The Data Analytics Lifecycle within Physical Security

The integration of data analytics into the physical security industry has the potential of ushering in a new dimension of efficiency and effectiveness in ensuring safety, protection and cost savings. The data analytics life cycle plays a pivotal role in this transformation, allowing businesses and organizations to take a business unit that was once a cost center and transforming it into a competitive advantage. Let's delve into the data analytics life cycle and its intersection with the physical security industry.

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Data analytics lifecycle phases:

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1. Data Discovery, Collection and Ingestion:

The data analytics life cycle commences with the acquisition and collection of data. Any data that is being considered for collection should be based on a business question that the physical security practitioner is trying to answer. In the realm of physical security, this step involves gathering diverse types of data such as video feeds, access control logs, intrusion alarms, incident management data and other sensor-generated data. While the data within these systems are key enablers, capturing this data in near real-time is one of the biggest challenges the physical security practitioner faces. Many of the described systems are legacy based systems and don't provide modern mechanisms to allow data ingestion within a business intelligence and analytics tool.

2. Data Preparation and Cleaning:

Once the data is collected, it undergoes processing and cleaning to ensure accuracy and consistency. This step is often referred to as Extraction, Transformation and Loading (ETL) or Extraction, Load and Transform (ELT) and involves removing noise, handling missing or erroneous data, transforming raw data into a usable format and loading the data into a data store. In the physical security industry, this could mean tagging video feeds to transform them from unstructured to structured data, aligning timestamps, correcting anomalies or removing duplicates and null values and landing the transformed data in a data analytics platform or a data warehouse.

3. Data Modeling:

With a deeper understanding of the data, models and algorithms can be created tailored to specific security data objectives. Basic or advanced statistical analysis, SQL queries and machine learning models can all be used to identify actionable insights. For physical security practitioners, it's very important that they identify the appropriate internal, external and technological resources required to effectively execute data modeling.

4. Deployment and Integration:

Once models are developed and validated, they are integrated into the selected business intelligence and analytics platform. Again, a physical security practitioner most likely will need to look to internal or external resources to assist with the deployment of selected models. Evolving technology around BI and analytics platform capabilities relative to model deployment should also be considered.

5. Monitoring and Optimization:

Post-deployment, continuous monitoring is essential to ensure the models perform as intended. Monitoring allows for real-time adjustments and optimizations based on performance feedback.

6. Communication Results through Reporting and Visualization:

The insights generated from data analytics need to be effectively communicated. Visualization tools are employed to present data trends, anomalies, and predictions in an easily understandable format. Dashboards and reports help security professionals and decision-makers grasp the security landscape and its impact on the overall business.

Conclusion:

Incorporating data analytics into the physical security industry has the potential to shift the paradigm from physical security being a cost center to a positive contributor. The data analytics life cycle forms the backbone of this transformation, facilitating a systematic approach to showing how physical security objectively impacts the greater business. To assist with the facilitation of the data analytics lifecycle within physical security, practitioners should consider domain specific analytics platforms like StratorSoft Analytics.

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Sources:

- "Data Analytics Lifecycle." https://www.rudderstack.com/learn/data-analytics/data-analytics-lifecycle/#:~:text=The%20phases%20of%20the%20data,and%

20communicating%20with%20your%20stakeholders.

- UNext Editorial Team. 31 October 2022 "Data Analytics Lifecycle: An Easy Overview For 2022." https://u-next.com/blogs/hr-analytics/data-analytics-lifecycle/

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