From raw data to actionable insights: an introduction to seamless integration 

This blog is written to be accessible for both seasoned data professionals and non-technical folk looking for inspiration on how to turn their data into something more useful (the right way). But if you’re the latter, perhaps skip the intro—we’re nerding out a bit.  

Having worked in the data world for some time now, I’ve been around the block enough to see my fair share of analytical awesomeness but also of downright data disasterology. I cut my teeth learning the do’s and don’ts in my master’s degree – only to be baffled seeing them all thrown out the window by enterprises in varying states of readiness as I entered the world of consulting. 

And call me jaded – but at this point, the biggest problem I see in data systems is people. Let me give you a practical example that, if you’ve any experience in data – you’ve probably found yourself quietly sobbing over before. (The dim glow of your 300-line SQL find & replace script in the background). 

Real-world data challenges: A consultant’s tale

Back as an analytics consultant when I was building a dashboard for a client, we were looking at adding some geographical context to their data, which came out of a behemoth CRM tool used by enterprises globally. However, the business was baffled by my vis as the numbers shown were coming up unexpectedly low.  

This being Tableau, I whipped up a quick data quality dashboard to look at some of the discrete values in our geo column. There before me, some 35 unique entries for the name of the same Hertfordshire city, a wild and unintelligible tangle of punctuation, misplaced capitals and flat-out misspellings.  

Software and data people reading this already wincing – they had a manual step in their data integration phase. Now, we could do a whole blog about the steps we’d take to fix that (and it’d probably be super interesting now, with recent advances in the field) but that’s not what I want to discuss right now. What I’d really like to speak about is seamless integration and how to take your raw data to actionable insight.

Breaking down the task: From raw data to actionable insight

Now, to do this, we’re going to break this task down into several discrete tasks and look at the considerations we should make in each phase. For those who may be reading this who are less tech-savvy, I’ll wrap the process into the context of something more relatable, cooking a meal.  

Data collection and ingestion (growing our food and delivering it to the kitchen) 

Let’s begin our metaphorical adventure in the field (of data collection). Just like in a kitchen, your vegetables started life in separate, disparate locations with no discernable relation to each other – usually so too does our data.  

Without integration (bringing the sources together), we would need to dash frenetically between the fields growing our produce, or even running between markets to find our scattered ingredients – every time we need them.  

Most people just want to visit a single place where they can find a variety of things they might need. For example, during COVID-19, everybody and their nan needed 400 rolls of toilet paper, just in case.  

Let’s do that with our data. In the data world, we can replace fields with different platforms, tools and vendors who provide us with a rich and vibrant tapestry of data sourced from IoT, customer interactions, enterprise systems and more.  

We typically want to bring these disparate sources together into one place where we can historise and analyze the results. For this, we typically use a data warehouse. Think of platforms such as ‘Snowflake’.  

Our objective here is to create a single location for people to search when they’re looking to leverage the collections we create. And we want this to take place in a regular, reliable manner, so we know we’ll have access to everything we need – when we need it. In this scenario, seamless integration brings disparate data sources together reliably and in a timely fashion. 

Data cleaning (washing & prepping our ingredients) 

Now, we’ve got our raw ingredients ready to cook up something special. But, before we get our hands on them, they got a little dirty whilst sitting out in the wild. Unless you’re an actual dog, – you probably aren’t eating your food straight off the ground. We’re going to need to clean it.  

There’s a fun adage in the data space which goes something like ‘rubbish in, rubbish out’. If the data you’re working with, i.e. the ingredients you are cooking with are a mushy pile of rubbish, so too will be the output.  

The reason you might want to automate this task is that data in modern context can be HUGE. We’re talking, Taylor Swifts carbon footprint huge, bigger in fact.  

It’s just not practical to do this manually. Let’s consider our food analogy again, there might be multiple checks we want to do to ensure our input is palatable. We might want to cut it open to check the inside, clean dirt off the outside, remove a husk or a rind, etc. Likewise, in data, there are multiple checks we might want to do, such as handling missing values, removing outliers or basic sanitation mapping grouping discrete values and more 

This only multiplies out the time and complexity cost with manual checking. Therefore, it should be clear why, if we want this step to work seamlessly, it is imperative it is carried out by a machine. Doing so increases reliability at this step, thus ensuring data quality and consistency downstream. 

Data transformation (chopping and marinating) 

Okay – so we’ve got our ingredients, cleaned them up – now it’s time to cut them and prep them for incorporating into our dish. This topic is the closest to my heart, it’s an aspect I’ve always found intriguing and fun.  

I look at Data transformation a little bit like digital Lego – putting all the little pieces together in a way that ensures the output is useful and interesting.  

To me, this is where we start to bring order to the chaos and our raw components start to take on a much more meaningful form!  

Today, we use tools like dbt to build our transformations in clean, modular steps that are easy to traverse, amend or document. But with any tool you use, a key tenant of seamless integration is to design your data flow, so we unify, standardise and ensure integrity. If we build things right, we build them once, and we can reuse them again and again, reliably and without fault.  

There are, of course, many different methodologies that can be applied – so specifically how you design your transformation is up to you. However, what is important is that it’s well engineered to provide integrity and consistency in your data therefore integrating seamlessly with tools and platforms that you wish to incorporate downstream. 

Data analysis (cooking our dish up) 

With prepped ingredients, the next step is cooking—the process where flavors meld and the meal takes shape. Data analysis is the culinary technique applied to these ingredients, using statistical methods, machine learning algorithms, and techniques to uncover patterns, trends, and insights.  

In effect, it’s the process of turning our plain, disjointed collection of raw inputs into something desirable and of value. Seamless integration here means ensuring that analytical tools have the correct access to the correct data they need to have access to sufficient processing capabilities to support any working load they are sending upstream. The result of which is cooking up timely and relevant insights crucial for decision-making. And last, but certainly not least… 

Visualization (plating and serving our meal) 

We’ve done much of the hard work required to turn our discombobulated data into useful information but there’s still a little more juice in those lemons left to squeeze.  

 The preceding steps have brought us to the point where we can start to identify useful information that we can use to guide our decision-making. Data visualization is the final piece in the puzzle, taking the prepared ingredients and serving them up to consumers in a way that is more palatable and easier to digest than our initial inputs. 

By visualizing our data, we can translate the patterns into forms that are more easily interpreted and understood by a human. Crucially though, by creating a workflow that seamlessly weaves together the preceding tools and workflows, we have created an experience that ensures that consumers of our data product have access to reliable, accurate, value-adding information that they can make important decisions on. Empowering organizations and employees alike to make faster decisions and with more confidence.  

Conclusion 

So, there we have it, gathering ingredients for gourmet indulgence – from field to plate. We know and understand the value of the process, but we can see that the way it’s carried out is every bit as important. Just as a chain is only as strong as its weakest link, an analytics workflow is only as reliable and its results only as accurate as the preceding steps. Therefore, we can see it’s paramount we ensure the chain that we’ve wrought is as seamless as possible.  

With such a dazzling variety of superb tech at our fingertips these days – the challenge is less about how to solve the technical challenge, but how to tie the dizzying array of technical solutions propping up your workflow, together. Which is why you should leave that job to a purpose-built platform designed for doing just that, Calibo.  

With Calibo, you can carve a new path, build from scratch with a technology stack you select – or integrate existing solutions you’re already using and let Calibo do the heavy lifting when it comes to orchestrating these tools.  

Learn more here.