PATIO: Technology Enablers at the Service of Business Outcomes
PATIO is Aligned Automation’s proprietary approach to integrate the landscape of digital technologies into an outcome-driven flow. The motivation for the name PATIO comes from the ability to be in a position to “leverage what is out there”. Connecting it to the digital technologies, PATIO stands for:
Machine Learning (ML)
Robotic Process Automation (RPA)
Natural Language Processing (NLP)
Business Intelligence (BI)
Data / Text / Process Mining
Machine Learning (ML)
Business Process Mgmt Software (BPMS)
PATIO… take advantage of what’s out there
Identifying patterns in order to make high accuracy predictions is the main benefit of supervised and unsupervised machine learning (ML) models. The outputs may take the form of:
- Identifying clusters, outliers, correlations for complex exploratory work
- Classifying elements into categories for scalable data enrichment
- Predicting numerical values for anticipating business conditions
Reducing the time and eliminating the errors associated with manual tasks through a programmable set of instructions is the value proposition of robotic process automation (RPA). This requires a detailed validation of the “if… then” business scenarios, to avoid situations where the wrong actions are triggered in a highly efficient manner.
Point of synergy: When the conditions to automatically trigger an action or a process are hard to determine, pattern finding can become a valuable input to identify and qualify the candidates for automation.
Achieving a two-way exchange between unstructured human expressions and structured computer data is possible thanks to natural language processing (NLP). In one direction, this translation allows for free text data to become consumable by analytical models. In the opposite direction, it expands the democratization of advanced analytics’ consumption by mapping analytical outputs (e.g.,statistical outliers) to human expressions (an alert e-mail message), often delivered through a business intelligence (BI) interface.
Incremental Point of synergy: structuring previously unstructured data becomes an additional input to machine learning models, which could increase their predictive accuracy and therefore increase the confidence to apply an automated routine.
Applying transformations to a series of inputs (e.g., numerical values, categorical attributes, free text, event logs) in order to generate incremental statistical and relational information is essentially mining for insights. The type of analysis performed and the output obtained depends on the type of input being interrogated:
- Text mining consumes unstructured data and produces statistical information associated to it; alternatively, the free text data may get transformed into structured data.
- (Structured) Data mining consumes structured data and produces statistical information
associated to it.
- Process mining consumes event log data and organizes the paths to produce statistical information on path frequency, variations, performance and conformance to target path.
Incremental Point of synergy: Artificial intelligence (AI) technologies like NLP and ML can expand and enrich the data that is available for mining, increasing the likelihood of revealing insights. Similarly, as task and process automation increase, the digital footprint that is produced becomes a more reliable and quickly accessible source of event log data for process mining applications.
Prescribing better decisions under uncertainty through reinforcement machine learning (ML) models, or orchestrating the process workflow to reduce waste (e.g., rework, redundancy, delays) through a business process management software (BPMS), are both examples of how these digital technologies can come together to optimize an objective function. The critical element though is aligning the optimization routine with the key business outcomes being pursued. Otherwise, another case study will be added to the list of technology deployments that fail to justify their investment.
Incremental Point of synergy: the intelligence generated during the mining activities is useful in prioritizing the optimization targets that will have the most impact on the business outcomes.