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Briefly Talking Business inTelligence INtervIew serIes

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Business Intelligence products have been around a long time, however in our view at DSA the opportunity for BI vendors could be on the verge of rapid expansion.

Two words explain why we believe this to be the case: “Data Democratisation”

Putting data and the ability to analyse that data in the hands of more departments and more people within those departments is becoming vital in staying competitive and relevant.

BI is at the sharp end of the Big Data Bubble. One of the challenges of Big Data is the process of “boiling down” the “noise” to extract data that can be meaningfully analysed. That’s where BI tools are starting to come into their own.

Big Data can pull data into manageable chunks that can then be extracted into BI tools for in depth, real time, meaningful analysis that will aid more insightful decision making.

The most simple and most widely used BI tool is the spreadsheet. In this data driven age with new forms of data that do not easily fit into a typical spreadsheet format, full BI products deliver a level of data insight that far exceeds what can be achieved in a spreadsheet. Quite simply, business’s that derive more insight from their data will thrive.

Choosing the right tool to democratise data in your own environment is possibly more important now than ever before. That’s why we are very pleased to publish this ebook compiling a group of interviews we carried out with some of the regions key BI players such as Tableau who have also supported the publication of this compilation e-book.

Each interview stands alone in its own right, but brought together this compendium provides valuable way to assess the strengths and focus markets for vendor’s offering.

We hope you enjoy reading the different viewpoints from each vendor and that it will help you fine tune your own thoughts as you decide which BI tool you are likely to try for yourselves.

Yours in Data and Storage Allan Guiam

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Once an aspiration for many people in ASEAN, the mobile device has empowered millions in Southeast Asia with a digital voice, and with that voice, a data footprint. Today, the world’s 7th largest economic region, with a vibrant population of more than 600 million joins the rest of the globe in the creation and consumption of data. By 2020, experts predict that there will be more data-bits produced than there are stars in our skies.

At Tableau, we believe that data analytics will be an essential skillset for a smarter, more connected 21st century. This is why we are committed to helping people see and understand their data.

Time and again, customers have validated our belief in an analytics application that is quick, easy and fun to use. In a Tableau commissioned research, we found that organisations which promote a data-driven culture, outperform those that don’t, by two to one. These data points keep us more committed than ever to enable people to answer questions, solve problems and derive insights from data by themselves. Ultimately, we aim to place that power in the hands of a much broader population, believing that the democratisation of data builds a better world.

Looking back, what started out at Stanford University more than 10 years ago in our bid to write software that would help people work with data analysis for better decision making has today benefited hundreds of thousands of individuals in over 150 countries.

Today, Tableau is proud to offer fast and easy analytics for everyone. The Tableau user comes in all shapes and sizes, ranging from start-ups to individuals and teams in multinational corporations, from government bodies to students working on school assignments. This is why we have offerings that are designed for everyone – including a Tableau Public that is available for free, Tableau Desktop that is also available on mobile, analytics served over the cloud in Tableau Online, and enterprise-grade analytics from Tableau Server. This way, users get incredible scalability that matches virtually every requirement.

In the same spirit, this ebook aims to offer readers a broad overview on what is available in today’s business intelligence space, while also serving as a testament to a group of organisations that continue to innovate and add value in the data analytics world. I would like to take this opportunity to express my appreciation to Data & Storage ASEAN for bringing these different insights together, and for shining light on the many perspectives of business intelligence trends, applications and stories.”

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1: WhaT’s The Difference BeTWeen Business inTelligence TOOls anD Big DaTa analyTics TOOls?

The main thing about working with data is the people. No matter how large or complex the data set, the key is what the user is trying to discover. At Tableau, we believe that everyone should, and can be empowered to be a user of data, not just data scientists. And to most users, there isn’t that much of a difference between Business Intelligence (BI) tools and Big Data analytics as long as the user is able to explore the data on hand, find answers, make discoveries, and make smarter decisions.

Having said that, the idea of Big Data is going mainstream because more people are recognizing the value of harnessing data that is all around them. From financial services to retail, people are finding that when complex data is visualised in charts or graphs. Also, if they can do so easily, they take the ‘Big’ out of Big Data, make data approachable, and enjoy the process of gleaning meaningful insights from it.

2: shOulD Bi Be puT in The hanDs Of all sTaff?

Data is recognized as ‘the oil of the 21st century’. We are finding more businesses, public sector organisations, and individuals increasingly turn to data to become both active contributors and beneficiaries across the Asia Pacific region. Keeping in mind how pivotal the role of data will be in fueling this smart region, we believe that - with the right safeguards and data governance policies in place - the more people in any organisation become fluent with using data, the more the organisation will stand to benefit from this data-first future.

Tableau helps people see and understand data. We have seen first-hand how self-service, user friendly data analytics and visualisation have reduced inefficiencies in organisations, and raised returns on investments - which explains why we champion the vision of ‘data for all’ with an almost-missionary zeal!

So yes, BI should be placed in the hands of as many people as possible. With Tableau, we are seeing how people of all levels can quickly analyse, visualise data and share information. Tableau users have diverse skill levels and work in all kinds of organisations, including fortune 500 corporations, small and medium-sized businesses, government agencies, universities, and non-profits. People are tapping Tableau for all kinds of use cases, including increasing sales, streamlining operations, improving customer service, managing investments, assessing quality and safety, studying and treating diseases, and improving education.

3: WhaT can a Bi prODucT achieve ThaT a spreaDsheeT cannOT?

Flash is certainly fast. Typically 10x faster than traditional disk systems. However, performance is not much use without control. You have to be able to allocate, manage and guarantee storage performance independent of capacity. Quality of Service control allows you to give applications the IOP performance they require, even in failure scenarios. To make the economics of Flash work, you also have to be able to share the infrastructure. The only way to achieve this is by being able to guarantee performance and eliminate the effects of ‘noisy neighbours’.

4: WhaT are The key cOnsiDeraTiOns When chOOsing a Bi prODucT?

We understand that different users may have different considerations, requirements and expectations from business intelligence and data analyses. For example, Tableau Desktop is useful for users who need all of their data at their fingertips, and would need to gather insights as quickly and visually as possible from large chunks of data. Tableau Server, on the other hand, enables sharing and collaboration of data across organisations to be quick and efficient, while Tableau Online is useful for organisations that are moving to the cloud and bringing their data with them.

This is why we offer a complete suite of

platforms all the way from trial versions to Server offerings. This enables users to scale forward or backward, without the worry of being constrained by limitations of conventional enterprise analytics and reporting platforms. Additionally, users have different skill levels. One key consideration for us at Tableau is that our offerings need to be easy to learn and use.

5: WhaT’s unique aBOuT yOur Bi Offering?

Tableau’s rapid-fire analytics is much faster. It is more collaborative and user-friendly at every step in the data workflow compared to more traditional BI tools. Tableau’s unique spirit of simplicity and visual aesthetics really delights our customers as they are easily able to get the answers they want.

Currently, more than 23,000 organisations rely on Tableau, and this ranges from massive organisations to small ones, as well as government bodies, individuals and students. This once again feeds into our vision of ‘data for all.’ Therefore, what is most unique about us – Tableau offers data analytics and visualisation that is suitable for almost anyone who needs to work with data.

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Business Intelligence (BI) is a subset of Big Data Analytics (BDA). Traditional BI tools allow you to scrutinise data based on your own hypothesis. The data needs to be standardised in specific format before it can be analysed. Perhaps the simplest form of BI is the spreadsheet. For example, you can create formulas to produce outcomes from a spreadsheet that has been fed a set of numbers.

With BDA, you can crunch structured and unstructured data in whatever format that the data is made available. And based on that, the BDA tool can generate a list of queries or questions for the user to choose from and decide what it wants to get out from the data. BDA helps you discover insights that may not have been possible previously.

The key difference is the output from BDA is comprehensive and incisive. BI gives you a dashboard view without the depth that can help make business decisions.

In summary, BI tools allow us to answer questions that we know i.e. “what are our KPIs from a data warehouse?”, while BDA allows us to answer questions that we do not know previously that we should be asking.


The question should be if we want our team to make better decisions. If the answer from the management team is a resounding yes, then the workforce needs to be trained on how to use the BI tools to differentiate the types of data and how data should be processed to produce desired results. But as stated previously, BI is a subset of BDA and in order for a team to get the most out of data, comprehensive solutions that process structured and unstructured data is needed.

This has implications for skills and training, as Big Data tools evolve they are becoming far more accessible to the average user. This means there is an opportunity to train staff to explore and discover data in Big Data - what data scientists primarily do today - in the same way that we have trained staff to use traditional BI tools, spreadsheets etc.


BI products can be customised to crunch massive amounts of data to provide useful information. With BI, you can draw correlations between different data sets, or between structured and unstructured data. For instance, you can analyse weather data from a myriad of sources including real-time data from IoT devices. And it can be done very quickly. A spreadsheet will not have the same speed as a BI product in handling large volumes of data, but spreadsheets are still a useful tool for presentation and manipulation of results from a BI tool.


The key considerations are easy to use, high performance and, sufficient functionality to cater to the user’s growing sophistication plus the same experience on desktop browser as well as mobile. You can tell that a user is becoming sophisticated from the type of queries that arise. IBM Cognos and SPSS are two examples of applications that give an extremely rich toolset for users to start small and scale as their needs and requirements grow.


The beauty about IBM is its broad and richly diverse range of product offerings that can cater to the casual user to the data scientist. Granted we did acquire many of the solutions but the great part is our ability to

integrate the solutions for enterprise use. For example, a casual user can use Watson Analytics as it is available online. More than 6,000 users from Asia Pacific have registered to use Watson Analytics. An enterprise or a business can opt to use either IBM Cognos or SPSS depending on their needs. A Malaysian telco, Celcom used IBM Unica to create targeted marketing campaigns.

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Many organisations have the equipment and expertise to handle large quantities of structured data, but with increasing volume and faster flows of data, they may lack the ability to “mine” and derive actionable intelligence. Big data analytics tools give managers insight into the business so they can make better choices.

Big data doesn’t always fit into neat tables of columns and rows. There are many new data types - structured and unstructured that can be processed to yield insight into a business. The idea is to “reduce” the data so that it can be put in a structured form. Then it can be meaningfully compared to the rest of your data and scrutinised with traditional or cloud business intelligence (BI) tools - by going a step further than Big data analytics tools to provide a deeper dive into data to enhance business performance.

Additionally, business intelligence tools aggregate data into levels so patterns can emerge to deliver greater insight into performance and enable organisations to be more efficient, agile and competitive. The challenge is deriving answers to business questions from available data so that decision makers can respond quickly to changes in the business climate.


Yes, however having multiple users and decision makers can pose challenges. The sweet-spot lies in how to ensure proper use and execution by all stakeholders without BI becoming a free-for-all, where everyone has access to analytics, and everyone is making decisions from different and inconsistent answers.

As such, it is important to have a solution that provides a secure, managed environment for users to load their own data, create their own analytics, and share their work. A centralised, cloud-based metadata layer such as Oracle BI Cloud Service ensures a single, consistent view of data. So even when users are uploading local and external content that isn’t curated, they’re

leveraging corporate definitions and standards in every analysis— because self-service analytics shouldn’t stand in opposition to security, quality and consistency.

With Oracle BI Cloud Service, self-service and governance go hand-in-hand, which means ‘Do It Yourself’ also means ‘Do It Right’. Sharing insights is also the quickest way to create transparency and drive change, as well as harnessing collective business acumen of your workforce and apply it to your top challenges.


Primarily, BI tools convert live data intelligently and offer insights. An agile analytics platform lets you quickly create compelling dashboards by using intelligent defaults and easy drag and-drop composition.

With BI, staff can easily share their work as they go, or share completed dashboards and applications, simply by putting them in shared folders. Roles and permissions provide fine-grained control of what people can see, ensuring they have access to content appropriate for them, even if the original analysis includes data outside its scope. Through the use of mobile BI, hot new insights won’t go cold while people are away from their desks. BI also empowers employees to work more directly with more data, which can radically reduce the resources needed.


Considerations will vary for different businesses depending on the size and industry. But as a basic level, there are three main areas to look at in order to determine the breadth and depth of a BI solution.

The first is to clarify and confirm the objectives of the solution and the sort of KPIs that should be achieved from implementing such a solution. Aside from the return on investment, decision makers can also consider other objectives such productivity improvements, increased

customer satisfaction ratings, cost reductions, amongst others.

The second area is looking into the company’s existing IT architecture and operational efficiency. This is to determine what extent of BI can be implemented without overwhelming those involved in the use of the solution. Last but not least, determining executive support and business involvement is key. All stakeholders should be in synch when it comes to goal setting, planning, developing and implementing the BI solution in order to ensure the earlier determined KPIs are met.


Oracle BI Cloud service is a comprehensive solution to create compelling business intelligence applications, combining the power of proven Oracle platforms with the speed, flexibility, and low cost of the Cloud. BI Cloud Service scales to empower users across your organisation, from small workgroups to the enterprise, making agile business intelligence accessible to everyone. Users enjoy an extensive analytics platform which offers an interactive interface with built-in guidance and tutorials so your team can be productive.

For HR executives, senior management, and analysts, the Oracle Transactional Business Intelligence (OTBI) Enterprise for Human Capital Management Cloud Service delivers advanced analytics, designed to share meaningful insight immediately. Focused dashboards are easy-to-use and intuitively invite users to explore data faster, resulting in a self-service model that eliminates the need to have custom one-off reports.

Users can also depend on Oracle Big Data Discovery, a single, easy to use product, built natively on Hadoop, which transforms raw data into actionable insight in minutes, without the need to learn complex products or rely on highly skilled resources.

Analytics are extended through the power of market- leading Oracle Database that is immediately available, predictable with system availability and patching so you have the latest features trouble free.

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Using an example to illustrate this, suppose my job is to analyse metrics associated with digital marketing programs.

We can run reports that give information like response hits and influenced leads. This ‘descriptive analytics’ gives answers to the question, ‘what has happened, where and when’. To probe deeper and understand ‘why’ customers don’t respond to promotional offers, ‘diagnostic analytics’ provides decision-makers the power to ask ‘why’ and ‘how’. Lastly, we can see ‘what is likely to happen’ through ‘predictive analytics’, enabling decision-makers to anticipate ‘what may’ occur. Business intelligence therefore allows you to understand your business data that is structured and not of huge volume or variety or velocity.

However, to provide more insights for campaign optimisation, I may need to analyse customer behaviors correlating vast volumes of data from point-of-sales systems. Big data analytics tools provide capabilities to gather and store the huge and fast-growing data from various sources into one place and help you find the questions that you might not think of.

Data is nothing more than a source. Rather than having different tools to support decision making, what is needed is a platform that gives everyone the ability to explore data, even big data, and ask questions across all their data sources.


Most people are aware of the term “The whole is greater than the sum of its parts”, quoted by Aristotle, one of the greatest philosophers of all time. This is because it suggests that people working together are routinely able to do more work than if their individual efforts were added together.

Now, think of the decisions being made in your organisation every day. There will be multiple levels of seniority, from senior executives, through to people on the front-line. Everyone is making all kinds of decisions on a daily basis. High-level strategic decisions will be made by executives and cascaded down the ranks in the organisations where more decisions will be made to drive actions. We will also need to allow people of any ranks to make the hundreds of micro-decisions required to do their jobs – without having to be told what to do. The reason is simple. In the time it takes to obtain permission to act, too often the opportunity is lost.

Therefore, giving everyone in the organisation the ability they need to make the smart, accurate, timely decisions makes a huge difference to your organisation, especially when you consider the collective impact.


The key advantage over spreadsheet reporting is BI’s ability to provide solutions that address user requirements for spreadsheet reporting (and a whole lot more), while giving IT control and manageability over the data. Users can move away from manually creating and maintaining the spreadsheets by hand, as this is extremely error-prone. BI allows users to continue to access and work with enterprise-wide data through a common data-model and governance framework, increasing control, accuracy and reliability of data.

While self-service is often the reason users turn to spreadsheets, they ignore the fact that they have to continually maintain the spreadsheet on a regularly basis, updating the data and formulas. Without a data audit trail and the lack of control, it is impossible to manage the risk of inaccurate or inconsistent information being distributed within the organisation, posing major compliance risks.

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Spreadsheets only provide finite sets of metrics or information from a limited set of data. They lack the visualisation and discovery capabilities that a BI product can provide, restricting the process of making discoveries through iterative exploration of data and the ability to deliver timely insights. BI provides a set of techniques and tools to make analytical results easily available to decision makers as well as operational workers, and empowers everyone to see a bigger picture of their data.


There are plenty of BI tools out in the market to choose from, and most will try to entice you with nice-looking visualisations and ease-of-use capabilities. Don’t be swayed by pretty pictures alone, remember that this is a working platform that people use as part of their day-to-day activities that can be an analyst at their desktop, an executive in a car, a management team in a boardroom or an employee who is using a workflow application day-to-day.

When choosing a BI product, it is important to select one that can support a range of scalable BI use cases to meet your differing needs across the business, leveraging common data and security models, in a common governance framework. Key considerations include:

• Dataintegrationandmanagementtoenable complex, scalable analysis

• Dataindexingtosupportfreeformexploration and discovery

• Governanceandsecuritytoprotectdataintegrity

• Libraryofmodernvisualisationstovisualise data

• OpenandstandardAPIaccesstofullcapability to extend and embed

• Toolkitstoaccelerateandsimplifydevelopment and creation

• Choiceofon-andoff-premisedeployment models

Lastly, users should have access to a broad ecosystem and community to inspire innovation.


Qlik brings a completely different perspective to BI through a unique and innovative platform approach, giving everyone the ability to answer not just ‘what happened’, but ‘why’ and ‘what will happen’.

Qlik supports a full spectrum of business intelligence and analytics use cases with centrally deployed guided analytics, self-service data discovery and embedded analytics in any enterprise or web application. Qlik provides agility for the business from upstream data sourcing and preparation, through to visualization and advanced analytics, and onto downstream collaboration and reporting but importantly, all within a governed framework that drives enterprise scalability and trust for IT.

Built on the patented QIX Associative Data Indexing engine, the Qlik Visual Analytics platform allows people to probe all the possible associations that exist in their data, across all their data sources, to see the whole story instead of just the partial views offered by query based or hierarchical tools from other BI vendors.

Beside innovative technologies, Qlik also brings to market specific industry and functional-level experience, delivering exactly what the customer needs to address their challenges. With over 35,000 delighted customers, Qlik regularly ranks at the top of independent surveys on customer satisfaction and value derived from the solutions it provides.

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In short, Business Intelligence 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.


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.


To me, both BI product and spreadsheet can be happily co-existing. It all depends on the need of the business users. If the business users are mainly performing descriptive analysis, then spreadsheet would be sufficient to meet their expectations, however, if the users would like to move from descriptive to predictive and /or prescriptive analysis, then the proper BI tool would be more appropriate for this purpose.


Below are some tips to look out for when choosing a BI product.

Key business problems to address

In general, users always at product 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,

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. Regardless of how good the BI product, 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.


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 organisation 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.

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Business Intelligence delivers quick and easy creation of multi-dimensional analysis of data, supports slice and dice, trending, forecasting and other analysis capability. Big Data Analytics involves data mining and predictive analytics to use past data for building model to explain the data and thus to predict specific parameter of new data. Big Data also includes Text Analytics on unstructured data where Natural Language Processing (NLP) deriving high-quality information from the text. Text Mining tasks include Text Categorisation, Concept/Entity Extraction, Sentiment Analysis, Document Summarization, and Entity Relation Modelling.

Big Data Analytics is a set of linguistic, statistical, and machine learning techniques that model and structure the information content of numerical and textual sources for Business Intelligence, therefore Business Intelligence and Big Data Analytics are highly related and are capable of supporting each other.


Business Intelligence supposed to be a tool for business user, it must be user friendly enough that most users can handle the tool with minimum training. While BI is a powerful tool for users to gain insight into their data, organisation may wish to limit the access of the tool to users who are trusted for such information in the organisation, otherwise constraints can be put to limit the kind of information and analysis that can be performed by individual users.

Some BI supports advance real world multi-dimensional view into the data where the output must be correctly interpreted to avoid misunderstanding of the data, such analysis should be put into the hand of users with proper training or proper understanding of the analysis.

So, it is really the choice of individual organisation, but if BI is put in the hands of

all staffs, then proper access right and security on the dimension and data must be setup, and training must be provided where necessary.


Many organisation who implemented Balance Scorecard started with spreadsheet, but later found insufficient on the tool for auto consolidation, tracking, and management of the performance indicators and therefore implemented BI for their Performance Management. Other aspect of BI like the ability to handle arbitrary timespan, performing cross subject analysis, handling huge datasets (billions of transaction), performing real time or near real time update from data source, providing Responsive Dashboard, more options for 2D/3D charting with annotation capability, alert and notification, those are just some of the example of features that spreadsheet cannot handle.

Advance BI with Big Data Analytics support also enables sentiment analytics and predictive analysis that spreadsheet cannot provide.


Ease of implementation - BI product must be easy to implement, because the easier the implementation, the easier shall the future maintenance and enhancement be. Buyers should hold a benchmarking test or a proof of concept to let BI vendors to demonstrate how easy to build the features needed by them. Through this, the buyer can be assured that the tool is suitable for their environment, performing the analysis that is needed, and is practical and usable for their organisation. Standard vendor demonstration may look nice and easy, but may later found not able to deliver same quality of work.

Scalable – BI with Big Data Analytics allows buyer to further implement Big Data when they are ready. Advance BI system also equipped with subsystems like Knowledge

Management, Performance Management and Enterprise Reporter. BI with workflow and web application framework scale the system to allow building of simple application for capturing data otherwise not available in current IT systems.

Support – How strong is the local presence and experience of the local team in implementing BI and their ability to provide consultation and advice for best practices to ensure high quality of implementation work.

Price – look at total cost of ownership.


Speedminer is more than a BI system that comes with Big Data Analytics, Knowledge Management, Performance Management, Help Desk, Social Network, Web Application Framework (with Application Builder and Workflow Engine), Integration Platform (able to establish bi-directional communication with other application) and other subsystems.

Speedminer Responsive Dashboard supports device of any resolution and optimise the display of information for best viewing experience.

Speedminer is CUBELESS in architecture and therefore easier to implement, and while it supports drill through like other BI product, it further provide navigation on the details transaction which cannot be supported by other BI and such feature allows Speedminer user to perform root cause analysis with greater confidence.

Speedminer is the only true real time BI with application built using the embedded Application Builder, meaning once data saved, the BI get updated concurrently with no separate process needed to update the CUBE.

Speedminer Integration Platform may act as the middleware to link up systems (internal and external) in any organisation, and as the data travels between the systems, Speedminer is capable to keep a copy of the data for analysis and therefore avoid the need to explicitly performing ETL to extract data again.

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