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What it takes to succeed with Big Data Analytics

What it takes to succeed with Big Data Analytics


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By Jacob Guldager-Løve, Senior Manager, Advisory

Big Data continues to be an area of much focus and interest, and challenges, for many organisations, but few have really succeeded in the area. KPMG recently helped a client with such a project focusing on Big Data Analytics, by assisting one of the largest payments providers in Europe with building three large data science Proof of Concepts (PoC) on one central Big Data Hadoop-based data lake. KPMG configured the Hadoop cluster, initially installed by Hortonworks, and subsequently ingested data from a number of source systems into the data lake. 

The data lake allows for multiple teams working on the data lake simultaneously, enabling the collaboration between data scientists and business experts across all three data science Proof of Concepts.


The analysis work performed focused on three domains:

  • Merchant Services
  • Fraud Services
  • Payment Services

The client is currently in the phase of proving the feasibility, value and potential commercialisation opportunity of the first advanced analytics results.

The success of this project was going beyond the technology, combining technology expertise with strong business understanding, the right capabilities and a stringent focus on defining strong use cases to drive valuable insight.

Data & Analytics has the power to create great value. However, it requires a business-first perspective, helping solve complex business challenges using analytics that clients can trust. The result should be analytics solutions and services that business leaders can believe in to help increase revenue, reduce costs and manage risk throughout the enterprise.

So what helped make this project a success, and what were the key focus areas for driving the value?

Use case-driven, an agile approach and visualising results

The right way to drive analytics initiatives is to base it on use cases. Getting to real value requires a clear focus on choosing use cases that can be proven, and then move the right use cases into production. The reason for working use case-driven is that the business may have a strong idea of where value may come from, but before it is proven, there is no real way to know if the data supports the use case. When working with use cases, we therefore recommend an agile approach, based on short sprints where use cases are continuously evaluated ensuring adaption and focus change if needed. If a use case turns out to be impossible to prove with the data, or there are other aspects (legal, commercial, etc.) preventing the realisation of the use case, it can be closed and focus can move to new use cases.



Use case-driven, an agile approach

Building capabilities

There is no doubt that the future of big data and analytics is bright, and undoubtedly, big data and analytics undertakings are very exciting, but they must pay off for organisations. To harness the power of analytics, companies must therefore invest in the required analytical skills in terms of both tools and staff. Organisations must invest in the development of employees' analytical skills to perform well operationally. However, not only data scientists are key to succeeding with Data & Analytics projects. Companies also need to consider data engineers, big data architects, visualisation experts and people with a strong data and business acumen who can translate data potential to business opportunities.

Analytics take time

To get the right value from analytics projects it is important to align expectations and have the right approach. The development of use cases often follows the traditional S-curve when measuring time and outcome. Initially it takes time to gather the needed data and get started. When the data lake has been established, interesting results can often be created in a fairly short time frame, but it may take a long time to optimise and fine tune the analytics models to the necessary level needed for production and commercial use. Be patient and value will surface.

We would be happy to discuss how we can help you start your Big Data Analytics journey.

KPMG P/S, Dampfærgevej 28, 2100 København Ø, Tel: +45 7070 7760, CVR no. 25578198

KPMG Acor Tax P/S, Tuborg Havnevej 18, 2900 Hellerup, Tel: +45 3945 1700, CVR no. 34082200

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