Data supply-chain transformation: Aligning to win

Is your organization geared towards providing quality data for analysis, intentionally or accidentally? While this may seem like an odd question, understanding organizational change levers can help uncover opportunities to improve your data supply chain processes.

A key challenge in organizational alignment is communicating the goals, metrics, and benefits of a process or function across departments. The true value of a strong data supply chain is improved data quality, but business leaders may overlook the need to communicate this widely across the organization. The cause may be rooted in psychology.

In psychology, the term “familiarity effect” used to describe scenarios where we are biased by increased exposure to content. How does this affect communication for IT leaders? We are exposed to the data and processes needed to gather, cleanse and analyze information, and we tend to project our understanding of it onto others in the organization. The reality is that each function in an organization is subject to familiarity and applies definitions based on the needs within its own function. It’s not surprising that marketing has different data requirements and key performance indicators than accounting; and without intentional communication across the organization, misjudgment occurs.

Certainly every organization has its own unique culture, and it’s worth considering other factors driving the need to communicate about the value of improved data quality: responsibility and accountability. For many organizations, the increased value of data has led to the creation of a new role as chief data officer (CDO).

Every new role in an organization must find “a seat at the table,” a well-worn definition of the need to communicate its organizational and business value. A recent study by MIT CDOIQ and Accenture shows that the role of CDO comes with many challenges. The top skill for CDOs is Change Agent and Evangelist, and 78% of CDOs say their roles are seen as more critical as companies look for competitive advantage.

To align the entire organization towards the common goal of improved data quality, multiple levers must be employed to change perceptions and behaviors. While an organization’s core culture affects the speed and durability of change, you’ll get the best results by encouraging positive, new behaviors (using a carrot instead of a stick brings longer-lasting benefits).

In addition to broad communication that helps the organization to have a common understanding of the goals, there are three other levers to improve results:

• Data Quality and Business Outcome – Metrics and Actions
• Role and responsibility definitions
• Departmental or individual recognition and rewards

Data Quality and Business Outcome – Metrics and Actions

Stakeholders, shareholders and executives measure a company’s performance based on business results. While the “value of data” is unquestionable to a business, data itself has no value. The value of data is directly related to achieving better business outcomes (higher revenue, lower costs, improved resource utilization, etc.).

Recognizing the value of data to an organization has resulted in many metrics and measures focused on data and its quality. The measure of Data quality has many dimensions, and making good business decisions with poor-quality data is challenging. Just as important, however, is aligning data metrics with the business decisions that data enables.

Metrics and measures are useful because they add meaning and context to the results. Aligning data measurements with business outcomes is a significant benefit as the organization can understand how data contributes to business outcome/value.

Role and responsibility definitions

Accountability for results begins with clearly defined responsibilities for each role. Testing your skills here is best done using MECE (Mutually Exclusive and Collectively Exhaustive) criteria to ensure each role has a clear responsibility and all responsibilities are assigned to a specific role.

For some organizations, the role of CDO in a job description can be used broadly enough to be a catch-all term – Security, Governance, Ethics, Reporting Analytics, Architecture and of course the elusive “soft skills”. It would be inappropriate to assign such a wide range of responsibilities to a single role. While the CDO role is certainly involved in many activities, it is good practice to narrow responsibilities to 3-5 key, critical items. Data responsibilities should align with existing core functions; For example, the legal department should remain responsible for ensuring compliance with current and evolving global standards such as data protection, storage/transport, etc. use of a RACI matrix is useful for aligning responsibilities with roles.

Departmental or individual recognition and rewards

Being part of the human tribe means we share common traits and essences recognized is a core trait that has been shown to drive satisfaction and performance. While data is dry and impersonal, people are involved in the data supply chain, and recognizing the contributions of individuals and departments can bring great benefits in transformation.

Many organizations link recognition and reward programs to performance appraisal cycles; However, creating a recognition and rewards program that focuses on improving the data supply chain and resulting data quality can be a powerful tool to accelerate change.

A critical concept is that recognition is meaningful and the organization is generally aware, with the ‘reward’ limited only by creativity – a dedicated parking space, a ‘golden ticket’ conferring a benefit, or gifts or money. Additionally, an award cadence helps accelerate change as departments and individuals compete for recognition (unscheduled spot awards can also be included to recognize special events, heroic accomplishments, etc.).

Leaders are interested in improving business outcomes that align with the organization’s mission, vision, and values. In today’s environment, data is more available than ever, and data-driven decision-making trumps gut instinct and intuition. Better alignment between these three levers can lead to significant improvements throughout the data supply chain that supports this data-driven decision making. Make it happen!