Data management
Best practices
Data unlocks invisible relationships between machine performance and inputs. Best practices for data integrity include:
Data/cyber security
A cyber security system must include protection for computerized elements of your digital information system. Cyber security is discussed in greater detail in the Technology and Risk of Adoption info sheet (to obtain a copy of this info sheet, contact
Data reliability/power quality
Computerized systems are sensitive to fluctuations in electricity supply. At least seven power quality issues may corrupt data. These are discussed in the power quality infosheet (to obtain a copy of this infosheet, contact
Data management skills
The skills and tools required to effectively manage and analyze data require training, collaboration and input across your organization.
Tools and training
Data management begins with tools and training. The tracking and control systems that generate data along with the skill sets required to understand those systems. The skills acquired through Canadian Industrial Program for Energy Conservation (CIPEC) training and using an Energy Management Information System (EMIS) are transferable to power quality controls. The awareness, data-gathering and data cleaning skills are a stepping-stone to digital mapping and digital integration (DI). With DI it is possible to develop key performance indicators (KPIs) that link efficiencies across procurement, maintenance, food safety, processing, shipping, sales, finance and human resources. The data will point to relationships. Your managers, experienced staff and subject matter experts will need to analysis and interpret the data. The endpoint is a system for predictive management and evidence-based decisions.
The critical junction in this game plan is with DI. Whoever you use to provide a digital integration platform should also be hired to provide the staff training it takes to identify, gather, maintain, clean, interpret, analyze, visualize and model your data. This requires team input from your staff and managers. Data can be gathered to identify time, process, volume, costs and other criteria. Your team needs to have the subject matter experience and expertise to recognize variables that affect what they are responsible for doing in your organization.
Field observation
Automated metering integration skills and technology unlock opportunities that include:
- how unplanned downtime affects productivity and finance. This is an opportunity for maintenance to use real data to justify predictive maintenance and to eliminate the cause of a series of linked costs.
- how sanitation practices affect sewer surcharges and hot water use. This is an opportunity to manage the variables that can impact input and waste costs.
- how robots impact the speed of order fulfilment and cost of specific tasks. This is an opportunity to repurpose existing workers away from repetitive workstations.
- the gross margin required to justify the marketing budget for a new customer. This is an opportunity for sales, finance, procurement and production to all meet their targets.
- how waste reduction, by-product upscaling and energy efficiency has impacted our retail carbon disclosure target. This is a sustainability story for your sales and marketing team.
Along with these questions, staff and managers also need the skills to interpret and present data for decision-making. Referenced against KPIs, DI is a platform that helps a business align operational performance with strategic goals.
Data management capacity gained through DI gets you roughly 80% of the way to achieving or adopting other practices, such as:
- enterprise resource planning
- traceability
- various ISO accreditation
- energy star accreditation
- validating the data for net zero and carbon credits
- modelling the impact of new equipment purchases
- modelling the requirements for new business
- responding to municipal water and wastewater plan requests
Data can be managed manually, which is highly labour intensive, or digitally, which once in place, is as simple as a keystroke.
A lesson learned from manufacturers already engaged with digitally integrated manufacturing is the extensive use of handheld scanners and barcodes to track inventory and reduce record-keeping touchpoints. This includes the labelling of materials from receiving and through processing. These manufacturers add a strip of digital tape to existing barcoded labels at receiving and add a strip of digital tape to or over work-in-progress (WIP) and finished goods to maintain traceability. The scanning process is digitally integrated to their inventory management, scheduling and shipping systems. This is an effective way to track WIP that is invisible to conventional inventory systems. When WIP is hidden from the system, it falls out of inventory valuation control and that is a hidden finance cost. It may also skew cash-to-cash cycles when WIP dwell time is more than 24 hours.
Another lesson learned has been published by Verdandis, a US-based IT company specializing in data cleansing and enrichment. They suggest a robust data-driven process, involving:
- supporting savings up to 17% for strategic sourcing of inputs
- reducing production costs 3–8%
- increasing plant uptime by 5–8%
For a detailed discussion on data management, data reliability and data security, please contact the Ministry of Agriculture, Food and Rural Affairs (OMAFRA) at