{"id":21677,"date":"2023-07-07T13:21:54","date_gmt":"2023-07-07T13:21:54","guid":{"rendered":"https:\/\/www.cleverrepublic.com\/?post_type=blog&p=21677"},"modified":"2025-10-02T14:34:07","modified_gmt":"2025-10-02T14:34:07","slug":"unlock-the-value-of-data-how-to-prevent-the-data-push-pitfall","status":"publish","type":"blog","link":"https:\/\/www.cleverrepublic.com\/nl\/resources\/blog\/unlock-the-value-of-data-how-to-prevent-the-data-push-pitfall\/","title":{"rendered":"Unlock the value of data: how to prevent the Data Push pitfall"},"content":{"rendered":"
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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.<\/p>

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 \u2018Data Push,\u2019 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 \u2018Data Push\u2019 approach and discuss the importance of Data Intelligence as a solution.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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The Guessing Game<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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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:<\/p>

\u201cThey 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.\u201d<\/em><\/p>

Data scientists might feel unheard and underutilised. In\u00a0the 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:<\/p>

\u201cIn 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.\u201d<\/em><\/p>

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.<\/span><\/div>

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 \u2018Data Push,\u2019 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 \u2018Data Push\u2019 approach and discuss the importance of Data Intelligence as a solution.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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The Pitfalls of Data Push<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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When data scientists push their work onto the business without a clear understanding of their needs, several issues can arise:<\/p>

Relevance:<\/strong>\u00a0The 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.<\/p>

Resistance:<\/strong>\u00a0Employees 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.<\/p>

Inefficiency:<\/strong>\u00a0Without 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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Preventing Data Push<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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To overcome the challenges of Data Push, organisations must prioritise Data Intelligence.\u00a0Data Intelligence<\/a>\u00a0encompasses 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:<\/p>