3 Steps to Simplify Data Preparation & Analytics

Imagine what your team could be working on if they weren’t spending 80% of their time collecting, cleaning, and organizing datasets. Unfortunately, for most BI & analytics software providers, this idea remains a fantasy.

That figure hasn't really improved either, despite everything that's changed about the tools available over the past decade. But just because this is the status-quo, doesn’t mean you should settle for it.

Slow and overly complex data preparation leads to several problems:

• Multiple applications storing different data for the same entities
• Low-quality or ‘dirty’ data impacting validity of analytics reports
• Poor data mapping and definitions

These pitfalls become major roadblocks, as the scale and complexity of analytics projects increases. Many of these take days, weeks, or even months to overcome, preventing your development team from working on new areas of the business.

In this article, we'll be covering what data preparation involves, a three-step guide to simplifying it, and how AI is changing what's possible without replacing the fundamentals underneath it.

Key takeaways

• Data preparation is the process of collecting, cleaning, structuring, enriching, and validating raw data before it's ready for analysis, and it still consumes the majority of most data teams' time.

• Nearly 80% of data teams spend more than half their time on data preparation rather than insight generation, a figure that's barely moved in a decade despite better tooling.

• Good data sourcing, the right integration tools, and staying on top of evolving datasets are the three foundational steps to simplifying data preparation at scale.

• AI is increasingly used to automate data profiling, error correction, and cleansing recommendations, but Gartner considers these augmented capabilities a must-have buying criterion, not a replacement for a solid data integration foundation.

• Data wrangling and data preparation are largely interchangeable terms, both describing the process of turning raw data into an analysis-ready format.

• A reusable, automated data preparation process pays for itself the moment you apply it to a second dataset, not just the first one.

What is data preparation?

Nearly 80% of data teams still spend more than half their time on data preparation rather than insight generation.

The data preparation process can also called data wrangling or data munging in different sources, largely interchangeable terms for the same underlying work, though wrangling sometimes emphasizes the structuring and reshaping side specifically, while preparation is used as the broader umbrella term.

Whatever term you use, the underlying stages tend to look the same across the industry: discovering and collecting the right sources, structuring the data into a usable shape, cleaning out errors and inconsistencies, enriching it with additional context, validating it against quality rules, and finally publishing it somewhere it can be used. Most of the frustration in data preparation comes from treating these as one undifferentiated blob of work, rather than distinct stages that can each be improved, or automated, on their own terms.

While data preparation is great for one-time jobs, ad-hoc queries, and ad-hoc ideas, for datasets that are in constant use, it is too manual, repetitive, and time-consuming to provide an effective solution. Left unaddressed, this is exactly the kind of thing that turns into one of the recurring pitfalls of a larger analytics project, slow, unreliable data quietly undermining the results everyone's relying on.

And that's where an optimized data integration process meets data preparation. By properly integrating your data sources, you can significantly reduce your time to value and ensure predictable, cost-effective scalability.

Sound like the solution you're looking for?

Let's take a closer look at how data integration can save you time and money in our three-step guide to simplifying data preparation and accelerating analytics.

Steps to simplifying data preparation

Each of these three steps builds on the one before it, so skipping ahead to fix your tools before your sources are reliable tends to just move the problem downstream rather than solve it. Here's where to start.

1. Start with good sources

Quality data sourcing is often overlooked in the data preparation process. But, when you jump straight into data cleansing, without questioning the reliability or frequency of your sources, you create a lot of needless extra work.

Sure, you'll save time short-term, but by kicking the can down the road, you make it harder to reconcile issues in the future. Ultimately, poor data sourcing causes frustrating quality, accessibility, and formatting issues. It's like polishing an old, beat-up car, you’re just glossing over the real problem.

That’s where data integration becomes your greatest ally. If you’re regularly updating and curating your sources, you’re not going to have time to massage all that data effectively. And, if you do, you’re probably limiting business growth in other areas. But, with a data integration platform, you have a permanent way to process data loads and make them available all the time. Instead of wasting 80% of your development time continuously preparing the same datasets, you can now focus more energy on your core services.

This is also the stage where it pays to be honest about a source's actual reliability, not just its convenience. A source that's easy to connect to but drops fields inconsistently, or changes format without warning, will cost you far more time downstream than the effort saved by skipping a proper evaluation upfront.

2. Choose the right tools

Now you’ve got a way to identify reliable data sources, you need to load the data into the right data integration platform. This is the gateway between a client’s data and your analytics engine, so it’s got a big role to play in the final outcome of the project.

This is the structuring and cleaning stage, and bigger or more complex datasets require faster, more efficient profiling and validation, examined closely for quality and formatting problems.

This is where a fast, consistent, and repeatable data integration process pays dividends. If you're waiting for someone to manually code new data transformations each and every time, it will take weeks or even months to start seeing value. Chances are this is a deal breaker for your client.

An effective data integration tool will also allow you to maintain and reuse data transformation templates, so you're not starting from scratch with each new connection.

Ideally, you want a platform that is accessible to both developers and business analysts, and enables most of your data prep to be done automatically, but allow domain experts to intervene to map, review or edit data where needed.

This is exactly the gap a tool like CloverDX Wrangler is built to close, giving non-technical users a visual, self-service interface for the enrichment and structuring work that would otherwise sit entirely with a developer.

3. Stay on top of evolving datasets

Whether you ingest data in batch or in near real-time, there’s often little opportunity to manually cleanse and standardize large volumes of data in the way your clients expect. Implementing streamlined data onboarding processes can help your team spend less time collecting, cleaning, and organizing datasets.

Proactively monitoring data quality and fixing issues before the start of your transformation helps prevent dirty data from corrupting your analytics project. To do this successfully, you’ll need to create data validation rules that assess each new record as it’s integrated into your analytics systems.  

But choose your attributes wisely. Too much monitoring produces overly complicated reports and makes it difficult for stakeholders to take decisive action. Conversely, too little monitoring leads to major oversights and consistent errors.

It's a delicate balancing act, but once you find the right combination of rules, you'll be able to use them over and over again. It's another case of doing the work upfront to save time later on.

Datasets rarely stay static for long either. A source that's stable today may add a field, change a data type, or shift a format next quarter, and a validation rule set that was calibrated for the old version won't automatically catch problems introduced by the new one. Revisiting your rules periodically, rather than treating them as a one-time setup task, is what keeps this step working as your data landscape evolves.

How AI is changing data preparation

AI is increasingly built directly into the data preparation process, automating tasks that used to require manual review.

In its 2024 Data Management Hype Cycle report, Gartner said organizations should make augmented data preparation features a must-have item when buying new data management tools, AI and ML capabilities that can automatically profile data, fix errors, and recommend cleansing, transformation, and enrichment measures.

It's worth being precise about what this changes and what it doesn't. AI can meaningfully speed up the mechanical parts of preparation, spotting a malformed date field, flagging an outlier, suggesting a likely correction, but it doesn't replace the underlying need for a solid, repeatable integration process to run on top of.

An AI model recommending a fix to messy data arriving through an ad hoc, unreliable pipeline is still building on a shaky foundation. The three steps covered above are what make AI-augmented preparation pay off, rather than just making a bad process marginally less painful.

This distinction matters as AI-driven analytics and machine learning become standard parts of the workflow. A model trained on inconsistently prepared data doesn't just produce a wrong number in a report, it can encode that inconsistency into every prediction it makes afterward, at a scale a human reviewer would never catch in time.

How CloverDX supports data preparation

The CloverDX platform is built to handle the full data preparation pipeline, from discovery through to publishing, in one place, rather than stitching together separate tools for each stage.

For the sourcing and structuring stages, Designer gives technical teams the flexibility to connect to virtually any source and build reusable transformation templates, so a process built once doesn't need rebuilding for the next dataset.

For the cleaning and enrichment stages, Wrangler gives business analysts and domain experts a visual, self-service interface to review, map, or edit data directly, without needing a developer for every routine adjustment.

And for the validating stage, built-in profiling and validation catches quality issues before they reach your analytics systems, with clear, actionable reporting rather than an overwhelming wall of alerts.

Because all three stages run on the same platform, a rule or template you build for one dataset doesn't stay siloed there. The same validation logic, mapping template, or transformation can be applied to the next client's data with minor adjustments, rather than starting from scratch, which is exactly what turns data preparation from a recurring tax on your team's time into a one-time investment that keeps paying off.

Final Thoughts: Prepare for the worst, deliver the best

‘By failing to prepare, you are preparing to fail’

We’re not sure Benjamin Franklin had data analytics in mind when he offered these sage words. But they’re just as relevant here. If you don’t handle data preparation correctly, you’ll waste a lot of development time and budget on reconciliation processes in the future.

It’s not a habit you want to build. Scale amplifies even the most basic errors and fierce competition in the data analytics and BI software market makes mistakes even more costly.

Good data preparation isn't about eliminating the work, it's about doing it once and reusing it forever after. Making efficient data integration a core part of your strategy, rather than relying on manual processes repeated for every new dataset, is what gives your developers the time back to deliver the kind of work that sets you apart.

The three steps in this guide, good sources, the right tools, and staying on top of change, aren't a one-time project either. They're the ongoing discipline that keeps data preparation from quietly eating your team's time back again a year from now.

Let's talk about what that could look like for your team.