When you’re submerged by a huge volume of digital files, it can be frustrating and time-consuming if you can’t put your hands on the information you need.
Flare's automated document tagging allows you to assign labels to important documents so that they are easier to organise and retrieve whenever you need them. Read more...
The ability to extrapolate and analyse data depends on relevant information being readily available and easy to find. A systematic search system that delivers a coherent structure and index for your business’s data is essential – which is why information taxonomies offer significant benefits that can make your organisation more efficient and productive.
The energy industry has worked with Big Data since before the term existed. Today a new set of Big Data challenges face CIOs; real-time data, data lakes, IoT, AI, ML, OSDU. The list goes on...
As part of Flare's Taxonomies, our Master Data Management tools allow companies to easily manage their master valid values and make them accessible to both Flare's Sirus suite and any other applications that may consume the data
As a business grows and enters new markets, it will also receive data from many new sources, which can be difficult to process, categorise, and store in a meaningful way.
Data is a fundamental part of the modern business landscape for both companies and consumers.
Accurate, relevant, and actionable data plays a crucial role in the growth of a business; if not meticulously managed, data assets can be an obstacle to growth.
Therefore, effective data management is a vital tool in enhancing data processes, improving the value of your data assets, and ensuring authenticity and quality.
At Flare Solutions, we can support your business with all aspects of data management. Read More...
With over two decades of providing data and information management consultancy services to the energy industry, Flare are perfectly positioned to help you develop your information management governance policies...
The benefits of Machine Learning are extensive and promise to transform the way that businesses operate, with significant cost savings, enhanced productivity, and better efficiency likely once an AI project has been implemented. However, the effectiveness of ML depends heavily on the quality of the data, so why is data cleaning fundamental to successful AI data processing?
The business world is constantly evolving and this is easy to see when you compare now to ten years ago. There have been some massive changes over this period and the amount of data companies collect now is certainly one. Large datasets are the norm for a lot of organisations, and gaining strategic insights from them is important. To enable this to happen, many businesses will engage in data cleansing.