Analytics helps Asean organisations read between the lines

Asean organisations need to develop an enterprise-wide approach to analytics and draw on customer insight if they are to maximize the business value of data

Asean organisations need to develop an enterprise-wide approach to analytics and draw on customer insight if they are to maximize the business value of data

In 2013, Accenture reported that 96% of Asean business leaders were com­mitted to the adoption of analytics or fact-based decision-making, sur­passing their counterparts in the UK (86%) and the US (85%).

However, close to 90% of Asean companies indicated that analytics is not yet deeply ingrained as an inte­grated, enterprise-wide approach. TechTarget takes a closer look to find out more about the state of analytics adoption in Asean businesses.

Nine days was all it took the National Library Board (NLB) of Singapore to generate in excess of 130 million “related articles” for more than 1.5 million newspa­per articles in the NewspaperSG collection of historic Singapore newspapers.

The NLB was able to automatically identify the related articles using a Hadoop cluster through the text analytics (TA) component of its big data programme. Had it done this manually, it would have taken 992 man-years, assuming it takes a person who works 42 hours per week one minute to identify each of the 130 million relationships, according to Kia Siang Hock, deputy director of technology and inno­vation at NLB Singapore.

The NLB consists of one national library, one national archive and 26 public libraries. According to available fig­ures from 2010 – now probably modest compared with today – two million members visited the libraries 36 mil­lion times to borrow from a collection of 1.5 million titles and 8.5 million items. In that year alone, the libraries processed more than 33 million loans and had more than 8.1 million digital user visits and 47.4 million e-retrievals.

Kia reveals that the NLB’s big data architecture consists of its enterprise data warehouse; data marts; extract, transform and load (ETL) tool; in-memory dashboards (including collection, patron and production dashboards); advanced analytics (including recommendation engine, collection planning, demand analysis and geospatial ana­lytics); content analytics; and text analytics.

Strategically, the NLB big data programme analyses and optimises the network of libraries nationwide, exam­ines the use of these libraries and how well they are serv­ing the community, and looks at how new libraries affect existing ones.

At the tactical level, it optimises the allocation of the limited collection development budget by finding the balance between the budget and demands at the library branches and collection categories, the costs and differing aging rates of the library materials and other parameters.

“Corporate KPIs [key performance indicators], human resource and finance dashboards are used to monitor trends and support decision-making,” says Kia.

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Future-proofing libraries

Operationally, demand forecasting tools are used to find out the number of copies of new and top-up titles to be purchased for each library branch, he adds. “Library branches use the analysis to adjust their programming, according to the local user and usage patterns observed,” Kia explains.

The NLB is working towards further collaboration with government agencies – including the Singapore Land Authority, Infocomm Development Authority of Singapore and the Ministry of Finance – on data sharing and domain expertise. It is currently exploring the effec­tiveness of data mining from the internet to better under­stand user behavior and proactively meet their needs.

“We are also looking at leveraging on the advances in multimedia content analytics, including image, audio and video analytics, to enhance the discovery of Singapore cultural and heritage content,” says Kia.

Early in the game

Lee Joon Seong, Asean advanced analytics lead at Accenture Digital, says Asean businesses are adopting two main classes of data and analytics capabilities. These are data manage­ment capabilities to manage data, and data science and ana­lytics capabilities to generate insights from data.

Data management includes the ability to ingest, orga­nise, store and manage data integrated and transformed from different sources. Traditionally, this includes data integration or ETL technologies and enterprise data warehouse and metadata management tools.

However, new big data technologies that enable inges­tion, storage and processing of large amounts of both structured and unstructured data at speed are stealing the limelight.

“Many Asean businesses already have some data mart or data warehouse and data integration in place. However, they are finding it increasingly difficult to scale these plat­forms to cater to the need to analyse rapidly growing and much more diverse sets of data cost-effectively,” says Lee. He points out that many have begun to revamp their data architecture to incorporate big data technologies.

On the other hand, the use of data science and ana­lytics tools to discover useful insights from data has also become more prevalent among businesses. The types of analytics include:

• Descriptive analytics, which focuses on answering “what happened” typically refers to the BI/reporting/visualisa­tion tool capabilities that provide users with drill-down/ slicing-and-dicing capabilities to analyse data;

• Predictive analytics, which focuses on answering the question of “what’s next”, where an example would be tools that enable the application of statistical tech­niques, such as regression analysis to predict propen­sity to purchase;

• Prescriptive analytics, which focuses on answering the question of “what is the best possible option”, where prescriptive analytics is built on top of predictive and descriptive capabilities;

• Cognitive analytics, which enables dynamic self-learn­ing/machine learning/natural language processing to perform complex reasoning and decision-making.

 Lee says Asean businesses are experimenting with big data technologies and prescriptive analytics technology, with very few exploring cognitive analytics.

 It may still be in its early stages, but he believes the use of this type of technology will gradually become more prominent as companies look at ways to industrialise and automate intelligent, insight-based decisions.

Michael Barnes, vice-president, research director at Forrester Research, observes that businesses across Asean increasingly view customer insights as a strate­gic asset and believe they are customer-centric.

“However, most are still hindered by ingrained approaches to business operations and corporate strategies that too often focus on products, not cus­tomers,” he notes.

 To sharpen analytics-related strate­gies, Barnes recommends the following:

• Link specific customer insight and analytics initia­tives to clearly defined, measurable business goals such as improving customer service and experience, driving revenue, increasing productivity, reducing costs and implementing sustainable continuous improvement strategies.

• Expand basic reporting capabilities. Delivering con­sistent, reliable, trusted data via timely reporting is only the most basic of requirements for driving insights and enabling a successful digital busi­ness model. Ensure that the data being captured, integrated and accessed is directly relevant to improving customer engagement and ultimately the customer experience.

• Move beyond traditional approaches to data manage­ment and access. Organisations must evolve beyond basic approaches to data management and access based purely on data governance and compliance mandates. Manual, aggregated and ad hoc reporting and data requests are not sufficient to deliver on a strategic digital vision.

This was last published in September 2015

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