Posts Tagged bigdata

May 12 2016

Big lessons from big data implementation – Part I

Each day 23 billion GB of data are being generated and the speed of generating big data is going double in every 40 month! Apart from their business data, organizations now also have humongous data available from google, Facebook, amazon, etc. They wish they can use all the available data to find useful information for doing their business better.  Let us look into big data deployment of a few organizations and learn from their experience.

Case 1: Rabobank

Rabobank is a Dutch multinational banking and financial services company headquartered in Utrecht, Netherlands. It is a global leader in food and agro financing and sustainability-oriented banking. Rabobank started with developing a big data strategy July 2011. They created a list of 67 possible big data use case. These use cases included:

  • To signal and predict risks, prevent fraudulent actions that the bank is running
  • To identify customer behavior  and to obtain a 360-degrees customer profile;
  • To recognize the most influential customers as well as their network;
  • To be able to analyses mortgages;
  • To identify the channel of choice for each customer.

For each of these categories they roughly calculated the time needed to implement it as well as the value proposition. In the end the Rabobank moved forward with big data application to improve business processes as the possibility for a positive ROI. A dedicated, highly skilled and a multidisciplinary team was created to start with the big data use cases. They were using Hadoop for analyzing big data. They selected social data, open data and trend data were integrated. So there data approach with a deluge of semi and unstructured mess. Hadoop is only part of a big data strategy. The key to success was the multidisciplinary team and that they embraced uncertainties and accepted mistakes to be made help them to overcome situation.

Problems faced during implementation

Rabobank didn’t store raw data, due to the costs and capacity issues. The data quality was not constant and the security issues were very high. Rabobank noticed that it was often unclear who owned the data as well as where all data was stored. Hadoop is different from older database and data warehousing systems, and those differences confused the users.

Lessons

  1. Specialized knowledge as well as visualizations is very important to drive big data success.
  2. Start with the basics & don’t stop at stage one. Big data implementation is continuous journey to reap data-driven insights.
  3. Not having the right skills for the job can be a big problem.
  4. The dangers of underestimating the complexity of a big data system implementation so focus on data management.

Case 2: OBAMA CARE

In 2010 the newly elected president of the United States of America government introduced Patient Protection and Affordable Care act. The main purpose of this act was the best of public and private insurance coverage for the population, and thereby controlling and reducing healthcare costs and requires them to interact with the government via a website to do so. The system is in essence a big data implementation problem with data being collected on a potential population in Excess of 300 million people across the entire country. Unfortunately the project not progressed as planned, and has become mired in technological controversy.

Problems faced during implementation

  • This act brought the country to default on its debt
  • Cost of Obama care – $1.6 trillion
  • Estimation of cost 2014-2024 – $ 3.8 trillion

Anticipation to prevent the problem:

  • They can take special knowledge as well as visualizations can prevent the loss.

Lessons

  1. The dangers of underestimating the complexity of a big data system implementation to focus on data management.
  2. The prior analysis and prediction complexity of data can prevent cause.
  3. Most of the data collected and stored in an agency’s transaction processing systems lacks adequate integrity so make sure that captured data meet integrity standard.
  4. Specialized knowledge as well as visualizations is very important to drive big data success.
  5. Not having the right skills for the job can be a big problem.

 

We shall analyze a few more cases tomorrow. Keep watching this space.

Disclaimer:

Views expressed on this article are based solely on publicly available information.  No representation or warranty, express or implied, is made as to the accuracy or completeness of any information contained herein.  Aaum expressly disclaims any and all liability based, in whole or in part, on such information, any errors therein or omissions therefrom.

References:

  1. The process of big data solution adoption By Bas verheji.
  2. Case study on Rabobank by Hewlett Packard enterprise.
  3. Big data for all : Privacy and user control in age of analytics by Omer Tene and Jules Polonetsky
  4. Realizing the promise of big data implementing the big data projects by Kevin c Desouza
  5. Case study on Obama care big data problem on patient protection and affordable care act
  6. http://www.mckinsey.com/business-functions/business-technology/our-insights/big-data-whats-your-plan.
  7. http://www.businessofgovernment.org/blog/business-government/implementing-big-data-projects-lessons-learned-and-recommendations.
  8. https://hbr.org/2012/11/the-analytics-lesson-from-the.
  9. http://dataconomy.com/hadoop-open-source-software-pros-cons/.