Author: Dr Charlotte Thurlow-Begg, Naval Architect – Ship Efficiency, IMC&S – QinetiQ
Developments in computational, communication and technological capabilities has enabled much larger quantities of better quality data to be captured and transferred for analysis at higher frequencies.
This methodology is often referred to as ‘Big Data’ and it has been a hot topic in the maritime industry for several years now; and it has strong evidence of working and is a trend that we see in the industry. Nevertheless, this infrastructure is not yet the norm and may not be affordable or feasible for many companies in the near to medium term. So what is an alternative method that will allow companies to capitalise on the value of their already existing datasets now, as well as align them for future improvements? QinetiQ have approached the data management subject from this angle and are calling this alternative methodology “Small Data”.
The QinetiQ Approach
The “Small Data” approach is based on the use of existing data collected onboard and onshore, which may range from traditional data reports to continuously monitored data from installed data capture devices. Bespoke solutions can be developed based on a four step approach: 1) data collection, 2) strategy, 3) processing, 4) analysis. However, this approach must first be underpinned by a comprehensive understanding of the existing datasets. This includes, but is not limited to, understanding the format, strengths and weaknesses of each data field. It is a fundamental step, particularly when considering the data will be used to produce information on which decisions could be based. Furthermore, it will support the identification of fundamental data gaps (e.g. important for compliance with relevant regulations), provide a valuable resource for future reference when solutions require the use of data, and inform the approach for data collection and strategy development.
The exact processes for data collection will be company and even ship specific due to the large variability often found in ship datasets. Once collected, the data can be used to conduct an exploratory analysis generating insight that will feed into the strategy.
The data strategy firstly focuses on how information can be used to encourage practical savings, rather than what analyses can be carried out: there is not much point producing a report if it does not get utilised. However, how to encourage practical savings from information generated insight is the tricky question. Important components to enabling identification of such solutions include, but again are not limited to: a fundamental understanding of the data; insight from an exploratory analysis; knowledge and experience in data analytics; holistic knowledge and experience of ship operational, technical and human factor requirements. Expert knowledge of ship technical and operational performance is imperative for ensuring that analyses, particularly those based on statistics, are interpreted correctly. Consideration of human factors is also key for enabling the practical aspect to improvements and for generating engagement around the business. Interaction with personnel will enable identification of existing procedure where modern data management could ease, simplify or add value; along with the most useful Key Performance Indicators (KPIs), at ship and company level, to support alignment with common objectives. Based on this, the outputs of an effective data strategy will layout: the requirements for what analyses should be carried out; what processing of the data is required; what data should be processed; how the data should be structured to provide versatility and ease of use. The tasks of structuring and processing the collected data should not be underestimated as many existing datasets are often complex, fragmented and inconsistent and present challenges for centralisation. With a strategy in place data processing and analyses can be implemented accordingly.
Capitalising on Wider Opportunities
The value in implementing modern and effective data management using the “Small Data” methodology is not just the ability to use existing data effectively to transparently produce performance insight. The structuring will also ease the process of complying with and managing relevant regulation and standards, for example: EU Monitoring, Reporting and Verification; the Ship Energy Efficiency Management Plan (SEEMP); ISO management standards. Furthermore, to engage personnel in operational improvements data management can be used as a backbone for: performance identification and feedback mechanisms; awareness and knowledge development; dynamic management tools – such as activating the SEEMP. By engaging personnel, the possible perceived negatives and barriers with performance monitoring can be managed, and an energy efficient culture improved upon.
As part of the “Small Data” methodology, QinetiQ is championing the need to engage and motivate personnel to ensure that the human aspects of any change programme are considered to leverage and obtain benefits for the company. Thus, “Small Data” does not only provide an alternative method to ‘Big Data’ by capitalising on the full value of existing datasets and resource via bespoke solutions; it extends beyond the production of performance reports and focused operations, to additional measures and strategies specifically designed to meet bespoke business demands and create a process, and culture, of continuous improvement.
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