Data scientists are often faced with selecting the most efficient algorithms to solve a given business problem. This process can be very time-consuming if they do not already know what they are looking for. By combining domain expertise and Big Data Science techniques, the Scry-Jidoka platform provides a decision support system (DSS) to help users choose and execute appropriate algorithms in order to analyze their data and solve the problem. To achieve this, Scry-Jidoka’s DSS uses a rich library of statistical and mathematical operators to analyze the settings in which various algorithms were previously successful, thereby providing appropriate recommendations. Some of the highlighted features of Scry-Jidoka are given below.
VISUALIZATION AND DESCRIPTIVE ANALYTICS
To use Scry-Jidoka, users input the processed (cleansed and harmonized) data, via Scry-Collatio or otherwise, and use its Graphical User Interface (GUI) to visually understand it. Given this data and the specific business problem, this platform filters for useful algorithms that may be proprietary or Open Source and stored in its library. Through the GUI, the users can see the utility of these algorithms for descriptive and statistical analysis, and then test their hypothesis using any of these algorithms. Finally, based on the outcome, users can either select algorithms individually or use Scry-Jidoka’s DSS to combine them and achieve better results.
PREDICTIVE AND PRESCRIPTIVE ANALYTICS
Using the given data, Scry-Jidoka helps users determine the parameters that are most relevant to the business problem. Based on these results, this platform predicts the dynamics of the key performance indicators (KPIs) for this problem. Finally, this platform provides prescriptive analysis and actionable insights as to how to improve these KPIs, thereby leading to enhanced performance.
OPEN SOURCE AND PROPRIETARY ALGORITHMS
The algorithms – both Open Source and proprietary – stored in Scry-Jidoka’s library are state-of-the-art. In case Open Source algorithms do not meet our needs, we create our own proprietary algorithms to solve specific problems; our algorithms incorporate concepts that are still actively under research by theoretical computer scientists including deep learning and hypergraph modeling. Furthermore, this platform contains many modified algorithms for analyzing unstructured data, audio and video data, and wave-forms data (e.g., ECG data).