What is the evolution of data information and how prepared are we for this? Big Data and AI technologies have already established their presence in almost every industry; the circumstances have now matured to bring forward Data Integrity. But how will this be achieved?
Engine B’s solution is to bring a universal open source platform based on Knowledge Graphs that make available background data knowledge, flexible search, uncover hidden data and risk management purposes. Let’s identify the necessity of Knowledge Graphs through user cases and its great potential in business application and let’s engage in an insightful discussion.
Innovation and Digitalisation is the successor of the Information/Data Era. Big Data & AI are the means to an end not the end goal. But what is the end goal? Going beyond Big Data, we need to integrate Quality data driven insights that will allow substantial improvement on service provision efficacy.
We need to uncover the knowledge hidden in our data. How can we achieve this? By creating standard and universally acceptable Data Models with the aim to produce Knowledge Graphs, a set of interlinked descriptions of entities.
This is where Engine B’s project comes in to fill this rather unmapped field, aiming to bring this concept to life as an open source standardised platform. This will allow businesses and services of all sizes across domains to have access and integrate this platform in their system based on their custom needs.
Engine B’s partners and backers include all four large accountancy firms, plus several of the next tier and challenger firms. Plus the accountants’ UK trade body, the Institute of Chartered Accountants in England and Wales (ICAEW). Engine B is also working with a number of leading law firms.
Why Knowledge Graphs? The main contribution of Knowledge Graphs is:
Provide background knowledge: A benefit already in use by many pioneer tech companies such as Google, that enables the user to retrieve sum up information about the data entity in search.
Flexible search: Relations are pre-existing between entities; therefore, it is easier and quicker for the analyst/user to retrieve any requested information, considering the staggering amount of time spent on retrieving client data before.
Uncover hidden data: As above, relations between data entities pre-exist and bearing in mind the query built by the analyst is not designed to connect specific data to retrieve information, unexpected correlations between data entities can be retrieved.
Risk Management Purposes: Drawing connections between unexpected events or information that would not be connected otherwise allows for the quantification of risk exposure within a complex network.
In this presentation we will show Engine B plans to use Knowledge Graphs in order to establish an open data standard, why this is a necessity, and how Knowledge Graphs promote Data Quality, Data Integrity and information exchange. We will also engage in an open discussion to exchange concerns, feedback and ideas.