August 10, 2023

Data-driven decisions: unlocking growth and overcoming challenges

Data-driven decisions: unlocking growth and overcoming challenges

Organisations that are ‘data-active’ are more productive than their reluctant counterparts in addition to quicker rates of adoption to big data, aligning to increased productivity. However, talent acquisition in data is increasingly difficult for organisations. According to a UK 2021 government study, half of UK businesses are recruiting for roles that require data skills with the most common being Data Analyst, Head of Data, Data Manager, CTO and Data Protection Officer. As the demand for data skills is high, over half of the recruiting UK large to medium businesses are struggling to find the appropriate skills. 

Underrepresentation in data

When producing machine learning technology, it is important to include a diverse variety of people in the decision making process. For example, in 2021 women make up 51% of the UK population but only 20% of AI and data professionals and 18% of users across the largest online global data science platforms. As diversity produces higher levels of cross collaboration and innovation than non-diverse counterparts, organisations that are aiming to scale and grow should include diverse members along their data journey.

Poor data quality: impact on revenue and project delays

Annually, poor data quality costs organisations an average of $12.9m. Beyond the immediate impact on revenue, long term this can increase the complexity of data ecosystems, create cloud confusion and delay projects. 

Case study: Uber

In 2017, Uber admitted they made an accounting error that meant they had to refund almost $50m to their drivers. This began from an update error in 2014 to Uber’s terms of service where they started skipping removing taxes and fees before collecting their 25% commission. As they did not have a data quality assurance practice or enough expertise, no-one discovered the error until it had accumulated into $900 per driver. However, DBA (database administrator) engineers provide a solution to poor data quality through constructing, monitoring and maintaining database standards and policies. 

Supply chain management: leveraging data for risk management and cost reduction

84% of CSCOs report that lack of visibility in the supply chain is their biggest challenge due to an overwhelming quantity of data, ineffective data organisation and sioled systems. Therefore, enhancing data processes and resources, can support risk management in addition to cost reduction. In one IBM case study, AI-enabled smart alerts and predictive analytics more than halved the duration of disruptions and quantity expedited shipment costs. 

Big data solutions (such as Hadoop) can analyse varied and unstructured data at high speeds, supported by machine learning, organisations are able to unify their processes and gain insights to optimise supply chain decisions. However, without the correct expertise, Hadoop can become costly as it requires strong and consistent communication to operate and build the multiple individual components and clusters whilst expanding. 

Customer centric insights

By leveraging data-driven insights, organisations can gain a competitive edge by understanding consumer behaviour and predicting market trends. This enables them to make informed decisions and adapt their strategies accordingly, positioning themselves for growth in the dynamic business landscape. Ultimately, embracing data as a valuable resource empowers organisations to proactively meet their customer needs and stay ahead in the market.

Case study: King Digital Entertainment

‍ In the gaming industry, data analytics allows organisations to predict player churn, likelihood of upselling or cross-selling on in-game currency or upgrades, the optimal marketing action through enhanced marketing attribution; all in turn driving an increased level of engagement.

One of King Digital Entertainment’s most popular games, Candy Crush, noticed a large abandonment rate around level 65. As there were a total of 725 levels, this abandonment was a big problem by increasing player churn and bounce rates, thus negatively impacting upselling and cross-selling opportunities. They utilised a combination of open-source Hadoop and in-memory analytic database of King’s 149 million daily active users (DAU) to discover the problem was around a particular element and when deleted user retention returned to normal levels.

Optimising processes and systems through data

Data provides organisations with the ability to gain valuable internal insights; allowing them to analyse and optimise their processes and systems. By leveraging data-driven intelligence, organisations can identify areas of improvement, streamline processes and enhance overall performance. This data-driven approach enables organisations to make data-informed decisions to improve efficiency and drive continuous improvement across their entire ecosystem.

Case study: PepsiCo

Across its 23 billion-dollar brands, PespiCo leverages Azure machine learning and predictive analytics to gain actionable insights from its vast data resources to understand consumer buying behaviour in more detail. In process consisted of:

  • Integrating existing datasets
  • Validating behaviour of the model with the aim of responsiveness and regulatory compliance
  • Deploying the model to a cloud environment to process incoming data
  • Monitoring the model for behaviour and business value
  • Retraining and replacing the model to improve performance

Whilst data migrations can potentially be expensive and confusing without the correct expertise, this process can be structured to become a continuous integration and continuous delivery pipeline.

Enhancing this process, enabled PepsiCo to integrate historic data such as consumer buying patterns and upcoming external major events such as school schedules, sports events and the weather to utilise predictive analytics to prepare their supply chain and maximise sales opportunities and capabilities. In employing data analytics for demand forecasting, provides PepsiCo with a more realistic and accurate depiction of the future.


‍As the quantity of information increases, organisations are confronted with more demands to harness and leverage their data effectively to stay competitive, launch innovative customer strategies and optimise the productivity of teams. Without the right expertise analysing this data and translating it into meaningful insights to decision-makers, organisations risk falling behind. 

Underrepresentation of diverse groups in data analysis and decision-making processes can result in biased and invalid results, while poor data quality can lead to financial losses and project delays. 

Therefore, organisations must prioritise data governance, diversity in decision-making, and data quality assurance to fully leverage the potential of their data and remain competitive in the digital era.

WeShape recognises the importance of diversity and inclusion in the tech industry, especially data. As signatories of the Tech Talent Charter and B Corp members, we actively promote a diverse supply chain and support organisations struggling with their diversity and inclusion initiatives or supply chain management. To find out more about our impact, click here for our 2022 annual impact report.

We have established partnerships with AWS and GCP to support organisations' cloud data migrations with minimal friction and expensive downtime. Calculate your organisations’ downtime cost here.To find out more about cloud technology, read our interview with cloud expert, Rob Coward.

Our variety of talent-based events, such as Tech Talks, London DevOps and Oops, enables us to have extended reach to a large talent pool, including the top 5% of associates. This allows us to act at pace to access unique data skill sets.

As data-driven cultures are customer-centric and often have to pivot quickly to adapt to market demand, within our on-demand consultant service, WeShape offers a dedicated account manager who will support your organisations’ goals with biweekly cadence calls and sprints. This removes the risk of silos, especially in big data environments such as Hadoop which requires continuous monitoring. 

Additionally, we are committed to engaging our associate network within 48 hours and deploying our skilled technical squads within two weeks of project acceptance. These streamlined processes enable organisations to work effectively and efficiently, maintaining a competitive pace with their data.

‍If you’re interested in booking a technical call to find out more about our solutions, book a quick chat with WeShape.


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About the Author
Charlotte Hamilton
Charlotte Hamilton

Charlotte is the social media assistant at WeShape. She has a 2:1 BA in English Literature and enjoys reading and writing in her free time. The Life at WeShape blogs are a fun way to get to know us once a month!

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