Transformation 101

Don’t Leave Your Data Out in the Cold: Sheltering Your Insights

Try to envision the amount of data your company collects, interfaces with, and uses on a daily basis. We’ll give you a moment…

An exercise like that is daunting to say the least, and capable of inducing a panic attack at its worst. That’s because “data” is a catch-all term that can include almost anything under the sun, as long as it’s information. Many companies start off with data storage placed on the backburner because they only have a few data points coming in, and this data might not be the driver of their early-stage business decisions. However, that stage lasts a lot shorter than founders think, and what began as a few interspersed data points can easily multiply into millions, billions, and even trillions of little pieces of data that are near-impossible to keep track of. Advancements in data warehousing and modeling have helped with the growing supply and needs of data management, but without a robust system in place, growing companies can have a multiplication problem on their hands like rats in a cheese cellar.

Getting Your House in Order

In the Today we’re channeling the energy of our friends in data engineering and architecture to talk about building a robust warehouse, because we believe nothing unifies those two groups together better than their joint love of building. But before you can pick up the virtual hammer and nails, it’s important to know what needs to get done. Having a place to store your data is important, but equally important is being able to use that data effectively. While a warehouse stores all your data in a nice place—roof over its head, perhaps central heating if you’re lucky—the data models are the blueprints that keep the warehouse running smoothly, both organizing and adding value to the incoming and outgoing data. With a well-designed model, your warehouse will be more efficient, more flexible, and easier to maintain in the long run. It’s up to you, the builders, to make sure the warehousing system you’re using falls in line with your company’s needs and future goals.

Thanks to our Analytics 101 lesson we know what to call these most important data needs: core performance metrics. We learned that these metrics are the bread and butter of data reporting, alongside your core dimensions, that unlock near-infinite potential for a company to capitalize on its data. But how do we design core metrics and dimensions into the system? That’s where modeling comes in!


Your Model, Your Way

The first step in designing a data model is understanding what information is important to your business. Start by defining the core metrics and core dimensions that most accurately track the performance and health of your company—the pulse of your business. In Analytics 101, we dive into how to pick and choose these performance indicators. Once your performance indicators are defined, it’s time for our newest warehouse designer to put together their vision of how to transform the data flowing into and out of your warehouse. *Hint hint* Keeping a data flow like this in mind can be a big help.

Data architects are really just analytical versions of chefs—the Gordon Ramseys of the data world (hopefully with less screaming and profanities). Data sources drop raw data into the architects’ laps like raw ingredients to a chef. It’s at that point the architect must find a way to combine and shape the data into something hearty and digestible, qualities that each data ingredient could not achieve on its own. Diligent architects understand that finding the right recipe and sticking to it is imperative. Without that foundation in place using a Data Building Tool, your transformation process can quickly break down as the level of complexity grows exponentially with each new data source. For example, one data ingredient might be sales reports in Shopify telling you who a customer is and what purchases they’ve made, while another ingredient is Google Ads telling you who a customer is and what ads they’ve engaged with. The magic comes from combining these two ingredients who have a common flavor (customer ID) into something more valuable than just ad performance and raw sales data alone: how much value the ads are providing over the lifetime of each customer.

While we encourage simplicity anywhere you can achieve it, we understand our suggestion is much easier said than done. Data transformation is executed through a programming language called SQL, which is how we communicate with the database. As any coder can attest, long strings of code can snowball into overly complex and hard to manage messes, especially if you’re trying to build functions on your own. The many connections between various sources, data sets, and transformations starts to form an intimidating web, one that even the most skilled SQL experts can get tangled up in with no idea how to escape.

This is especially true for growing and evolving data sets, where the functions need to evolve with the data so the end result, our KPIs, can stay up to date. One misstep and your KPI data suddenly becomes old news, or worse, your entire code could break. 

It should go without saying that designing your data model is not something that can be done halfway, because you may feel the consequences for years to come. We’ll go over just a few of the ways poor implementation of your data model can wreak havoc within your warehouse, starting with the dreaded…

3 “I”s of Data Integrity Failure

  1. Incorrect data – no explanation necessary, incorrect data is worse than no data at all. Unless you want your whole company in chaos, best not ignore this one.
  2. Inconsistent data – nothing wipes an analyst’s confidence more than seeing multiple sources returning different data for the same query.
  3. Incoherent data – a messy warehouse is impossible to read or understand, which prevents data from being used at all.

Additionally, without proper implementation, the quality of life of your warehouse takes a major hit. Here are just a few examples:

  1. Cost of ownership increases when architects and engineers have to frequently fix things, and updates to the system slow to a grinding halt.
  2. Missed opportunities begin to pile up thanks to slow query times and poor scalability of the system.
  3. And finally, as the warehouse grows with data and the pressure on the model mounts, you’ll also have a problem integrating with new systems that were meant to help you scale.

A (Not-So) Beginners Guide to Data Modeling

Navigating this transformation minefield is in fact so tricky that many billion dollar companies have built and marketed huge products dedicated to this very science. Microsoft, IBM, Accenture, and many more have huge stakes in companies wanting to outsource their entire data chain.

But what if I want to maintain some independence over my data management? That makes total sense, but unfortunately it’s no cake walk. Let’s go over some steps a typical company might go through when they’re trying to manage data on their own.

Step 1
Data points need a warehouse to call home, so you’ve got to get it up and running. Once you’ve gone through the disorienting process of finding a warehouse you like (even if you know nothing about warehouses), then comes the installation. Think software installers, security checkpoints, passwords, resetting passwords, and long waits on hold with support. All those IT complications can really drag you down.

Step 2
It’s time to pull data into the warehouse. Oh, look at that, there’s not just one password now, but one password for EACH DATA SOURCE you need to sign into. Then, the actual pulling of the data requires having all your APIs hooked up perfectly. Be prepared for another time intensive process with even more troubleshooting and of course, another call to support.

Step 3
Start cleaning the data. This comes through a process called data validation. In short, that means staring at your screen real close to make sure all your numbers are correct, in the right format, and don’t contain any surprises. Only then can you actually trust the data coming in—otherwise it’s anybody’s guess.

Step 4
Your KPIs need clear definitions. Why are definitions so important? Because we don’t want one department defining a KPI one way and another department going rogue. You’d be surprised how common this actually is, and it always muddies up the reporting process. Reporting requires one function and spits out one result, so everyone in the company needs to be on the same page as to what that function is. We call this “business logic,” the process of syncing up the different departments on what’s most important to the company.

Step 5
(Are we not done yet?) Breaking out the SQL magic. Hopefully you have a SQL wizard on standby because you’re going to need someone to write the code that combines the different data sets together. What happens behind the scenes is typically thought of as a “black box” to most people, but you still have to have someone who knows what’s happening back there, manipulating the data to come to the right conclusions on KPIs.

Step 6
Last, but certainly not least, you need to acquire an orchestration tool. Remember learning the Order of Operations in grade school? Does PEMDAS ring any bells? Orchestration tools are important guardrails to make sure all the transformations you plan on doing happen in exactly the right order. Because some transformations require previous transformations to happen first, a tool like this ensures your data multiplies before it does addition (so to speak).

Boy, that is a lot of steps just to get yourself set up to start capitalizing on your data. Don’t worry though, we’re not just here to scare you. For every data problem, there’s a solution right around the corner, and we don’t want to leave you out in the cold.


Sheltering Your Insights Shouldn’t Be This Hard

Analytics Odyssey noticed a gap in the market that other data management companies just weren’t satisfying. Today’s businesses need a more holistic solution to their data management that can keep you in the cockpit when it comes to your data, but also automates a lot of these processes for them, cutting down on countless hours of administrative tasks, troubleshooting, etc. Thankfully, Analytics Odyssey provides all of these steps in one place. Now there’s no need to search across the web for a myriad of tools that match to each individual step. With Analytics Odyssey, you can get everyone on the same page, all in one place. Make miscommunication a thing of the past. Plus you can finally hang up on that support call repeating “we’ll be with you shortly” for the last six hours, because now you have your own dedicated team eager to help.

In case you aren’t convinced yet, here are a few more benefits of good data modeling to help sweeten the deal.

  1. Performance: Optimize database performance by indexing data and specifying the appropriate data types and sizes for fields.
  2. Speed: Retrieve and manipulate your data in a snap.
  3. Space: The final frontier. Reduce the overall storage space required for your database.
  4. Integrity: Stand tall and confident in the integrity and accuracy of your stored data.
  5. Security: Squash security risks. Identify potential vulnerabilities from the start and design your database in a way that minimizes those risks.
  6. Accessibility: No more gibberish. Data is easier to understand and utilize, and therefore more accessible across the entire company.
  7. Integration: Connect and conquer. Enjoy simplistic integration and data sharing with other systems, so you can evolve as your data evolves.
  8. Scalability: Grow your data capabilities in harmony with the size and scale of your growing business.

Contact Analytics Odyssey today to see how our data analytics and management platform can “transform” how you do data, and don’t forget to keep an eye out for our second lesson, Transformation 102, coming soon!