effective drugs, and improving existing ones. Therefore, an efficient data storage solution is necessary for the operational process of data acquisition.
One of the common challenges in the data management process is data quality. Often the desire to put all the information into data lakes turned the lake into a «swamp». Data that does not correspond to reality reduces efficiency. Therefore, it is important for companies moving to data-driven management to emphasize how they measure and address quality gaps.
Data infrastructure can affect data quality. Built-in AI-based tools can ensure that information is stored correctly, taking into account requirements, mandatory checks and security measures. Data infrastructure presents enormous opportunity while eliminating the human costs and energy inefficiencies of legacy storage systems.