As data becomes more intrinsically key to business success, the implementation of a coherent data strategy becomes paramount. This section explores the components of good data strategy management, covering topics such as scalability, new technology, multichannel co-ordination, avoiding the data silo problem, and developing a data driven culture within the enterprise.<\/p>\n\n\n\n
The truly \u2018smart\u2019 computer brains we have today have arrived amidst a rapid maturity of the technology. Until very recently, the capabilities of AI had initially been more often confined to areas such as gaming and rather simple chat bots, and I\u2019m sure at the time many of us would have been cynical about the prospects of using these techniques in a scientific setting in the near future. Such has been the speed of development though that these techniques have very quickly gone from being helpful for some more ordinary tasks to becoming extremely powerful tools to drive insight and innovation.<\/p>\n\n\n\n
Looking specifically at text and data mining, for example, the benefits from a high level are clear \u2013 by deploying computers to examine tens of thousands of scientific papers at once, across all disciplines, we can identify clues for further investigation and eliminate wasteful avenues of research in a fraction of the time it would take even a large team of people. The head start it will give your team in tracking down the answers or clues they\u2019re looking for is tremendous \u2013 even before you consider any cost savings you will find as a result of those rapidly delivered insights.<\/p>\n\n\n\n
And from a technical perspective, we have a good understanding of how to deploy these tools and the coding required to get the answers we\u2019re looking for.<\/p>\n\n\n\n
It is, however, between these two points that things start to slip. How exactly do you write a business case for text and data mining if you don\u2019t know exactly what you\u2019ll get back? For many, text and data mining could be seen as nothing more than an expensive leap of faith. In truth, it\u2019s nothing more than a mental block.<\/p>\n\n\n\n
As with any research project, it\u2019s important we remember nothing is guaranteed in research. The same is true when asking a human to do a job rather than a computer. There are basic principles to help you narrow down your search, but you cannot guarantee that the answer you\u2019ll get is the answer you want. A computer will just find the answers much more quickly.<\/p>\n\n\n\n
Understanding that and embracing that mentality is key to overcoming one of the major human barriers to accelerated digital techniques.<\/p>\n\n\n\n
Another major barrier exists \u2013 and it is perhaps even more fundamental to developing the brief. For years, computer scientists have enshrined the GIGO mantra \u2013 garbage in, garbage out. It is with good reason. Ask the right questions, you\u2019re more likely to get the answers you want. Of equal importance is to ask the right questions of the right data \u2013 the higher quality source material, the better chance you\u2019ll find the answers you need.<\/p>\n\n\n\n
This is a basic scientific principle in itself, so it underlines the importance of not overthinking the process of developing the brief, but sitting down with someone who understands both the language of science and computing (whether that\u2019s one person or two) to help flesh out the brief in a way that won\u2019t just improve the results of your project from a digital perspective, but will deliver a robust framework for the human research team, who will have an understanding on what kind of clues the text and data mining project will unveil for them.<\/p>\n\n\n\n
Over time, this process can become even more finely attuned and allow even greater integration between those digital techniques and scientific principles, helping us truly realise the incredible potential of AI and machine learning.<\/p>\n","protected":false},"excerpt":{"rendered":"
As data becomes more intrinsically key to business success, the implementation of a coherent data strategy becomes paramount. This section explores the components of good data strategy management, covering topics such as scalability, new technology, multichannel co-ordination, avoiding the data silo problem, and developing a data driven culture within the enterprise. The truly \u2018smart\u2019 computer […]<\/p>\n","protected":false},"author":14428,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"off","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[10],"tags":[],"class_list":["post-1406","post","type-post","status-publish","format-standard","hentry","category-gallery"],"_links":{"self":[{"href":"https:\/\/people.utm.my\/azhari\/wp-json\/wp\/v2\/posts\/1406","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/people.utm.my\/azhari\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/people.utm.my\/azhari\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/people.utm.my\/azhari\/wp-json\/wp\/v2\/users\/14428"}],"replies":[{"embeddable":true,"href":"https:\/\/people.utm.my\/azhari\/wp-json\/wp\/v2\/comments?post=1406"}],"version-history":[{"count":1,"href":"https:\/\/people.utm.my\/azhari\/wp-json\/wp\/v2\/posts\/1406\/revisions"}],"predecessor-version":[{"id":1407,"href":"https:\/\/people.utm.my\/azhari\/wp-json\/wp\/v2\/posts\/1406\/revisions\/1407"}],"wp:attachment":[{"href":"https:\/\/people.utm.my\/azhari\/wp-json\/wp\/v2\/media?parent=1406"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/people.utm.my\/azhari\/wp-json\/wp\/v2\/categories?post=1406"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/people.utm.my\/azhari\/wp-json\/wp\/v2\/tags?post=1406"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}