APAC buyer’s guide to BI and analytics

Use cases

Data analytics as used in businesses today typically refers to the ability to discover, interpret, extract and communicate meaningful patterns in data, according to Bipin Singh, senior director of product marketing at Tibco.

“A large number of people use the term visual analytics where they leverage computer programs to graphically interpret that data and its relationships. Business intelligence is often used in advanced analytics to predict future conditions and make data-driven decisions,” he says.

When applied to specific business domains, data analytics could ease day-to-day business operations like finance and sales reporting, or data-driven decision-making in areas such as human resources (HR) as more organisations realise the need to understand, retain and maximise the potential of its people while managing cost, says Shenoy.

CK Tan, director for strategic client advisory at Qlik Asia-Pacific, sees most use cases for BI and analytics focused on achieving customer excellence by providing analytics to front-line employees to ensure they are well-equipped with real-time, actionable insights.

“We are also seeing increased adoption of use cases that involve organisations providing data and insights to their clients, suppliers and community through public portals,” he adds.

Key considerations

The ability to create business value from BI and analytics tools is critical, though this requires the tools to be organised around enterprise information pipelines, according to IDC.

For one, they must integrate with broader data and business processes and systems that make up the pipelines. That includes integration with products of other suppliers, and fitting into existing workflows with minimal retraining and onboarding.

“Both LOBs and IT need to play well in this team sport of BI and analytics, so that a balance can be achieved between agility and control in an enterprise”

The increasing use of BI and analytics by non-technical experts also means that the tools must be easy to use and intuitive. Tibco’s Singh says having data visualisation capabilities helps to turn data analysis into visual representations for people to easily consume and process data.

Also, the growing use of artificial intelligence (AI) and machine learning means that BI and analytics tools must now support those capabilities, ideally with graduated capabilities ranging from no-code or low-code, to calling powerful open source libraries such as TensorFlow and even bespoke code development.

Besides technical factors, there are business considerations as well, such as setting clear data goals, keeping use cases in mind, costing and the market landscape, according to Qlik’s Tan.

On data goals, Tan advises enterprises to consider what they would like their data to do and where it can deliver value.

“Are you looking to better understand key performance indicators? Or empower more of your workforce to make data driven decisions? Or solve a specific business problem in your industry? By having a clear understanding of your data goals, organisations can easily define what tools are needed to achieve them,” says Tan.

Tan advises enterprises to choose an analytics platform that can accommodate all their use cases within a unified, governed framework. If self-service data visualisation is high on the priority list, for example, think about how business users – who require more than just read-only capabilities – can best consume the output.

This can be as simple as pairing modern BI solutions with augmented analytics systems that carry AI components to help users get more out of their analyses. It is also worthwhile having a fully open analytics platform that can be customised and extended to support new use cases.

Tan also advises enterprises to get a good sense of costing. For an analytics platform, the total cost of ownership can include several factors on top of the initial licensing fees.

“Look for competitive pricing, but make sure you compare apples to apples. Also, beware of hidden costs, such as extra software licensing for underlying technologies and ongoing support costs. Often, the software cost is only a small part of the total cost,” he says.

Finally, as more cloud software companies are acquired, there is a chance of having data residing in multiple cloud locations, on-premise systems and repositories. To avoid facing supplier lock-in, choose a platform that offers true multicloud capabilities.

Skills and culture

The adoption of BI and analytics tools, however, does not guarantee success for enterprises seeking to become more data-driven. In fact, there are multiple ingredients needed to define success for the enterprise, says Julian Quinn, senior vice-president of Alteryx Asia-Pacific.

“There’s a need to have the workforce with the right skills. At Alteryx, we recognise that any data worker in an organisation has the capability to tackle sophisticated analytical tasks, if empowered with the right training, technology and tools,” he says.

“Whether brand new to data or wanting to sharpen their knowledge of advanced, predictive analytics, we have a programme that will equip one with the skills needed to enter a desirable field or make strides in their current career path.”

SAP’s Shenoy says BI and data analytics also need to be seen as a strategic asset, and not just an IT project. To spur adoption, current practices, such as relying on spreadsheet reports, must be stopped to change the mindset and behaviour of people.

At the same time, organisations must create an environment of trust to empower their employees to be agents of change, says Tableau’s Gan.

Noting that this goes both ways, Gan says businesses should be open and transparent in setting clear expectations and guidance around data ethics, while employees should be accountable for their actions and use business resources in a responsible way.

“Only then will the organisation be able to effectively instil a strong data culture, and leverage this to inform their business strategy and decision-making process. In doing so, they can remain agile in an ever-changing business landscape and continue to make effective decisions quickly,” he says.

There’s also the perennial need to foster closer partnerships between business and IT teams.

“Numerous BI and analytics initiatives have failed historically when they are IT-led and not tightly related to business goals,” says Tibco’s Singh. “Enterprises have learnt from their past mistakes and are now making sure the projects are well-scoped and well-defined by the business and well-partnered by IT.

“The lines of business [LOBs] need self-service capabilities for agility, but also need the help of IT to make sure correct controls are in place when projects move out of prototyping and pilot phase into production phase. Both LOBs and IT need to play well in this team sport of BI and analytics, so that a balance can be achieved between agility and control in an enterprise.”