Having a carefully thought-out data strategy is an essential part of any organization’s success in today’s environment. The data strategy drives culture and shapes future actions by providing an organization-wide understanding of what data is and how the company intends to leverage it. In a world mired in ever-evolving technologies around data ingestion, analysis, and reporting, it is harder than ever to right-size an organization’s data strategy for their needs and goals.
The ideal data strategy for any organization is one that allows for repeatable risk mitigation, forecast gains, and smart portfolio management. When creating an organizational data plan, consider why you collect this information, how you use it, whether you apply data insights (or don’t), and what problem(s) it solves.
You may find yourself in need of complex data techniques to address the issues your team faces. Procurement professionals are often intrigued by the benefits of emerging techniques like machine learning (ML) and artificial intelligence (AI) – rightfully so as these tools are among the most powerful and efficient in the data realm. The truth is that most procurement organizations often lack both enough data and the right kind of raw data to apply ML and AI to their spending or sourcing projects. Naturally, this makes it difficult to achieve any real breakthroughs in automation or improved efficiency on your own. Bleak as this may seem, there are effective ways to go about obtaining and applying the right kind of data to leverage these exciting technologies.
THREE KEY STEPS FOR
TO OPTIMIZE YOUR DATA STRATEGY
THREE KEY STEPS FOR
TO OPTIMIZE YOUR DATA STRATEGY
STEP 1: Combine Available Data to Create an Internal Baseline
Any given organization’s data strategy must include a collection of intracompany data regarding specific goods and services. Information from internal stakeholders around why a product or service was chosen in the first place – like ease and quickness, competitive intelligence, or promise of innovation could shape future purchases.
By collecting this intracompany information from the outset, as well as recording data sources to refresh their dataset more easily, a given organization will be able to define their baseline/current steady-state position. This step may include, but is not limited to, collection of supplier audit data, personal testimony from current innovation partners, and review of product or service issues alongside any available transactional data.
STEP 2: Integrate Contextual Data
Procurement organizations often get stuck in the process of utilizing transactional information without also gathering contextual data. Without tapping into external sources like third-party data (import/export databases, monthly/weekly adjustments to exchange rates, et cetera), governmental publications (i. e., the Energy Information Administration, Bureau of Labor Statistics, Census data, et cetera), and other relevant data points, these teams are missing out on crucial insights. Best-in-class procurement data strategies will take various relevant external data sources into account on an ongoing basis.
As an example, for an organization that manages large volumes of corrugate packaging, they would be remiss not to utilize reports around pulp mills, follow the fluctuations in imports/exports of pulp and paper products, and may even go as far as following fuel and transportation to fully develop their supply chain view.
STEP 3: Utilize Relevant Data Techniques to Review and Analyze your Data
By now you have your datasets and third-party reporting, you’ve likely been able to uncover a few “pain points” for your organization and you’re probably looking to transition to data analytics and forecasting. For companies who find themselves with disparate types of data, various data sources, or simply with particularly large datasets, your typical data analysis software may not be robust enough to make this analysis as quick and easy as needed. Traditionally, procurement analytics have focused on understanding past procurement spend and supplier performance, but increasingly the focus is shifting toward AI-driven prescriptive decision-making. This reflects an evolution from “descriptive” analytics to “prescriptive” analytics.
While your organization may not have ML and AI expertise on-hand, we recommend that organizations assess the analytics-as-a-service products market for solutions. For example, ProcureAbility’s analytics-as-a-service product, PureSpend, helps organizations to get to the next stage. With tailored dashboards, secured data warehousing, and a focus on client autonomy and data accessibility, our services support any number of client-requested data needs that can be refreshed at the click of a button.
If you are interested in learning more about optimizing your procurement data strategy, please reach out to our Analytics and Intelligence experts.