Home Asia-Pacific III 2015 Big Data is transforming the way we live, work and think

Big Data is transforming the way we live, work and think

by Administrator
Heru SutadiIssue:Asia-Pacific III 2015
Article no.:4
Topic:Big Data is transforming the way we live, work and think
Author:Heru Sutadi
Title:Executive, Director
Organisation:Indonesia ICT Institute
PDF size:362KB

About author

Heru Sutadi is both Executive Director for the Indonesian ICT Institute and a University lecturer.
He is involved in ICT research in Indonesia for example: spectrum pricing, cloud computing, m-commerce, cyber security, Big Data and social media.

For ten years he worked for various telco companies in Indonesia and other countries. Heru was then elected as Commissioner of the Indonesian Telecommunication Regulatory Authority in 2006, reelected for a second term in 2009 until May 2012.

Heru Sutadi graduated in electrical engineering, he then continued his studies taking a MSc in Communication and then PhD Electrical Engineering. He also took the Executive Program in Graduate School for Business in University of Cape Town, South Africa. In addition to his formal education, Heru has followed various courses and training e.g.: spectrum management, interconnection, mastering the internet, and regulation about telecommunication, broadcasting and online media.

Article abstract

The presence of Big Data transforms how we live, work and think; across various sectors such as: financial services, e-commerce, telecommunications, government, political activity, including the transportation sector.

Full Article

What is Big Data?

Big Data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze. This definition is intentionally subjective and incorporates a moving definition of how big a dataset needs to be in order to be considered ‘Big Data’. The definition varies by sector depending on what kinds of software tools are commonly available, and what size of datasets are common in a particular industry.

Big Data includes, for example, Hadoop [a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation].; and SQL [Structured Query Language) a special-purpose programming language designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS).

With Big Data there are four main dimensions, which include: volume, velocity, variety and veracity. By ‘volume’, data grows in all types. Every day in the world there is 2.5 quintillion bytes of data (2,5×1018) bytes. Regarding ‘velocity’, Big Data can analyze 500 million call records in real-time to predict customer churn. Concerning ‘variety’, at present, data consists of two major parts, ‘structured’ and ‘unstructured’: as text, sensor data, audio, video, streaming, and log files. The final dimension is ‘veracity’. One in three business leaders in Indonesia do not trust their information to make decisions. How can they act on the information that is hard for them to believe? Building trust in Big Data is a major challenge as the current diversity and number of sources increases.

The presence of Big Data transforms how we live, work and think; across various sectors such as: financial services, e-commerce, telecommunications, government, political activity, including the transportation sector.

Imagine, if before we traveled using vehicles, such as private cars, in many large cities, congestion inevitably occurs. With the development of GPS technology, which is mated with the analysis of Big Data, congestion can be predicted from the traffic patterns of a city which, in turn, can be downloaded via mobile applications such as Google Maps or Waze apps.

In an election for governor or president, the hectic social media where every person writes an opinion and needs to be heard, voters’ concerns, voting intentions and candidates can be processed with Big Data analysis, for example, the recent Presidential Election in Indonesia. The President Joko Widodo was predicted to win only a few days before the election, with the use of Big Data analysis, monitored on social media such as Twitter and Facebook.

For telecom operators, the use of Big Data is also very useful. Operators in countries with large numbers of prepaid users, can gauge which regions spend the most money ‘topping up’, including date, time, place, amount.

With E-commerce service providers, Big Data can be used to look at peak times visitors stop by to shop online, what items are viewed and/ or purchased, to determine the kind of goods to hold in stock, and prices, the e-commerce service should provide.

In the health sector, Big Data can analyze outbreaks of diseases and possible population movements of people affected by the outbreak.

Big Data can also be used to precisely determine the type and level of criminal activity, and predict future patterns.

Key success
Big Data is not, essentially, a technology problem but rather a ‘business problem’. That is, first, how to create business value. Improving the quality/ accessibility of enterprise data is not an end-in-itself. Data strategy must be driven by an understanding of how information can enable or improve business processes. Data strategy does not need to identify all possible business benefits, but it should at least define several that are material to the business in question and measurable.

For most businesses, data is an active asset that is captured, created, enhanced and used in many business processes and applications. To manage this dynamic environment, the data flows across systems and processes needs to be organized in a coherent way. These capabilities organize technology platforms and business processes based on their function in the ecosystem: capturing and creating data cleansing and organizing it, mining business insights from it, and using intelligent reviews whose insights drive actions in the business.

Beside which, we have to identify critical asset data where not all the data in the business is ‘critical’. In fact, most data is specific to an application, business function or transaction. Last but not least, data governance and strategy is not a mere ‘project’, but rather a continuous company function


There are very many Big Data challenges, the first of which is how to protect the data, especially relating to privacy. Currently, in various analyzes, the data used is the company’s internal data, or open data such as data on social media, financial statements of companies on the stock exchange and so forth.

Many countries already have rules on data protection but many do not. What needs to be agreed is which data that is not open to the public including, say, social media tweets or profiles on Facebook, although data in instant messaging or SMS, is actually data that is private and cannot be used in Big Data analysis.

The other challenge for the future is knowing how fast and in what form data will grow, and coping with ‘unstructured’ data (or unstructured information) that is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well.

Very many sectors can benefit from the presence of Big Data, which can produce a ‘victory’ for those who use it, such as the political world, online commerce, health, financial and other services. However, looking ahead, there are also many challenges respecting data protection and privacy, as well as large volumes of unstructured data.


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