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The 3 myths of social network analytics

Madura McCormack | Sept. 14, 2012
There’s more to it than just mining and analysing

Social network analytics have been a major game changer in businesses, but with every trend comes issues. According to Dr Kam Tin Seong, associate professor for Information Systems at Singapore Management University, when it comes to social network analytics, the same problems of data integration and data consistency are ever prevalent.

During his presentation at the Business Intelligence Asia Pacific Summit 2012, Dr. Kam shared what in his opinion are the "3 Myths" that plague social network analytics. The two-day summit held in Singapore was conducted by the Global Science & Technology Forum, a specialised training and certification provider and independent promoter of global research and development across multiple fields of science and technology.

Even though data analytics is available for business to leverage on the power of social media, translating that into dollars is not as simple as it sounds, says Dr Kam.

Myth 1: Data analysis is easy 

"Businesses don't get value from these analytics. On a social network you may see your advertisement get 10 likes or even a 100 likes. But so what? Likes don't generate sales," Dr Kam said.

The trick is to convert and use this data knowledge by identifying the major influencer that is apparent in every network cluster of social media.

Dr Kam noted that by finding a social media user with a strong follower base, businesses will be able to tap in and convert the analysis into dollars.

"Did you know that research shows most doctors are left-handed? Yet [medical] software developers don't make their applications with this in mind. It's all about making the technology effective."

The idea is to engage the end-user says Dr Kam. Businesses need to be driven to use analytics by making it simple, something he dubs "every day analytics".

"There are two types of analytics in my opinion; day-to-day analytics to understand sales and customers, and complicated analysis that may require PhD knowledge. For day-to-day analytics, you don't need an IT specialist to claw the data for you."

The associate professor notes that time and training is needed when doing data analysis but making analytics simple for the end-user will ultimately drive business.

Myth 2: Analysts don't need time to think

Analysts work at the speed of thought, not the speed of light. Drawing from his experience working for the financial sector, Dr. Kam has a message for C level executives, "We can't give you the analysis yesterday."

"Most of the time they [C level executives] want the analysis not today, or tomorrow but yesterday. Businesses need to understand that data analysis, to even gather the relevant data will take time."

Myth 3: You can buy an off-the-shelf BI system 

 

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