THE INSIGHT OPPORTUNITY.

Uncovering the next generation of information intelligence.

Knowledge simply isn’t working for 1.14 billion knowledge workers.

We spend at least 25% of our time searching for information at work – plus almost the same again recreating what we can’t find. But often raw information isn’t even what we really need.

Knowledge workers and their organisations need intelligent insights, gleaned from interconnecting the information they already know about – and the information they don’t.

They need to do more with information if they’re going to maximise their potential to innovate.

Traditional search technology can’t deliver these results, but automated insight technology will.

Read on to find out what the next generation of information intelligence for smart organisations looks like – and how to get there.


What you'll get with this eGuide:

  • Understand the challenges presented by today's information landscape - and how they affect knowledge workers
  • Learn what 'insight' really means - and how automated enrichment processes help you maximise information potential
  • Take away 5 real-life business scenarios where insight is more valuable than information - ideal to share with colleagues
  • Examine the next generation of automated insight technology and how it can benefit your organisation


Contents

INTRODUCTION - What is this?

CHAPTER ONE - Information intelligence is at a crucial juncture.

CHAPTER TWO - Why (and where) businesses need insight capabilities.

CHAPTER THREE - Enrichment. The ‘secret sauce’ to move you beyond search.

CHAPTER FOUR - Where next? How to start unlocking information insight.

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INTRODUCTION

What is this?

Organisations globally are generating and storing more data than ever (if you don’t know exactly how much, check out page 5 in our PDF download of this eGuide). You don’t need us to tell you that this information boom is a huge opportunity for innovation, efficiency, and commercial advantage. But for the world’s 1.14 billion knowledge workers, it only means one thing – growing piles of siloed information to wade through, and less time making critical decisions or innovating new solutions.

Already, knowledge workers spend over 25% of their working week searching for the answers they need from business systems (Forbes, 2022).

Search technology isn’t working for today’s knowledge workers.

Knowledge workers across all organisations share common challenges in the context of rapid information growth. All roads lead to being unable to find the information you need – or unlock the insight that exists in the unknown spaces between that information.

Across any business, there are knowledge workers that need better insights to solve their departmental challenges, like:

  • Securely handling personal data
  • Efficiently developing or maintaining key products or assets
  • Proactively identifying new commercial opportunities
  • Strategically introducing new systems or applications
  • Intelligently managing cases, investigations, or research projects

You don’t want tools to locate the information you need, or clues about where to go looking – you just want the answer, straight up. And that answer might not just be returned information; often it’s insight, inferred from connecting the dots between multiple pieces of information.

US defence secretary Donald Rumsfeld’s famous axiom that “there are known knowns, known unknowns, and unknown unknowns” has never been more pertinent to anyone trying to navigate and mine their organisation’s information enterprise. And it’s these “unknown unknowns” that represent a wealth of opportunity when it comes to information intelligence. They contain the golden insights that can drive innovation, operational efficiency, and best-in-class performance.

Traditional enterprise search technology slaps a plaster over the problems raised by information growth, siloed storage, and unnecessary access limitations. And it doesn’t address the insight problem. Soon, search won’t be able to keep pace.

This eGuide explores the challenges today's enterprises and their knowledge workers face, why search technology won’t cut it anymore, and what the next generation of AI-powered insight solutions looks like.

We hope you find it informative and enlightening, but also optimistic about the future. If you’ve got thoughts or questions on any of the topics, ideas, or solutions discussed, we’d love to hear from you. Get in touch with us at enquiries@aiimi.com and we’ll be happy to chat.


CHAPTER ONE

Information intelligence is at crucial juncture.

The volume of data organisations hold is only increasing, with the world storing 200 zettabytes of it by 2025.

Sometimes it seems that data underpins everything we do. That’s because in many ways, it does. From influencing what we watch to determining whether we can see friends and family in a pandemic, data is the deciding factor. In the corporate world, Gartner predict that by 2023 more than 33% of large organisations will have analysts practicing decision intelligence and decision modelling.

But there's a huge barrier to organisations placing intelligent information work at the heart of their everyday operations - and we’re at a critical juncture with the opportunity to overcome it.

As ever-growing data remains siloed across departments and organisations persist with traditional search technologies, knowledge workers can’t find the basic information they need from a growing quagmire – let alone advanced insight.

On average, only 1% of all company information can be seen by employees, with 90% of all searches denying users access to the information they need.

Putting valuable insight into the hands of knowledge workers is the key to enabling them to drive business innovation, efficiency, and growth. But until organisations can close the delta between the vast amounts of information they generate and the insight capabilities they possess, they won’t reap the benefits of citizen data scientists, enterprise knowledge democratisation, or a more enlightened workforce.

Putting valuable insight into the hands of knowledge workers is the key to enabling them to drive business innovation, efficiency, and growth.

“From our analysis, 73% of the documents companies create have never been used since the day they were created. People are systematically rebuilding information that’s already there.” Steve Salvin, Chief Executive, Aiimi

Often businesses already have the information they need to provide the answers to their knowledge workers’ questions — they just don’t know it.

As Aiimi’s Chief Executive, Steve Salvin, points out, “From our analysis, 73% of the documents companies create have never been used since the day they were created. People are systematically rebuilding information that’s already there, wasting valuable resources and time.” The key obstacle businesses face is how to make sense of their existing information, cut through the Redundant, Obsolete, and Trivial (ROT), and turn raw information into intelligent insights. And if knowledge workers across a business can be served these timely insights, without the need for human prompting, that has the power to transform their work and business performance.

In this eGuide, we’ll evaluate the options for augmenting insight capabilities and explore how knowledge workers will drive organisations forward - if they have the right automation technology and intelligent insights.

There’s a gap between the amount of information organisations generate, and their ability to realise intelligent insights from it. And it’s only widening.


CHAPTER TWO

Why (and where) businesses need insight capabilities.

44% of people have made a wrong decision because they were unaware of information that could’ve helped. [Gartner 2021]

And 43% of people report failing to notice important information because of the sheer volume of applications they use and volumes of data they’re presented with. Information obscurity and information overload are simultaneously crippling our access to insight.

Gartner predicts that by 2026, employees will reduce their time spent looking for information by 50% as information finds them in the context of their current work activity. But even this shift from ‘search’ to ‘served’ isn’t enough to ensure they can harness actionable, intelligent insights from their information. To innovate and do their jobs more effectively, tomorrow’s knowledge workers need insights delivered to them in context, not just raw information.

Insight requires deep analysis of multiple data and information entities. It considers metadata, past experiences, and myriad contexts that surround each piece of information. With billions of information entities in play (and always growing) and so many variables to consider, mapping and understanding context, history, and relationships between information is impossible to do manually. Plus, the most valuable insight may be derived from connecting the dots between information entities that wouldn’t normally cross paths in their siloed systems or business departments.

“When you’ve got the insights you need, you can see what’s not working – and make informed, innovative change.” Steve Salvin

At the rate information is growing, and its complex geography across the enterprise, the only way organisations can ensure their insight capabilities are fit for purpose is to introduce intelligent technology that takes them beyond search.

If automated knowledge sharing processes steered by AI technology aren’t put in place to instantly capture and share insights, internal knowledge stacked up over the years can get easily lost in siloed systems, benefitting potential competitors, rather than your own organisation, when skilled knowledge workers move on to new pastures.

That’s why organisations need technology to improve their insight capabilities. But where exactly can intelligent insights deliver the most impact for a business and its employees?

Simply put, anywhere where knowledge worker employees are. From Sales & Marketing to Product Engineering, Data Protection & Legal to Research teams. Powerful insight technologies will flex to truly understand and generate insight from the information that lives in every business area, crossing departmental boundaries and system silos to democratise knowledge and develop a more enlightened workforce.

FIVE SCENARIOS WHERE INSIGHT EATS INFORMATION FOR BREAKFAST

1. COMPLIANCE TEAMS HANDLING PERSONAL DATA

Scenario: You’re a DSAR Manager working through a mounting backlog of Subject Access Requests from customers and ex-employees. With 30 days to complete each one, and a small internal team, you can’t keep up with the problem manually. Your existing enterprise search solution is helping you find files relating to your Data Subjects, but you’re certain things are getting missed and it’s only a matter of time before a data subject complaint lands you in hot water with the ICO.

  • The information you have = a result set of files containing specified keyword(s), gathered by running a simple keyword search across the most likely locations.
  • The insight you need = all the files that relate to your data subject, from across the enterprise, with personal data entities highlighted and an intelligent risk score allocated to the file so you can resolve any storage or access concerns at the same time.

2. ENGINEERING TEAMS MAINTAINING PHYSICAL ASSETS

Scenario: You manage a team of engineers who maintain a local water treatment plant. It was built decades ago, so site plans and technical documentation are scattered and have been scanned in to create digital copies. You want to locate everything that contains the asset’s code so you can get visibility over ongoing maintenance activities and proactively avoid unexpected repairs.

  • The information you have = the locations of the treatment plant’s files, but you must open each one and review its contents manually to see if its relevant; and you’re not sure if other relevant information might exist elsewhere.
  • The insight you need = a single view of all files and data relating to the asset, showing the relationships between each file, the key information it contains (even if it’s a scan of a paper document), and a clear view of what’s outdated or been superseded.

3. SALES TEAMS LOOKING FOR OPPORTUNITIES AND MARKET INSIGHTS

Scenario: You’re part of a growing sales team with ambitious pipeline targets to drive commercial growth in line with the business strategy of diversification into a new market – local government. You need better insight into this market to help you find potential opportunities for your products.

  • The information you have = Right now, you’re manually finding and reviewing third-party publicly available information, like local council publications, for any hint that your product could solve a challenge. Looking for keywords and skim reading are the only tools you have to interpret this information.
  • The insight you need = to automatically crawl, index, and enrich any unstructured information sources to unlock brand-new insights – like visual networks showing the relationships or themes shared between files, maps plotting referenced geographical locations, analytical views showing patterns in the data, timelines highlighting key events, and quick document summaries auto-generated using machine learning.

4. IT & SECURITY TEAMS DECOMMISSIONING LEGACY APPLICATIONS

Scenario: You’re an IT Manager working with the CIO and CISO of your organisation to decommission a legacy finance application with minimal disruption to the department and maximal value to the business. Your CIO wants to understand what information is in the system and how she might bring it into the business, but your CISO is concerned about commercially sensitive data being accessed by employees outside of the finance team.

  • The information you have = you’ll likely migrate everything across into a Cloud storage location and grant access to the same set of users, using your current legacy decommissioning and migration solution. Nothing deleted, nothing added – it’s a cheaper filing cabinet.
  • The insight you need = to know exactly what information is in the system and whether it’s sensitive, high-risk, or contains personal data, so you can determine where to move it to and who should have access. To turn unused, inaccessible information into evergreen content that’s interconnected with information from across the business to generate insights for the finance team.

5. FRAUD TEAMS INVESTIGATING MISCONDUCT AT REGULATED FIRMS

Scenario: You’re a Case Manager working in the Fraud team to identify organisations involved in criminal activity. Indicators of fraud are multi-faceted and difficult to pinpoint; you need to track and monitor behaviour over time, keeping your focus on detailed activities and one eye on the bigger picture.

  • The information you have = multiple datasets about the organisations your team is investigating, from yearly accounts to trading activities, and data records for the key individuals on your radar. With this data and information in various file formats, identifying patterns manually is time consuming and ineffective.
  • The insight you need = to detect, be alerted to, and track patterns of behaviour across datasets and uncover relationships between entities. You need to see the big picture in a way you just can’t do manually with so much data, visualising key insights chronologically, geographically, or analytically as needed.


CHAPTER THREE

Enrichment. The ‘secret sauce’ to move you beyond search.

Machine Learning-based, automated enrichment processes are a vehicle to accessing, using, and sharing knowledge across your enterprise – that’s why they’re at the heart of the next generation of intelligent information technologies.

But what is enrichment? And how does it transform raw information into something much more valuable? Insight.

Information or data enrichment is the process of taking either unstructured data (like Microsoft Word documents and PDF files) or structured data (like data from SAP or a CRM system) and adding additional context to it – think labels, metadata, and classifications – so users can better structure, navigate, and use the information. For today’s organisations, there’s no way enrichment can be done manually – information is simply too vast. But with Machine Learning, it’s possible to fully automate enrichment, creating new insight and value from large volumes of information that could never have been enriched manually. These enrichment steps can include everything from extracting text content from documents or classifying documents and clustering them into logical groupings, to image recognition and named entity recognition (extracting key business entity types from information, like site codes or NI numbers).

Enrichment has endless practical applications for unifying structured and unstructured data and generating insight. Like extracting all the site and asset details from CAD drawings so that we can automatically attach them to their SAP asset records. Or categorising inbound emails into a customer service centre, conducting sentiment analysis, and then routing them automatically to the best department to handle them. Or highlighting personal data entities in a document so they can be quickly reviewed and redacted.

Using automated enrichment, organisations can bring together their two previously separate worlds of structured data and unstructured content. And by creating a rich picture of the whole information landscape and highlighting the unseen relationships between instances of unstructured and structured information, you start to uncover previously inaccessible and hidden insight.

Automated enrichment is a crucial enabler for organisations who want to extract value and wealth from the masses of information that flow through their core processes – and start generating new, even more valuable data on top of what they already have.

But most businesses are organised around different teams, each focused on delivering their own separate functions. This structure leads departments to store their data and content in silos; for example, only allowing fleet managers to view information within the fleet management system or supply chain teams only being given access to the asset management system. Enrichment generates the best complex insights when you can layer multiple data sets – which makes the reality of business data silos quite problematic. At Aiimi, our experience with customer organisations has shown us that data and content silos cause several issues.

“Often, non-compliance, avoidable mistakes, or incomplete understanding occur because one piece of information has fallen between the cracks. Information is simply not joined up.” Steve Salvin

To enrich all structured and unstructured data and unlock insights in context, organisations must break down these data and content silos and interconnect information from across the enterprise. Enrichment works at its best when information can be assessed in context, when there are more connections to be drawn and relationships to be identified between previously disparate information entities.

For knowledge workers, enrichment is the key to providing a whole new level of AI-powered data insight. And the faster they get it, the sooner they can achieve business goals to step ahead in our competitive globalised economies.

How can organisations break down their silos and start using automated enrichment to join the dots between information and generate new insight? Find out in Chapter Four…


CHAPTER FOUR

Where next? How to start unlocking information insight.

So far, we’ve looked at: why the traditional enterprise search model won’t scale to manage information growth; how access to insight can transform the success of knowledge workers across a business; and why moving away from information silos towards interconnected, enriched information can open up a world of insight opportunity.

What’s next? How do you take this from a utopian vision of information and insight into a practical reality that works for a large organisation with reams of information, pressing business challenges, and vital data protection commitments?

This is not just a pie-in-the-sky concept for how we’ll work decades from now; the technology to harness automated ML enrichment processes and deliver intelligent insights from information into the hands of those who need it is already replacing stilted enterprise search solutions. It’s being adopted by data-focused business leaders and used by innovative knowledge workers.

Insight engines are a new class of technology – and they’re purpose-built for the job.

They take a different approach to other information solutions. Information is indexed, classified, and enriched. At Aiimi, we think insight engine technology is the ideal way to create an interconnected data mesh that spans your entire organisation, connecting the dots between disparate information to unlock never-before-seen insights.

This mesh of interconnected information is so much more valuable than its raw information components – crucial derived insights are uncovered from bringing more than one entity together and exposing relationships. Plus, this mesh is always up-to-date and ready to be queried to provide the insight you need for all kinds of business use cases.

Insight engines allow businesses to leave all information where it is and simply crawl all sources to create an index, rather than requiring IT teams to migrate information to a single repository or platform before teams can start generating insights from it. The software advancements powering insight engine technology enable both structured data and unstructured content to be discovered whilst it remains in file shares, email servers, or data lakes. Not only this, but they can also surface and deliver this data at the right time and place, so users can become aware of information previously obscured from them. Unknown unknowns, if you will.

Insight engines apply relevancy methods to describe, discover, organise and analyse data. This allows the existing or synthesised information to be delivered proactively or interactively, and, in the context of digital workers, customers or constituents, at timely business moments. (Gartner)

The role of the insight engine is firstly to raise awareness, by shining a light on data that’s important to an organisation at that moment in time and to make it visible. As Aiimi’s Steve Salvin puts it, “Essentially, you’re now able to ask your information a question and get an answer back – not just a search result that may or may not provide what you need.”

“Essentially, you’re now able to ask your information a question and get an answer back.” Steve Salvin

Insight engines allow knowledge workers to see the whole data picture – to gain macro-level insights into information patterns, trends, and usage – and to dive into the finer details and explore relationships between entities.

Insight engines also facilitate the connection of people within an enterprise, making it easier to share and disseminate knowledge. Applying contextual tags to existing stores of data opens up the opportunity to discover all the people who’ve created or modified a document on a topic, making it simpler to tap into knowledge networks outside of your own.

Aiimi’s Chief Executive, Steve Salvin, describes insight engines as adding “myriad other dimensions to let you consume the knowledge in a way that makes sense to you.” Or, to put it another way, the future approach to business information can be thought of as like the experience of browsing an intelligent, recommendation-driven streaming service such as Netflix.

Netflix first presents viewers with a thumbnail image of each film’s trailer, along with a description of the plot, the actors, year of release, rating, genre, and so on. It then also suggests similar films, highlights what other people have watched and provides recommendations based on what you’ve seen before. This all seems simple and obvious to us now, but it’s actually incredibly subtle and smart. By presenting information in this way, viewers can assimilate contextual information at speed and more coherently, which leads to improved decision making. The same can be said for knowledge workers across all kinds of organisations.

“The Aiimi Insight Engine adds a myriad of other dimensions to let you consume the knowledge in a way that makes sense to you.” Steve Salvin

In much the same way, the insight engine allows businesses the opportunity to start visualising information in a way that’s meaningful to them.

For example, when searching for an invoice, the thumbnails of other invoices from that supplier are immediately visible, or the purchase order related to that invoice can be viewed. Just as we now see Netflix’s way of displaying information as the obvious way to do it, we’ll soon feel the same way about how insight engines present information to us at work.

A new way to see information

The insight engine’s ability to find new relationships between data is the key to unlocking value. And its intelligent visualisation capabilities (at Aiimi, we like to call these ‘lenses’ within our technology) make it possible to consume and interpret these valuable insights in the most intuitive way. From visualising information entities across vast and complex data sets geographically or distilling key events into a clear timeline, to displaying relationships and connections between entities graphically or creating a visual knowledge network.

Bringing together disparate sources of data in this way can be extremely powerful. As Aiimi’s Chief Exec Steve Salvin highlights, “data and content are really one information landscape, so surfacing insights comes from being able to join them together.”

These applications provide businesses with a different way to interact with data and information. The ability to contextualise information moves us beyond a focus on big data. Instead, businesses are now able to make better use of their captured data and turn it into transformational insights.

“Data and content are one information landscape, surfacing insights comes from being able to join them together.” Steve Salvin

Software platforms such as the Aiimi Insight Engine have the power to reconnect knowledge workers to data by presenting it to us in ways that better suits our cognitive abilities. Not only that, but by surfacing it contextually, we’re reminded of its history, relevance, and ongoing importance.

Forward-thinking businesses are already realising the potential of machine learning and AI technologies to put intelligent information capabilities in the hands of their knowledge workers, so they can reap the full potential value of a growing data estate.

Intelligent search, cognitive search, and insight engines are rapidly overtaking standard enterprise search solutions to ensure that knowledge workers - ‘the most valuable asset of a 21st-century institution’ according to Peter Drucker – can do what they do best... lead enterprise productivity and ensure profitability with a competitive edge.

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