Big Data Issues Challenges

TABLE OF CONTENT

1.0 INTRODUCTION 2
2.0 THE OBJECTIVE 2
3.0 THE ANALYSIS OF ISSUES AND CHALLENGES 2
3.1 THE ENABLER FOR ORGANIZATION 2
3.2 THE END USE 3
3.3 THE SKILLS 3
3.4 THE CAPABILITIES 4
3.5 THE DATA SOURCES 4
4.0 RECOMMENDATION 5
5.0 CONCLUSION 6
6.0 REFERENCES 7
CHALLENGES AND ISSUES IN IMPLEMENTING BIG DATA IN MALAYSIA

1.0 INTRODUCTION

Every organizations are now speaking about Big Data and some has made it into practice. The initiative of harnessing data to amplify capabilities in achieving organizational objectives has made it possible for practitioner as well as senior management in their decision making.

In Malaysia, the culture of acceptance of Big Data to help in decision making is agreeable. However, the infrastructure for adoption are not fully understood. Nevertheless, the public sector and private sector are in the transition phase from curiosity and enthusiasm to buy in and across startup.

2.0 THE OBJECTIVE

This report illustrate the issues and challenges in more detail of Big Data in Malaysia respectively.

3.0 THE ANALYSIS OF ISSUES AND CHALLENGES

Every organization has the opportunity to leverage big data to its advantage – to drive accurate and timely decisions that can materially affect its business and organizational goals. Following are the challenges in implementing Big Data:

3.1 THE ENABLER FOR ORGANIZATION

Some of the organizations viewed that their data assets has effectively deriving tangible benefits. From a discussion with Greg Whalen, Pivotal’s Chief Data Scientist, Asia Pacific and Japan, revealed that many organizations are using their existing data to identify new correlations which provide profits through their manipulation of data history. Some of the managers believe they are already using their data assets effectively with the newer approaches in data mining, exploratory visualization and comparison across external data sets may shed new light on unknown opportunities. Studies shown that organizations has increasingly spend 25 percent on big data in 2014 compared to 2013 as a turning point investment. Meanwhile, the ICT industries has an increasing technical fragmentation in big data, therefore, there are opportunities for boutique firms in Malaysia to meet specialist technical needs for global market.

3.2 THE END USE

Due in improving customer service and experience, retention, acquisition, cross-sell and up-sell to be competitive in the industry, big data was held important for businesses. In fact, the end use for big data are used to monitor the high ranking of social trends of social data. Therefore, the adoption of big data initiative projects readiness in Malaysia are high amongst managers rather than the personnel. Studies shown that the interest of big data is primarily driven by improving business growth and profit instead of efficiency. This also means that the managers need to align the external and internal goals of having big data. For example, the approach in the change of product and service development would need data to increase operational efficiency. Managers might value to think of risk management, customer retention, customer acquisition, product and service innovation, customer behavioral profiling and forecasting supply and demand. On the other hand, the the practitioner had more focus on internal operation, such as supply chain monitoring and infrastructure and asset monitoring. Therefore, the business need to understand the needs of applying big data in order to reduce cost while increasing the value.

3.3 THE SKILLS

The vary of organizations helps to produce sustainable Big Data ecosystem in desired skills acquisition between ICT and non-ICT. Generally, ICT firms are participating in big data ecosystem as solution providers while the non-ICT firms are developing deep analytical capabilities pertinent to their business needs. Some organizations may participate in the Big Data ecosystem as both producers and consumers, especially organizations in the ICT and marketing services sectors.
However, due to the hype surrounding techniques there is a need to also promote the value of fundamental applied math and statistics skills. The most commonly desired skills are special data analysis, modeling and simulation. The intermediate and advanced are the following skills:
a) Big and Distributed Data (eg. Hadoop, MapReduce)
b) Algorithms (eg. computational complexity, CS theory)
c) Machine Learning (eg. decision trees, neural nets, SVM, clustering)
d) Back-End Programming (eg. JAVA/Rails/Objective C)
e) Visualization (eg. statistical graphics, mapping, web-based dataviz)

3.4 THE CAPABILITIES

Capabilities are desired characteristics and features that may encountered by many industry segments, be applicable to multiple end-uses and/or rely on some combination of skills to produce and consume. In order to help the organization to perform analyses and make decisions much more rapidly, they need to go through the sheer volumes of data. Capabilities are beyond having the access to the data - it is the handling of the data that creates value.
The top three priority is to have a real time insights from realtime data streams, uncovering patterns (e.g. segments, correlations) from multi-structured datasets, and data visualization to reveal patterns and trends.
To do so, it takes lot of understanding to visualize the data as part of your analysis in a right shape. For example, if the data comes from social media content, you need to know who the user is in a general sense - such as a customer using a particular set of products - and understand what it is you’re trying to visualize out of the data. Without some sort of context, visualization tools are likely to be of less value to the user. One solution to this challenge is to have the proper domain expertise in place. The organization need to make sure the people who analyze the data have a deep understanding of where the data comes from, what audience will be consuming the data and how that audience will interpret the information.

3.5 THE DATA SOURCES

Data is the new oil. The wider social and business impact of Big Data initiatives is dependent on the quality of data and information on which they are based. The value of data for decision making exist only if the data is accurate and timely. It becomes even more pronounced when considering the volumes of information involved. The organization need to have a data governance or data management in place to ensure the data is clean. The data source would help most organization in getting started by combining the internal and external data source.
Data environments whether open or proprietary are becoming increasingly complex as new business models emerge. In Malaysia, complex social problems such as traffic congestion or local crime could be addressed by open data and crowd-sourcing approaches, in both diagnosing problems and in providing feedback mechanisms from citizens. Open data from government sources support the big data ecosystem especially for social problems that have many dimension. For example, access to health care is also a function of transportation, location and income. Ministries can utilize internal data management for service optimization. However, the weariness of the bureaucracy and red tape would be a hindrance to to the big data initiatives. Moreover, the issues of privacy, resource cost and perception of Malaysian professionals to have public cloud.
There also a concern on organizations which only have a single source of data rather than combining the internal and external sources. External sources may benefit some organization in getting started with the basic goal of identifying data sources. For example, respondent who were likely to highly-value their customer behavioral profiling will depend on third party data such as social media.
In addition, the public cloud vendors need to identify and address the concern of privacy, resource sot, and perceptions specific to Malaysian professionals.

4.0 RECOMMENDATION

1. To foster a data data-centric organizational culture, where organizational processes, decisions, and objectives are supported by leveraging a variety of data resources.
2. To equip the organization to manage and make decisions based on real-time data, in order to remain competitive.
3. To cultivate multi-disciplinary data science teams, consisting of specialists with advanced skills in mathematics and statistics, domain experts, and others.
4. To combine data from internal sources within organization together with external sources (including public and proprietary third-party sources) to spur innovation.
5. To create a ‘data dictionary’ of data assets relevant to your organization, to minimize friction for data science projects across the organization.
6. Large organizations, and especially government, should make concerted efforts to release data to the public in machine consumable form to unlock value hidden within their data assets.

5.0 CONCLUSION

This report shows the progress level of maturity of Big Data analytics in Malaysia. Even though the support was there at the executive levels, however, the aspirations and enthusiasm for Big Data is not uniformly matched with resource commitment. There are several inhibitors that prevent organization in making headway with Big Data projects, including recruiting the appropriate skills, the ideation of novel, high value use cases and access to datasets.
Big Data involves not just technological evolution, but a cultural shift in the way of work and share knowledge. These values include an increasing emphasis on transparency and continuous innovation. The advent in Big Data brings rapid change but also emphasizes interest in classical disciplines such as mathematics and statistics. The pitfalls in the use of Big Data should be at the forefront in considering adoption.
The privacy rights of individuals will also constrain how organizations collect, manage and distribute data. However, we believe the productive value of Big Data analytics in societies outweighs drawbacks if these concerns are respected and managed carefully. We have no doubt that the Big Data analytics industry will continue to mature and evolve in unexpected ways in Malaysia that will support the nation’s competitiveness in the global economy.

6.0 REFERENCE

1. Big Data In Malaysia : Emerging Sector Profile 2014. (2014) (1st ed.). Malaysia. Retrieved from http://bigdataanalytics.my/downloads/big-data-malaysia-emerging-sector-profile-2014.pdf
2. Addressing Data Volume, Velocity, and Variety with IBM InfoSphere Streams V3.0. (2014) (1st ed.).
3. Sas.com,. (2014). What is Big Data?. Retrieved 5 December 2014, from http://www.sas.com/en_us/insights/big-data/what-is-big-data.html

References: 2. Addressing Data Volume, Velocity, and Variety with IBM InfoSphere Streams V3.0. (2014) (1st ed.). 3. Sas.com,. (2014). What is Big Data?. Retrieved 5 December 2014, from http://www.sas.com/en_us/insights/big-data/what-is-big-data.html