I have had many hats in my career. The largest one today is client-side and consultant. I've worked in non-governmental organizations, licensing/permits, product, and marketing; every one of them heavy on data governance, organization, and analysis. Managing sources of truths have me front and centre to how the organization treats data and information. One common thread is that every division and every department interprets the data in a distinct way.

When working in the product management division for a publisher, the data we collect, the same tables, charts, and graphs are fuel for the gruelling all hands meeting debate session. Everyone has a strong opinion on a seemingly neutral product (the data). When I worked marketing in the e-commerce sector, the same thing happened but worse. It forced me to learn that data is only as divisive as the cohesion of the organization. One organization genuinely had teams that were using the data as a starting point toward an agreeable consensus, while the other was using data as a tool to blame.

Overly Opinionated Dashboards

This divide comes down to familiarity of the platform. If you’ve spent any time working client-side or as a consultant, you know that managing a single source of truth is hard enough internally. But when you step into the marketing department, you run into an entirely different beast: the rented dashboard.

Every major marketing platform serves up its own highly opinionated UI. This is true from marketing analytics to a full blown ERP. The problem is that these platforms aren’t taking a stance on your organization's success; they are taking a stance on their own indispensability. They are designed, from the ground up, to validate the marketer's decision to use them.

They highlight generous attribution windows, vanity metrics like "Impressions" or "Engagement," and bright green up-arrows next to "Return on Ad Spend" calculated by their own grading rubric. The dashboard's opinion is simple: Keep spending money here. This makes it incredibly difficult for non-technical marketers to understand what their key metrics are. When you live your entire day inside interfaces engineered to make you feel successful, you start adopting the platform's definition of success as your own. You lose sight of the unvarnished truth of the bottom line. I’ve sat in countless cross-departmental meetings where marketing brings an entirely different reality to the table. Mind you, not because they are being deceptive, but because they have been conditioned by an ecosystem of third-party dashboards that actively force feeds the platform's subjectivity over the true business impact.

The Intrinsic Value of Traffic

I remember clearly getting into a fight when calculating performance projections with a non-technical marketer. They insisted on only counting number of sessions as a key metric. My background was from product, they were from a big agency group campaigner; I was the new guy hired to fix things. The clash boiled down to: "Including users per session makes me look bad." This was for internal so we needed to account the additional variables which would affect not only marketing but also sales and logistics. The Marketing Head took their side, the next leaders meeting was of course chaos.

Sessions equal eyeballs, and a high volume of eyeballs justifies the campaign spend. But inside an operational organization, traffic has a completely different intrinsic value. A single user returning for five separate sessions doesn't mean logistics needs to prepare for five potential orders; it means one user is highly engaged—or perhaps highly confused by the checkout process. If you forecast organizational operations based on the agency definition of traffic, you break the supply chain.

To a marketer optimizing strictly for platform delivery, a session is a victory condition. It means the ad was clicked and the landing page loaded. But to the rest of the business, traffic without revenue is not a victory—it is a massive, confusing liability. High volume that fails to convert immediately tanks your overall conversion rate and skews your customer acquisition cost. It leaves sales and logistics scrambling, trying to figure out if the company is dealing with a sudden drop in product appeal, a broken checkout button, or just a massive influx of unqualified, low-intent clickbait traffic.

This means big sessions hold massive extrinsic value because it justifies their retainer, lowers their Cost Per Click (CPC), and keeps the algorithm happy. The platforms themselves fuel this perspective. When a marketing team brings purely extrinsic metrics into an internal leadership meeting, they are essentially importing an ad network’s definition of success into a boardroom that desperately needs operational truth.

You see the exact same clash happen when an SEO team successfully ranks a page for a high-volume, broad-match keyword. The organic sessions skyrocket. The marketing department claims a massive, zero-cost victory, pointing to the hockey-stick growth on their dashboard. Yet, the sales does not show any impact giving the misinformed impression that SEO is simply a broken channel. It is the fundamental foundation for the blame game.

Misinformed Data Interpretation: A Regional Issue

Working in APAC, I see this problem recurring mostly in South East Asia. As a relatively late adopter of popular technology, SEA users basically leapfrogged the consumer technology evolution by skipping personal computers and straight to mobile. There were internet cafes throughout the 90s (this was true for Indonesia as it was in Philippines) but these were mostly visited by the young middle class who were technologically savvy.

I say this comparing it to another region I'm familiar with: Australia. Seeing the user behaviour in Australia is vastly different. Based on my findings desktop users averaged around 35%-40% across several sectors, SEA would average as low as 18%. Personal computers were a luxury for SEA when the first dotcom bubble popped, but the flood of cheap android phones – not to mention how Facebook was popularly "the internet" for some time in the 2010s in this region – gave way to a large chunk of the population access to the world via internet.

This translates to how decision making is done based on what the general population understand. Many non-technical stakeholders and decision-makers do not have an intuitive understanding of how underlying digital infrastructure works. This isn't born out of simple ignorance; it is the byproduct of an environment where access to baseline technology was historically limited, making the foundational mechanics of the web completely alien to most people. Interpreting data from a foreign source can be daunting, which explains why an opinionated dashboard can do more harm than good. The monolithic hierarchical style commonly found in an organization's social structure also does not help course correct these decision making habits.

The SEA consumer journey is inherently fragmented and deeply conversational. A user might use Google search for top-of-funnel research, but they don't necessarily want to check out via a traditional website cart. The actual transaction happens in a fragmented ecosystem of super-apps, marketplaces, third party payment processors, or via direct social commerce. The customer didn't bounce because the intent was low; they bounced because they couldn't find the link to buy it on their preferred marketplace, or they couldn't find a call to action button to negotiate with a human.

There Is No I In Team: The Us in Business

There is a common term in Indonesia called "ego sektoral". It may sound latin and it may be called something else where you are from. It is basically a bureaucratic mentality where agencies, departments, or sectors prioritize their own interests, authority, and prestige over shared goals, resulting in poor coordination, inefficiency, and hindered national productivity. Its mostly designated for the public sector but it bleeds out into the private sector as well.

Analytics as a job function can not constrain its loyalty to one department. Is there such a thing as performance analytics or product analytics or commercial analytics? Yes! But its not which department it belongs to, but its loyalty.

This is arguably the hardest position to hold for an in-house data team. You might sit under the CMO or the VP of Product, but you are holding the numbers that dictate the reality for Sales, Operations, and Finance. When you have access to the entire dataset, you lose the luxury of departmental ignorance. You can see, how a pushing a high projection number in marketing is actively creating a bottleneck in logistics.

But building that requires a deeply uncomfortable, yet entirely necessary, level of professional courage from the analyst. It requires standing in a room with the department head who hired you and telling them their metrics are hurting the broader business. It requires understanding that while you may manage a specific department's data, your loyalty belongs entirely to the holistic reality of the business.

The nuanced nature of data makes it impossible to take sides especially when we have unrestricted access to it. You work for the company, not your superior. When I was client side the first thing I do when joining a company is meet as many cross departmental people as possible, figuring out the business process, integrating it with my work. Now as a consultant, I don't ask clients what solutions they have in mind, but what their goals are and how the campaign / project / business works end to end. Solutions are built with what data you have, not what you imagine you want. If someone really needs a specific solution, we go through the process of guiding them in building their ideal technical requirements to make it happen.

A good company does not have "ego sektoral", the people there work for the big picture, the bottom line. Together.

— Andaru Pramudito is a Senior Analytics Consultant with a background in sociology, bringing a people-centered perspective to data, governance, and analysis across sectors. He writes about how organizations make sense of information and turn it into decisions without pretending data speaks for itself.