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Enabling Business Value Creation through Data-Driven Analytics
Enabling Business Value Creation through Data-Driven Analytics

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Analytics has become the backbone of every large organization and is instrumental in fostering growth. By reducing operational costs, accelerating responses, and promoting agility in crucial business strategies, analytics tools and solutions have pushed processes to generate value and create a long-term business impact. The benefit of infusing analytics into business operations promises to minimize process proliferation, increase process efficiency through standardization and the adoption of best practices.

With the improvement in availability of data, there is a renewed focus on analytics enabling organizations to build data analytics team that delivers results. Several companies actively purchase data analytics technology but don’t give enough thought for the creation of their data team. Building a centralized analytics function has led to more value creation than some technology or software advances of the past.

In many cases organizations are working in silos while executing smaller projects, which can add value, but their long-term impact might be at risk. Embracing a product centric mindset and ways of working executed through multidisciplinary agile squads while ensuring collaboration between global and local teams will avoid duplication and enable scalability.

Today, organizations make investment and valuation decisions based on local point of view, which can vary a lot depending on the maturity of the business unit. Over time analytics will provide more rigor through frameworks to help us decide where and how much companies need to invest by leveraging the DVF framework and also enable us to calculate value delivered, leveraging standard measurement archetypes. These will be fostered by a global taxonomy enabling us to have a measurement platform to test and compare algorithms.

While leveraging internal data is critical, organizations increasingly need to process and analyze related external data from sources like social media, blogs, image and video sites, sensors, and more. We use Spotlight, a ML algorithm that targets campaigns to consumer segments. It takes campaign and consumer data to understand what is the optimal consumer audience that will respond to a given campaign. Businesses must garner insights from customer and consumer data, analyze the data pool, and offer insights and predictions that enables businesses to test hypotheses and evaluate outcomes that can be put into practice.

Integrating analytics in the core of the business and making them a part of the functions and making them analogous across verticals will help ignite growth in analytics domain and create career paths and incentives to develop and retain top talent. For example, our Portfolio Optimization Platform defines a high-performing portfolio that will be used as a benchmark based on sell-out data & historical Net Revenue from POCs. It helps to identify business opportunities in terms of volume and coverage comparing each POC to the benchmark portfolio and prioritizing SKUs for initiatives that have a relevant incremental NR. Once the right portfolio has been defined for each POC, recommendations are connected and deployed by existing value delivery platforms like tele-sales, BDRs and BEES.

Analytics solutions mix technology and human input to further refine results. The way forward, is to democratize AI, creating applications that can enable the use of analytics in other verticals and equip them with the training and tools to garner meaningful insights and become an extension of the analytics community.

Today, analytics teams have evolved a lot and although some markets might sit on the higher end of the curve, globally, the vertical is still in its infancy. The aim is to change shift the needle and invest in world class tech like Auction Edge. It is a ML model and aims to optimize the design of auctions for sourcing by taking historical data on attributes and outcomes of past e-auctions and learning from them to recommend optimal values of auction parameters. We are also investing in tools like Brewdat and MSFT Azure DevOps to enable us on our overall journey.

To ensure a constructive convergence of analytics tools and governance, businesses will need to expand analytical capabilities, roles and processes, anticipate changes in products and practices, plan for convergence of platforms and facilitate collaboration between data and analytics communities across the organization. Many companies believe that gathering data and putting figures through a data visualization tool is sufficient, however, it is only the start.

Data is the major factor promoting corporate expansion and is crucial to preserving a competitive advantage and commercial relevance. Organizations should therefore establish the groundwork for streamlining their data management processes and real-time analytics while creating a culture that is data-driven to keep the needle moving in the right direction.

 

 

About the author

Surodeep Chaudhuri, Director – Analytics, GAC, AB InBev GCC

Surodeep Chaudhuri, Director – Analytics, GAC, AB InBev GCC

 

 


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