Traditional online analytical processing (OLAP) systems struggle to keep pace with the velocity, variety and volume of data that modern organisations generate and analyse every day. Initially designed to work with structured data, the present semi-structured or unstructured data formats commonly encountered in big data environments overwhelm conventional OLAP architectures.
It complicates the data integration and modelling process, restricting the ability to gain holistic intelligence from diverse data types.
As data demands change and volumes grow exponentially, traditional OLAP systems are also strained for performance and scalability. Additionally, they are not designed to natively support the integration of machine learning and AI models for advanced predictive and prescriptive analytics. This slows down the organisations in harnessing the full potential of their data.
The limitations of traditional OLAP systems in handling the challenges posed by modern data stacks, extend beyond technical constraints. These limitations give rise to a profound need for Explainable AI (XAI) solutions, as organisations strive not only to extract valuable insights but also to generate trust in those insights.
As decision-making increasingly becomes data-driven, the “black box” nature of traditional OLAP leads to scepticism and hesitation within organisations as the process fails to provide transparent explanations for the patterns and insights it uncovers.
Explainable AI provides interpretable and human-understandable explanations for the results it produces. XAI helps answer critical questions such as “What are the implications of a particular trend” or “How did we arrive at this result” and “What factors are contributing to it?”
These explanations enhance trust in the reports and enable domain experts to validate and fine-tune AI models, improving their overall effectiveness.
Augmentation of OLAP with a graph-based approach while supporting XAI combines the strengths of traditional OLAP with the versatility of graph databases and algorithms. Graph structures are designed to model complex relationships and dependencies within data with a visual map that is easy to comprehend.
With an augmented architecture, intricate connections between data points open for exploration, revealing a deeper level of patterns and insights that traditional OLAP systems might overlook.
This powerful combination of OLAP and graphical capability enhances the depth and breadth of analytics. It retains its ability to handle structured data efficiently, while also adding the ability to work with semi-structured and unstructured data formats which are prevalent in modern data environments.
A synergy of OLAP, graph-based methods and XAI fosters a pioneering step towards more informed, trustworthy and effective data analytics.
Next, let’s look at the application of this unique stack in two representative industries – Healthcare and Financial Services.
Healthcare Applications
The sheer volume of data that a hospital network needs to analyse for intelligence to guide treatment plans or allocate resources poses significant challenges. Integrating graph technology with their OLAP architecture can unlock deeper insights for them.
Graphs can be utilised to represent intricate relationships between patients, medical procedures, medications, and healthcare providers; constructing a holistic view of a patient's healthcare journey.
Here are some areas that this technology stack can be of significant advantage:
Patient journey mapping: Graph algorithms can help visualise and analyse the entire patient journey from initial diagnosis to treatments, surgeries, and follow-up care. This helps in generating personalised patient care plans that are based on histories, outcomes, and preferences.
Referral networks and specialist collaborations: Graphs can reveal networks of healthcare providers, showcasing referral patterns and collaborations between specialists. This insight can be used to foster timely interventions, effective teamwork, and coordinated patient care.
Clinical trial matching: Matching eligible patients with ongoing clinical trials based on their medical history, demographics and treatment preferences increases the likelihood of successful trial participation and accelerates medical research.
Medication adherence and interaction analysis: Modelling patient-medication relationships with graph algorithms identifies adherence patterns and tailored treatment plans.
Resource optimisation: Graphs reveal hospital resource utilisation enabling efficient scheduling, resource allocation and operational efficiency improvements.
Explainable treatment plans: The graph-based approach allows for transparent and comprehensible explanations for treatment plans enhancing patient trust and understanding. This may include key medical factors and relationships that influenced the recommended course of action.
Overall, graph-based approach with XAI helps medical practitioners to enhance patient trust and confidence in the healthcare provider's expertise- resulting in better outcomes.
Financial Services
When financial institutes seamlessly integrate graph technology into their OLAP framework, they open the door to a wealth of insights. Graphs become instrumental in modelling intricate relationships among various entities such as customers, accounts, loans, and transactions.
A comprehensive view of a customer's financial history goes well beyond the capabilities of traditional OLAP systems.
Taking risk assessment as an example, OLAP systems often struggle to analyse the multiple interconnected data points involved in evaluating a borrower's creditworthiness. However, the fusion of OLAP and graph technology delivers superior credit risk insights instantly.
Let us consider more application areas within the financial services domain:
Identifying financial networks: Graph algorithms uncover hidden connections among customers, accounts, and transactions. This reveals insights like clusters of accounts associated with specific industries or customers frequently engaging in joint transactions.
Detecting anomalies and fraud: Graph-based anomaly detection raises red flags for unusual patterns or suspicious activities such as unexpected large transactions from accounts that have no prior history of such behaviour.
Assessing risk profiles: Banks gain a nuanced understanding of customer risk profiles by analysing relationships and interactions with graph technology. This includes evaluating income source stability, assessing asset diversity, and spotting potential risk indicators.
Portfolio diversification: Analytical insights using graphs aid in optimising investment portfolios by highlighting correlations between different asset classes and diversification opportunities to manage risk.
Credit scoring enhancement: Graph algorithms consider not just individual credit scores but also the influence of a customer's network. For instance, having financially stable connections can positively impact a customer's creditworthiness.
Explainable loan approvals: When processing loan applications, the graphical approach provides a clear rationale for decisions, highlighting key factors and relationships that influenced the outcome.
Conclusion
The challenges posed by traditional OLAP systems in the face of modern data stacks, along with the need for explainable AI, have paved the way for an innovative solution – the fusion of OLAP with a graph-based approach while embracing XAI principles. This unique combination not only addresses the limitations of traditional OLAP but also enhances the scope of data analytics.
As data continues to be the driving force behind decision-making in various industries, the tech stack comprising OLAP, graph-based techniques, and XAI holds the potential to redefine how organisations leverage their data.
It enhances not only their analytical capabilities but also their trust in the insights they derive. This promises to be the future of data analytics – one that is both powerful and explainable, leading to better outcomes and more informed decision-making.
