For sourcing and procurement professionals, the future isn’t about fancy high-end gadgets as it’s about identifying new solutions for long-term efficiency and continuous business growth.
We all know about the enormous potential of digitalization for the entire business landscape, and how massively it had evolved over the past few years, opened up new opportunities, encouraged development, and enabled sustainability.
The lively evolution of technology and business processes has also molded several major sourcing trends that should be considered, focused on, and efficiently used when taking a long-term approach. These trends are clustered around topics such as:
- hardware
- software
- direct material
- data
- professional services
- telecommunication
Sourcing processes can be challenging and complex as they come. Hence, in order to be impactful, they require Data, and the data must come from somewhere. This article is about to focus on one of the most dynamic trends in sourcing – Data Sourcing.
What is Data Sourcing?
Rapid technological advancements have allowed for the harvesting and management of numerous types of data, and the majority of today’s business strategies heavily rely on its analysis.
Data sourcing can be observed from 2 perspectives:
– As a content-driven process that involves identifying data catalogs and sources needed for business use cases, understanding them, extracting them, and transporting them.
– As a commercially-driven process that involves self-generating and purchasing data. In this case, there are two commercial areas in third-party data that require special attention: price and standard terms. Especially the avoidable small point of “Commercial” and “Private” use of public and private data.
Data sets and catalogs are the objects of interest for sourcing, including:
- Datasets and related metadata information covering specific use cases.
- Data catalogs, which are various target-specific datasets.
- Data reseller agreements that make a significant difference in pricing.
Data can be purchased as structured or unstructured information. Several main cost drivers should be taken into account and mitigated:
- Reducing the number of APIs and their easy-to-use capability.
- Increasing data maturity through curation, augmentation, and cleansing.
- Avoiding over-engineering and focusing on necessary data.
- Digitizing and automating highly manual process-driven reports.
- Defragmenting the data environment and simplifying the architecture.
- Reducing the number of premium vendors and avoiding data source duplicates.
Where is the data coming from?
Data sources vary greatly and include a wide range of data points such as:
- databases
- spreadsheets
- XML files
- scraped web data
- hard-coded data
- flat files
- live measurements from physical devices
- streaming data services
- AI data, and more
Although they share the same goal of providing location and description, most data source types can be classified into 3 categories that are stored, accessed, and used in different ways:
File data sources: These data sources contain all of the information within a single, shareable computer file. They have no names, are not registered to individual applications, systems, or users, and are editable like any other computer file.
Machine data sources: These sources have names, reside on the machine, and cannot be shared. Like other data sources, they provide all the information, but require a DSN (Data Source Name) to invoke the connection or query.
Domain-specific organizational data sources: These data sources contain all of the information for machine learning and artificial intelligence, which are becoming increasingly integrated into everything businesses do, but they require connectivity, quality, and access to data to do so. The most common of these sources is new (orchestrated) information from A.I.-driven data interpretation.
Data sets are also provided by a wide range of supply partners. There are hundreds of supply partners from which data can be received.
The most important data clusters are:
- Own Data
- Process Data
- Production Data
- Generative AI Data
- Self-Digitized Data
- 3rd party Data
- Risk Data
- Public Data
- Reference Data
- Ecosystem Data
However, the majority of today’s organizations (some say more than 80%) lack intelligence-gathering maturity, especially when it comes to surveying and reporting on thousands of data sets, their actual demand, analysis, and quality measurement.
Therefore, it is crucial to have preprocess as a crucial step to acquire and implement information that can help achieve our business goals.
How can Data be governed?
In order for businesses to analyze data effectively, data governance is crucial. This includes defining the responsibilities for managing data, as well as deciding what data will be used in your business.
In most cases, enterprise types of data and related data catalogs provide in-depth information, tracking, and assessment of thousands of products, vendors, industry service providers, data products, applications, and technologies.
Ideally, cloud-first approaches and a data operating model built around federated, standards-based data architectures and disciplined domain-based data governance should replace on-premises approaches. This facilitates the development of data assets that are reusable, sustainable, and easily accessible. As a result, the time required for data engineering is drastically reduced, and the stability and maintainability of applications are increased.
Improving productivity and performance can be achieved by establishing data dictionaries, creating traceable data lineage, and implementing data quality controls.
What is Big Data?
Big data is a term that represents data exceeding the capacity of conventional databases such as spreadsheets, ERP systems, transactions, EDI invoices/purchase orders, and competitor pricing. This is due to its size, speed, or unstructured nature. It is not the same as “a lot of data” because it is variable, massive, and less predictable, particularly in procurement and supply chain management. Taking advantage of big data can provide a significant competitive edge, with examples including videos, images, blogs, voice and audio files, emails, social media, weblogs, page contents, text messages, and XML documents.
By 2025, it is estimated that 73% of business processes in procurement and supply chain management will depend on big data and advanced analytics. Standard or tailored solutions and service providers, such as Titan MIS, are crucial for large companies with complex requirements.
The benefits of using big data include improved decision-making, reduced costs, increased productivity, and enhanced customer service. However, there are also cybersecurity risks, talent gaps, and compliance complications to consider.
A new area of Generative AI?
Generative AI is a subfield of machine learning that trains computers to generate new data similar to the data it has been trained on. This differs from traditional machine learning, which involves recognizing patterns and making predictions based on those patterns.
The main objective of generative AI is to enable computers to create realistic data similar to what humans produce. This is achieved by training algorithms on large datasets to identify patterns and learn from them. Once learned, the algorithms can generate new data that fits the same patterns.
Generative AI has the potential to transform work across all industries and should be considered in data sourcing. Easy-to-use generative AI applications, such as ChatGPT, DALL-E, and Stable Diffusion, are democratizing technology by processing huge amounts of data. Large Language Models (LLMs) can potentially “know” everything an organization has ever known, including its history, context, nuance, and the intent of its products, markets, and customers.
By using generative AI, businesses can save time and money on product development and increase efficiency. Additionally, generative AI can help businesses stay ahead of the competition by creating unique products and content.
To ensure the successful adoption of new technology, there are 6 essential factors to consider:
- Start with a business-driven mindset and dive in.
- Take a people-first approach by involving stakeholders and ensuring their buy-in.
- Prepare your proprietary data for integration with the new technology.
- Invest in a sustainable technology foundation that can adapt to future changes and advancements.
- Accelerate innovation within the ecosystem by collaborating with external partners.
- Level up your responsible AI practices to ensure ethical and transparent use of the technology.
Data Sourcing Checklist – ready, set, go!
To implement a valid data sourcing process, companies need to ensure that several business aspects are on point. Here are some essential steps:
Define your objectives
To focus on the data-sourcing activities that have the biggest impact, it’s important to have a clear and concise understanding of the desired business outcomes. Define what type of data is required, how to manage its supply, and how to implement quality checks.
Profile the data
The first step is to find a proper data provider (such as Titan MIS). Ensure that the data has the right structure, granularity, age, frequency, and availability.
Collect raw data
Collecting the data from the earliest possible point increases its flexibility and manageability, reduces risks of data quality, and limits exposure to uncontrolled manual intervention.
Quality control
Ensure that data reporting is of the highest quality, and implement quality controls as early in the data supply chain as possible. Use metrics to increase visibility and oversight of quality.
Avoid collecting unnecessary data
Focus on the important data and avoid the complexity of increased data without actual use.
Use Data Profiler Solutions
Data profiling is about examining, analyzing, and cataloging data. It produces critical insights that can be further leveraged. Use data profiling solutions such as Titan MIS for cataloging datasets, product licensing, and rights management.
Use fully integrated Smart Data Marketplaces
Use integrated platforms that streamline purchasing, deployment, data offerings, and facilitate their access to ensure quality, consistency, and security. Data marketplaces such as Titan MIS are populated exclusively with HQ data feeds from vetted providers.
Adapt to changes
Every era carries its own business requirements, and data sourcing is no exception. Hence, organizations should avoid being rigid and adapt to all sorts of changes without impacting their key operations. That includes proper planning, new acquisitions, systems, and knowledge transfer.
A.I. Friendly
Data is the fundamental bedrock of generative AI. Therefore, data suppliers should invest heavily in technology so that companies and their AI engines can consume their products via APIs and tailor them to fit their own use cases with prompt engineering techniques like prompt tuning and prefix learning.
Conclusion
The significance of data sourcing in running a successful business has never been greater. Therefore, adopting a premium approach to data processes and sourcing events is crucial. This includes having a clear vision, a skilled team, great collaborations, and the proper technology to handle and document data effectively.
What is your thought on this matter?
