14 sectors for applying Big Data and their input datasets

Jun 2, 2015

14 sectors for applying Big Data and their input datasets

Hello folks, 

In the vast majority of talks with clients and prospects about Big Data, we soon realized an astonishing gap between the business itself and the expectations of Data Analytics projects. Therefore, we carried out a research to respond the following questions: 

  • What are the main business sectors that already use Big Data?

  • What are the most common Big Data results per sector?

  • What is the minimum dataset to reach the results per sector? 

The summary is organized in the table below.

,Business type / sector,Raw data examples,Business Opportunities,, ,"1 - Bank, Credit and Insurance ","Transaction history. Registration forms. External references such as the Credit Protection Service. Micro and macro economic indices. Geographic and demographic data.","Credit approval. Interest rates changes. Market analysis. Prediction of default . Fraud detection. Identifying new niches. Credit risk analysis.",, ,2 - Security,"Access history. Registration form. Texts of news and WEB content.",Pattern detection of physical or digital behaviours that offer any type of risk.,, ,3 - Health,"Medical records. Geographic and demographic data. Sequencing genomes.","Predictive diagnosis (forecast). Analysis of genetic data. Detection of diseases and treatments. Map of health based on historical data. Adverse effects of medications / treatments.",, ,"4 - Oil, gas and electricity",Distributed sensor data.,"Optimization of production resources. Prediction / fault and found detection.",, ,5 - Retail,"Transaction history. Registration form. Purchase path in physical and/or virtual stores. Geographic and demographic data. Advertising data. Customer complaints.","Increasing sales by product mix optimization based on behaviour patterns during purchase. Billing analysis (as-is, trends), the high volume of customers and transactions, credit profile by regions. Increasing satisfaction / loyalty.",, ,6 - Production,"Data management system / ERP production. Market Data.","Optimization of production over sales. Decreased time / amount of storage. Quality control.",, ,7 - Representative organizations,"Customer's registration form. Event data. Business process management and CRM systems.","Suggestion of optimal combinations of company profiles, customers, business leverage to suppliers. Synergy opportunities identification.",, ,8 - Marketing,"Micro and macroeconomic indices. Market research. Geographic and demographic data. Content generated by users. Data from competitors. ","Market segmentation. Optimizing the allocation of advertising resources. Finding niche markets. Performance brand / product. Identifying trends.",, ,9 - Education,"Transcripts and frequencies. Geographic and demographic data. ","Personalization of education. Predictive analytics for school evasion.",, ,10 - Financial / Economic,"List of assets and their values. Transaction history. Micro and macroeconomics indexes.","Identify the optimal value of buying complex assets with many analysis variables (vehicles, real estate, stocks, etc.). Determining trends in asset values. Discovery of opportunities.",, ,11 - Logistic,"Data products. Routes and delivery points.","Optimization of goods flows. Inventory optimization.",, ,12 - E-commerce,"Customer registration. Transaction history. Users' generated content.","Increased sales through automatic product recommendations. Increased satisfaction / loyalty.",, ,"13 - Games, social networks and platforms (freemium)","Access history. Registration of users. Geographic and demographic data.",Increase free users conversion rate for paying users by detecting the behaviour and preferences of users. ,, ,14 - Recruitment,"Registration of prospects employees. Professional history, CV. Conections on social networks.","The person's profile evaluation for a specific job role. Criteria for hiring, promotions and dismissal. Better allocation of human resources.",,

Conclusions

  • The table presents a summary for easy understanding of the subject. However, for each business there are many more variables, opportunities and of course, risks. It is highly recommended to use multivariate analysis algorithms to help you prioritize the data and reduce project’s cost and complexity.

  • There are many more sectors in which excellent results have been derived from Big Data and data science methodology initiatives. However we believe that these can serve as examples for the many other types of similar businesses willing to use Big Data.

  • Common to all sectors, Big Data projects need to have relevant and clear input data; therefore it is important to have a good understanding of these datasets and the business model itself. We’ve noticed that currently many businesses haven’t been yet collecting the right data in their systems, which suggests the need pre-Big Data projects. (We will write about this soon). 

  • One obstacle for Big Data projects is the great effort to collect, organize, and clean the input data. This can surely cause overall frustration on stakeholders.

  • At least as far as we are concerned, plug & play Big Data solutions that automatically get the data and bring the analysis immediately still don’t exist. In 100% of the cases, all team members (technical and business) need to cooperate, creating hypothesis, selecting data samples, calibrating parameters, validating results and then drawing conclusions. In this way, an advanced scientific based methodology must be used to take into account business as well as technical aspects of the problem.