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The Keys to Unlocking Agribusiness Potential
Technology Adoption & Healthier Data
Technology Adoption & Healthier Data
The hidden costs of poor data health in the ag input supply chain
Which side of that gap are you on?
There’s a technology and data gap in agribusiness
Technology adoption and improved data analytics in agriculture are becoming table stakes to compete. But the Ag input supply chain finds itself at an interesting crossroads as it relates to technology adoption and data health in the agribusiness ecosystem.
What is data health?
Data health refers to the overall quality, accuracy, and completeness of data that is collected, stored, and used in various contexts such as data analysis and decision-making. A dataset with good data health is one that is reliable, timely, and trusted.
For over a decade, there has been an emphasis on technology investment at the grower and farmer level. Precision ag tools, farm management software, and robotics and automation are seen as the darlings of innovation in the agriculture industry.
But the Ag input supply chain has been left behind, and that lack of focus and attention has created a status quo of antiquated technology and poor data. Some of the largest commercial agribusinesses are operating in a hamstrung manner.
When the foundation of all good decision-making is the data that informs your options, the accuracy, completeness, and timeliness of that data become critical. It can make or break critical business decisions.
Consequently, subpar data health can have big implications for your business. And there’s a long list of realities that companies face as they navigate decision-making with inefficient data:
Black box data issues with no visibility from commercial partners
Over-time and late nights for staff to hit year-end data reconciliation deadlines
Large, recurring annual expenses for data quality services
Delayed market, price, and product prices for hampered business insights
Poor sales experiences for customers due to a lack of digital sales enablement
But these hurdles are not simply the “cost of doing business.”
The real cost of poor data health
Squeezed profit margins
Inaccurate, incomplete, and untimely data hinders the ability to take advantage of margin opportunities.
Losing customers
Competition with better data will provide products and services more aligned to customers' needs.
Inability to attract talent
Next generation talent will be fluent with data and expect to use modern digital technologies in the work place.
Next generation talent will be fluent with data and expect to use modern digital technologies in the work place.
"When the foundation of all good decision-making is data, the options, accuracy, completeness and timeliness of that data can make or break essential business decisions"
John Brubaker
Chief Executive Officer, Smartwyre
Industry Examples
Data health and technology adoption missteps are costly
The 2022 holiday travel event by the major U.S. airline included 15,000+ canceled flights. While a harsh winter storm was originally blamed for the travel disruptions, it became clear that there were underlying issues as other major airlines quickly bounced back and Southwest continued to struggle.
It was later determined that outdated scheduling software – the data and systems supporting it – was the culprit. A lack of investment in scheduling software combined with the cascading flight cancellation data compounded communication issues. With over 50-plus years of advancements in aviation, there were still components of the systems from as far back as the 1970s.
The entire December event cost Southwest $825 million in lost revenue, passenger reimbursements, and other costs. Plus, playing ‘catch up’ and addressing these data processing and system errors will mean a hefty digital and IT infrastructure investment. With an 18-month runway to get all those IT systems up and running, the airline could be exposed if another issue related to weather and its systems arises.
Department of Labor in Washington, D.C.
When COVID-19 created market uncertainty in 2020, many companies and businesses were forced to close, leaving millions of people across the U.S. without jobs. Department of Labor systems in states like Florida, New York, and California were completely unprepared for the volume of claims, as decades-old digital tools struggled to keep pace.
Plus, when programs like Pandemic Unemployment Assistance were created by the federal government, systems couldn’t accommodate the rules and guidelines in real-time, adding further to the delays and backlogs in processing claims. In Kansas, the system’s inability to verify data and catch errors led to $440 million being distributed for fraudulent claims.
In many states, the lag in investing in technology and data infrastructure for decades meant that unemployment departments had to hire additional staff, work with outside vendors, and take other prompt measures to address issues.
Leaders vs Laggards
Technology adoption and
the impact on revenue
Leaders widening the revenue gap
In many industries, like agriculture, there is a clear divide: Leading businesses at the forefront of technology adoption with a keen eye on leveraging data versus laggards who don’t.
And the gap between those two groups has only widened since the start of the COVID-19 pandemic.
A recent Accenture report, ‘Make the Leap, Take the Lead’, found that prior to 2019, Leaders in technology adoption and innovation had a growth rate that was 2x that of Laggards.
And the gap has only widened more since the start of the COVID-19 pandemic.
In the past three years, Leaders are now growing at 5x the rate of Laggards on average.
The tactics and strategy of a leader
Leading organizations make a variety of decisions and strategic moves that lift them above their competitors.
For example, as it relates to their IT departments, Leaders will shift budget from operations to innovation-related activities, noting the compounding effects it will have across their business.
With that shift in budget, priority in the IT department becomes speeding up software development cycles, changing business processes and building new capabilities. Plus, the IT budget doesn’t grow, and maintenance and operations become facilitated via the cloud, making them more cost-effective.
These updated systems and processes give Leaders strategic agility and scalability. Meanwhile, the new capabilities and efficiencies help compress transformation initiatives, further widening their lead over Laggards.
Attention agribusinesses
Leverage modern technology to improve data health
Technology adoption and good data health go hand-in-hand with improved decision-making and winning business strategies. Therefore, it’s not surprising that top-tier organizations in industries like finance, health care, and transportation, among others, make strategic decisions to invest in those digital capabilities.
In agriculture, it’s no different. Leading agribusinesses adopt new technologies and improve data health to improve decision-making and remain market leaders.
These leaders create space between themselves and others in the industry by optimizing for two key data attributes: data quality and data timeliness. In the Ag input supply chain, this includes creating robust, real-time data channels as it relates to grower information, product data, incentive programs, and transactions.
The data health divide in agribusiness
The Ag input supply chain has lagged in technology investment and normalized outdated data processing procedures. Some of the outdated activities around data management seen in Ag:
Time-sensitive information being sent by email
Massive amounts of data rekeying
Complex spreadsheets requiring expansive history and knowledge to update
Large annual data consolidation and clean-up events at reconciliation time
Status Quo
Modern processing
Batch-processing
Daily processing
Manual efforts to update data
Automation tools and techniques
Gathering data from multiple sources
Leverage pre-consolidated, validated information
Information delayed by weeks or months
Real-time data updates
Marginal improvements in data health
Data health over 99%
It doesn’t have to be this way. Proven technologies used across non-Ag industries are being leveraged to improve overall data health. The shift is underway.
Smartwyre Improves Data Health
Improve the quality of
grower information
Crop input organizations track and manage critical information for all farmers in a grower data set. But the quality of those data sets can often be suspect.
Spreadsheets are filled from a mix of data feeders, like ERPs and CRMs, that don’t talk to one another. Data formats and attributes are not consistent. 3rd-party groups attempt to enhance the data’s utility, but merely increase the disorder.
These variables collide to create a common problem: the overall quality of the grower data is low.
The downstream effects are highlighted when it comes to recording sales and calculating incentive payments. Massive, manual efforts to review grower records and transaction data become the solution.
But there is a better way.
Today, Smartwyre uses advanced techniques and tools to improve the quality of grower data. Fuzzy matching, smart address validation, rules wizards and transaction data correction are used to help ensure that entity information is correctly matched and updated into the correct organization hierarchy. Data exceptions are queued for immediate resolution – and Smartwyre is updated to automate this change going forward.
High-quality grower data allows for:
Crop advisors and sales teams to have additional business insight
A complete, accurate view of grower sales transactions and profitability
Real-time, in-season incentives for growers based on current market data
Better understanding of incentive liabilities
More robust supply chain planning
Related entities
Easily identify and manage all relevant entities within a grower organization.
Data transactions
Validate data and address issues at the source.
Grower Information
Track critical information such as GLN's, stewardship IDs, and customer IDs in one location.
Speak with Smartwyre
CONTACT SMARTWYRE TO IMPROVE YOUR DATA HEALTH
Contact Smartwyre To Improve And Manage Data Quality
Driving Agribusiness Performance. Connecting retailers and suppliers to improve productivity and commerce.
Leaders vs Laggards
Technology adoption and
the impact on revenue
Leaders widening the revenue gap
In many industries, like agriculture, there is a clear divide: Leading businesses at the forefront of technology adoption with a keen eye on leveraging data versus laggards who don’t.
And the gap between those two groups has only widened since the start of the COVID-19 pandemic.
A recent Accenture report, ‘Make the Leap, Take the Lead’, found that prior to 2019, Leaders in technology adoption and innovation had a growth rate that was 2x that of Laggards.
And the gap has only widened more since the start of the COVID-19 pandemic.
In the past three years, Leaders are now growing at 5x the rate of Laggards on average.
The tactics and strategy of a leader
Leading organizations make a variety of decisions and strategic moves that lift them above their competitors.
For example, as it relates to their IT departments, Leaders will shift budget from operations to innovation-related activities, noting the compounding effects it will have across their business.
With that shift in budget, priority in the IT department becomes speeding up software development cycles, changing business processes and building new capabilities. Plus, the IT budget doesn’t grow, and maintenance and operations become facilitated via the cloud, making them more cost-effective.
These updated systems and processes give Leaders strategic agility and scalability. Meanwhile, the new capabilities and efficiencies help compress transformation initiatives, further widening their lead over Laggards.
Smartwyre Improves Data Health
Improve the quality of
grower information
Crop input organizations track and manage critical information for all farmers in a grower data set. But the quality of those data sets can often be suspect.
Spreadsheets are filled from a mix of data feeders, like ERPs and CRMs, that don’t talk to one another. Data formats and attributes are not consistent. 3rd-party groups attempt to enhance the data’s utility, but merely increase the disorder.
These variables collide to create a common problem: the overall quality of the grower data is low.
The downstream effects are highlighted when it comes to recording sales and calculating incentive payments. Massive, manual efforts to review grower records and transaction data become the solution.
But there is a better way.
Today, Smartwyre uses advanced techniques and tools to improve the quality of grower data. Fuzzy matching, smart address validation, rules wizards and transaction data correction are used to help ensure that entity information is correctly matched and updated into the correct organization hierarchy. Data exceptions are queued for immediate resolution – and Smartwyre is updated to automate this change going forward.
High-quality grower data allows for:
Crop advisors and sales teams to have additional business insight
A complete, accurate view of grower sales transactions and profitability
Real-time, in-season incentives for growers based on current market data
Better understanding of incentive liabilities
More robust supply chain planning
Related entities
Easily identify and manage all relevant entities within a grower organization.
Data transactions
Validate data and address issues at the source.
Grower Information
Track critical information such as GLN's, stewardship IDs, and customer IDs in one location.
The Keys to Unlocking Agribusiness Potential
Technology Adoption & Healthier Data
The hidden costs of poor data health in the ag input supply chain
Which side of that gap are you on?
There’s a technology and data gap in agribusiness
Technology adoption and improved data analytics in agriculture are becoming table stakes to compete. But the Ag input supply chain finds itself at an interesting crossroads as it relates to technology adoption and data health in the agribusiness ecosystem.
What is data health?
Data health refers to the overall quality, accuracy, and completeness of data that is collected, stored, and used in various contexts such as data analysis and decision-making. A dataset with good data health is one that is reliable, timely, and trusted.
For over a decade, there has been an emphasis on technology investment at the grower and farmer level. Precision ag tools, farm management software, and robotics and automation are seen as the darlings of innovation in the agriculture industry.
But the Ag input supply chain has been left behind, and that lack of focus and attention has created a status quo of antiquated technology and poor data. Some of the largest commercial agribusinesses are operating in a hamstrung manner.
When the foundation of all good decision-making is the data that informs your options, the accuracy, completeness, and timeliness of that data become critical. It can make or break critical business decisions.
Consequently, subpar data health can have big implications for your business. And there’s a long list of realities that companies face as they navigate decision-making with inefficient data:
Black box data issues with no visibility from commercial partners
Over-time and late nights for staff to hit year-end data reconciliation deadlines
Large, recurring annual expenses for data quality services
Delayed market, price, and product prices for hampered business insights
Poor sales experiences for customers due to a lack of digital sales enablement
But these hurdles are not simply the “cost of doing business.”
The cost of
poor data health
Squeezed profit margins
Inaccurate, incomplete, and untimely data hinders the ability to take advantage of margin opportunities.
Losing customers
Competition with better data will provide products and services more aligned to customers' needs.
Inability to attract talent
Next generation talent will be fluent with data and expect to use modern digital technologies in the work place.
"When the foundation of all good decision-making is data, the options, accuracy, completeness and timeliness of that data can make or break essential business decisions"
John Brubaker
Chief Executive Officer, Smartwyre
Industry Examples
Data health and technology adoption missteps are costly
The 2022 holiday travel event by the major U.S. airline included 15,000+ canceled flights. While a harsh winter storm was originally blamed for the travel disruptions, it became clear that there were underlying issues as other major airlines quickly bounced back and Southwest continued to struggle.
It was later determined that outdated scheduling software – the data and systems supporting it – was the culprit. A lack of investment in scheduling software combined with the cascading flight cancellation data compounded communication issues. With over 50-plus years of advancements in aviation, there were still components of the systems from as far back as the 1970s.
The entire December event cost Southwest $825 million in lost revenue, passenger reimbursements, and other costs. Plus, playing ‘catch up’ and addressing these data processing and system errors will mean a hefty digital and IT infrastructure investment. With an 18-month runway to get all those IT systems up and running, the airline could be exposed if another issue related to weather and its systems arises.
When COVID-19 created market uncertainty in 2020, many companies and businesses were forced to close, leaving millions of people across the U.S. without jobs. Department of Labor systems in states like Florida, New York, and California were completely unprepared for the volume of claims, as decades-old digital tools struggled to keep pace.
Plus, when programs like Pandemic Unemployment Assistance were created by the federal government, systems couldn’t accommodate the rules and guidelines in real-time, adding further to the delays and backlogs in processing claims. In Kansas, the system’s inability to verify data and catch errors led to $440 million being distributed for fraudulent claims.
In many states, the lag in investing in technology and data infrastructure for decades meant that unemployment departments had to hire additional staff, work with outside vendors, and take other prompt measures to address issues.
Department of Labor in Washington, D.C.
Leaders vs Laggards
Technology adoption and
the impact on revenue
Leaders widening the revenue gap
In many industries, like agriculture, there is a clear divide: Leading businesses at the forefront of technology adoption with a keen eye on leveraging data versus laggards who don’t.
And the gap between those two groups has only widened since the start of the COVID-19 pandemic.
A recent Accenture report, ‘Make the Leap, Take the Lead’, found that prior to 2019, Leaders in technology adoption and innovation had a growth rate that was 2x that of Laggards.
And the gap has only widened more since the start of the COVID-19 pandemic.
In the past three years, Leaders are now growing at 5x the rate of Laggards on average.
The tactics and strategy of a leader
Leading organizations make a variety of decisions and strategic moves that lift them above their competitors.
For example, as it relates to their IT departments, Leaders will shift budget from operations to innovation-related activities, noting the compounding effects it will have across their business.
With that shift in budget, priority in the IT department becomes speeding up software development cycles, changing business processes and building new capabilities. Plus, the IT budget doesn’t grow, and maintenance and operations become facilitated via the cloud, making them more cost-effective.
These updated systems and processes give Leaders strategic agility and scalability. Meanwhile, the new capabilities and efficiencies help compress transformation initiatives, further widening their lead over Laggards.
Attention agribusinesses
Leverage modern technology to improve data health
Technology adoption and good data health go hand-in-hand with improved decision-making and winning business strategies. Therefore, it’s not surprising that top-tier organizations in industries like finance, health care, and transportation, among others, make strategic decisions to invest in those digital capabilities.
In agriculture, it’s no different. Leading agribusinesses adopt new technologies and improve data health to improve decision-making and remain market leaders.
These leaders create space between themselves and others in the industry by optimizing for two key data attributes: data quality and data timeliness. In the Ag input supply chain, this includes creating robust, real-time data channels as it relates to grower information, product data, incentive programs, and transactions.
The data health divide in agribusiness
The Ag input supply chain has lagged in technology investment and normalized outdated data processing procedures. Some of the outdated activities around data management seen in Ag:
Time-sensitive information being sent by email
Massive amounts of data rekeying
Complex spreadsheets requiring expansive history and knowledge to update
Large annual data consolidation and clean-up events at reconciliation time
Status Quo
Batch-processing
Manual efforts to update data
Gathering data from multiple sources
Information delayed by weeks or months
Marginal improvements in data health
Modern processing
Daily processing
Automation tools and techniques
Leverage pre-consolidated, validated information
Real-time data updates
Data health over 99%
It doesn’t have to be this way. Proven technologies used across non-Ag industries are being leveraged to improve overall data health. The shift is underway.
Smartwyre Improves Data Health
Improve the quality of
grower information
Crop input organizations track and manage critical information for all farmers in a grower data set. But the quality of those data sets can often be suspect.
Spreadsheets are filled from a mix of data feeders, like ERPs and CRMs, that don’t talk to one another. Data formats and attributes are not consistent. 3rd-party groups attempt to enhance the data’s utility, but merely increase the disorder.
These variables collide to create a common problem: the overall quality of the grower data is low.
The downstream effects are highlighted when it comes to recording sales and calculating incentive payments. Massive, manual efforts to review grower records and transaction data become the solution.
But there is a better way.
Today, Smartwyre uses advanced techniques and tools to improve the quality of grower data. Fuzzy matching, smart address validation, rules wizards and transaction data correction are used to help ensure that entity information is correctly matched and updated into the correct organization hierarchy. Data exceptions are queued for immediate resolution – and Smartwyre is updated to automate this change going forward.
High-quality grower data allows for:
Crop advisors and sales teams to have additional business insight
A complete, accurate view of grower sales transactions and profitability
Real-time, in-season incentives for growers based on current market data
Better understanding of incentive liabilities
More robust supply chain planning
Related entities
Easily identify and manage all relevant entities within a grower organization.
Data transactions
Validate data and address issues at the source.
Grower Information
Track critical information such as GLN's, stewardship IDs, and customer IDs in one location.
Speak with Smartwyre
CONTACT SMARTWYRE TO
IMPROVE YOUR DATA HEALTH
Speak with Smartwyre
Contact Smartwyre To Improve And Manage Data Quality
Contact Smartwyre To Improve And Manage Data Quality
Speak with Smartwyre
Contact Smartwyre To Improve And Manage Data Quality
Contact Smartwyre To Improve And Manage Data Quality
Driving Agribusiness Performance. Connecting retailers and suppliers to improve productivity and commerce.
Driving Agribusiness Performance. Connecting retailers and suppliers to improve productivity and commerce.
Solutions
For
Resources