The development of enterprise-class applications has become increasingly important for enterprises seeking to access high-quality data. As they attempt to manage a data-driven organisation to achieve their corporate purpose, enterprise leaders must create frameworks to connect dependencies across processes for reliable information. When data is effectively interpreted, it becomes the key to unlocking critical business insights.
Today’s enterprises are attempting to optimise and exploit data – which they rightly regard as a valuable asset – for actionable insights. It is necessary to be secure and accurate to better support decision-making and satisfy demanding customer requirements. Not only that, but this data must be easily accessible, with exploration functions that provide analytics for obtaining actionable insights.
Recent advances in data analytics, business intelligence, machine learning and artificial intelligence have enabled enterprise organisations to better detect and accurately forecast operational difficulties. This exponential improvement in the ability to analyse enormous historical data sets and millions of pages of unstructured text to track patterns and identify potential problems is revolutionary.
By creating business intelligence dashboards, organisations can instantly understand where to increase efficiencies, when to manage costs and how to impress customers with the right financial solutions in the long run. Businesses should also focus on new methods using data to predict and mitigate risk effectively to ensure business success. Further, data analytics tool develops a single source of truth for compliance and method to build trust among customers.
Digital transformation leads to improved analysis and use of data. Through this transformation, enterprises can implement more effective business and finance practices. Although many enterprises recognise the importance of having an adequate data infrastructure, it is not yet reliable enough in many countries.
The rate at which data is generated is increasing all the time. People are preparing themselves to function and engage with data in the larger environment. In addition, the necessity to improve usable data analysis while becoming increasingly data-driven in decision-making is unanimously acknowledged.
This cannot be done without reliable analytics tools capable of desegregating and connecting previously siloed data, making it manageable from a single place.
Enterprises must be more perceptive in times of business uncertainty to detect and respond to new technology opportunities that drive digital transformation. Hyperintelligence – a term that has only lately been coined – makes data accessible to employees at their convenience.
For forecasting and strategising, data must be gathered from reliable and personalised sources. The simple card, which is considered the future of Data Analytics, aims to give important data insights for specified keywords across most web apps.
OpenGov Breakfast Insight on 25 May 2022 at Sheraton Towers Singapore aimed to provide the latest information on how enterprise is using data analytics to drive mission outcomes.
Data as the new oil
To kickstart the session, Mohit Sagar, Group Managing Director, and Editor-in-Chief, OpenGov Asia delivered the opening address.
Data on a global scale has taken on an entirely different dimension and Singapore is no different. In fact, compared to other countries in the region, the nation is well ahead of the curve and leads in data analytics. The public sector has spent a huge amount of money on technological innovations.
“Data can enable governments to make informed decisions,” Mohit asserts,” but people use data for different reasons.”
Data is no longer confined to technical departments. Successful projects must be partnerships between lots of different groups with different goals, mindsets, levels of understanding and ways of working. Breaking down silos is vital to collaboration and innovation.
Mohit believes that access is not enough – people need to possess the tools and skills. He believes that people have been looking at data abundantly but are looking for new insights. “You must look at the same thing from a different perspective. To do so, you need a new lens and develop a new culture.”
The crux of the matter is, he says, “Are organisations asking the right questions and maximising the use of data?”
In closing, Mohit emphasises the importance of partnerships that could help leverage data analytics for an organisation. “Use technology and minimise customisation. Do not try to customise technology – use readily available tools.”
By working with the right people, a company can accelerate its digital journey towards effective digital transformation. In light of this, he encouraged delegates to look for experts to partner with who could ease their transformation journey.
Value creation through data analytics
Ram Kumar, Chief Data and Analytics Officer, Cigna spoke next on how to create value through data analytics.
Ram begins with a definition of a data-driven organisation: it performs analytics/advanced analytics and builds data-driven intelligent (AI) solutions to drive business outcomes. Using data does not necessarily mean an organisation is “data-driven” or has a “data-driven culture”.
Expanding on what a data-driven culture means, Ram adds that it is related to how the “lifecycle of data” is managed and governed effectively and efficiently which would enable an organisation to organise, enable/democratise its data for consumption to drive activities acceptably. Ultimately, it is to use data to make informed decisions, create value, resolve conflicts and manage risks.
Ram shared his organisation’s data and analytics vision: a data-driven culture that is built on democratised data and intelligent data-driven insights that drive affordable, simple and predictable healthcare for our partners, customers and communities.
People often take data for granted, Ram feels. Organisations need to recognise that data is an enabler and should democratise it – make it accessible and available. Once data is democratised, anything is possible.
A true data culture for Ram is when data is on the balance sheet and information is more valuable than hardware and software.
He also shared what a data-driven value creation prioritisation framework looks like:
Organisation Priorities
- IM (Information Management) Strategic Priorities/Themes
- Country and Regional Business Priorities
- Enterprise Strategic Priorities/Themes
Data and Analytics Capability Maturity Assessment
- Data Lifecycle Management & Governance
- Data Analytics
- Data processes
- Data Privacy, Ethics & Security
- Data Monetisation
- Data Value Creation & Measurement
- Data Architecture
- Technology supporting Data enablement
- Data culture
IM Data and Analytics Strategy and Roadmap
- Data and Analytics Platform – Op & Analytical
- Tools and Technologies
- Operating Model
- Ways of Working – Partners, Central/Local
- Frameworks, Standards, Processes
- Data Governance Model
- Data and Technology Architectures
- Skills and Capabilities
- Data-driven culture
- Quarterly Review and Validation
Data strategy should be agile and reviewed every three months to ensure that it aligns with value creation.
Business Use Cases Prioritisation
There are criteria that the business has to meet before looking at the use case that the organisation wants to implement. This is to ensure that it can be operationalised:
- Business Use Cases for Regions/Countries
- Technology Use Cases for Regions/Countries
- Link to strategic themes e.g., affordability
- Business Use Cases Prioritisation and Value Creation Framework – Implementation feasibility/effort vs. business value
- Business Use Cases Accountability Register
- Waiting List Use Case Register
- Quarterly Business Use Case Validation
Business Use Cases Execution and Operationalisation
- BI, Descriptive, Predictive, Prescriptive & Disruptive Analytics (inc. ML/AI)
- Automation
- Data Stage Gate Review Process
- Smart Data Governance
- Operational Analytics
- Value Measurement and Reporting
- One Page Case Study
- End to End Execution and Delivery Process Continuous Improvement
In conclusion, Ram is a true believer that organisations need to own their data strategy. True transformation can only happen when organisations focus on business value creation through use cases or data foundational work.
Deepening organisational use of data analytics
Kyung-Whu Chung, Director, Sales Engineering, APAC at MicroStrategy spoke next on the different stages of data analytics and maturity.
“Why do we do analytics?” Kyung-Whu begins. “There are different reasons, ranging from growth, efficiency, user experience, quality, risk.“
There are huge benefits for organisations to using data analytics, including improved efficiency and productivity. Better data analytics leads to faster and more effective decision-making and results in better financial performance. Data analytics also assist organisations to identify and create new promising products and services.
While benefits are clear internally, there are advantages for the consumer as well. Customer satisfaction and experience are both critical for a company to thrive was the key. Data analytics help better understand consumer behaviour, trends, demand and identify issues. It improves customer acquisition and retention with enhanced customer experience.
Kyung-Whu acknowledges that in the past, most might have used data analytics for efficiency, but mature organisations today are using data to increase customer services and drive topline growth. The way that data is being used today is in driving more significant business outcomes.
As Kyung-Whu shares the different stages of data maturity, he explains that stages one to three are based on business requirements and the building of a trusted environment. However, stages four and five are looking at data as an asset and zooming in on data adoption, working the data as a life cycle.
In closing, Kyung-Whu agrees with Ram that data strategy has to be owned by the organisation. Everyone will be at a different stage, he believes. What is important is knowing where organisations are at and how they would like to move forward. He encouraged delegates to expand their thinking and embrace a multi-tool environment. A data-driven culture can only be built on data democratisation, enabling everyone to access every process and every app. Collecting data is only a start, organisations need to enrich the data to gain deeper insights.
Interactive Discussions
After the informative presentations, delegates participated in interactive discussions facilitated by polling questions. This session is designed to provide live-audience interaction, promote engagement, hear real-life experiences, and impart professional learning and development to the participants.
It is an opportunity for delegates to gain insight from subject matter experts, share their stories and take back strategies that can be implemented in their organisations.
The opening polling inquired on the stage in which organisations are at based on the Business Intelligence Maturity Assessment. Most (61%) are at level 3 (strategic) while the remaining delegates are either at level 2 (tactical) (11%), level 5 (transformative) (11%), or level 4 (mission-critical) (17%).
Kyung-Whu remarks that it is often not a neat distinction in stages. There are a few dimensions: people, process, technology and the data itself.
The next poll inquired about the main challenge delegates face in their data strategy journey. Half (50%) chose a lack of data culture/literacy/skill across employees as their primary concern. Under a third (31%) thought that missing an overall strategy that crosses departments and teams is their biggest obstacle. Just over a tenth (13%) indicated data governance, data privacy and security concerns, while the lack of a centralised tool for sharing and collaboration was troubling for 6% of the delegates.
One delegate felt that it is easy to get the tool in place, but the challenge lies in getting people to use data to generate insights and share insights. Mohit echoed that it is a cultural issue on top of the possibility of needing to navigate legacy technology.
Kyung-Whu agrees that data culture and literacy are important. He is glad that organisations are looking into people because it shows a shift in the maturity of organisations.
Another delegate said that the level of maturity and adoption varies across the different agencies in his group of organisations. To analyse data, organisations need to have common data dictionaries as well as a governance structure that is agile enough to adapt.
On the top analytic adoption challenge in their agency, most (41%) found unstated factors to be the issue. Over one-third (35%) found data quality and accuracy concern the biggest obstacle. The remaining thought that the lack of talent and training (18%) or found the limited access to analytics challenging (6%).
Kyung-Whu commented that it is important to ensure that people are speaking the same language. There needs to be more bridging between business and IT.
When asked about their agency’s biggest data management barrier, 38% found data accessibility and sharing the biggest stumbling block. A quarter (25%) found the ability to analyse data in real-time the biggest challenge. The remaining delegates found providing trusted data to be a hindrance (19%), regulatory compliance (12%) or data collection/cleansing (6%) their biggest barriers.
Mohit believes that if businesses learn to use data, they will become ten times stronger. Kyung-Whu adds that the key is to get people to use data through trigger points. At the right moment, if the data shows up and is pushed to people, it can prompt people to ask the next or the right questions. The more people are enabled, the more data regulation becomes important.
Inquiring as to what business users do when they have new data requirements, an overwhelming majority (80%) would approach data analysts in their business unit for support. Of the remaining, 13% would raise a Helpdesk ticket for IT (Information Technology) support, while 7% would go with their gut feeling.
On being queried about the application that delegates spend most of their working days on, just over half (53%) spent their time on productivity applications (like Microsoft Office), followed by email (29%) and then business intelligence applications (18%).
Looking to know whether delegates have considered zero-click experience for data, over two thirds (67%) need more information, 22% have not considered it while 11% have.
Conclusion
The Breakfast Insight concluded with remarks from Kyung-Whu who reiterated the role of data analytics and the need for agencies to begin leveraging it. He urged agencies to become data-driven and advised them to accelerate their digital transformation.
Kyung-Whu suggests a paradigm shift that would help with the use of data analytics – bringing intelligence to the general audience (70%) who would not ask questions about data. The key is to offer them “answers to their first questions.”
Instead of getting people to reach out to analytics platforms, the strategy should be about injecting intelligence to where people are – through zero-click analytics to solve the problem that we just discussed.
In closing, he invited the delegates to reach out to his team to explore ways they could deepen the data analytics maturity in their organisation. He emphasised that it is a long-term journey that MicroStrategy has walked and would be willing to undertake.
