Empowering business transformation through data decoupling and analytics

In today’s rapidly evolving digital landscape, harnessing the power of data has become a critical priority for businesses across industries. To delve deeper into the subject, we had the privilege of interviewing CK Tan, Senior Director at Qlik, a leading data integration and analytics company. With his extensive experience in the field, CK Tan shared valuable insights on how companies can effectively manage sensitive data, liberate legacy systems, leverage real-time data, and enable smarter decision-making through the adoption of data decoupling and advanced analytics technologies. In this conversation, Minu Sirsalewala, Executive Editor – Special Projects, explores CK Tan’s expert perspectives, highlighting key challenges, successful projects, emerging trends, and actionable advice for businesses looking to unlock the full potential of their data assets.

How can BFSI companies effectively manage their sensitive data while leveraging the benefits of data decoupling?

Digital decoupling architecture fosters a culture of adoption in financial institutions, enabling them to benefit from cutting-edge technology for managing applications and data. In addition, quick wins help teams achieve short-term goals and boost confidence in pursuing new initiatives.
The BFSI sector invests in information security to manage sensitive personal and financial information due to external threats, reliance on third parties, and increased internet use and mobile apps. A robust data privacy policy is necessary to comply with data privacy laws. Companies must invest in safe data storage environments and implement rich data lineage and auditing capabilities to avoid reputational costs from data breaches.
For example, the National Stock Exchange (NSE) has significantly reduced the time needed to integrate and load data for some activities performed since deploying Qlik. This reduces manual work as tasks that took three to six hours only take 20 to 35 mins now. As a result, Qlik has helped NSE with 75% time savings and has aided the regulatory teams of NSE in driving better efficiencies by facilitating data analysis and improved decision- making capabilities.

What are the key challenges companies face when it comes to liberating legacy systems and adopting new data technologies, and how can they be overcome?

There are several challenges that organizations face when it comes to modernizing their IT infrastructure and data management practices. Firstly, legacy system dependencies pose significant obstacles as they are deeply ingrained in the company’s IT infrastructure and often rely on other interconnected systems. Secondly, the existence of data silos, where large volumes of data are stored in different systems and formats, makes it difficult to integrate and analyze data seamlessly across the entire organization. Additionally, employee resistance to change can hinder the implementation of new tools and processes, stemming from a lack of understanding or fear of job loss. Lastly, adopting new data technologies necessitates a thorough review of data governance policies and practices to ensure proper management and utilization of data resources.

Companies must prioritize data liberation and technology adoption to overcome legacy system challenges. Additionally, to fully leverage real-time data, organizations need to align their operational decisions with the speed of data availability. This involves optimizing the data-to-action pipeline, reducing the time taken to access relevant data and increasing the frequency of acting upon it. Alongside technological advancements, data literacy plays a crucial role in enabling efficient decision-making. Moreover, the increased velocity of decision-making generates a wealth of data patterns that can be analyzed, providing opportunities for decision- mining and further insights. It is important to have a clear data strategy, build a strong data team, communicate effectively, and invest in data governance practices and technologies to alleviate resistance to change.

In your experience, how can companies enable smarter decision-making by leveraging their data assets, and what role does technology play in this process?

In today’s fragmented and time-constrained world, the goal of providing the right information to the right user at the right time remains crucial. However, it’s no longer necessary to inundate everyone with all the data. Prescriptive and recommendation-oriented insights can be delivered straight from the data, simplifying the process. Effective data storytelling goes beyond charts and visuals; it must be connected to actionable outcomes, ensuring that the information leads to meaningful actions. AutoML plays a key role in predicting future events and recommending optimal actions.

By incorporating alerting, reporting, and automation, data stories can be seamlessly integrated into workflows, ensuring timely access to relevant information. The shift towards embedding micro- stories within work systems enhances data storytelling, transforming insights into actionable outcomes that drive decision-making and meaningful actions. Companies can also implement Artificial Intelligence to analyse and visualise their data assets and make informed decisions. This involves data democratisation, integration, real-time and advanced analytics, and visualisation. Technology is crucial in providing the infrastructure and tools necessary for this process. Cloud-based data analytics platforms, Machine Learning algorithms, and real-time data processing technologies are just a few examples of technologies that enable more intelligent decision-making.

Here are some points covering the possibilities:

•By analyzing historical data, predictive analytics can help companies forecast future outcomes and identify opportunities and risks. Applying ML algorithms to identify patterns enables businesses to pivot strategies with agility.
•IoT sensors and streaming data analytics empower.
companies to monitor business performance and promptly respond to issues, thereby triggering fast decision-making.
•Beyond simply converting data into graphical forms, data visualisation involves more. A company’s business intelligence (BI) strategy must include this crucial skill. Because you may reveal new insights and make your points more effectively when you select the appropriate visualisation to emphasise the most crucial components of your data. And as a result, your company may take wiser decisions and achieve greater results.

Design thinking is an important component of your approach to consulting. How do you incorporate it into your work, and how does it help businesses to reimagine their processes and adopt a data-first culture?

When building a data-first culture within an organisation, many organisations will focus on training users to improve their data literacy and analytical skills. However, this can be a challenging and lengthy process, as most people will expect technology to work for them. The key to success is making it easier for the users to work with data to achieve their desired behaviour. Design thinking helps by taking a human-centred approach to always needing to start with people, to build empathy to get a deep understanding of why they desire to use data and how does it tie back to the viability of the business. What is technically feasible, for example, to handle ever-growing data from digital services or products that you may be offerings, like IoT and sensor devices? Are you also considering shifting from on-premises to the cloud to take advantage of its flexible consumption and scalability? Lastly, is good quality and timely data in place to meet the downstream needs like reporting, machine learning and data-driven applications.

With the shift to a data-first culture, the role of the data and BI team is also transforming to that of a product team, often made up of multi-disciplinary team members from business and technology working together to build data products that deeply embedded in business processes and oversee a capability end-to-end, from strategy to delivery and continuous enhancements.

What are some of the emerging trends and technologies in the data and analytics space that you believe will have a significant impact on businesses in the near future?

We have seen the Metaverse, and now the conversations on ChatGPT and its integration, and India has been a part of this transition and is ahead in accepting technological improvements. Here are two developments that will be game-changers in the industry.

Market consolidation opens new opportunities:

•Isolated systems, such as those for data management, analytics/AI, visualization, data science, and automation, are now being consolidated.
•Combining these operations creates new possibilities
and simplifies collaboration between data producers and consumers. Agile data pipelines can be built around business objectives by starting with the desired product, outcomes, or decisions.
•Interoperability is enabled through shared standards
and APIs, and convergence is further simplified when a provider serves multiple market groups. The aim is not to go “all-in” on one data stack, which could result in vendor lock-in or undermine compliance. Instead, consolidate the data among platforms that can operate with numerous stacks.

Multi-cloud approach:

•Organizations moved quickly to update programs and shift data to the cloud during the pandemic, but similar problems from the on-premises environment are reappearing. For example, cloud warehouses or lakes require addressing data transportation, transformation, metadata catalogues, and other issues. This drives investment in various software components, including semantic layers, data integration, transportation, sources, and observability.

Finally, what advice would you give to businesses that are looking to leverage their data assets and achieve meaningful insights?

Data assets enable important insights and data- driven decision-making, leading to better customer understanding, operational efficiency, and new growth opportunities. Additionally, data assets can help businesses avoid risks and adapt to new trends.

Here are some ways how:
1)Understanding organisational KPIs: For organizations to achieve success, each department should know its KPI without goal setting it is hard to measure success. For each department to use the data in an efficient manner the defined KPI will help to get insights in real-time for decision making on changes/improvement.
2)Real time data for results: Data is being churned out with every click, it’s essential to have a strategy for using the best of the data available for their departments to keep a track of the progress from planning to results, data helps in understanding the deviation.
3)Using data from multiple sources: Too frequently, data is isolated in several operating systems and cannot be instantly and automatically accessible for analysis. However, a contemporary strategy might incorporate data from several sources and make it accessible through analytics. Utilising real-time data enables the development of actionable insights and decisions. The ultimate goal of analytics should be an organisation that can integrate its major business functions and respond quickly to insights generated by a single system.
With this approach, businesses can leverage their data assets and achieve meaningful insights that drive business value and enable them to stay ahead of
the competition.