Founder and Product Manager at Fiddler laboratories, an explainable AI platform that delivers confidence, visibility and insight into AI
Companies are generating an increasing volume of data at a 61% CAGR. As a result, companies have made a transition to a data-driven decision model to create a competitive advantage.
The traditional BI workflow is to produce tailored summary data views and analyzes to inform decision making from vast oceans of underlying data stores. These mostly manual efforts do not keep pace with increasing data speeds that require a way to derive rapid information from large data sets. Additionally, data approximation obscures the information, data selection adds human bias, and the information is not precise.
Artificial intelligence (AI), especially machine learning (ML), can be used to automatically discover complex relationships in data and speed up the process of information generation. Augmented analytics is an emerging analytical paradigm with integrated ML and AI. This improves data selection, data analysis, information generation and normative decisions.
Let’s understand the main types of analyst requests.
1. Description. Describe what happened based on past data. For example, in-store sales fell in Texas last month.
2. Diagnostic. Explain why it happened. For example, store sales plummeted due to ineffective promotion in San Antonio.
3. Predictive. Plan what will happen next. For example, store sales will continue to decline in Texas.
4. Prescriptive. Recommend a plan of action. For example, pause or replace promotion in San Antonio.
To meet these demands, an analyst manually sifts through the data and creates statistical models to find patterns that answer the business question posed, selectively eliminating the data in the process. Investigating business issues in this way takes considerable effort.
Augmented Analytics boosts traditional analytics with:
• Faster decisions by providing a selection of data, speeding up diagnosis and automating information generation.
• Accurate decisions by leveraging all data and capturing its complex relationships.
• Data democratization with real-time shared reports and easy access for data research.
Therefore, the augmented analysis is expected to increase from around $ 4 billion in 2017 to about $ 30 billion by 2025. The application of AI to the generation of insights encourages business analysts to acquire ML skills, giving rise to a new role: that of citizen data scientist. This role allows organizations to accelerate their data projects by complementing limited data science talent.
The challenges of augmented analytics today
While augmented analytics bring many benefits, its challenges hamper those benefits. The black box nature of information-driving AI paradoxically injects haze and doubt into this AI-generated information. This makes information particularly difficult to understand, analyze, and query for confident decision making, let alone instill trust with other stakeholders.
Recent reports on alleged biases in lending, credit, hiring, and healthcare algorithms highlight the risks of a lack of transparency in AI solutions. Additionally, citizen data scientists bring domain expertise to business problems, but lack easy-to-use tools to support their proficiency level in advanced data science workflows.
Enter explainable AI: analytical transparency of the black box
Explainable AI is the next generation of AI that opens the black box so humans can understand what’s going on inside AI models to ensure decisions made by AI are transparent, accountable and trustworthy.
An explainability-centric analytics solution delivers a deep understanding of the information generated that helps analyze underlying relationships, understand problem drivers, and diagnose issues, saving significant time and effort. informed decisions.
While an augmented analytics solution without explainability could accurately predict a drop in store sales for the next month, explainability may indicate why it would drop. Likewise, while an augmented analytics solution is capable of prescribing that a cohort of users receive a giveaway, explainability will help you convince your business partners to fund this proposition.
Explainability allows humans to actively participate in the AI process by providing feedback when needed. This ensures the possibility of human monitoring of the AI to correct the course when needed. With explainable AI, teams can finally better understand diagnostic, predictive, and prescriptive business insights, empowering businesses to confidently make reliable decisions, accelerating data-driven decisions across the organization.