Decision Intelligence is the use of artificial intelligence and high-power data analysis to optimise the decision-making process for individuals and within businesses.
It enables non data scientists to deliver high value insights that answer critical business questions.
Our enterprise solutions unlock simulation workflows that enable the creation of a closed loop Digital Twin.
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Compass: EngineTM enables Decision Intelligence by automatically linking disparate data sets.
The term digital twin is becoming pervasive across all industry sectors driven by trends such as the Internet of Things and Industry 4.0 and is also being realised in applications as diverse as wind farms, jet engines and even supply chain management.
Yet not everyone’s definition of a digital twin is the same. Many will focus on the things you can do with the virtual representation such as build machine learning, artificial intelligence, analytics and simulations to describe and predict the behaviour of this virtual representation.
Secondly, there is a growing gap between the business and executive demand for data led insights and the availability of data scientists and advanced data science and coding skills across their organization.
Thirdly, traditional data science tools and technologies are high cost and complex to use – limiting wide spread adoption and effective scalability.
Fundamentally, therefore, Decision Intelligence solutions are mission critical for organisations that seek to be data led. They tackle the data deluge head-on by eliminating the cost and complexity of advanced data science, scale the right tools to the right people at the right time and transform data into actionable intelligence at speed.
Practical, real world examples are the best way to understand what Decision Intelligence is and the impact it can have. Consider three examples that have been applied to some of the key issues facing us today – environmental decision making at speed, sustainable data strategy development and business process digital twins.
Environmental Decision Making at Speed: With 80% of data having a geospatial component, i.e. a place in three dimensional space, many data based analyses deliver intelligent insights that are location related. Organisations like the Rainforest Trust need to make decisions about which ecosystems to protect and the faster they can make these decisions, the more of the planet they can save. One big challenge they face is the amount, complexity and diversity of the data they now have available to use to help them make the best possible investment decision. All these different data sets, such as the latest list of endangered species, where these species range, the ownership of the land etc must be interrogated holistically. With traditional approaches, linking and preparing the data for analyses can consume hours, days and even weeks of a data analysts time – even before any real analysis has been performed. With Decision Intelligence solutions, much of these manual processes are eliminated through analytic process automation and simplified through no-code workflows. In this way, the time to decision can be slashed by as much as 80%.
Sustainable Data Strategy Development: With the anticipated tripling of the amount of data by 2030 and the majority of this data not being used to create value for an organization, simply storing this so-called dark data has been estimated to create over 6 million tonnes of CO2 emissions per year – running counter to the ESG commitments being made by more and more organizations and demanded by their customers, investors and regulators. New, advanced, yet low or no-code approaches to data linking and discovery are now being democratised beyond the core data science community to other domain experts and non–data analysts to enable them to better understand and prioritise the data that adds the most value to their organisation, who is using what data and how frequently. In this way a sustainable data strategy can be developed with different data sets assigned to hot, warm or cold storage or even eliminated altogether. Graph based approaches to data organisation are one such technology that can underpin these sustainable data strategy initiatives and have been identified as a critical enabling technology in Gartner’s Tech Radar.
Business Process Digital Twins: In parallel with the increased focus on the use of advanced data science for applications in the areas of sustainability and environmental protection, the historic business focus on competitive advantage and innovation, process efficiency, cost savings, productivity, profitability and margin remains as important as ever. Though pioneered in industries developing physical assets, such as aerospace, oil and gas or industrial equipment, the concept of the digital twin is now rapidly spreading to the worlds of business, banking and finance, culminating in the business process digital twin. While the application could hardly be more different from a digital twin of a physical asset, the end goal remains the same – to create a digital replica of a real world process that is bi-directionally synchronised with data streaming from the real world process, being analysed in the digital replica and resulting actions being driven back into the real world – all with the intent of delivering a better process that operates faster and at lower cost. Emerging examples of a business process digital twin include call centre operations automation, simulation, optimisation and scale up and manufacturing, warehouse and logistics operations improvement.
Considering the clear value delivered by Decision Intelligence, the need for this value across all sectors of industry and the common challenges organisations are facing to deploy it, Slingshot Simulations is uniquely positioned to help.
Our Compass: EngineTM graph based decision intelligence platform-as-a-service solution tackles the three main challenges customers face on their journey to becoming data driven organisations.
Overcome the data deluge: By using industry proven, IP protected graph and bi-graph technology developed for over a decade, Compass: EngineTM enables rapid, automatic linking and discovery of highly disconnected data sets at scale. From this integrated foundation, organisations can unleash the power of machine learning and artificial intelligence, simulation and digital twins and slash the time to return on investment of their digital transformation initiatives.
Democratise data science: The no-code, intuitive data analytics workflows within Compass: EngineTM enable non-data scientists and specific domain experts to leverage the power of advanced data science in their own workflows, reducing the critical path reliance on typically small, focused data science teams
Remove the cost barrier to scale: With flexible pricing tiers from a free community version to customisable and modular enterprise solutions, Slingshot Simulations is able to offer a very low total cost of ownership model, substantially removing the cost barrier to entry for companies in the early stages of their digital transformation and delivering a cost scalable path for those ready to move to the next level.
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