Published by Jessica Weisman-Pitts
Posted on November 18, 2021
By Michalis Michael, CEO of DMR
Unstructured data (UD) has never been more important than now, and its importance will skyrocket during this decade. It only takes a glance at the recent “State of Unstructured Data Management Report” to reveal how seriously UD is being taken, with 45 per cent of respondents intending to invest in UD analytics software as a matter of urgency.
The report also notes that most IT leaders – 62.5 per cent, in fact – will spend more on UD storage by the end of 2021 than they did in 2020.
These statistics tell us that the possession and understanding of UD is being recognised as increasingly essential. This is hardly surprising: over 90% of all data available to humanity is unstructured, and the business applications of this treasure trove of knowledge are enormous and untapped.
Of course, the potential insights that UD offers come with an important caveat. UD is formidable: it’s intimidatingly vast and exceptionally complex. IBM estimates that, on a global scale, we generate approximately 2.5 quintillion bytes of UD every single day.
Though UD might be a treasure trove, it’s treasure protected by a fiendishly complicated locking mechanism.
The scale and intricacy of UD and its potential for insight underscore the significance of the companies – the technologists and data scientists – who hold the key to unlocking its potential. In order to take advantage of UD, then, it’s important to understand both what UD is, and how the right specialists can use it to unveil powerful business insights and create value.
What is UD?
UD is mostly text, audio, images, and video. It is, perhaps, best understood in relation to its more accessible counterpart: structured data (SD).
SD is, in essence, organised data (numbers in tables): we’re used to seeing SD on spreadsheets, for example, or other highly managed environments. And due to its organised nature, SD holds some obvious advantages – it can be readily used, searched, understood, and actioned.
Moreover, as IBM has pointed out in a recent article, SD “does not require an in-depth understanding of different types of data and how they function”: “users”, they note, “can easily access and interpret the data.”
The same can’t be said for UD. UD is any data which is not organised in tables and spreadsheets and which, by extension, can’t be processed or manipulated via traditional data methods. There is an additional complexity though which is the most prohibitive of all: UD is expressed in natural language and there are hundreds of them that a global business would be required to analyse and understand. SD on the other hand is usually expressed in numbers which are almost universally understood.
According to IBM, 95% of businesses prioritise UD management – and it’s easy to see why, given the vast potential that such data possesses.
As the above implies, UD can include practically any form of data or information: tweets, call centre audio files, any kind of text, sensor data, and images all fall under the purview of UD, and they can solve myriads of business problems – provided that somebody can make sense of UD’s multilingual, tangled, disorganised complexity.
It’s worth repeating that the amount of UD the world creates on a daily basis would fill ten million Blu-ray disks which, if stacked, would be ats tall as four Eiffel Towers.
Consequently, UD requires a potent combination of sophisticated AI technology and specialism in the field of data science in order for its inherent value to be harnessed effectively, meaning that the enormous business value of UD is inexorably linked to the companies who make it work. Thankfully they exist; most are start-ups or early-stage companies.
Harnessing the power of UD (and its interpreters)
Why, then, do businesses invest in UD? What benefits can properly harnessed UD create?
In the world of marketing intelligence, UD is exceptionally valuable. By using AI to navigate through breath-taking volumes of customer interactions with brands, it is possible to find unprecedentedly nuanced and immediately actionable insights into customer experience – how customers think and feel about a company, service, brand or product – on a scale and level of nuance hitherto undreamt of.
UD can also provide insights that directly impact companies’ bottom lines, especially in the world of trading and investment. By trawling through large quantities of news articles and headlines in order to find information that may influence the fluctuation of stock prices, for example, UD analysis technology can have a direct impact on trading and investing. .
However, this kind of scenario also underscores the value of accurate insights. If UD analysis is poorly managed, inspiring wrongfooted financial decisions, then traders – to extend the previous example – stand to lose millions.
As such, while UD’s considerable advantages understandably attract a lot of interest from major businesses, it is only as powerful as the technology and people harvesting and annotating that data to turn it into actionable insight.
Consider, for example, the sentiment analysis so vital for marketing intelligence and CX measurement: in order to get the best results, human curators are essential in order to train bespoke AI to accurately detect emotion.
Or, to return to our trading example, the news headlines and articles that can provide valuable insight need to be presented free of ‘noise’. Customised machine learning models can help the “signal,” or the proverbial needle in the haystack, and avoid irrelevant data – without this kind of specialist approach, social media monitoring (for example) can wrongly identify huge swathes of UD as relevant, giving incorrect insights and therefore removing all the intelligence value of UD.
Conclusion: investing in the analysers
Clearly, there’s no mystery surrounding IT leaders’ ever-increasing investment in UD – knowledge, in marketing, alternative data, and countless other organisations, is power. But for investors looking to capitalise on the power and utility of UD, there is much to be gained from investing in those select organisations capable of performing the alchemical task of transforming meaningless data into essential insights.
The real value of UD rests in the hands of the technology and AI specialists who can wield it – and this is a cause well worth investing in.
By Michalis Michael, CEO of DMR
Unstructured data (UD) has never been more important than now, and its importance will skyrocket during this decade. It only takes a glance at the recent “State of Unstructured Data Management Report” to reveal how seriously UD is being taken, with 45 per cent of respondents intending to invest in UD analytics software as a matter of urgency.
The report also notes that most IT leaders – 62.5 per cent, in fact – will spend more on UD storage by the end of 2021 than they did in 2020.
These statistics tell us that the possession and understanding of UD is being recognised as increasingly essential. This is hardly surprising: over 90% of all data available to humanity is unstructured, and the business applications of this treasure trove of knowledge are enormous and untapped.
Of course, the potential insights that UD offers come with an important caveat. UD is formidable: it’s intimidatingly vast and exceptionally complex. IBM estimates that, on a global scale, we generate approximately 2.5 quintillion bytes of UD every single day.
Though UD might be a treasure trove, it’s treasure protected by a fiendishly complicated locking mechanism.
The scale and intricacy of UD and its potential for insight underscore the significance of the companies – the technologists and data scientists – who hold the key to unlocking its potential. In order to take advantage of UD, then, it’s important to understand both what UD is, and how the right specialists can use it to unveil powerful business insights and create value.
What is UD?
UD is mostly text, audio, images, and video. It is, perhaps, best understood in relation to its more accessible counterpart: structured data (SD).
SD is, in essence, organised data (numbers in tables): we’re used to seeing SD on spreadsheets, for example, or other highly managed environments. And due to its organised nature, SD holds some obvious advantages – it can be readily used, searched, understood, and actioned.
Moreover, as IBM has pointed out in a recent article, SD “does not require an in-depth understanding of different types of data and how they function”: “users”, they note, “can easily access and interpret the data.”
The same can’t be said for UD. UD is any data which is not organised in tables and spreadsheets and which, by extension, can’t be processed or manipulated via traditional data methods. There is an additional complexity though which is the most prohibitive of all: UD is expressed in natural language and there are hundreds of them that a global business would be required to analyse and understand. SD on the other hand is usually expressed in numbers which are almost universally understood.
According to IBM, 95% of businesses prioritise UD management – and it’s easy to see why, given the vast potential that such data possesses.
As the above implies, UD can include practically any form of data or information: tweets, call centre audio files, any kind of text, sensor data, and images all fall under the purview of UD, and they can solve myriads of business problems – provided that somebody can make sense of UD’s multilingual, tangled, disorganised complexity.
It’s worth repeating that the amount of UD the world creates on a daily basis would fill ten million Blu-ray disks which, if stacked, would be ats tall as four Eiffel Towers.
Consequently, UD requires a potent combination of sophisticated AI technology and specialism in the field of data science in order for its inherent value to be harnessed effectively, meaning that the enormous business value of UD is inexorably linked to the companies who make it work. Thankfully they exist; most are start-ups or early-stage companies.
Harnessing the power of UD (and its interpreters)
Why, then, do businesses invest in UD? What benefits can properly harnessed UD create?
In the world of marketing intelligence, UD is exceptionally valuable. By using AI to navigate through breath-taking volumes of customer interactions with brands, it is possible to find unprecedentedly nuanced and immediately actionable insights into customer experience – how customers think and feel about a company, service, brand or product – on a scale and level of nuance hitherto undreamt of.
UD can also provide insights that directly impact companies’ bottom lines, especially in the world of trading and investment. By trawling through large quantities of news articles and headlines in order to find information that may influence the fluctuation of stock prices, for example, UD analysis technology can have a direct impact on trading and investing. .
However, this kind of scenario also underscores the value of accurate insights. If UD analysis is poorly managed, inspiring wrongfooted financial decisions, then traders – to extend the previous example – stand to lose millions.
As such, while UD’s considerable advantages understandably attract a lot of interest from major businesses, it is only as powerful as the technology and people harvesting and annotating that data to turn it into actionable insight.
Consider, for example, the sentiment analysis so vital for marketing intelligence and CX measurement: in order to get the best results, human curators are essential in order to train bespoke AI to accurately detect emotion.
Or, to return to our trading example, the news headlines and articles that can provide valuable insight need to be presented free of ‘noise’. Customised machine learning models can help the “signal,” or the proverbial needle in the haystack, and avoid irrelevant data – without this kind of specialist approach, social media monitoring (for example) can wrongly identify huge swathes of UD as relevant, giving incorrect insights and therefore removing all the intelligence value of UD.
Conclusion: investing in the analysers
Clearly, there’s no mystery surrounding IT leaders’ ever-increasing investment in UD – knowledge, in marketing, alternative data, and countless other organisations, is power. But for investors looking to capitalise on the power and utility of UD, there is much to be gained from investing in those select organisations capable of performing the alchemical task of transforming meaningless data into essential insights.
The real value of UD rests in the hands of the technology and AI specialists who can wield it – and this is a cause well worth investing in.
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