Why Organizations Struggle to Derive Value from Data

Why Organizations Struggle to Derive Value from Data

In the rapidly evolving digital landscape, businesses are investing significant resources into data and analytics with the expectation of reaping substantial rewards. Yet, a startling disconnection looms between the promise of data analytics and the actual value it delivers. Recent studies show that there is still a huge gap between expectations and actual value creation.

  • According to a McKinsey Global Survey, a mere 15% of organisations believe they are fully capitalizing on their investments in data and analytics. Link
  • Deloitte’s 2022 survey of chief data officers uncovers a similar trend, with only 35% of organisations achieving their desired business outcomes from data and analytics initiatives. Link
  • NewVantage Partners’ 2021 survey further underscores this issue, revealing that 76% of executives feel their organisations are battling to keep up the momentum in their data and analytics endeavours. Link

At Data Edge, we’ve witnessed this disconnect firsthand. Our experiences align with these statistics, revealing a complex landscape where the expected value of data analytics often remains an unfulfilled promise. In this blog post, we dive into the reasons behind this gap and explore strategies to bridge it, turning potential into performance.

Why Companies Struggle to Derive Value from Data, Analytics and AI: A Closer Look

In the modern business landscape, data is often likened to a new form of currency or oil. Despite this recognition, many companies find themselves unable to unlock the true potential of their data analytics investments. The reasons for this shortfall are multifaceted and often interlinked. Below are some of the key pain points in our experience.

Leadership and Organizational Ownership

Most organisations have been around since long before the digital revolution and face significant challenges in adapting their organisations to take full advantage of data. Common challenges are legacy systems and infrastructure, problems with data accessibility and quality, a company culture where data is not emphasized in decision making and a lack of digital expertise. 

Active involvement and ownership from top management is crucial in aligning efforts to utilize data as a strategic asset and a lack of involvement will have a detrimental effect on progress. 

Misconception of Data Analytics being an IT function

Another common misstep is the perception of data and analytics as a purely IT function. This siloed approach restricts the integration of data analytics into broader business strategies. Data is and should be viewed as a core business asset, not just a technical resource, to fully leverage its capabilities for decision-making and strategic planning.

The Predominance of Output-oriented Focus

Many organisations fall into the trap of being output-focused rather than outcome-focused. The emphasis tends to be on what activities are done and what is produced rather than what the actual business outcome is. Doing a lot of things does not necessarily mean achieving positive business outcomes and data analytics can significantly help in identifying what adds value and what does not.

The Skills Gap and Cultural Challenges

The lack of data literacy and skills within organisations is often a substantial barrier. A workforce skilled in collecting, analyzing, and interpreting data is crucial for effective data utilization. Additionally, many companies struggle with establishing a strong data culture where data is not only valued but is the foundation for informed decision-making.

Technological Limitations and Scalability Challenges

On the technical front, the inadequacy of tools and technologies is a prevalent issue. Many organisations continue to rely on outdated or inflexible data analytics tools, limiting their ability to extract, process, and analyse data effectively. Furthermore, as companies grow, their data needs evolve. An inability to scale data capabilities to meet these growing demands can render initial data investments ineffective over time.

Forging a Path Forward

Overcoming these challenges requires a holistic approach, starting from the top. Educating and involving leadership in data initiatives, fostering a data-driven culture, investing in the right tools and skills, and focusing on actionable outcomes are key steps towards harnessing the full value of data analytics.

By addressing these issues, companies can transition from merely collecting data to truly harnessing its power to drive informed decision-making and sustainable business growth.

Key Preconditions for Organizational Readiness

To truly become more data-driven and realize a return on investment from data and analytics, the proper organisational readiness needs to be in place. We believe that the following drivers are really important to realise the potential of data, analytics and AI.

  • Management understanding and commitment: The first step towards becoming a data-driven company lies in the understanding and commitment of the management group. A deep appreciation of the possibilities and what is necessary to create value from data is crucial. This involves recognizing the strategic importance of data and analytics, beyond its technical aspects. 
  • Clear responsibility and alignment with business goals: Data analytics should not be viewed as just an IT initiative but as a core business strategy. Assigning clear responsibility for data analytics within the management team and ensuring it aligns with business objectives is essential. This alignment guarantees that data initiatives support and drive key business goals. 
  • Fostering a data-driven culture: Cultivating a culture where decisions are made based on data is vital. This means prioritising business outcomes over functional outputs, where the focus is not just on data collection or analysis but on how these efforts translate into tangible business results. 

Accelerating Value Creation: Essential Pillars

In parallel with working on organizational readiness we need to establish an effective setup for delivering data, analytics and AI initiatives. When comparing leaders and laggards in this space we have identified three key pillars that, when established, ensures value creation both short and long term.

  • Establishing clear priorities and strategy: Setting clear priorities, objectives, and establishing a coherent strategy for data analytics ensures that efforts are focused and aligned with the company’s broader goals. This strategic alignment helps in maximizing the long-term impact of data initiatives. 
  • Value-centric analytics process: Having an analytics process that emphasizes value creation is key. This means each step in the data analytics process, from data collection to analysis, is designed to drive meaningful business outcomes. It is important to realize that this extends well beyond just building BI reports or AI models to also cover decision-making and actually creating tangible business value.
  • Ensuring that the proper capabilities exist: As already outlined, becoming data-driven is a truly multidisciplinary effort and scaling this across an enterprise requires key capabilities that are keeping up. It is important to realize that these capabilities do not only mean technical infrastructure and tools but also ways of working, organisation design, skills development and recruitment etc. Building these capabilities ensures that the company has the right tools, skills, and procedures in place to effectively harness data.

Conclusion: Bridging the Data Value Gap

The transition to a truly data-driven organisation is challenging, yet essential for success in today’s business world. The evidence has been clear for a long time that data-driven organisations outperform their peers and with recent advances in AI it can be argued that lagging behind poses an existential risk for companies.

Overcoming obstacles such as leadership gaps, misconceptions about the role of data analytics, skills shortages, and technological limitations requires a strategic and holistic approach. Emphasizing the importance of aligning data analytics with business goals, nurturing a data-centric culture, and investing in the right tools and skills is crucial for tangible outcomes.

In our upcoming posts, we’ll delve further into the various solutions and methodologies we employ while collaborating with our clients. Our focus is on generating tangible business value through data analytics. This includes conducting educational sessions, performing opportunity assessments, and establishing a value-driven analytics process. Stay tuned for more insights!

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