How to Fix Your “Data Organizational Strategy” – Compelling Arguments for the C-Suite

In the modern business ecosystem, data is not just a buzzword — it is central to the success of corporate strategies. With the recent proliferation of AI technologies, the urgency for organizations to harness their data has amplified, underscoring the need for a competitive edge.

Although there is a plethora of material on the nuances of these technologies and their optimal deployment, there is a significant gap in discussions about the organizational architecture that can best leverage the power of data. The data team’s organizational structure is as important as (if not more than) the technologies themselves in defining the success of a company’s Data Strategy.

It determines how efficiently a company can interpret, address, and implement data-driven solutions for maximum impact.

Consider traditional departments like Finance or IT. Their organizational roles are clear-cut, typically under the leadership of the CFO or CIO respectively. Yet, the positioning of the Data team is a matter of debate. The answer to the question of where the Data team should report will vary based on who you ask, when you ask, and how you ask the question.

During my career spanning Fortune 500 organizations and startups across various industries, I have reported to the CEO, CTO, CIO, CPO, CFO, and COO with the CMO being my primary stakeholder in most organizations. The executive to whom I reported typically depended on who was the biggest evangelist or had the best understanding of why a data discipline was needed in the organization.

The rise of titles like Chief Data Officer (CDO) and Chief Analytics Officer (CAO) indicates an increased strategic focus on data. However, the roles, reporting hierarchies, and the scope of their influence differ across organizations.

The foundations of an effective data organizational framework rest on two pillars: the clarity of data roles and a coherent reporting structure.

1. Clarifying Data Roles

Confusion often clouds the distinction between various data roles. This is perhaps expected given the nascent nature of many data disciplines. However, this ambiguity can lead to recruitment challenges, misaligned expectations, and retention problems.

For instance, while Data Analysts primarily extract insights from data, Data Scientists employ machine learning algorithms for decision automation. The roles might converge in some areas but are fundamentally distinct. The evolving data landscape has also ushered in titles like Analytics Engineer and ML Engineer, each with unique specializations. Organizations must therefore standardize their role definitions in line with industry norms to ensure they recruit, retain, and motivate the right talent.

As an example, hiring a Machine Learning Expert as a Data Scientist and really giving them Data Analytics work will only result in morale issues and inefficiencies regardless of the compensation you provide them. Ultimately, they will move on from your company and it will be a lose-lose situation for the employee and the employer.

2. Evolving Reporting Structure

In the journey of a company's growth, the structure of its data organization plays a pivotal role in its ability to effectively harness data. This structure isn't static. It evolves in tandem with the company's size, goals, and data fluency and maturity in the company.

While a centralized organization makes more sense in a startup environment or one where the data fluency and maturity are low, a hybrid approach (centralized in reporting lines but dedicated to business teams) has proven to be successful in most companies once you grow to Enterprise scale where the leadership has good data literacy.

Centralized structure: In the initial stages of a business, especially in startups, a centralized structure is highly effective. Here, a singular data leader is responsible for all data-related decisions. This encompasses setting team priorities, determining the technology stack, and guiding hiring strategies.

The same is true for a company where the leadership team and organization have little understanding of the investment, complexities, and trade-offs needed to tap into the value of data.

Benefits of a centralized structure:

• Unified vision: A single leader ensures the team marches to a consistent drumbeat, ensuring cohesion and clarity.

• Rapid decision-making: With fewer bureaucratic layers and centralized prioritization, decisions are swift, catering to the agile nature of startups.

• Operational efficiency: At this stage, data is primarily a tool for operational enhancement rather than a strategic asset (unless data is the core product powering your business - E.g., Google, OpenAI, etc.), and a centralized approach ensures optimal utilization.

Hybrid approach: As businesses expand and the different functions in the organization start to become data literate, the sheer volume and diversity of data-related needs grow. A centralized model might struggle to keep pace. Enter the hybrid approach, which combines centralized oversight with functional alignment to specific business units.

Benefits of a hybrid approach:

• Business context integration: Data teams can work closely with specific business units, ensuring that data solutions are tailored to unique business needs and challenges.

• Unified best practices: Despite decentralization in functions, a central authority ensures standardized best practices, data governance, and technological coherence.

• Scalability: This model can adapt to varying scales, from mid-sized companies to vast multinationals.

Customization in the hybrid model: Larger corporations might further refine this model based on their specific needs. For instance, some might embed dedicated data analysts, scientists, and engineers within individual business units, retaining centralized control only over critical areas like data security, governance, and architecture.

Conversely, smaller enterprises might choose to embed only data analysts in business units, centralizing the more specialized roles like data engineers and scientists.

Unfortunately, a lot of companies decide to move to Decentralized mode for the Data team once they grow large citing speed and control. This means each leader lands up building their own data disciplines including Data Engineering, Data Science, and Data Analytics for their organization.

Ironically, while such firms would never consider a decentralized finance or IT team that’s not housed under the CFO or CIO respectively, they tend to underestimate the expertise required for a cohesive data framework.

Decentralization often spawns isolated data systems, redundancy, disjointed metrics, and an inability to harness the full potential of data — eroding the competitive advantage, especially in an AI-driven future.

Additionally, separating teams like Data Analytics from Data Platforms can be counterproductive. Drawing an analogy, this is akin to having financial controllers and financial planning and analysis units operate independently, rather than unified under the CFO.

Such separation often leads to duplicate roles, tension between these disciplines, and diminished organizational value. For example, you may see the Data Analytics or Data Science organization trying to hire Data Engineering expertise because they cannot get into the priority list of the Data Platform team.

On the flip side, you will see the Data Platform team hiring Data Scientists or Data Analysts to either service questions that stakeholders asked of them or to jump on the bandwagon of Machine Learning and AI to prove the value of their organization.

At a minimum, such organizations should consider setting up a CDO discipline or a COE for Data which can oversee enterprise-level standards including Data Governance, Metrics Definitions, Infrastructure, and Vendor Decisions.

While this may not solve all the organizational issues mentioned above, it will help provide a framework for the various functions to more efficiently tap into the value of their core asset - data.

In conclusion, having a central framework is key to extracting value from data and making it a core strategic asset and differentiator for your organization. But, forging and scaling an optimal Data Organizational Structure is as much an art as it is science.

It demands visionary leadership with the autonomy to act. You need to understand the business needs, data literacy, and data monetization opportunities, and evolve the organization as the data usage matures. As the landscape evolves, particularly with emerging technologies, the right strategic and structural choices will delineate industry leaders from the rest in the upcoming decade.

About the Author:
Roopesh Varier is a distinguished data professional with an illustrious career spanning over two decades, marked by engagements across multiple Fortune 500 corporations. His expertise has been instrumental in establishing and leading data science and analytics divisions within a diverse range of industries including technology, finance, entertainment, and healthcare.

Noteworthy among his accomplishments is his seminal role in orchestrating the initial big data warehouse at Symantec, accelerating the adoption and scaling of big data infrastructures within American Express, and delineating the key metrics pivotal to Roku’s IPO.

Currently, Varier leads the Data & Analytics charter at SmartAsset, a forward-thinking FinTech startup. He is active in the academic sector teaching Data Strategy and related courses. He used to teach at Hult and currently teaches at U.C. Berkeley.

Additionally, Varier lends his expertise as an advisor to several analytics-centric startups in Silicon Valley, fostering the next innovation wave in the data arena.