How to Build Internal Stakeholder Alignment Around Data Initiatives

The C-Level Conversation has Evolved to Include Data Engineering

In the past, the role of Data Engineering was to handle the technical aspects of data management, extended across many layers of an organisation’s hierarchy below the senior executives. Unless an incident caused by data systems occurred, data engineering was

Why Data Engineering is Now a C-Level Discussion

Previously, Data Engineering was seen as an operational function carried out by technical experts who worked on the lower rungs of an organization. Until the systems failed, it was not visible to executive management. Organizations have undergone such a change that Data Engineering is now seen by executives as a contributor to the organization’s ability to achieve growth and compete. The impact of Data Engineering is now felt throughout the organization, as evidenced by a CEO’s request for insight into the current status of a company’s AI projects and an executive requesting information about the Data Engineering processes in order to make a decision about how much money to spend on the project.

Recent Developments

In the past, Data Engineering was primarily a support function. It supported the operational aspects of the business by keeping data pipelines running and dashboards updated. Business decision-making was slower, due to smaller volumes of data being available, and there was a greater tolerance for lags between when a decision was made and when it was implemented.

As organizations evolve, they are moving toward tighter feedback loops, where executives want to see the results of their decisions within days rather than months. Digital products, such as mobile applications, now generate enormous amounts of behavioral data that need to be processed and analyzed very quickly. Additionally, the emergence of AI and automation in recent years has raised organizations’ expectations for making data-driven decisions.

As a result of these factors, Data Engineering has a direct impact on whether an organization has timely and reliable information or is reliant on out-of-date information.

The Link Between Data Engineering and Executive Priorities

Data engineering intersects with a variety of issues directly aligned to the C Suite.

Revenue and Growth

Revenue generation relies on accurately attributing to a point in time, segmenting customers, and predicting future sales volumes based upon accurate analysis of historic or future market circumstances. Data pipelines that are built on unreliable platforms and/or architecture affect inconsistency in the organization’s narrative surrounding the path to growth as well as the time in which it needs to react to market change. Effective data engineering provides a clear foundation for establishing the confidence a business must have in order to invest.

Product Innovation and Velocity

The Executive is responsible for delivering a product/service roadmap to the market and also holding his/her company accountable to be ahead of their competition. Without rapid response back to “what worked” and “what didn’t work,” product innovation will slow down considerably. Data Engineering forms the basis of experimentation, validating features and creating a “wealth of knowledge” for informing continuous improvement.

Risk, Compliance, Trust

Poor-quality data and lack of governance expose an organization to regulatory risks, as well as to reputational damage, which will ultimately rest with the Executive of the organization. Reducing the risks associated with producing incorrect information and audit findings is enhanced through the use of Reliable Data Systems.

Cost Control/Efficiency

Cloud Data Platforms constitute a growing percentage of the total technology investment an organization incurs. Poorly architected systems and platforms increase an organization’s overall costs and mask inefficiencies in processes that have been operating in organizations for many years. Without complete understanding of the trade-offs between Data Engineering Platforms, Executives cannot manage Technology Investment Margins effectively.

Why it’s No Longer Sufficient to Delegate Responsibilities for Data Engineering

There are a substantial number of Executive Leaders (C-Level Executives) who believe that by acquiring a strong data engineering team, they will no longer need to devote time on the issue. This perception tends to fall flat as organizations have scaled.

When there is no C-Level Executive Alignment:

• Teams will typically construct Data Engineering Processes and Solutions to meet local business unit needs; therefore, teams will be optimizing based upon their individual business unit goals rather than having an Enterprise-Wide Goal in mind.
• Platform decisions and the business strategy associated with those decisions will begin to drift over time.
• Data Standards will vary across business units.
• Investments will not be completed with well-defined measures of success.

To properly engage C-Level Executive Leaders within data engineering, the role of the Executive is not limited to the selection of tool, schema review, etc., but should also incorporate establishing priorities for the organization, defining outcomes the organizations wish to achieve, and creating an outcome-based performance measurement process to hold their data teams accountable for delivering business impact.

The Executives Role in Modern Data Engineering

An effective method of involving the C-Level Executive continues to be through a systematic and

The impact of Data Engineering on Executive Priorities

Data Engineering has a direct impact on many of the executive’s key priorities.

• Growth & Revenue
  Accurate attributions, segmentations & forecasts are core elements of revenue strategy. When the data pipelines are not built effectively, the conflicting data-driven narratives regarding growth will impede an organization’s ability to react quickly to changes in the marketplace. An effective Data Engineering process (pipeline) creates race and timing-free openings for growth.
• Product Velocity / Innovation
  The executive is responsible for delivering the organization’s roadmap and remaining competitive. A product team’s inability to receive rapid feedback results in poor innovation. Data Engineering is the backbone for supporting rapid experimentation, feature validation and enhancing continuous improvement.
• Reducing Risk, Compliance, & Building Trust
  Data quality issues or governance failures expose organizations to regulatory risk & potential reputational damage. The executive is ultimately accountable for these issues. An effective Data Engineering platform minimizes the risk of costly errors and audits.
• Controlling Costs / Improving Efficiency
  Increased spending on cloud data platforms is increasing rapidly. A poorly designed platform increases costs & hides process inefficiencies. An executive must understand the trade-offs in Data Engineering to effectively control the margin.

It is not enough to simply delegate the Data Engineering responsibilities to a strong Data Engineering team

Many executives believe that if they hire a strong Data Engineering team, this removes the necessity for them to be involved in Data Engineering efforts. This belief becomes increasingly irrelevant as Data Engineering becomes the focal point of entire organizations.

Without executive engagement:

• Data teams optimize for local and/or immediate needs versus optimizing across the enterprise on common themes;
• Platform decisions become disconnected from the overall strategic vision;
• Data standards are created in silos;
• Investment decisions do not have clear success criteria.

To be effective in the 21st Century Digital World, C-level executives must be engaged in every aspect of the Data Engineering Process, which includes both building the Data Engineering team and the organizational Data Engineering strategy and decisions.

An executive’s involvement in Data Engineering requires a defined process and pattern. Executives must:

• Express the relationship between data and the execution of business strategy
• Determine the most important metrics for their organization
• Take ownership of and be accountable for data products
• Balance speed of execution with appropriate risk evaluation
• Evaluate investment opportunities based solely on the expected benefits rather than hype

Leaders engage with product and engineering strategy in very much the same way. Data engineering falls within the same governance processes as those in product and engineering.

How AI Has Speed Up This Evolution Into the C-Suite

AI initiatives have brought a lot of attention to data engineering among executives, who are quickly learning that the effectiveness of their models and tools is wholly dependent on the data that they are fed.

A large percentage of common challenges that arise from implementing AI are related to gaps in their organization’s data engineering, including:

• Inconsistently trained data sets.
• Poor traceability and explainability of their data set.
• Frequent delays in data refresh cycles.
• Limited resources and access to high-quality features.

As AI becomes a key point of differentiation between competitive organizations, executive management must also realize that having all the necessary elements in place regarding data engineering is essential prior to starting an AI strategy.

Signals For Executive Attention Regarding Data Engineering

When many organizations have outgrown the need for merely having a data engineering function, there are distinct signals that will prompt an executive to step in and give additional attention to that area:

• Recurring disputes over Key Performance Indicators (KPIs) accuracy.
• Inability to validate product or growth initiatives in a timely manner.
• AI projects that have been unable to reach production.
• Rapidly increasing cloud data expenses but with no evidence of value being derived from that expense.
• Numerous compliance/audit issues pertaining to the data used for a variety of purposes.

In those cases, executive involvement in data engineering is imperative.

Data Engineering Errors Executives Commonly Make

Even when leaders recognize the need for data engineering to successfully implement their AI strategies, they may encounter significant challenges if they make certain mistakes when interacting with data engineering:

• Overemphasis on the toolset; the evaluation of vendors should take place only after defining the expected outcome.
• Consider IT as an ASRs.

Data Ownership Is a Shared Responsibility

Investments in foundational infrastructure do not yield quick return on investment (ROI), rather they require patience while you establish the infrastructure necessary to support data operations within an organization.

All Items are Priorities

When organizations make data management initiatives priorities on every level, then organizations lose the ability to make difficult decisions that create value. The inability to make tough decisions will stall the ability to develop new data-driven business models.

Avoid These Mistakes with Clarity and Consistency

The First Step in Building Executive Engagement in Data Strategy is Aligning Data Strategy with Business Strategy

To build executive engagement, organizations must align business and data strategies. Connecting business and data strategies can be accomplished by implementing the following five steps.

By Connecting Data Strategy/Investment to Business Growth, Speed, and Trust, Executive Engagement Is Reinforced

Executive Engagement with Data Strategy Is Key to Success

Executive engagement with data strategy requires the ability of executives to understand the correlation between their corporate goals (success metrics), data investments, and the outcome of a successful data strategy. To create the understanding between these three components, it is important for organizations to define and understand how these three components are interrelated.

Data-Driven Decision-Making

In today’s digital economy, organizations that treat data as a C-level priority have seen significant shifts in key performance indicators (KPIs).

• Trust in data results in faster decisions.
• Product teams are able to iterate and produce results quickly.
• Organizations’ Artificial Intelligence (AI) powered solutions are deployed to production more quickly.
• The perspectives of leaders from all levels of the organization are aligned.

The limitations of an organization developing and maintaining the same level of understanding of how its data supports its business objectives and goals will ultimately limit its ability to compete.

Brand Positioning

Logiciel Solutions views data engineering as a strategic capability and one that should be a key part of executive conversations. Logiciel supports executives in making connections between data foundational solutions and their business growth, speed, and trust objectives by supporting executive engagement in data engineering through the establishment of a collaborative relationship between data engineering and business strategy. Organizations that maximize executive engagement in the data strategy will move faster and incur fewer disruptions in their operations than organizations that do not.

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