Automatic for the People: Data Management, Collaboration and Innovation
Automatic for the People: Data Management, Collaboration and Innovation - Written by Mark Bell, Head of Transportation and Infrastructure, 1Spatial.
Transportation - A Driver of Growth
The movement of people facilitates the economic advancement of our countries, ensuring that we thrive and grow as societies.
But as the world’s population increases, pressures on existing transportation networks continue to intensify. Driven by the increased population demand, owner operators are more exposed to the operational and environmental risks associated with a network at near capacity. This ultimately impacts customers through both decreased service levels, and increased network related carbon emissions.
Organisations can counter current pressures by innovating through the use of new or improved technological, organisational or commercial structures. Key to these innovations is good quality, well maintained, timely and well trusted data.
Smarter Data, Smarter Transportation, Smarter World
During the preparation for our Smarter Data Smarter World conference in November 2018, my team and I were faced with the dilemma of filling an afternoon with relevant and engaging content for a room of Transportation Data Experts. We also faced the challenge of what to do in the dreaded post lunch ‘graveyard slot’.
After discussing different approaches, we decided that whatever we did, it should be collaborative, innovative and provide value to the attendees. In short, we needed people to be active and engaging with each other in conversation. Everything pointed towards a team-based exercise, so we decided that we would split the event attendees into sector focused groups and work towards a common goal.
The goal we chose, was to understand the common data challenges facing the Transportation Industry from the perspective of the Data Experts in the room. The people who are ultimately tasked with managing their enterprise’s ever-increasing source of competitive advantage; it’s data.
Drawing out the Challenges
The question we posed was ‘what data challenges are we currently facing within our organisations. The plan was to break out into smaller, industry sector focused teams and draw the challenges faced by our organisations onto an A1 page, ready for presentation back to the wider group at the end of the exercise. The only rule was no text – let the pictures paint a thousand words.
By using the simple drawing technique favoured by organisational behaviour academics such Professor Tina Kiefer, creativity and collaboration was more rapidly fostered within the newly formed teams. As we discussed and drew the challenges onto the paper, the images sparked further discussion and collaboration, resulting in a lengthy list of challenges per group.
The Results, Further Analysis and Subsequent Findings
After the event, we summarised and aggregated the output from each sector group, recording the challenge details and associated impacts, validating the data with further customer conversations during the early part of 2019.
After running the analysis, we then categorised the challenges into one of three types: technical (data and systems), organisational (people and process) or commercial (overwhelmingly finance). These results produced two key findings:
1. Challenges have a technological focus - Unsurprisingly for information gathered from technically minded people, the largest group of challenges presented were technological, followed by organisational and commercial as shown below:
|Challenge Type||Number of Unique Challenges|
2. Challenges have a cost reduction focus - When the results were analysed further, we discovered that six of the eight common challenges posed risks to cost, either through fines or operational inefficiencies. This indicated a downward pressure from organisational management to drive efficiencies into organisations. Only two challenges, 2 and 3, were focused on the impacts of revenue growth.
|1||Data licensing||Commercial||Adhering to the various policies and licenses that govern third party data.||Risk of fines from contractual breaches.|
|2||Data monetisation||Commercial||Inability to monetise proprietary data.||Loss of potentially new revenue.|
|3||Balancing needs||Organisational||Balancing stakeholders needs for best economic benefit.||Missed opportunities to improve your business.|
|4||Workforce communication and efficiencies||Organisational||Difficulty in driving efficiency into workforce data capture.||Unneeded costs.|
|5||Data duplication||Technological||The burden of duplicated data and no single source of truth.||Unneeded costs.|
|6||Huge data volumes||Technological||Swamped by too much data.||Unneeded costs.|
|7||Poor data quality||Technological||Low quality of supply chain data.||Unneeded costs.|
|8||Poor data visibility||Technological||Poor visibility of data availability across an enterprise.||Unneeded costs.|
With the challenges aggregated, we then discussed how they could be tackled, proposing potential solutions. These solutions also produced a third key finding:
3. Solutions have an automation focus - Of the eight challenges, we realised that five could be resolved through the approaches of tooling automation and processes simplification. All tangible and achievable activities for the people who are facing the associated challenges.
|Challenge Number||Name||Approach Type||Approach||1Spatial Solution|
|1||Data licensing||Implement a Data Governance Framework.||A data license register, holding commercial agreements and conditions.||Data Governance Framework.|
|2||Data monetarisation||Improve business development activities.||Take a market led approach to opportunity identification and exploitation, driving which data products are created for internal and external consumption.||Data Monetisation Planning.|
|3||Balancing needs||Improve decision making with better use of data.||Using clean, meaningful data, look at whole life costs of projects to provide a more holistic view on their return on investment.||Data Integration and Cleansing.|
|4||Workforce communication and efficiencies||Automate tooling and streamline processes.||Streamline processes and automate data capture tools to free up workforce teams capacity to accomplish higher value tasks.||Mobile Workforce Digital Transformation.|
|5||Data duplication||Automate tooling and streamline processes.||Automate the validation, integration and enhancement of data capture and data change across your enterprise.||Location Master Data Management.|
|6||Huge data volumes||Automate tooling and streamline processes.||Automate the management and access of data across your enterprise.||Location Master Data Management.|
|7||Poor data quality||Automate tooling and streamline processes.||Automate the validation and enhancement of data provided by your supply chain.||Supply Chain Data Quality.|
|8||Poor data visibility||Automate tooling and streamline processes.||Automate the management and discovery of data across your enterprise.||Location Master Data Management.|
Industry events risk missing the opportunity to foster not just two-way conversation, but more detailed periods of team-based collaboration. By providing a psychologically safe environment for like-minded people to meet, collaborate and innovate, we were able to distill three key findings and three key insights from the challenges the Transportation Data Experts said they faced.
In conclusion, the insights from the exercise were:
- Solutions are reusable across Transportation sectors - The majority of the transportation data challenges discussed at SDSW 18 were common across the various transportation sectors such as road, rail and ports, meaning potential solutions could be leveraged between sectors to reduce deployment costs.
- Solutions are ‘spend to save’ - 75% of the challenges focused on cost reduction, indicating a justifiable route to securing funding, through ‘spend to save’ initiatives.
- Solutions are within the remit of Data Experts - 50% of the challenges were technological in focus and 60% of proposed solutions sharing the same solution type; Automation of tooling and simplification of processes. This later figure indicates that more solutions are within the grasp of Transportation Data Experts, reducing the reliance on other departments.
With demand increasing on Transportation networks, owner operators will need to be more innovate, collaborative and agile to succeed. Importantly, they will need to harness their data more effectively to drive operational efficiencies and increase their insights. This is what we aim to facilitate at Smarter Data Smarter World, and we hope you see you there this year!