Analytics Odyssey: Recent Episodes
Analytics Odyssey
DataOps 101Evolution of the Modern Data Stack: Demystifying Data’s Notorious “Black Box”“What in the heck is a Modern Data Stack and why should I care?”
It’s a common question we get with companies just starting out on their data journey, and frankly, it makes a lot of sense. The title can be both confusing and daunting for first timers (and even for some data vets out there).
For those who don’t know, a data stack is a collection of tools centered around the gathering, manipulation, and use of data. As technology has developed since the first days of the data stack, the stack’s role has grown to include more tools that make the process even more efficient and the end results more useful. However, if data stacks have been evolving for decades, why do we settle on such a static naming convention: the “Modern” Data Stack? If we’ve learned anything from the silly naming of artistic eras like “Modernism”, one might question what we’ll have to name the next era of data tools. Do we resign to calling it the “Postmodern” data stack (and then even worse, the “post-postmodern data stack”)?
Fortunately, the term “modern” could be less indicative of an era, and more indicative of a core competency of today’s data stacks, which is their ability to stay a relevant and efficient part of an evolving data landscape, incorporating new software and technical advancements efficiently into existing frameworks. Almost paradoxically, in order for data stacks to remain truly “modern,” the systems we use, as well as the individual instances of data themselves, must evolve with the needs of today’s companies and their customers.
Here we’ll take a closer look at the history behind the data stack and the developments that have kept our data systems up to date and “modern,” even within an era of rapid technological development.
The Stack: An Origin Story
It might be impossible to imagine now, but the original digital data storehouse started in the 1950s, recorded on old-school punch cards. Any data you would want to save came with tons of labor and even larger amounts of space to store and maintain the absolute beasts that early computing is famous for. Cards had to be manually fed into machines that recorded the data, and afterward, data was stored in its physical form, once again by teams of people that organized and watched over these gigantic stacks of data much like librarians and their countless volumes.
Thankfully, humanity would soon evolve beyond this laborious era of floors filled with punch cards and teams to manually feed them and into the era of the mainframe and even the personal computer—an era of technology that in some ways still resembles the technology of today.1970s: The ETA for ETL
Many of the foundational principles and advancements in the history of data management were introduced in the 1970s. Alongside the development of relational databases, companies could now extract and integrate data from multiple sources, leading to the principle of Extract, Transform, Load, or ETL. Still used by many professionals to this day, this simple, yet powerful idea of collecting and converting data to encourage consistency laid the groundwork for many new advancements in the field throughout later decades.With data now being stored on magnetic tapes or disks, computers were becoming more capable of efficiently handling large volumes of data. The birth of the query language SQL in the 70s also led to more clear and streamlined data queries that could be used by more people. Combined with new hardware developments and the relative ease of communicating with databases through SQL, ETL was set to flourish in the following years, starting with the emergence of the data warehouse.1980s: IBM, The Warehouse Powerhouse
Believe it or not, it wasn’t until the 1980s that the current vision of what a data warehouse should look like really took form.
In the midst of IBM’s era of market domination over the computing space, researchers in the company began playing with better ways to centralize and consolidate their data from different sources to generate reports. Though computers may have decreased in size by this point, allowing widespread use among a variety of job roles, IT was still the undisputed overlord of data, and their work was the definition of “siloed.” While databases had progressed to the point of having separate repositories specifically for analytics, all inquiries had to be dealt with through IT from beginning to end. Those outside the department started to grow antsy for a method of accessing data themselves without the need for an IT degree of their own.2000s: Data Demands Digital
Thanks to both the internet boom and bust of the 90s and early 2000s, the sheer volume of data generated by trackable activities accelerated rapidly. Data experts struggled to keep up with the needs of these big corporations and the potential to profit from this “digital gold.” Since the methods of old could no longer manage, up-and-comer Amazon looked to the clouds for an answer.
Amazon Web Services and others like Microsoft Azure and Google Cloud Platform revolutionized how these mountains of data could be processed and stored by sending it to the cloud. With easily-expandable digital storage, companies had the potential to scale their data solutions faster and with more flexibility, becoming less reliant on large, hardware-intensive in-house IT teams. However, it would take a next-generation tool to fill in the gaps still left by these first-generation cloud data solutions, including their price point that only appealed to huge-scale corporations and their unimpressive speed. It was time for Redshift to make data possible for the “Average Joe.”2010s: “Redshifting” into the MDS
Despite the major leap that cloud data processing was, many companies did not have the resources or infrastructure to fully take advantage of this movement until 2012. That’s when Amazon Redshift changed everything. The first data warehouse to run natively in the cloud, Redshift addressed many of the problems faced by previous cloud solutions, namely their cost and ease of use. Now more companies than ever could get real data analytics without having the deepest of pockets. Small to medium-sized companies finally had a realistic solution for managing their growing data needs. Not only that, Redshift’s innovative architecture made data processing much faster—like lightning fast compared to its predecessors.The effects of Redshift on data management were so far-reaching that the era following Redshift’s release later became known as the “First Cambrian Explosion.” With Redshift leading the charge, many more solutions used this tidal wave as a time to research and release their own innovations to complement it. Several well-known products inspired by the successes of Redshift include Snowflake, dbt, and BigQuery.The Aftermath and Defining the Next Great Era
Many believe that the technological wave of innovation ushered in by Redshift solidifies our definition of the “Modern” Data Stack as we see it today. We generally consider the following features when deciding if a data stack is truly “modern:”* It must be cloud-based * It’s both modular and customizable * It considers best-of-breed first (choosing the best tool for a specific job, versus an all-in-one solution) * It’s driven by metadata
While massive developments have slowed since this “golden era” ended after 2016, we’re still happy to enjoy smaller developments in the tools we use within the Data Stack. Nonetheless, all it takes is one tool to jumpstart the next explosion of data innovation and company productivity.Some think the “next big thing” will stem from the recent explosion in AI tools, while others believe it will come as a solution to a well-known data problem, such as gaps in the “data feedback loop.” But maybe that innovation is available right now, just waiting to be picked up. We believe Analytics Odyssey could be that game-changer companies have been searching for.With Analytics Odyssey’s low barrier to entry, anyone can learn to use their data more efficiently in a matter of hours, not months. Not only that, the Analytics Odyssey platform is designed with adaptability in mind—ready to evolve with you and your data. As the “modern” data stack changes with new technologies to integrate with, you won’t have to worry about falling behind the competition or emerging technologies leaving you in the dust.The post Evolution of the Modern Data Stack: Demystifying Data’s Notorious “Black Box” appeared first on Analytics Odyssey.
Transformation 101Don’t Leave Your Data Out in the Cold: Sheltering Your InsightsTry 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 OrderIn 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 WayThe 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
- 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.
- Inconsistent data – nothing wipes an analyst’s confidence more than seeing multiple sources returning different data for the same query.
- 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:
- Cost of ownership increases when architects and engineers have to frequently fix things, and updates to the system slow to a grinding halt.
- Missed opportunities begin to pile up thanks to slow query times and poor scalability of the system.
- 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 1Data 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 2It’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 3Start 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 4Your 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 6Last, 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.
- Performance: Optimize database performance by indexing data and specifying the appropriate data types and sizes for fields.
- Speed: Retrieve and manipulate your data in a snap.
- Space: The final frontier. Reduce the overall storage space required for your database.
- Integrity: Stand tall and confident in the integrity and accuracy of your stored data.
- Security: Squash security risks. Identify potential vulnerabilities from the start and design your database in a way that minimizes those risks.
- Accessibility: No more gibberish. Data is easier to understand and utilize, and therefore more accessible across the entire company.
- Integration: Connect and conquer. Enjoy simplistic integration and data sharing with other systems, so you can evolve as your data evolves.
- 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!
The post Don’t Leave Your Data Out in the Cold: Sheltering Your Insights appeared first on Analytics Odyssey.
Case StudyFive Star Franchising Rapid Growth With Analytics Odyssey’s Data PlatformIntroductionFive Star Franchising is a leading home services franchise company based in Utah with over 400 franchise partners across the United States and Canada. Beginning as Five Star Painting, the franchising firm has since evolved to encompass five brands: Five Star Bath Solutions, Gotcha Covered, ProNexis, Bio-One, 1-800-Packouts, 1-800-Textiles, Mosquito Shield under the holding company Five Star Franchising.
After creating a proven system, Five Star Franchising brings franchise partners the tools and support needed to maximize entrepreneurial potential in growing a home based business.
As the company grew, they faced a significant challenge in understanding the performance of their five brands and each brand location. Feeling frustrated at the fractured data spread across multiple CRMs, the executive team knew the organization needed centralized and specialized reports to transform its data and empower its franchisees to be successful. That’s when they turned to Analytics Odyssey.
Through their partnerships, Analytics Odyssey provided valuable insights into the franchise performance, and made it possible to access year over year reporting for all 400 locations in less than 30 days. Additionally, each location owner was able to measure their performance across the brand. These data insights proved to be valuable to the accuracy and growth of the Five Star Franchising business.
The Challenge of Tracking PerformanceFive Star Franchising executives recognized an untapped potential for optimizing business decisions through better understanding and tracking of performance for each brand and location.
The company lacked resources to effectively implement a large data warehouse with various pipelines customized to each franchise location. They needed a team who could help collect and sort all that mystified data. Recognizing the potential in Analytics Odyssey’s data platform and consulting services, the executive team at Five Star Franchising was confident this solution could solve their problem.
They were excited to work with experts who could advise on their data roadmap going forward, and who could grant access to preferred tools so they could continue maintaining the technology along the way.
Why Five Star Franchising Chose Analytics OdysseyThe Five Star Franchising team was looking for someone who would not only develop what they needed, but who could teach them to be self-sufficient down the road. They wanted to be able to maintain and update reports on their own, without relying on outside help. Analytics Odyssey fit the bill as it showcased both of these key factors in its business model.
When Analytics Odyssey provided the expertise and technology that Five Star Franchising needed, all within a budget that made sense, they knew this solution beat out other data analytics solutions they had investigated.
“We are extremely happy with the way they’ve organized and transformed our data, and thrilled to have a partner with their expertise to work with and advise our existing data personnel,” said CTO of Five Star Franchising, Shane Mackay.
How Analytics Odyssey RespondedAfter engaging with Analytics Odyssey, the team immediately addressed the challenge of understanding and tracking performance of the franchise firm’s brands and brand locations.
The first step was to understand their data roadmap and identify the key metrics that would give the executive leadership team the insights they needed. Analytics Odyssey collaborated with Five Star Franchising to develop a strategy for obtaining revenue reports from flat file sources in order to generate high-level reports quickly. They also created a framework for filling in all the missing data pieces for these reports once the CRM integrations were completed.
The team of data experts worked closely with Five Star Franchising to develop reports that would meet their needs, while involving them in the process. This fostered learning and understanding so that the executive team could become self-sufficient in the future.
In the early days, Five Star Franchising was pleased with the data insights that Analytics Odyssey’s organized datastack unveiled, the mentoring they received, and the progress being made for their own brands.
By prioritizing the needs of the client and fulfilling their commitments, Analytics Odyssey established a strong relationship with Five Star Franchising and earned their trust, elevating their confidence in the product’s ability to achieve their data analytics goals.
The ResultsBy implementing Analytics Odyssey’s comprehensive data stack, Five Star Franchising saw several key successes including the ability to identify top performers and track trends in same-store sales. This provided invaluable insights into the performance of the brands and allowed brand presidents to make data-driven decisions to drive growth and improve the bottom line. Each brand president is now able to measure each locations performance across the brand.
Another key success was the ability to access year-over-year reporting for all 400 locations in less than 30 days. This gave the leadership team a much-needed overview of their performance, and it allowed them to identify areas for improvement, or where data was previously inaccurate.
“Our data was all fractured and in different CRMs, and we really needed specialized reports that worked with each of our brands. Analytics Odyssey provided the expertise and team we needed all within a budget that made sense. We are extremely happy with the way they’ve organized and transformed our data.”
-CTO of Five Star Franchising, Shane Mackay.
Five Star Franchising faced a significant challenge in understanding the performance of their brands and brand locations. However, by working with Analytics Odyssey, they were able to overcome this challenge and achieve significant growth in revenue. They also gained valuable insights into their performance, and were able to access year over year reporting for all 400 locations in less than 30 days. Additionally, each location owner was able to measure their performance across the brand.
With the help of Analytics Odysseys comprehensive data stack and consuslting services, Five Star Franchising was able to become self-sufficient in data analytics, and continue to thrive in their business.
If your franchise is looking to track your data more efficiently to make better-informed decisions, we’d love to help. To scale your franchise more quickly and effectively, contact our team at info@analyticsodyssey.com or schedule a demo here.
About Five Star Franchising
Five Star Franchising, a leading home services franchise company with 400 franchise partners across the US and Canada, faced challenges in understanding the performance of their brands and each brand location due to fractured data spread across multiple CRMs. Analytics Odyssey, through its data platform and consulting services, provided valuable insights into the franchise performance, and made it possible to access year-over-year reporting for all 400 locations in less than 30 days. By implementing Analytics Odyssey’s comprehensive data stack, Five Star Franchising achieved significant milestones in data management and became self-sufficient in data analytics.
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FOR IMMEDIATE RELEASE
ANALYTICS ODYSSEY PARTNERS WITH FIVE STAR FRANCHISING
Franchisors empowered with comprehensive and attainable data analytics to make better decisions faster through Analytics Odyssey’s modern data stack.
PROVO, FEB 17, 2022 — Analytics Odyssey, data analytics solutions provider, announced its partnership with Five Star Franchising, a leading home-service franchising firm in Utah. The partnership aims to help each franchise brand optimize its operations, improve performance, and scale quicker through data-driven insights.
As part of the partnership, Five Star Franchising will white-label Analytics Odyssey’s advanced analytics platform allowing real-time tracking and analysis of key performance indicators (KPIs). This will enable franchisors to make informed decisions about their operations, marketing, and sales strategies across all their brands. Performance reports can easily be generated and shared with the Board of Directors and equitiy partners through the data platform.
In addition to providing the data analytics platform, Analytics Odyssey will also supply Five Star Franchising partners with customized training, support, and consulting services to help them fully utilize the platform and gain the maximum benefits from their data.
“We are excited to partner with Five Star Franchising to bring our data analytics solutions to their franchise system,” said Derek Doel, CEO of Analytics Odyssey. “We believe that data-driven insights are essential for any business looking to succeed in today’s competitive landscape, and we look forward to helping franchisors optimize their operations and improve performance through our platform.”
“Before working with Analytics Odyssey, our data was fractured and dispersed throughout different CRMs,” said Shane Mackay, Chief Technology Officer of Five Star Franchising. “We really needed specialized reports that worked with each of our brands. Analytics Odyssey provided the expertise and team we needed all within a budget that made sense. We are extremely happy with the way they have organized and transformed our data, and thrilled to have a partner with such expertise to work with and advise our existing data personnel.”
To learn more about Analytics Odyssey’s comprehensive datastack, please visit www.analyticsodyssey.com.
For more information and media inquiries, contact info@analyticsodyssey.com.
About Analytics Odyssey
Analytics Odyssey helps businesses make confident decisions by taking advantage of the power behind their datastack. They provide advanced data tools like data warehousing, modeling, and DataOps, as well as end-to-end management and consulting to empower employees at every step of the data chain, from analyst to executive. With Analytics Odyssey, franchisors can access their data whenever they need it, right at their fingertips.
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Analytics 101Zoom Out and Drill Down: The Benefits of Core PerformanceIt’s your first day at the new job, your Analytics degree still fresh off the press, and you’re ready to start moving the needle for your company. As an analyst, your job is to inform. In 2023, high-level business decisions can’t be made on a whim or based on simple hunches; we need the data. Your job is to collect and interpret massive deposits of data and provide decision makers with real insights backed by real data.
Here’s the problem. On Day 1, the company dumps their massive data warehouse into your lap and casually tells you “have fun” as they walk away. Needless to say, your stress levels just spiked. Even with your skills, knowledge, and practice with data, you don’t even know where to begin. Data in its rawest form is nothing short of a beast, and when you have millions of data points to manage, it can feel like you’re raising a house full of toddlers all by yourself—just with fewer dirty diapers.
Of all the questions running through your head, the loudest is probably “Where do I even begin?” or “How do I parse through millions of data points to focus on only what’s important: the holy grail that can drive my company forward?” To answer this, we need to talk about something called core performance.
What is Core Performance and Why Should I Care?In the Data Analytics profession, core performance is the set of metrics or indicators that demonstrate your level of success. They’re the meat and potatoes of your operations, the helm of the ship that either keeps your company on track, or prepares you for a major course correction. These metrics are indispensable since data analysts act as the first line of defense against possible market shifts that can make or break the company. If it’s happening in the market, you can bet it’ll show up in the data first. If you think of the CEO as the king of the company, the data analyst would be the trusted advisor who’s always seen whispering into the king’s ear.
As you’ve probably picked up by now, core performance is kind of a big deal; it helps your company set performance targets and track their progress, it gives you red flags for areas of improvement before they become emergencies, and it helps give your company some direction toward their goals—much like a captain’s compass.
“So I understand why Core Performance is important, but how do I distinguish this from all the other data points floating around in the ether?” That’s a fantastic question. And as many of you know, the answer to all great questions is always: It depends.
In all seriousness, Core Performance can differ wildly from company to company. The spectrum of possible company goals is huge, and even a single company goal may change from year to year (or month to month!). While one company might treat their Sales team like royalty and every dollar they bring in as manna from heaven, other companies might see networking and building partnerships as a healthier long-term goal.
No two companies are the same, so no two data reports should be the same either. The sales-team-centric company may prioritize leads generated, customer acquisition costs, and value per customer as their core performance, while others might put social media impressions, partner sign ups, or manufacturing costs at their core. Just remember, a critical tenant of core performance is making sure you’re tracking the right metrics at the right time. Other examples you might want to consider as your core performance metrics include: revenue, profits, customer satisfaction, employee retention, and employee efficiency. The ultimate responsibility of the analyst is dissecting high-level company goals, and connecting them with the metrics that give the best picture of the goal’s completion.
By now, the question “Why should I care?” should be pretty self-explanatory, but just in case, we’ll sum it up for you: core performance is what informs the decision makers of your company where they’re headed and at what speed. Without these key metrics in place, data analysts will be left floating in a sea of data, grasping for whatever metrics can keep them afloat the longest. Accurate reporting is crucial to every business no matter what their goals are, and having the right core performance metrics in place gives management the ability to zoom out and drill down into all levels of company performance.
Through the Looking Glass: Telescope or Microscope?One of the biggest benefits to using core performance metrics is that they can paint you a data picture through a variety of lenses, from telescopic to microscopic.
As most data analysts can attest, members of the executive team are frequently in need of reports, forecasts, and analysis in order to make their next move. Business strategy is often like a game of chess—if you want to win, each move must be carefully considered and calculated. You won’t get very far against someone who understands the game if you just move your pieces around carelessly (perhaps the reason why I always lose).
Just as a chess master might take a step back and look at the board as a whole, considering all the possible moves and how each scenario might play out, so should executives view their business from time to time. We’ll call this the telescope approach: focusing on the big picture and high-level factors at play, momentarily setting aside the small details that might cloud your view of the stars.
Core performance is essential to providing this zoomed-out perspective because if you don’t have the right metrics nailed down from the start, executives won’t be able to make use of your reports—or worse—you send them chasing a target that’s not in line with the company’s goals. Imagine looking through your telescope at a beautiful constellation, only to have your friend inform you that you’re actually looking at a couple of planes in the air.
The second important use of core performance metrics is the ability to drill down to the most specific details to make changes on the front lines, because as the visionary artist Vincent van Gogh once said: “Great things are done by a series of small things brought together.” We’ll call this the microscope approach: identifying concrete details that are either helping or hindering your company goals. Perhaps that’s an underperforming employee who needs extra guidance, or a sales process that’s becoming a bottleneck for other teams.
Having your core performance metrics in place and clearly identified allows executives and managers to better prioritize resources based on how closely a proposal is to the core metrics. For example, if improving employee retention is one of your top goals for 2023 and surveys have indicated that employees value a “fun workplace,” maybe shelling out for that foosball table takes priority over paying for newer and better software extensions.
The drill down technique allows you to solve smaller problems before they pile up into a company killer. With the right data from the analyst team, executives are better equipped to delegate tasks to various levels of management that will ignite change in the trenches of your business operations.
Not an Overnight SuccessUnfortunately for many lightning-fast companies, a data analytics strategy takes time to build. You can’t expect a new hire such as the one first described to be able to revolutionize the company overnight. However, through implementation of core performance metrics, your company can strengthen its reporting process for more reliable, accurate, and—most importantly—helpful insights every step of the way.
But at this point, you may be asking yourself, “Is this really worth it? What can I actually expect to get out of a process like this?” which makes sense. Changing the way you think about data takes time, and having reservations about making that kind of commitment is normal. Let’s explore some of the benefits we’ve seen from companies who have risen to the challenge and focused their efforts into defining their core performance:
- Increased revenue
a. Increased customer lifetime value and reduced churn.
b. More efficient sales and marketing processes. - A better support network and chain of command
a. This is especially helpful for franchisees who rely on outside management for guidance. - Machine learning capabilities
Perhaps your eyes glistened at the word “revenue” or it was a combination of the three that excited you, but if you’ve gotten this far, that means you’d probably be willing to try something new with your data. While we can’t promise you’ll see results tomorrow, you’d be surprised how fast the following four steps will fly by when you’re motivated to make it happen.
Step 1: Establish Core Metrics for each department of the companyDid you think you were going to implement core metrics for a single department on its own? Think again! While core metrics can be useful for any team who takes advantage of them, they’re an absolute game-changer when applied across the board. When departments work together with the same structured goals in mind (or differing goals that complement one another) it’s easier for a unified vision to come into clarity, both within the data and in practice. Here’s a list of questions to ask yourself and other departments to help ensure core metric alignment:
- What metrics are you currently tracking and how?
- What data are you currently collecting?
- What are your company’s main goals and objectives?
- What metrics are your competitors using?
- How will you use the metrics in your decision making?
- Who is responsible for tracking these metrics?
Step 2: Establish Core Dimensions that describe the detail of each metricAside from the aforementioned metrics, don’t forget to choose your core dimensions carefully as well. These will help you filter your data more effectively, allowing you to see patterns and identify trends more quickly. Achieving the level of granularity that best fits your company’s needs is important. Granularity refers to the specificity of each dimension you’re collecting, such as time, deciding to track year, date, or date and time. Identifying the ideal granularity is a delicate balance; greater detail comes at the sacrifice of simplicity, so choose wisely. If it’s not feasible or necessary to collect certain data every minute, for example, then it may suffice to just track the date.
Step 3: Combine, & transform source tables into a centralized data modelAs Benjamin Franklin famously puts it, “Everything has a place, and everything in its place.” The same goes for data. A disarray of source tables will make your job a living nightmare, so the process of transforming and cleansing your data is crucial.
- After choosing your level of granularity, transform and cleanse your data to be uniformly compatible with your centralized model. Analytics Odyssey can help with this.
- This may involve aggregating data to a higher level of granularity to ensure the data mapping corresponds with a common set of dimensions.
- KEEP YOUR DATA MODEL ACCURATE AND UP-TO-DATE. Yes, I’m looking at you. In order to stay reliable and useful, you must monitor and test data quality regularly.
Step 4: Create a dashboard where core metrics match KPIsIn your dashboard, input your core metrics as KPIs along the top bar, allowing you to keep track of them all in real time. Never miss a beat: this kind of dashboard integration allows you to always have your hand on the pulse of the company. We know finding the right dashboard tool is hard. Thankfully, Analytics Odyssey can help with that too!
Here’s an example to get you thinkingA dashboard like this turns you into a data superhero. With the ability to filter all your data by specific decisions, you can save brainpower to put to finding real solutions instead of searching for the most important data pieces like needles in a haystack. Here, everything you need comes at the click of a button. Want to find sales volume by location? Click, done. Want to see a table of your sales over time? Click, click, “your table is ready, sir.”
In the following example, sales numbers and quotes are your core metrics, while location, owner, brand, email, status, and date are your core dimensions. Drill down into your data with the power of filters, allowing you to go from telescope to microscope in a matter of seconds, and vice versa. The best part is you can get all your data, all your insights, all in one place and from anywhere, even from your laptop!
Next upIf you feel like you’re ready to, you may want to check out our next lesson in the Analytics Series, 102, where we discuss Comparisons.
The post Zoom Out and Drill Down: The Benefits of Core Performance appeared first on Analytics Odyssey.
FOR IMMEDIATE RELEASE
ANALYTICS ODYSSEY LAUNCHES NEW DATA STACK PRODUCT TO PROVIDE FRANCHISORS AN EFFICIENT AND EFFECTIVE SYSTEM FOR STORING, MANAGING, AND ANALYZING DATA
Analytics Odyssey’s modern data stack provides strategic end-to-end data management to give business leaders a confident, data-driven organization.
PROVO, Jan 26, 2022 — Analytics Odyssey founder develops and launches new data platform to handle a larger volume of data, process data more quickly, and provide more advanced analytics capabilities to franchisors after spending decades in the data analytics field.
“Data is the brain of any business,” says Analytics Odyssey founder and CEO, Derek Doel. “I know first-hand how frustrating it is to build complex reports over and over again to keep up with evolving data-points. Businesses and franchisors need a way to make decisions faster, more accurately, and more confidently.”
The Analytics Odyssey data platform takes a united approach, combining customized roadmapping and end-to-end data management solutions at a fraction of the time and cost. Created by a team of data strategy experts, the platform provides strategic resources to achieve trustworthy and quickly-accessible data to give decision-makers confidence behind their data.
As companies grow and franchise, data management becomes increasingly complex and difficult to update. Now, with Analytics Odyssey’s data system, all data is centralized in one place making it simple, usable, and easy to manage. Business Leaders, Data Analysts, Business Analysts, and Data Engineers can make effective data-driven decisions for their businesses.
To learn more about Analytics Odyssey’s comprehensive data stack, please visit www.analyticsodyssey.com.
For more information and media inquiries, contact info@analyticsodyssey.com.
About Analytics Odyssey
Analytics Odyssey provides customized data management solutions, designed to streamline your data and provide actionable insights. Our platform is created by experts in data strategy and provides centralized data systems for simplified management. With Analytics Odyssey, decision-makers can make informed decisions with confidence, knowing that their data is trustworthy and accessible. Our solutions save time and cost compared to traditional data management approaches and are ideal for growing companies and franchises. Let Analytics Odyssey help you make data-driven decisions for your business by providing data right to your fingertips.
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Episode 100: Data Literacy Qlik Global Head of Data Literacy, helping individuals and organizations realize their data and analytical potential by bringing to light and enhancing skills in data literacy. When not found within data and analytics, he can be found with his family or trail running the mountains of Utah.
The post Data Literacy in SAAS appeared first on Analytics Odyssey.
It takes discipline to build your data products Author: Derek Doel (Founder and Podcast Host) Hello, my name is Derek Doel and I’ve been in the Data Science discipline for the last decade. In that time I’ve seen small businesses and enterprises alike change their perspective on the value that data has for their company.…
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Establishing the base foundation: 3 steps to building descriptive analytics The data for your business does not exist until your business designs a product (or integrates with one) that generates it. Once everything is in place and the lights turn on, it’s your customer’s usage that creates the data you want to mine. But what…
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The post Baby Steps to Complete Analytical Maturity appeared first on Analytics Odyssey.
Ever wonder how engineers can step into a company, join a development team, and start building software right away? It’s because the way a business logically builds its database, is the exact same way their brains logically think. Whether you’re aware of it or not, your mind is constantly building a database and storing your…
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The post How to create a mind palace and populate with Data appeared first on Analytics Odyssey.