Data Analysis

Misconceptions of Procurement Analytics: Data strategy is a one-time initiative

Having spent the last couple of years working with purchasing and accounts payable data from IBM and clients across a variety of industries, the IBM Procurement Analytics as a Service team established a data strategy framework with a broad business perspective. We believe this practical framework can be leveraged by any business unit and industry.

Many factors require careful consideration to bring about an effective and well-crafted data strategy. This article outlines those factors and highlights that departments beyond IT are essential to bringing a successful data governance program to life. Our framework is built around the data lifecycle: from need assessment through consumption. The “breadcrumbs” for applying this scheme can be found in every business.

Align data strategy to business goals

More and more companies view data as a strategic asset, instead of a by-product of their business activities. According to McKinsey, data-driven organizations are 23 times more likely to acquire customers, 6 times as likely to retain them, and 19 times as likely to be profitable. However, according to a 2019 NewVantage survey, 77% of businesses report that “business adoption” of big data and AI initiatives continues to represent a big challenge for them. Why?

A significant clue is that 42% of companies do not assess, measure, or monitor data governance. Data Governance incorporates data, roles, processes, communications, metrics, and tools that help organizations formally manage and gain better control over data assets.

Data activities are often isolated within the IT branches and processes are often duplicated or redundant, losing scalability and driving up costs. Without data governance, a company cannot realize the true worth of its data. Lacking an objective measure for prioritization results in incoherent decision-making drives up costs and affects revenue. When the budget gets tight, projects without clear business value will experience funding and resource cuts, further increasing the effects of this vicious circle, affecting IT and non-IT departments alike. Successful enterprise-wide data governance can only be achieved when business goals are aligned with the data strategy.

When business goals and data strategy are aligned, employees at all levels can understand the importance of data and their role to treat data as an important asset in their daily work. Being a data steward becomes part of the company’s culture, ensuring clear roles and responsibilities. The alignment aids in uncovering cause and effect in the operations and leveraging customer insights, which enables data projects to receive business support with the appropriate resources.

So how do we link data strategy with business goals? Start with a top-down approach to understand the business goals and priorities. Then, examine what, why, and how data is necessary to achieve them. Where do you want to see your company in x years? How can data help you to get there? Simultaneously, from the bottom-up, gather technical domain knowledge by consulting with department leaders to understand the current issues, requirements, and challenges they face, what data is available, and what is necessary to overcome these challenges.

A few ideas to consider when aligning your data strategy with your business goals:

• Start simple and iterate to refine your processes and define use cases.
• Understand your customers and your internal end-users to build user-friendly, not just “shiny,” tools.
• Define data strategy success factors and KPIs.
• Revise and adapt data strategy to coincide with business strategy changes.
• Analyze the current data ecosystem and discover its innovative potential, creating a “desired state” that supports business goals.
• Identify gaps between your current ecosystem and the desired state and set up a roadmap to unlock the transformative power of data.

Acquire and process data

The next key consideration is how to process and store data and make it accessible to everyone in the organization who could benefit from it. Businesses have relied on data warehouses and Extract-Transform-Load (ETL) processes for years to get a consolidated view of the data. Today, the ETL method of integrating data from multiple sources is still a core component of an organization’s data integration toolbox. However, today’s data velocity demands an infrastructure that enables an exchange with frictionless data flow at its core. Endpoints (applications, processes, people, or algorithms) interact with the data hub, potentially in real-time, to provision data into the hub or receive data from it.

A data hub is designed for the rapid exchange of information needed by today’s organizations. The hub captures and reconciles any type of data (meta, master, operational, analytical, etc.). Then, it delivers the data in multiple desired formats without necessarily storing it physically in a central place. Using search-based applications and API services, data consumers can easily discover and get instant access to data they can trust. With minor upskilling, employees can easily start taking advantage of the data hub in their daily work.

Enrich data

Once data is available, it is cleaned, categorized, and combined with external sources. These enhancements provide context and enable companies to make informed decisions.

Depending on the business goals, different types of data enrichment can be pursued, but in all cases, data must come from a trusted source. Without reliable data, leaders cannot be confident their decisions are rooted in facts and reality.

However, keeping the data consistent and up-to-date is a continuous, high-maintenance process. Over 50% of businesses spend more time cleaning data than using it. Regular updating is paramount to keeping the data current, and machine learning algorithms are offering a way to streamline that process.

After all, if the business decision-makers cannot trust the data within their organization, how can stakeholders and customers know they are in good hands? Information that is not correctly maintained and distributed can prove harmful to the integrity of business decisions.

Another important element is the implementation of an effective procurement taxonomy. Tracking every dollar an organization spends is a seemingly impossible task. With the help of business experts, setting up such taxonomy hierarchies can aid in classifying transactions that allow us to cross-index spend data against vendor categories and monitor progress towards set strategies.

In an IBM whitepaper, Marco Romano (then IBM’s S2P Chief Data & Analytics Officer) details the three characteristics of an effective taxonomy that can feed a cognitive engine:

1. Flexible — yet global
2. Multi-dimensional — with an ability to combine elements from different sources
3. Situation-based — or, as Marco wrote: “It is not about how you buy, but rather what you buy. I would argue even further that an appropriate taxonomy is about identifying how you resolve a business problem through products or services”

Apply Artificial Intelligence (AI)

In recent years, the accessibility to Machine Learning (ML)-powered AI has dramatically increased, yet we find that better data continues to beat better algorithms. Consequently, in AI initiatives, most time is spent designing and managing data, rather than developing algorithms. Failing to focus on extracting relevant fields or cleaning data will diminish the initiative’s overall effectiveness and significantly increase the likelihood of errors. The solution is to involve procurement experts in Data Science projects to introduce domain knowledge in addition to the technical expertise.

ML models are organized into complex AI systems that feature many interrelated data components. The data strategy must be built on reusable, general-purpose components with well-understood elements. Concentrating efforts solely on technical requirements can be another peril of the initiative. Focusing on the data requirements instead will yield better results for the ML-infused capability.

Analyze data consumption

Many organizations fail to support decision-making, because they are trying to please everyone in the organization, instead of selecting a few focus areas.

When designing your data strategy, you need to articulate a clear goal on which the analyses can be built. Focus on four approaches:

• Understand your clients — customer profiles, the scope of activities, industry trends
• Create better products and services — personalized offerings to beat competitors
• Improve processes
• Data monetization — selling or licensing non-sensitive information

After you define the analytical goal for your data, choose a business case to implement that reflects the processes within the selected segment (procurement, finance, manufacturing, etc.).

A common pitfall in data strategy implementation is assuming that employees will be able to work with data visualizations and dashboards. For a successful outcome, you need to involve data translators in your strategy team to bridge the gap between technical and operational teams.

A data translator’s primary role is to bridge the expertise of data scientists, engineers, and analysts into business functions. They need to have a thorough understanding of their departments, including the strategic goals to the processes that they are running, and certainly the industry where they are working in. With their help, you are more likely to uncover key pain points, underlying opportunities, and prioritize business problems that will drive the most value when solved. Data translators can deliver insights from your data through analytics. They are also a key player in transforming the company culture, so that everyone is involved in the data strategy, and treats it as their responsibility, not just the CIO.

Navigating the data ecosystems is undoubtedly taxing. Business and technology both change in unprecedented rhythms, but at the same time, change opens the door for transformation. Avoiding these misconceptions, acknowledging the various challenges, and understanding the key success factors will help you chart a course and make any necessary corrections along the way to a well-crafted and scalable data strategy.