Is your organization aligned to deliver quality data for analysis by intent, or by accident? While this may seem like an odd question, understanding organizational change levers can help uncover opportunities for improving your data supply-chain processes.
A key challenge with organizational alignment is communicating across all departments the goals, metrics, and benefits of a process or function. The true value of a strong data supply chain is improved data quality, but leaders might miss the need to communicate that broadly across the organization. The cause may be rooted in psychology.
In psychology, the term “familiarity effect” is used to describe scenarios in which we’re biased by increased exposure to content. How does this impact communications for IT leaders? We are exposed to data and processes needed to gather, cleanse, and analyze information, and we tend to project our understanding of that to others in the organization. The reality is that each function in an organization is subject to familiarity and applies definitions based on the demands within their own function. It’s not surprising that marketing has different data needs and performance measures than accounting; and without intentionally communicating organization-wide, misperceptions occur.
Certainly, each business has its own unique culture, and it’s worth to other factors driving the need for communication about the value of improved data quality: responsibility and accountability. For many firms, the increased value of data resulted in the creation of a new Chief Data Officer (CDO) role.
Any new function in a business must find “a place at the table”, a trite way of defining the need to communicate their organizational and business value. A recent study by MIT CDOIQ and Accenture shows the CDO role has many challenges. The top skill for CDO is “change agent and evangelist,” and 78% of CDOs report their roles are being seen as more critical as businesses search to find competitive advantage.
Aligning the entire organization to work toward a common goal of improved data quality requires using multiple levers to modify perceptions and behaviors. While the baseline culture of an organization impacts the speed and durability of the changes, the best results come from incentivizing positive, new behaviors (using a carrot rather than a stick yields longer lasting benefits).
In addition to broad-based communication to help the organization have a collective understanding of the goals, there are three additional levers to improve outcomes:
- Data quality and business outcome – metrics and measures
- Role and responsibility definitions
- Departmental or individual recognition and rewards
Data quality and business outcome – metrics and measures
Stakeholders, shareholders, and executives measure a company’s performance based on business outcomes. While there is no question about the “value of data” to an organization, there is no value in data itself. The value in data is directly related to achieving better business outcomes (increased revenue, lower cost, improved asset utilization, etc.).
The recognition of data’s value to a business has led to many metrics and measures focused on data and its quality. The measure of data quality has many dimensions, and it’s challenging to make good business decisions with poor quality data. However, just as critical is aligning data metrics with the business decisions enabled by that data.
Metrics and measures are useful because they give meaning and context to outcomes. Aligning data measures to business outcomes is a significant benefit, because the organization can understand how data contributes to business outcome/value.
Role and responsibility definitions
Accountability for results starts with having clearly defined responsibilities for each role. Testing your capabilities here is best accomplished using the MECE criteria (Mutually Exclusive and Collectively Exhaustive), to ensure that each role has a unique responsibility, and all responsibilities are assigned to a some role.
For some organizations the role of the CDO in a job description may be defined so broadly as to be a catch-all – security, governance, ethics, reporting analytics, architecture, and of course the elusive “soft skills”. It would be unreasonable to assign such a broad scope of responsibilities to a single role. While the CDO role certainly is involved in many activities, good practice is to limit the responsibilities to 3-5 key, critical items. Responsibilities for data should align with existing core functions; for example, Legal should remain responsible to ensure compliance with current and evolving global standards like privacy, storage/transport, etc. Utilization of a RACI matrix is useful in aligning responsibilities with roles.
Departmental or individual recognition and rewards
Being part of the human tribe means we share common characteristics, and being recognized is a core characteristic that has been consistently 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 deliver big benefits in transformation.
Many organizations link recognition and reward programs to performance review cycles; however, creating a recognition and reward program focused on improving the data supply chain and resulting data quality can be a powerful tool to speed change.
A critical concept is to have recognition be meaningful and broadly aware to the organization, with the “reward” only limited by creativity – a special parking spot, a “golden ticket” that brings a perk, or awards of gifts or money. Additionally, having a cadence to delivering awards helps speed change as departments and individuals compete for recognition (unplanned spot awards can be included as well, to recognize special events, heroic accomplishments, etc.).
Business leaders are interested in improving the 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 outperforms guts and intuition. Better alignment across these three levers can deliver significant improvement across the data supply chain that feeds this data-driven decision-making. Make it happen!
