Breaking the “Bad Data” Myth from the Start
Every executive has heard (or uttered) some version of: “Our data is bad.” It’s a common refrain when reports don’t line up or dashboards throw perplexing numbers. But what if the data itself was never the real culprit? In truth, the information inside your core systems of record – your CRM, ERP, HRIS, supply chain platform, etc. – is usually well-structured, clean, and purpose-built for its domain. Customer orders in Salesforce, payroll entries in ADP, or inventory records in SAP are carefully validated and governed at the source. The breakdown begins the moment humans start extracting and moving that data elsewhere. It’s a bit like cybersecurity: IBM found 95% of breaches result from human error, not faulty firewalls. Your enterprise systems are fortified sources of truth, but one careless action – an exported CSV here, a tweaked spreadsheet there – and the integrity starts to crumble.
Consider a simple scenario: a finance team exports revenue data to Excel for a custom analysis. A marketer pulls a similar report into a Google Sheet to reformat it for a presentation. Meanwhile, an analyst loads the same data into a data lake to merge with web analytics. None of these copies have the full context or controls of the source system, and each is modified for a different purpose. By the time that data circulates back to decision-makers, there are several conflicting “versions of the truth.” The original data wasn’t bad at all – it was solid when it lived in the CRM or ERP – but after all the well-intentioned human reshaping, it’s anyone’s guess which version is accurate. Like a message distorted in a game of telephone, the content gets diluted with each handoff. The irony is profound: we blame “bad data,” when in reality the trouble started after the data left its safe home.
Where Good Data Goes Bad – The Real Causes
If the raw material (the source data) is usually fine, we need to ask: What turns it “bad” in the eyes of the business? The answer is almost always people and process. In the rush to make data “useful” for everyone, well-intended teams often introduce chaos. Let’s diagnose how this happens:
Copying data into centralized repositories – and warping its relationships: A system like Salesforce or SAP maintains complex relationships (customers linked to orders, employees to payroll records). Export that data into a generic warehouse or lake, and often those relationships break or flatten. Foreign keys get lost, tables are denormalized, and important business rules don’t always come along for the ride. One data quality expert notes that “data integrity issues (like broken relationships, orphan records, missing links) produce misleading aggregations and downstream errors”. In short, moving data without its full context can distort the original meaning of the information.
Over-engineering transformation pipelines: To make disparate data “play nice” together, companies build elaborate Extract-Transform-Load (ETL) pipelines. These pipelines often evolve into Rube Goldberg machines of SQL scripts, custom code, and lookup tables that only a few experts truly understand. When something goes wrong, few people can audit or explain the logic. As one commentator put it bluntly, “someone messes stuff up in a data pipeline” more often than we’d like to admit. Every extra step – parsing, joining, reformatting – is another chance for an error or a misinterpretation to slip in. And crucially, if a source system changes (a new field added, an API updated), suddenly dozens of downstream processes may break – kicking off a frantic scramble to patch things up.
Combining data with mismatched definitions: Enterprise data integration is fraught with semantic challenges. Different systems (and teams) define things in conflicting ways – what counts as an “active customer,” what time frame defines a “late shipment,” or how each department calculates “profit.” When you slam these datasets together in a warehouse or BI tool without aligning definitions, you get metrics that don’t reconcile. Research highlights that when teams define key terms differently – say, what constitutes a “customer” or when a deal is “closed” – data becomes fragmented and untrustworthy across systems. The data didn’t change, but the meaning did, and that’s enough to throw off the whole analysis.
Rebuilding the same metrics in every tool: In far too many organizations, multiple versions of critical metrics live in parallel. The sales team maintains a pipeline report in Excel, finance has a revenue figure in the BI dashboard, and operations uses a custom SQL query for their number – all purportedly “total sales,” yet none agree. This redundancy is the enemy of consistency. It’s how you get a meeting where the CEO holds up one report and the CFO another, each with different figures sourced from different processes. Siloed access and lack of shared context lead teams to replicate efforts and produce conflicting results. Everyone is working hard, but in effect duplicating (and complicating) the truth.
Continuous maintenance and firefighting: Every time something changes upstream – a new product line in the ERP, a re-segmentation of customers in the CRM – it triggers a cascade of manual updates in all the downstream spreadsheets, dashboards, and data marts. New column added? Now hundreds of reports might need revisions. Schema change in an API? Suddenly the data pipeline fails overnight. The result is an ever-spinning hamster wheel of maintenance. It’s no wonder data engineers report spending nearly half their time just maintaining pipelines (44% on average, costing about $520,000 annually per company). All that effort is essentially undoing breaks and misalignments we introduced by copying the data in the first place.
Let’s be clear: Your Salesforce didn’t randomly corrupt its own records. SAP didn’t suddenly mix up your inventory counts on its own. The loss of integrity happens in the handoffs – in our rush to warehouse, report, and manipulate data without preserving context and governance. And it exacts a heavy price. Gartner estimates poor data quality costs the average company $12.9 million a year, and MIT researchers peg the cost at 15–25% of revenue lost due to downstream data issues. That’s not because the source systems failed – it’s largely due to the friction, rework, and errors introduced after the data left the source. A famous cautionary tale: in 2012, JPMorgan Chase’s “London Whale” trading fiasco was exacerbated by a spreadsheet error – a simple copy-paste mistake in an Excel model that miscalculated risk, contributing to a multi-billion-dollar loss. The data wasn’t “bad” coming from the trading systems, but a human reshaping of that data in a shadow system (Excel) led to a disastrous decision.
The diagnosis is inescapable. Enterprises don’t have a data problem; they have a data handling problem. The moment your data leaves its source, it enters a perilous journey of ad-hoc transformations and fragmented contexts. So why do we keep doing things this way?
The Legacy Approach: One Warehouse to Rule Them All (and Why It Falls Short)
For years, the prevailing wisdom in enterprise data architecture was “pour everything into a central warehouse or lake, and all your silo problems will be solved.” In theory, a centralized data warehouse would unify the company’s information, break down silos, and provide a single source of truth for analytics. In practice, it often hasn’t worked out that way. In fact, many companies have ended up with more silos, not fewer – just different ones. Here’s why the traditional approach often falls short:
Silos simply shifted locations: Piling all data into one warehouse can create a false sense that silos are gone. Yet, if each business unit dumps data in with its own assumptions intact, the silo mentality persists inside the warehouse (one team’s tables vs. another’s). Moreover, large enterprises sometimes spin up multiple warehouses and lakes – one for marketing, one for product, one for international subsidiaries, etc. – because a single model can’t cleanly accommodate all needs. The outcome? You’ve just recreated silos in a new form, with expensive infrastructure to boot. And even with a centralized repository, people often export data out of it into spreadsheets or local tools, spawning new mini-silos on the fly. The net effect can be more fragmentation, hidden behind the veneer of a unified platform.
Lagging and stale data by design: Traditional ETL processes usually run on schedules – maybe nightly, maybe hourly for the ambitious. That means your “single source of truth” is often several hours or days behind the live source systems. In today’s world, that lag can be critical. A warehouse that updated last night is of limited help when a customer calls this morning about an order issue. As one technology expert noted, when data is copied out via ETL, the best case might be data from “yesterday’s end-of-day” – acceptable for some historical analysis, but far from ideal for operational needs. Stale data leads to stale answers, and frustrated business users who find that by the time data is in the warehouse, it’s already old news. This is how you get the phenomenon of teams bypassing the beautiful warehouse you built and hitting the source systems directly for the latest info, because the central repository isn’t up to date.
Ballooning costs at every layer: Centralizing data isn’t just a technical endeavor – it’s a costly one. You pay in multiple ways. First, there’s infrastructure cost: storing petabytes of duplicate data and running heavy compute to transform and query it. Then, engineering and maintenance cost: armies of data engineers to build and fix pipelines (remember that 44% of their time spent on maintenance). Add governance and tooling cost: special tools to catalog the data, test data quality, manage permissions, etc., all because the data is now twice-removed from its original context. One study found companies often maintain dozens or even hundreds of custom data connectors and schemas just to funnel data into a warehouse, a multi-year effort consuming precious talent. And these costs tend to increase the farther data gets from the source – more things break, more reconciliation needed, more confusion to sort out. Essentially, for every dollar you invest in copying data, you might be spending another dollar (or more) cleaning up the side effects. It’s a textbook ownership cost nightmare.
Lost domain knowledge: Perhaps the subtlest problem is that a warehouse is a generic environment trying to house very specific domain data. Each source system (CRM, ERP, HRIS, etc.) was designed with rich domain logic – the CRM knows what an opportunity lifecycle is, the ERP knows how to apply revenue recognition rules, the HR system knows the org hierarchy. When we pour data into a warehouse, we often have to rebuild these rules and contexts from scratch in a new form. It’s like translating a novel into another language – some nuance invariably gets lost. Teams end up re-coding business logic (sometimes inconsistently) across various reports and models. If the rebuilt logic doesn’t perfectly match the source, you’ll see discrepancies. For example, the finance system closes the books with one algorithm, but your BI team computed revenue with a slightly different formula in the warehouse – now finance and BI reports conflict, and you’re in a meeting arguing about which number is right. This reinvention of the wheel is not only inefficient, it’s prone to error. As one data governance expert observed, many pipeline inconsistencies boil down to individuals being “allowed or able to create those inconsistencies” by redefining metrics on their own. The warehouse made it technically possible to recalculate anything; human nature ensured that we did, each in our own way.
In summary, the legacy “big warehouse” mindset often trades one set of problems for another. Yes, you might solve some initial silo issues by consolidating data, but you introduce delay, high costs, and a brittleness that cracks under real-world changes. The overarching lesson? The further data drifts from its original source and context, the harder it becomes to maintain fidelity and trust. That recognition is fueling a new approach – one that turns the old model on its head.
A New Model: Keep the Data at the Source, Bring the Logic to the Data
Imagine if you could query and reason across all your enterprise systems without exporting a single CSV or building a single brittle pipeline. Instead of copying data to the analytics platform, you bring the analytics (and automation) to the data. This is the essence of a new model gaining traction, and one practical example of it is Adaly – a platform built on the idea that your data is already correct where it is, so why not leave it there?
Adaly is not another warehouse or system of record. Think of it as a connective layer that plugs directly into the systems where your data already lives and is correct, and then allows you to ask questions, get insights, and even trigger actions across those systems in real time. The key differences in this approach address the very failure points we discussed:
No more endless data copying – connect instead of collect: Adaly connects to your internal systems (Salesforce, SAP, Oracle, Workday, Adobe, you name it) as well as external and third-party sources in real time. Rather than duplicating all that data into yet another repository, it queries and combines on the fly. This means the data you see is always the live, up-to-date information straight from the source system – not yesterday’s snapshot. By eliminating bulk batch exports, there’s no opportunity for a human to quietly introduce an error during an extract or mis-label a column in a spreadsheet. The original relationships and context stay intact because you’re effectively looking at the source directly, just through a unified lens. One Adaly principle: “No more data lakes – just talk to your real-time data and get answers with full context and citations”. In other words, it doesn’t copy the lake; it lets you fish from all ponds at once.
Preserve meaning and lineage end-to-end: Since Adaly maintains connections to each source, it also maintains an understanding of where each piece of data comes from and what it means in that system. Ask it a question like “What was our revenue by product line last month?” and it can fetch the numbers from the finance system and the product catalog from your ERP, but crucially, it knows these figures in context. It can even provide a citation back to the source record if needed, so you have traceability (imagine seeing that a number in your report links back to an entry in SAP or Salesforce that generated it). This baked-in lineage and context means no more black-box transformations that can’t be explained. You get transparency that traditional pipelines often lost. Essentially, Adaly brings context back to the center of data analytics, where it belongs.
Less pipeline, more pipeline automation: Removing the human middleman from data transformation does more than prevent errors; it collapses the time and effort needed to get answers. Adaly uses a combination of deterministic automation and AI reasoning to handle a lot of the “data prep” work that used to chew up countless hours. Need to join data across a dozen systems? That happens behind the scenes, without you writing bespoke code for each API. Need to apply a business rule? The platform can be taught once, and it’s applied uniformly (versus every analyst doing it slightly differently). By taking people out of the loop of reshaping and re-modeling data, Adaly eliminates the largest point of failure and delay. The result is dramatically less complexity – one company leader described it as moving from herding dozens of disconnected tools to having an “intelligent agent” that coordinates insight and action. For your teams, that means far less time wrangling data and more time using it.
Cuts cost and chaos, not corners: An approach like this has a very appealing side effect: massive reduction in data duplication and the associated costs. Early adopters of Adaly’s approach have seen significant savings, for example reducing storage and ELT (extract-load-transform) costs by around 72% by eliminating all those redundant copies and transformations. This isn’t about ripping out your existing systems – Adaly doesn’t replace your CRM or your BI tool – it’s about streamlining the data plumbing between them. In fact, Adaly is positioned as a net cost saver: you can stop investing in so many ETL processes, data warehouses, and even third-party analytics tools, because a lot of that work is handled in one unified platform. The platform essentially pays for itself by consolidating what used to be a sprawling, high-maintenance data stack. Less infrastructure to maintain, fewer pipelines to break, fewer siloed tools – that all translates to real dollars saved and far less operational drag on your organization.
Trust through a single source of actual truth: Perhaps most importantly, this model restores something that has been sorely missing: trust in the data. When your analytics are directly reflecting what’s in the system of record, it’s much harder to argue about whose number is right. There’s no “my spreadsheet vs. your dashboard” standoff – they’re all drawing from the same well. Adaly’s ethos is to give every team a “single source of truth” again, but not by forcing everyone into one giant monolith, rather by federating the questions to the right systems. The platform unifies the view without distorting the source. That means a salesperson and a finance manager can both ask of Adaly, “What were the sales for product X last quarter?” and get the same answer, with confidence it’s directly from the official sales system with all the proper definitions applied. It essentially de-risks your data use: people can trust the answers because they know those answers haven’t passed through ten unguided transformations to get to them.
Adaly is one example of this new philosophy in action – it’s an “enterprise cognition” platform as the company calls it, connecting data and insights across functions in real time. The broader point is this: the future of enterprise data isn’t about making yet another copy of your data, but about leveraging the data where it already resides, in real time, with AI-assisted context. By doing so, you dramatically reduce the opportunity for human error, you reduce latency to insight, and you reduce the cost and effort of chasing the data around. In short, you get to focus on using data, not wrangling it.
From People Problem to AI-Driven Future – A New Era of Enterprise Data
It’s time to flip the script on “bad data.” The next generation of data-driven enterprises will realize that they never really had bad data – they had bad data practices. The solution is not a new patch or a stricter policy; it’s a fundamentally different architecture that minimizes those human touchpoints where things go wrong. By returning to source systems as the bedrock of truth and letting technology bridge them intelligently, companies can finally break the cycle of blame and rework.
The implications are exciting. Imagine an enterprise where data flows as freely and accurately as blood in a healthy body – every organ (department) gets the oxygen (information) it needs, without clots and blockages. Questions can be answered on demand because the answers are coming from the living source, not a stale archive. Executives could confidently base decisions on real-time dashboards knowing everything ties back to audited systems of record. Data engineers and analysts, freed from grunt-work of pipeline maintenance, can turn their talents to forward-looking analysis, predictions, and AI models that drive the business. In fact, a solid data foundation is a prerequisite for effective AI in operations – if your AI is training on janky, piecemeal data, you can’t trust its recommendations. By keeping data truthful and consistent at the core, platforms like Adaly provide a reliable launchpad for AI and automation to truly transform how work gets done.
The takeaway for leaders is clear: you do not have a data quality problem inherent in your source systems. You have a people-and-process problem that was built into an outdated data architecture. The great news is that this is a solvable problem. By embracing a “back to the source” mindset – and the modern tools that enable it – you can eliminate huge swaths of complexity and cost. It’s not about throwing out your existing investments, but rather connecting them in a smarter way. When you do that, you’ll find the nagging issues like conflicting metrics, slow reports, and endless reconciliation meetings start to fade. Instead, you gain speed, trust, and a newfound agility in decision-making.
Your data was never the enemy. The real enemy was the convoluted journey we forced that data to take. Going forward, the companies that win will be those who shorten that journey, or skip it altogether. They will operate on live knowledge, not yesterday’s news. They will treat data as a strategic asset at the source, not a problem to be cleaned up downstream. And in doing so, they’ll unlock a foundation for AI-era operations that is lighter, faster, and far more accurate.
In the end, fixing the “data problem” isn’t about fixing data at all – it’s about fixing how we use it. The truth is already there, inside your enterprise. With the right approach, you can finally set it free, and let your teams run with it. As we move into this future, platforms like Adaly are showing that we can have our cake and eat it too: the richness of our source data, without the pain of traditional data wrangling. That is a future worth aiming for – one where we stop fighting our data and start trusting it as the single, unambiguous voice of truth that it always had the potential to be.