Unlock the value of data: how to prevent the Data Push pitfall

Most organisations recognise the tremendous potential of leveraging data to drive insights, make informed decisions, and gain a competitive edge. However, many organisations still struggle with data adoption. Clever Republic interviewed data scientists at various firms to uncover what is holding them back in maximising the value organisations derive from data through advanced analytics and artificial intelligence.

We found a common pitfall that many organisations encounter: the misalignment between their data science teams and the business side. This misalignment often leads to a phenomenon we call ‘Data Push,’ where data scientists struggle to understand the specific needs of the business and end up pushing their work onto employees who are unprepared or uninterested. In this blog post, we will explore the challenges posed by the ‘Data Push’ approach and discuss the importance of Data Intelligence as a solution.

The Guessing Game

One of the key reasons for the Data Push pitfall is a lack of communication and collaboration between the data science team and employees in other departments. Employees often remain unaware of how data science can benefit their work, leading to a significant gap in understanding and expectations. Consequently, employees either do not actively propose use cases to data scientists, or propose use cases with little value. This makes it difficult for the data science team to align their efforts with the specific needs of the business. One data scientist told us:

“They raise analysis issues instead of prediction issues. They just want simple facts to put in their presentations. That is not what we data scientists were hired for. So, we hope that there is an actual data science task there, but in the end such projects kind of slowly bleed to death.”

Data scientists might feel unheard and underutilised. In the absence of clear use cases from departments, data scientists are left to guess what the business requires. They rely on their expertise and assumptions, which may not always align with the actual needs of the organisation. Another data scientist explained:

In our case we mostly create our own work. We will try to think of a use case ourselves even though we are not the ones performing daily operations. This makes it difficult to find out what can be improved. But yeah, then we just guess, or try to investigate ourselves to find use cases.”

As a result of the situation described above, the data science team invests time and resources into projects that do not provide significant value to the business. This leads to frustration and scepticism among both data scientists and business employees.

The Pitfalls of Data Push

When data scientists push their work onto the business without a clear understanding of their needs, several issues can arise:

Relevance: The work of data scientists may not address the immediate challenges or opportunities faced by the business. This makes it difficult for employees to see the value and relevance in the insights or solutions provided.

Resistance: Employees may resist adopting data-driven approaches when feeling disconnected from the decision-making process. Lack of understanding and involvement can breed scepticism and reluctance to change.

Inefficiency: Without input from the operations side, data scientists may spend considerable time and effort on projects that do not align with organisational priorities or have limited impact. This leads to wasted resources and missed opportunities.

Preventing Data Push, the Data Intelligent way

To overcome the challenges of Data Push, organisations must prioritise Data Intelligence. Data Intelligence encompasses connecting data, policies, processes, technology, and people to ensure the availability, integrity, and usability of data. By implementing effective Data Intelligence practices, organisations can prevent frustrations and wasted resources. Additionally, they can bridge the gap between data science and operations:

  • Collaboration and Communication: Establish channels for regular communication and collaboration between data scientists and employees from various departments. This enables a deeper understanding of the business needs and encourages employees to propose use cases that can benefit from data science.
  • Use Case Identification: Encourage employees to identify areas where data-driven insights or solutions can make a significant impact on their work. This proactive approach empowers employees to actively participate in the data science process and ensures that projects align with real business needs.
  • Prioritisation and Resource Allocation: Implement a transparent prioritisation process for data science projects. This ensures that resources are allocated to initiatives that have the most significant potential to drive value and meet the organisation’s strategic objectives.
  • Continuous Learning and Education: Conduct training sessions and workshops to educate employees about the potential applications of data science in their respective domains. This empowers them to understand how data science can help in their work. It also encourages a data-driven mindset across the organisation.

The pitfall of Data Push can be a significant roadblock for organisations seeking to harness the power of data science. However, by prioritising Data Intelligence practices, organisations can bridge the gap. Clever Republic helps organisations become more Data Intelligent. Interested in working with us or simply sharing ideas? Feel free to reach out to us and schedule a (virtual) meeting!

Frequently asked questions:

Data push occurs when there is a misalignment within an organisation between the data team and the business. This misalignment often leads to a phenomenon we call 'Data Push,' where data scientists struggle to understand the specific needs of the business and end up pushing their work onto employees who are unprepared or uninterested. 

Relevance, Resistance and inefficiency can arise as a result of data push. 

  • Collaboration & communication
  • Use case identification
  • Prioritisation & resource allocation
  • Continuous learning & education