Unstructured data represents one of today\u2019s most significant business challenges. Unlike defined data \u2013 the sort of information you\u2019d find in spreadsheets or clearly broken down survey responses\u00a0\u2013 unstructured data may be textual, video, or audio, and its production is on the rise.<\/p>\n
In fact, by some estimates, as much as 80-90% of new data is unstructured<\/a>, and that presents real challenges from a data management standpoint.<\/p>\n How can businesses make meaning out of unstructured data and generally manage all of the information they\u2019re generating in a productive way? It\u2019s a challenging process, but it is possible with the right array of tools.<\/p>\n The first step towards making use of unstructured data is to find a way to centralize this information, and this is a top priority for many businesses today. In fact, 56% of businesses say that getting their unstructured data into the cloud<\/a> is a top priority.<\/p>\n Migration is not quite management, but it\u2019s the first step towards organizing and evaluating unstructured data, and that\u2019s important. The biggest barrier to this task, however, is a lack of IT capacity and budget \u2013 but businesses that can successfully budget for these expenses may find profitable insights in that data, more than making up for those initial costs.<\/p>\n The next aspect to addressing unstructured data is extracting more concrete information from it, and this may be the most complicated element. How do you quantify unstructured data? There are a number of approaches, but AI is one of the most important tools because by using innovations like natural language processing (NLP), the system can identify frequently used terms, evaluate tone, and much more.<\/p>\n Once businesses can see \u201cinside\u201d their unstructured data, there\u2019s a lot to explore. Better data management can drive business growth<\/a>, and can help provide direction for a number of operational changes. For example, businesses have used information derived from unstructured data to improve safety, advance healthcare outcomes, and automate business facilities based on worker insights \u2013 but let\u2019s take a closer look.<\/p>\n One type of unstructured data common in the healthcare industry is imaging, whether that\u2019s a CT, MRI, or an X-ray. Radiologists can be slow in evaluating non-emergent imaging, and the human eye is only so sensitive. When imaging is paired with AI<\/a> technology, however, facilities can turn over imaging results more quickly and with greater accuracy.<\/p>\n Another area of healthcare that is well-positioned to benefit from better unstructured data management and analysis is drug development. This may seem like a fairly structured area, but given what we know about unstructured data production, pharmaceutical research generates a lot more of it than you might realize. The industry also struggles with prioritizing and organizing such data, which has a negative impact on collaboration and product development in the pharmaceutical industry<\/a>.<\/p>\n As noted regarding the pharmaceutical industry, collaboration is a critical business function and the inability to collaborate can be a serious hindrance to progress \u2013 and this is true across industries. With unstructured data, though, you can\u2019t just send along a data set and, depending on how you aim to evaluate or manipulate the information at hand, businesses often need to be able to exchange, comment on, and modify large files across teams and locations. So, what\u2019s the best way to tackle this task?<\/p>\nCentralizing Information<\/h2>\n
Find Out What\u2019s Inside<\/h2>\n
Collaboration Considerations<\/h2>\n