Short Description: Businesses have been using data and analytics for decision making for many decades. Yet, the companies are struggling to derive significant value from their investment in analytics. This is largely due to taking a limited view of how analytics can be leveraged, discounting its actual potential.

analytics for business growth infographic

Businesses have been using data and analytics for decision making for many decades. Yet, the companies are struggling to derive significant value from their investment in analytics. This is largely due to taking a limited view of how analytics can be leveraged, discounting its actual potential. Analytics is largely viewed as a predictive or prescriptive modelling exercise. Modelling is one aspect of analytics, and it is imperative to understand all the four layers of analytics for deriving long-term value for business.

The Four Layers of Analytics

1: Business Context

When looking to implement analytics, businesses tend to outline their business context at a high level. It is important to delve in to fine details in order to properly define the problem areas analytics must provide insights on. At this point, it is also critical to identify KPIs to measure the business outcomes.

2: Analytics Modelling

With problem areas clearly defined, this layer is about establishing interdependence between multiple variables (business drivers) based on a relevant hypotheses. This is an iterative process, and requires continued experimentation to improve the model. Using cutting-edge technologies like Artificial Intelligence (AI) and Machine Learning (ML) can help advance the accuracy of the model until maximum value is delivered.

3: Data

High quality data is pre-requisite for developing a relevant analytics model. Poor quality of data will result in inaccurate results from the model. Organizations need to access and leverage high quality data from various sources. This data would then have to be prioritized, cleaned, made consistent and correlated. Lack of optimized enterprise data results in organizations spending eight to nine times more money for every dollar spent on data modelling.

4: Technology

The real value of analytics-based solutions is derived when the technology is seamlessly integrated with the corresponding business processes. For instance, if a credit card business intends to leverage analytics for evaluation and grant of credit, then it is crucial to link the application to credit card point of sales for actionable insights.

In addition to the above, organizations must appropriately adjust their operating model for deriving optimal value from analytics-based solutions. Learning from companies like Google, Facebook and Amazon, who have successfully leveraged analytics to their advantage, a cross-functional and agile operating model is a must-have.

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By Data Solutions Group