For business professionals across a wide swath of industries who leverage enterprise resource planning (ERP) software in their day-to-day—the future is now.
The snowballing advancements in artificial intelligence (AI) are now knocking on the door of ERP systems, heralding next-level advancements in automation, optimization, and data-driven competitive advantages ERPs enable. This means the smartification of ERPs is happening now, as you read this.
But…why now?
AI-related disruptions have been rippling through the modern business world for nearly 15 years. ERP software became widely adopted in the 1990s and has played a role in business ever since. Why, then, has it taken so long for these two important technological forces to converge?
Three words, and one increasingly important concept:
Data. Supply. Chains.
What is a data supply chain?
A data supply chain is a formalized series of steps designed to manage the lifecycle of data within organizations. It is conceptually similar to traditional supply chains, which are designed for efficient transportation and distribution of physical goods while maintaining their quality and overall value along the way.
A newer practice in data management, data supply chains have evolved to meet the growing trend of treating data as a product (as opposed to a disposable commodity). In this way, a data supply chain mimics the logistical aspects of traditional supply chains—facilitating the collection, processing, analysis, and distribution of information within and across an organization. At the same time, these systems work to maintain optimal data quality and integrity as these processes take place.
As a system, a data supply chain is typically composed of several key components:
Data sources: These components are the organizational origin points where data is either generated or collected. Depending on an organization’s size and needs, a data supply chain may have a variety of data sources, including company databases, social media, public datasets, and data products like dashboards or data marts.
Data acquisition systems: Technology and tools utilized to gather data from an organization’s data sources. Examples commonly include data collection methods like APIs, direct data entry, web scraping tools, or automated data capture technology like radio frequency identification (RFID).
Data storage solutions: Infrastructure used to store data for processing and analysis. These may include traditional databases, data warehousing solutions, or cloud storage services that enable flexible, scalable data storage.
Data processing and management tools: Techniques and software used to clean, transform, and manage data, helping correct inaccuracies, remove duplicates, and keep data formatted consistently. Collectively, these typically include ETL (extract, transform, and load) systems, data integration tools, and data quality management software.
Data analysis and analytics platforms: Components that analyze data to extract insights by helping organizations identify patterns, trends, and correlations in their data. Examples may include machine learning platforms, business intelligence (BI) tools, and complex data analytics frameworks, in addition to statistical analysis software.
Data security and compliance mechanisms: Unsurprisingly, data supply chain components will also include technologies and protocols that ensure compliance with legal and regulatory standards to protect data privacy. These will include access controls (e.g., DAC, MAC, ABAC), data masking, encryption, and auditing systems.
Data distribution systems: Methods and technologies needed to share data insights within and outside of an organization are also vital. These can involve dashboard applications, reporting tools, data-sharing APIs, and automated workflow systems that get valuable info into the hands of operations teams and decision-makers.
Data archiving and disposal: Finally, we have the aspects of the data supply chain required for securely archiving and deleting data. Data lifecycle management policies and tools that manage, retain, archive, and safely destroy data fill this role.
Why it took data supply chains to enable ERP smartification
To be clear, data supply chains are not the only reason ERP solutions are finally beginning to hitch their horses to the AI stampede.
However, there are many specific reasons the advent of data supply chains has freed ERPs to fully utilize the potential AI brings to the table:
Bolstering data availability and quality
A major prerequisite for effective AI implementation is the consistent availability of large amounts of high-quality data. Once implemented, data supply chains organize and manage the flow of data from data sources.
In doing so, data supply chains ensure that ERP systems gain and maintain access to the requisite firehose of clean, accurate, timely data—creating the robust data environment needed for training AI models and ensuring their accuracy and reliability.
Improving data environment integration
As with most aspects of data management, data supply chains break down silos within organizations as they begin improving data integration across departments. Integrated data environments are crucial for AI functionality within ERP systems. Increasing overall data integration leads to more comprehensive insights and resilient decision-making processes.
Fomenting real-time data processing
Data supply chains that support real-time data processing functionality are essential for supporting the up-to-the-minute data AI applications require. This is especially important for ERP solutions that provide demand forecasting that helps manage inventory and sales data in real time.
Enhancing scalability and flexibility
Data supply chains provide the scalability and flexibility needed to handle the increasing volume and variety of data that modern businesses generate. This scalability is crucial for AI applications, which often require substantial computational resources and the ability to adapt to changing data inputs and business needs.
Fortifying data security and compliance
AI applications often involve sensitive data, and the security and compliance measures integrated into data supply chains ensure that data is handled securely. This is particularly important for ERP systems, which must comply with various regulatory requirements regarding data privacy and security.
Beyond ERPs: 9 ways data supply chains are benefitting organizations overall
It’s worth broadening the aperture here as well to make sure we fully appreciate the holistic impact data supply chains are having within organizations. While these benefits certainly vary across industries, the following nine are proving common and of critical importance:
1. Increased data quality and integrity
As one of the latest additions to the data management toolkit, data supply chains are proving to be exceptional at ensuring optimal data quality and consistency—cleaning, normalizing, and validating data within an organization. Knowing this level of data quality is consistently available, data teams can more confidently support the use of advanced analytics and more insightful reporting.
2. Platforming advanced technologies
By establishing data quality as part of an organization’s bedrock, day-to-day operations benefit from a more robust data infrastructure. This not only supports more sophisticated uses of automation but also better positions data teams to adopt and implement more sophisticated technologies as they become viable.
3. Enhanced productivity and efficiency
Extensively automating data collection, processing, and distribution through data supply chain implementation reduces manual effort, therefore minimizing human error. In addition to streamlining workflows and speeding up general processes, this automation also helps keep data teams out of the weeds, freeing up valuable time to invest in more strategic, mission-critical tasks.
4. Cost reduction
Optimizing data flows and improving overall data management capabilities helps organizations reduce the overhead cost associated with storing, processing, and retrieving data. Even better? As data supply chains facilitate better data-driven decision-making, resource allocation also improves, leading to further cuts to operational expenses.
5. Risk management and compliance
Cost reduction without the need to compromise risk management and compliance is the peanut butter and jelly of the data management world. Data supply chains enable exactly this—providing invaluable means for monitoring, analyzing, and reporting on risk factors while ensuring compliance with relevant protection regulations by enriching data access, data handling, and data archiving/disposal practices.
6. Agility and scalability
Compared to competitors that don’t have them, organizations using data supply chains are able to adapt, scale, and pivot with more agility as markets change. Increasingly, the ability to do so is crucial for maintaining resilience and responsiveness in data-driven industries.
7. Innovation and new opportunities
Data supply chains improve an organization’s ability to identify trends, patterns, and gaps in the market. By identifying these opportunities for innovation sooner, teams gain the advantage—enjoying more time to explore and develop new products, services, and business models.
8. Improved organizational decision-making
Additionally, data supply chains enable the integration and analysis of data from diverse sources. This can provide comprehensive insights that inform strategic and operational decisions across the organization. Untempered access to real-time data and advanced analytics also allows business leaders to make more informed, data-driven decisions that increase their competitive advantage.
9. Enhanced customer experiences
Finally (and ultimately), organizations are using the insights gained from data supply chains to better understand customer needs, preferences, and behaviors, facilitating more effective and engaging customer engagement strategies.
Key focus areas for planning data supply chain implementation
Just as when logistics professionals plan to implement supply chain management, there are specifics data pros should focus on to help ensure their data supply chain initiatives are successful:
- Strategic alignment and clear objectivessome text
- As with all mission-critical initiatives, make sure you set the stage with a clear strategy for your data supply chain and confirm that this strategy aligns with overall business goals and objectives.
- Cultural change and trainingsome text
- As part of this strategy, don’t sleep on organizational culture. Those organizations that are already embracing data as a product may have less to focus on here. However, the more data leaders work to ensure a culture that both understands and values data quality, the more fluid the data supply chain implementation will be.
- Sometimes, this doesn’t take more than providing basic (yet consistent) training and resources to ensure staff—at all levels—will effectively support their data supply chain.
- Data governance and performance measurement some text
- Plan how the data supply chain will function as part of your established governance framework, further clarifying roles, responsibilities, and standards for overall data quality and security.
- Evaluate key performance indicators (KPIs) and metrics to ensure, post-implementation, you can continue to track progress and measure the effectiveness of the data supply chain against set goals.
- Resilience and the need for continuous improvement
- Be sure to identify potential disruptions and vulnerabilities within the data supply chain, along with contingency plans to enhance its resilience.
- Establish methods of assessment and optimization to ensure that data supply chain processes will improve continuously over time. Additionally, redouble efforts to identify new technologies and methodologies that can improve the supply chain post-implementation.
- While key software and systems like ERPs stand to benefit on day 1, the data supply chain itself deserves its own love and attention (and plenty of investment) over time.
Embracing the chain, and ensuring the first link holds true
So, while data supply chains are (finally!) helping teams glean the full potential of enterprise-scale ERPs, they’re redefining what’s achievable through organization-wide digital transformation. Digital supply chains certainly earn attention for making this possible.
But let us not forget, the strength of any chain—logistical, data-centric, or the one keeping your bike safe during the workweek—is only as strong as its first link. As the stakes increase, data contracts should be an essential aspect of any supply chain initiative.
Learn more about how, and why, data contracts can help the smartification of ERPs extend throughout your organization and beyond.
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