With its potential to transform business enterprises, data analytics is increasingly being adopted by leading companies including E-commerce players to derive business insights from raw data and remove guesswork from decision making. Innovation-driven companies such as Google, Tesla, and Uber have used the power of data analytics to enter new markets, build their customer relations, and streamline internal processes including supply chain management.

Despite its many advantages, many companies are equally struggling to extract maximum value from their investments in data analytics tools. In order to achieve business transformation, business must adopt an end-to-end approach towards analytics and not just focus on any one aspect. End-to-end analytics comprises of the following 4 layers, namely:

  • Business context and planning
  • Analytics modelling
  • Data
  • Technology

This article outlines why business enterprises must focus on all 4 analytics layers in order to maximise its benefits.

Layer 1 – Business context and planning

Smooth running of daily operations is crucial for the success of any business enterprise. The effectiveness of data analytics depends to a large extent on the operational efficiency of the company. Even in the areas of operational inefficiencies, data analytical tools can use raw data to pinpoint these areas, which require improvements.

However, most business enterprises do not completely link their business context and planning with their implementation of data analytics. While business operations and planning are planned at a broadly higher level, they are not adequately integrated in analytics, thus making them non-measurable for overall business impact.

 

 

For example, data analytics can be identifying an E-commerce marketing channel that needs improvement or in determining if the logistical department is order more (or less) inventory than it needs.

Layer 2 – Analytics Modeling

Analytics Modeling layer is a key element in data analytics required to understand the business data, make accurate data-based predictions, and to extract valuable insights for making correct business decisions.

 

 

Most business enterprises fail to recognize that analytics modelling is an iterative process with built-in continuous learning and improvements. A/B Testing is a key requirement to improve the modelling layer. Through repeated iterations, AI and machine learning technologies can help in improving the accuracy of this model in order to deliver maximum business value.

Layer 3 – Data

While modern business enterprises are able to generate large data volumes, the collected data is often unclean and available in different formats, thus making the data cleaning process very expensive. On an average, for every $10 spent on data modelling, $80-90 is spent on improving the data quality.

Marketing efforts have also been transformed due to the large volume of data on social media platforms. Companies can now tap into what customers are talking about their products and can deploy customer engagement metrics such as social listening, click-through rates, and bounce rates to determine the success (or failure) of marketing campaigns and promotions.

Layer 4 – Technology

The fourth and final layer in data analytics is the technology layer where analytics needs to be integrated into the business process. For example, for a business process revolving around evaluating and approving credit card transactions, the analytics-powered process must be linked to all online (and offline) points of sale of the credit card to determining the feedback.

 

Business data along with data analytics are valuable for any business enterprise to gain insight and identify areas of improvement in their processes. Utilizing and integrating all the 4 layers of data analytics (discussed in this article) is critical for deriving the maximum benefits of this innovative technology.