Analytical waste or the hidden threat to business success

By Vishal Soni, Senior Technical Product Marketing Manager at Alteryx.

  • 6 months ago Posted in

In this ever-changing environment, where uncertainty has become the new reality, leveraging data to make the right decision is vital for businesses to increase their competitive advantage and stay ahead of the game.  

 

However, a recent study by McKinsey reports that managers lose more than a half-million days per year on ineffective decision-making. There is a clear problem amongst organisations when it comes to accessing and utilising data for their own advantage, in a time efficient way.  

 

In today’s turbulent and unpredictable economy, where speed to insight is critical, the opportunity to reduce analytical waste is too important to miss. 

 

Identifying areas for improvement 

 

First, it’s essential to understand what analytics and data waste involve. Waste can occur in many forms, such as irrelevant data collection and analysis, late or incomplete analysis, inefficient use of resources, or misalignment with business objectives. In today’s increasingly competitive and fast-paced business landscape, decision-makers must avoid wasting time and resources on analytical initiatives that do not enable them to deliver unique, actionable business insights.  

 

Alteryx’s decision intelligence research found that decision fatigue is the biggest challenge for UK businesses, with 30% refreshing their data sets and updating their analyses daily. This can lead to tremendous amounts of manual work and repetition bloat of the typical analytics processes, and therefore stalling the time it takes to deliver insights and often resulting in missed opportunities or irrelevant conclusions.   

  

Collecting and analysing as much data as possible can be tempting, but the information value of data is perishable and decays with time. Without effective data and analytics strategies that ensure all users across the business can harness data and make empowered decisions, organisations will need help to implement their analytical resources effectively to promote decision intelligence across every department.  

 

On top of this, without an analytics foundation built on clearly defined objectives communicated effectively between teams, analytic initiatives may not align with the organisation's strategic objectives. Typically, these scenarios occur when there is a lack of clarity, alignment and collaboration between the various stakeholders involved in data and analytics efforts.  

 

Key steps to reducing analytical waste  

 

The first step is to identify what the organisation wants to achieve through its data initiatives and analytics strategies which will remove bottlenecks and improve the effectiveness of data and analytics processes. Does your business rely on specialists to analyse the data, or can the process be handled by other people in the business who are familiar with the business problem? This is how managers and operational teams closest to the problem will be able to focus only on the most relevant data while avoiding collecting data that does not add value.  

  

Secondly, the quality of the data needs be emphasised, ensuring that the data collected is accurate, reliable and relevant. Any bottlenecks around data access or uncertainty around how the data will be used may impact the results of the analysis. If your data culture lacks democratisation and empowerment, this can waste time and effort. The value of good data is limited if it is not easily accessible to employees. Data sharing and access should be the norm within businesses, not the exception.  

 

To eliminate analytical waste, real-time architectures are the way to go, but automation is a tool that is increasingly being used to make the analytical process more efficient. 72% of business leaders in the UK think using automation would be beneficial for analysis, although only 20% of business decisions in the UK are supported by automation.  

  

Take the example of a retailer that only monitors its inventory on an ad hoc basis. They run the risk of being out of stock in case of a sudden event or a peak season – an approach that will be costly in the long run. Therefore, it is necessary for businesses to develop a detailed data management plan that specifies when and how data will be collected, stored and processed efficiently to facilitate optimal decision-making. The quicker you can access and analyse the data, the faster you can react to a business event, like a change in the competitive landscape, and develop the appropriate business response.  

 

The importance of maintaining the human element 

 

In an economy where real-time decision intelligence is critical to success, and automation is at the core of the fight against analytic waste, it is ironic that the human factor plays such a major role in eliminating analytical waste. A human-centred approach to analytics is crucial for long-term success. Business leaders need to invest in training their decision-makers, regardless of skill level, to ensure they have the skills and knowledge to participate in the data and analytics process. Decision-makers should regularly monitor analytic performance to identify areas where waste can be reduced. This includes auditing the use of data, identifying bottlenecks and analysing the results of analytical projects to identify where improvements can be made. 

 

Organisations should also encourage collaboration between business and IT teams to ensure that their data and analytics efforts align with the business's long-term objectives. By being close to the problem, empowered by analytics in their decision-making, and supported by accurate and real-time data to deliver decision intelligence, employees can help develop their organisation into an agile business that can adapt and react to changing circumstances effectively. 

   

A holistic approach is needed to eliminate analytical waste, especially as the volume and complexity of data continues to grow. In the coming years, data will continue to play an increasingly critical role in the success of businesses. The effective management of data will help create accurate and timely data pipelines that anyone can access and analyse – which is critical to fully harness its power. 

 

Companies that take proactive steps to reduce analytical waste today will be better positioned to take advantage of opportunities tomorrow. It is the only way they can remain competitive, drive innovation and deliver better services to their customers. 

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