Queenie Wong, Head of Data Management Centre of Excellence, SAS Malaysia
SAS Institute is arguably one of the most well recognized names in data analytics world having started out in 1976. It was started as a project called Statistical Analysis System (SAS) at the North Carolina State University to create a "statistical analysis system" that was originally used primarily by agricultural departments at universities in the late 1960s.
Today it is the world's largest privately held software business and its software is used by most of the Fortune 500. Data & Storage Asean spoke to Queenie Wong, Head of Data Management Centre of Excellence, SAS Malaysia, to get the company’s perspective in the growing big data analytics market in Malaysia and ASEAN.
DSA – What’s the difference between Business Intelligence tools and Big Data Analytics tools?
Queenie Wong: In short, Business Intelligence (BI) taps into the relational database (which is mainly an internal relational data source) for analysis and the data is being processed at the summary level. Whereas Big Data analytics usually comprises of both internal and external large data set for analysis. The data sets are both structured and unstructured and usually are untapped by conventional BI tools.
DSA – Should BI be put in the hands of ALL staff?
Queenie Wong: Theoretically it should be that way, as different levels of people require different levels of data sets and all levels of decisions will have a collective impact on an organisation.
DSA – What Can a BI product achieve that a spreadsheet cannot?
Queenie Wong: To me, both BI product and spreadsheet can happily co-exist. It all depends on the needs of the business users. If the business users are mainly performing descriptive analysis, then spreadsheets would be sufficient to meet their expectations. However, if users would like to move from descriptive to predictive and /or prescriptive analysis, then a proper BI tool would be more appropriate for this purpose.
DSA – What are the key considerations when choosing a BI product?
Queenie Wong: Below are some tips to look out for when choosing a BI product.
Key business problems to address
In general, users always select products based on features and functions without fully defining the business problems to address. Apart from that, there is no one single silver bullet to address all the business challenges, and therefore, prioritising your business problems is essential.
Getting business users buy-in
The owner of the BI product would be the business user, not the IT/MIS department. Therefore, it is essential to include business users in the tool evaluation process. Regardless of how good the BI product is, it would be ineffective if business users are not utilising it in their day to day operations.
Easy to use and maintain
A good BI product comes with a short learning curve whereby users can adapt to it easily, and at the same time, it should be easy for the IT/MIS to maintain. Therefore it should be server-based with a thin client architecture.
Able to scale up and out
Not all BI products can scale well. The growth of the business and the data that is generated with that growth would ultimately impact to the performance of analytics tools. Therefore, it is important to ensure that the selected BI product can scale both up and out for future expansion.
DSA – What’s unique about your BI offering?
Queenie Wong: SAS Visual Analytics provides a complete platform for analytics visualisation, enabling you to identify patterns and relationships in data that weren’t initially evident. Interactive, self-service BI and reporting capabilities are combined with out-of-the-box advanced analytics so everyone can discover insights from any size and type of data, including text. Users of all skill levels can visually explore data on their own while tapping into powerful in-memory technologies for faster analytic computations and discoveries. It’s an easy-to-use, self-service environment that can scale on an enterprise wide level. It’s designed for anyone in your organization who wants to use and derive insights from data – from influencers, decision makers and analysts to statisticians and data scientists. It also offers IT an easy way to protect and manage data integrity and security.