Taxwise Or Otherwise

In the past months, I’ve had discussions with banks, business groups, and regulators about how data has become one of the most precious assets of an organization. Most, if not all of us, share the same perspective that data is indeed a key enabler of organizational growth. However, maximizing the value of data continues to be a big challenge.

My varied professional roles have allowed me to manage, process, analyze and transform both financial and non-financial data — delivering reports to internal and external stakeholders. From my experience and recent conversations, I came across two key misconceptions regarding data.

In today’s fast-paced environment, key business decision-makers need quick and comprehensive insights to remain relevant in the markets where they operate. Regulators likewise require more perspectives in crafting policies and guidelines to make the financial markets more resilient.

As the need for information-driven insights intensifies, users and decision-makers only become aware of the data gaps as they perform their analysis. To augment this, organizations tend to collect even more data to hopefully address the gaps and enrich the analysis. Additional manual processes are carried out to bridge these requirements and still provide the reports and analysis to the stakeholders. However, additional processes and data collected put pressure on the timeliness of the results.

Some analysts are lucky enough to be given a few weeks to process and interpret data, but most are not. This is driven by the principle of timeliness — the value of insights from the data declines over time. As such, analysts need to be agile enough to be able to provide quick results albeit without compromising completeness and accuracy. Tools and techniques for data analytics need to enable this balance of attributes.

Based on my experience, the activity and results of data analytics are often interesting. The variety and depth of insights that can be obtained are quite limitless as well. To get the appropriate insight often requires a well-trained eye or a closer look to understand the data better. However, as the need for timeliness kicks in, most analysts (as they perform business-as-usual activities) are forced to rely on readily-available data. They analyze through existing tools and models to be able to address the information needs on time. With this intense time pressure, more often than not, preparers can only respond with “that’s what the data tells us” when asked to explain their outputs. This being the case, it is quite difficult to conclude that the results of the analyses are complete and accurate — since that’s “the data.”

This is a common experience for report preparers and users alike. From this scenario alone, we realize that data, managed correctly or not, has a great impact on the quality of the analysis, decisions, and reasoning of stakeholders.

To address these misconceptions, organizations can start by revisiting and changing their mindset. Let’s take a step back and ask ourselves, “Am I letting my data govern me?” One telltale sign is when we only look at them at the middle or end of the process (i.e. when we look deeper during our analytics when everything has been collected) and worse, get stuck with “the data” we have.

As we try to answer the question, let’s consider these actions to handle data more effectively and realize its benefits:

1. Look at data from start to end, and top to bottom. We need to look at data from a holistic perspective, i.e. from collection to use, per producer and consumer. When doing so, organizations must realize that proper collection, handling and analysis of data are critical to maximizing its benefits.

Looking at data from start to end and top to bottom allows organizations to identify critical data elements, their primary data sources, and the ultimate data consumers. Once identified, organizations can have a stronger grasp of what impacts them, as well as how these data will influence their decisions and strategic actions. More importantly, the “meaning” of data should be defined to agree on a common understanding of what each data element intends to describe.

2. Invest time to manage it. Most organizations are becoming increasingly technology-led and data-driven due to the fast-paced and dynamic environment we are in. This requires agility, but being agile does not mean that we do things quickly without a care. While we want data to be available at the earliest possible time, quality remains an important consideration. After all, data analysis can only be valuable when based on quality data.

Organizations should invest time and resources managing the data. This starts with defining the data governance and management strategy, the key stakeholders (producers and consumers) and their responsibilities and the standards that the organization need to adhere to. Control points and compliance need to be established to provide checkpoints across the data life cycle. Data quality should be a business goal considering its various elements — accuracy, completeness, consistency, timeliness and coverage, to name a few. Organizations need to know what data they hold and where they are, and be able to describe and categorize these data to drive proper use and management.

With all the changes happening across different industries and territories, we are usually confronted with more increasing quantities of data and more complex tools in data analytics. However, the resulting analysis from them can only be valuable when founded on good quality data — a scarce resource among organizations. Insights from good-quality data can have a significant impact on an organization’s ability to make good decisions and to sustain overall growth.

Reflecting more deeply on data management made me realize that existing technology, data analytics, tools, and models will only result in valuable insights and better management decisions when anchored on good-quality data arising from good data governance and management. Are you governing your data or do you let it govern you?

The views or opinions expressed in this article are solely those of the author and do not necessarily represent those of PricewaterhouseCoopers Consulting Services Philippines Co. Ltd. The content is for general information purposes only, and should not be used as a substitute for specific advice.


Jamil Saripada is a manager with the Risk Consulting practice of PricewaterhouseCoopers Consulting Services Philippines Co. Ltd., a Philippine member firm of the PwC network.

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