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Why Enterprises Need an Insights Engine.

by Matt Eustace

Data exists everywhere. When you look at the structure of an organisation, you quickly realise that everyone is an information worker now – creating, managing, sharing, and using information in their daily work. Whether you’re an engineer or a construction worker, you’re working from the data you have and creating new data. So, how do we get value from such vast amounts of information? We require innovative tools, like an insights engine, that can handle the complexity and scale of data in a modern enterprise and turn it into actionable insight.

We spoke to Aiimi’s Head of Solutions Engineering Matt Eustace about why enterprises need an insights engine that uncovers and interconnects the entire data picture.

What is an Insights Engine?

Insights engines are an evolution of what search engines used to be. While search engines only look at the documents an organisation holds, an insights engine reaches all information across the enterprise and externally. Using AI and machine learning techniques, the insights engine can discover, interconnect, and enrich all your information, regardless of format (structured, unstructured, or semi-structured) or storage system (internal, external, cloud, or on-premises). Through this, the insights engine is able to deliver synthesised information (insights) to information workers, proactively or interactively, at the exact moment they need it. There are four key stages that insights engines move through to supply these actionable insights to users:  

Discovery Stage

The Discovery Stage is where the insights engine discovers all information, whether that’s structured data or unstructured content, no matter which system it lives in. It automatically crawls all repositories, cloud, web, or on-premises, to capture an up-to-date, centralised index of everything.

Enrichment Stage

In the Enrichment Stage, insights engines use a range of tools, AI, and machine learning techniques to enrich all information and data with added structure and context. The approach to enriching a document, text, or data record may change based on the organisation’s domain, end-user requirements, or the type of insights needed. 

These tools include:

  • Term Extraction to automatically identify set terms like name or ethnicity;
  • Pattern Matching to find specific sequences of characters like serial numbers or email addresses;
  • Proximal word recognition in conjunction with patterns to spot things like dates of birth;
  • Named Entity Recognition to find the names of people, organisations, and places;
  • Phrase and Topic Detection to understand topics;
  • Large language models to recognise objectivity/subjectivity;
  • Sentiment Analysis to determine how positive or negative the content is; and
  • Geotagging to find and plot locational data.

These techniques enrich your information by applying labels (synthesised meta data) across your enterprise, helping you understand what you have, how and where information is stored, and which processes are in place.

Next, an insights engine classifies all information and data. Each document or data record is analysed then grouped into clusters. An enterprise typically has many clusters of information. Once they’ve been grouped and given a meaningful name, the insights engine can then automatically label the records. Insight Engines can also identify relationships between records through Entity Linkage.

Repository Stage

The Repository Stage is where all indexed data and metadata lives. It stores the enriched information index, along with security information and other configuration data. An insights engine can house hundreds of millions of pieces of information, yet gives users blisteringly fast response times.  

Gateway Stage

The Gateway Stage is where all information is secured with an authentication gateway that respects access permissions from source systems, and integrates with your corporate security protocols, like multi-factor authentication and single sign-on.

What’s the difference between data, analytics, and insights?

Data is the original source material; it’s where the answers to your organisation’s biggest challenges are hidden, and it may or may not have the characteristics that make it easy to extract insight. An insights engine uses the techniques above to find, classify, analyse, and create insights and predictions from the data. It can give you valuable information about a data point in relation to other data points, to tell you if it’s an anomaly, a trend, or linked to other data in some way. Analytics is how the insights engine contextualises and visualises those findings so that the end-user can easily get value from the information.

What value does an insights engine bring and how does it differ from a search engine?

A recent survey by Gartner found that 47% of digital workers struggle to find the information or data needed to perform their jobs effectively. The survey also revealed that 66% of respondents agreed better business outcomes could be achieved if IT provided universally accepted and supported applications and devices to get work done. Without the right applications, digital workers struggle to find the information they need, make wrong decisions due to a lack of awareness, aren’t alerted with relevant notifications, and miss out on important updates.

At its core, an insights engine delivers information and value across the enterprise by giving you a comprehensive picture of your entire data universe. This means information workers can get the right answers first time, make better informed decisions, and perform better in their jobs – enabling the whole enterprise to gain the competitive edge needed in a data-driven world.

Historically, you could pull up all the records in your organisation that matched your query, but they wouldn’t tell you a story or explain what the data was showing. You’d have to open each document or record individually and then manually correlate that information with what you’d seen previously. 

An old-fashioned search engine will only look at the documents an organisation currently holds. An insights engine extends that reach to any type or source of information, structured and unstructured data, internal or external to the organisation - encompassing email, web content, and social media for example.. It makes information and data easy to consume and available to other applications and business processes across your organisation.

With a search engine, you’d receive a list of items matching your search. With an insights engine, this information can be delivered in more useful ways that serve your business processes, such as a relationship map that visualises the information and shows its context in relation to other information. Search engines also tend to work in isolation, whereas an insights engine can provide end-to-end experiences and feed insight into other environments.

Gartner’s Hype Cycle for Natural Language Technologies 

Gartner’s Hype Cycle for Natural Language Technologies 2023 shows recent advances in artificial intelligence and machine learning have called for innovative methods in the field of natural language technologies – pushing insights engines to the ‘Slope of Enlightenment’ phase.

With the exponential growth of content and data, both internal and external, organisations are facing more challenges in getting the insight they need to make better decisions. Insights engines can solve this by combining data discovery with AI technologies to retrieve and synthesise information and data, making insight, analysis, and automation possible. For the enterprise, this will mean:

  • Increase in the quality, quantity, and variety of data used to inform an enterprise
  • Custom-made applications serving localised use cases – amplifies relevance and deeper context
  • Seamless user experiences – embedding information in applications where it’s needed
  • Ability to enable ‘wide data’ to be synthesised as information throughout the digital workspace (semantic search development)
  • Programmatic access to content supporting digital transformation
  • Demand for multiple insight applications to be developed and delivered cohesively.

Why should enterprises insist on the entire data picture and what does the Aiimi Insight Engine do differently?

Each enterprise app’s view (and its AI) is limited to a small section of your data. As a result, they can’t get to the answers the user needs, and with so many apps all working at once, the user can be overwhelmed by notifications. At the same time, there’s no cohesive governance if each app works independently and nothing is connected.

The Aiimi Insight Engine discovers all information, no matter where it lives; it treats structured data and unstructured content the same, to interconnect everything and unlock your entire data picture. And it’s only by having this complete picture that you get accurate, timely answers, and data-driven decision making. The user gets to the right answer, and the insights engine delivers secure enterprise AI, with the governance layer that is so essential for any organisation to fully and safely use AI such as LLMs – so your employees, partners, customers, suppliers, and investors can trust their data is handled correctly.

Aiimi’s Insight Engine is agnostic, so it doesn’t matter which platforms or operating systems an organisation uses. Nor do we move an organisation’s information into our own product – we leave everything in situ, so there’s no need to move anything. Created with the user at the forefront of the design process, our insights engine can transform data into an enterprise’s most valuable asset, empowering every area of the business to achieve greater outcomes and avoid failure.

Get the entire data picture with the Aiimi Insight Engine 

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