How You Can Overcome Big Data Roadblocks In Public Safety
Big data is probably the most complex piece of any public safety strategy. I could ask a group of 30 individuals to explain it to me and I’d probably get 30 different answers. So let’s start on an even footing. Big data analytics in public safety is the process of examining large and varied data sets to uncover hidden patterns, unknown correlations, abnormal behaviors, risks, and other useful information that can help law enforcement agencies and governments make more-informed decisions and assess and mitigate threats more efficiently.
The Data Mix
Highly dependent on data, specialized analytics and applications can significantly improve operational response, predict criminal activities, and identify patterns that could take years for an analyst to detect.
Big data analytics applications allow criminal analysts and predictive analytics professionals to analyze large amounts of structured data, in addition to the less conventional data sets that often provide infinite value. For law enforcement agencies, the need to assess a mix of structured, semi-structured, and unstructured data, for example, social media content, witness and people statements, and mobile phone records, captured via the ever growing number of sensors connected to the Internet of Things is becoming increasingly critical.
In simpler terms, big data analytics provide a means of analyzing complex sets of data to draw conclusions that can assist law enforcement agencies predict, investigate, and assess criminal activities and public safety threats better. Similarly, business intelligence helps agencies make better decisions relating to business operations, performance, and human resources management.
The development of a big data strategy is not a simple task. Agencies often concentrate on the tool as opposed to true business requirements and the real needs of big data. Data availability is probably the most important piece to define and resolve.
Five of the Biggest Big Data Roadblocks
- Data is scattered in various silos across agencies or multiple agencies
- A lack of understanding of what’s required (What do we want to focus on?)
- Lack of governance
- No real big data champion to provide direction
- Potential pitfalls that can trip up organizations implementing big data analytics initiatives, including a lack of internal analytics skills
Although the term big data has been used for several decades, it’s relatively new in the field of public safety.
Hadoop’s distributed processing framework was launched as an Apache open source project in 2006, planting the seeds for a clustered platform built on top of commodity hardware and geared to run big data applications. By 2011, big data analytics began to take a firm hold in organizations and in the public eye, along with Hadoop and the various big data technologies that had sprung up around it.
The Tech and the Tools
Law enforcement agencies make great use of structured data, the majority being stored in record management systems and intelligence data bases. Unstructured and semi-structured data types typically don’t fit well in traditional data warehouses or data lakes, and their potential value is often ignored as analysts concentrate on relational databases filled with structured data sets.
Unstructured and semi-structured data does not fit well in this model
Another challenge revolves around the inability for the data warehouses to handle the processing requirements that are omnipresent in assessing sets of big data. Timeliness, frequency, velocity and volume are imperative in the management of emergencies.
Big Data Analytics Uses and Challenges
To be truly effective, big data analysis must include internal data and open source data; however, often the required information is not under the purview of the agency. In addition, streaming analytics applications are becoming common in big data environments. The emergence of cloud technology makes it possible to collect, organize and analyze massive amounts of data under reliable architecture that meets law enforcement agencies’ big data analytics needs.
As big data analytics tools and processes mature, organizations face additional challenges. But, they can benefit from their own experiences, innovations by other users and analysts, and technology improvements. Big data environments are becoming a friendlier place for analytics because of upgraded platforms and a better understanding of data analysis tools.
Before starting the analytical modeling process for big data analytics applications, public safety agencies must have analysts who truly understand what is expected of them and who can thoroughly understand and determine what data must be considered to produce accurate findings.
I’ve often been quoted as saying that “It’s a reverse engineering process: First you define what your goal is and then revert back to the data source you need for the big data process.”
One basic question is how much data should public safety agencies incorporate into predictive models. Big data analysis techniques have been getting lots of attention for what they can reveal about customers, market trends, marketing programs, equipment performance and other business elements. For many IT decision makers, big data analytics tools and technologies are now a top priority.
The Big Data Journey
You need to ask yourself the following question. Are you able to define and identify the risks you are trying to mitigate though your big data investment?
Big data for public safety is risk-based public safety. Your first task is to make sure that risk identification is a factor in your big data strategy. Based on that risk, it will be a lot easier to identify which big data technology you need for the public safety threats you are facing.
Do you need a predictive strategy to identify potential crime locations and suspects? Do you need a video cloud to monitor high risk areas and individuals? Do you need a data mining system to identify investigative avenues or crime trends?
Every country, every city is facing public safety issues. You need to focus on your highest risks. Once you have decided what needs to be addressed, the real work can begin.
For more information, take a look at our FusionInsight HD and FusionInsight Libra solutions.
Disclaimer: Any views and/or opinions expressed in this post by individual authors or contributors are their personal views and/or opinions and do not necessarily reflect the views and/or opinions of Huawei Technologies.