Alright, so your business has a ton of data—great! But now what? It’s like having a pile of ingredients in your kitchen but no recipe. Data is fantastic, but without a game plan, it won’t do much for you. So how do you turn all those numbers into something that provides incremental value to business leaders that they don't already know?
According to a Salesforce study in 2023, 80% of business leaders said data is critical in decision-making at their organization. They cited benefits like faster decision-making, building trust within the organization, improves focus and reduces uncertainty. However the same study found that 67% of leaders were not leveraging data for complex strategies like pricing due to having inadequate tools to generate insights from their data.
Business leaders are experiencing one of the toughest economic markets of our time, but they have an untapped advantage for better decision-making: their data.
Juan Perez, Chief Information Officer, Salesforce
The CIO of Salesforce is focused on data literacy training for employees to establish a data culture that supports business goals and improves resilience ahead of a potentially turbulent economic future. “The secret to driving true insights is marrying data with analytics. A combination of data, analytics, and the necessary data skills enables companies to maximize their technology investments and uncover opportunities that drive business strategy and strengthen customer trust,” said Perez.
Data literacy is critical for success in any organization in the modern economy where data is everywhere and tells the unspoken tales of potential opportunity and existing waste. Getting value from data is simpler than you might think--we'll break it down into steps with a digestible example.
Step 1: Data - You’ve Got a Lot, but So What?
First things first, you've integrated data from all your business software and stored it in a data warehouse, including marketing, sales, customer interactions, and website traffic data. But just having data isn’t enough. It’s like having a phone book (remember those?), but no idea who to call. What matters is how you are using your data.
Let’s say you are selling online and in-store. Over the past year, you’ve been tracking customer behavior. Tons of people are buying your products, but where do you go from here?
Step 2: Information - Organizing the Chaos
Now that you’ve got all this data, it’s time to organize it into something useful. This is the step where you turn random numbers into valuable information. You do this by segmenting your data into categories. Think of it like decluttering your closet–grouping similar things together makes it easier to figure out what’s really going on.
You might separate your customers into groups–those who shop online vs. in-store, those who subscribe to a service vs. those who make one-time purchases. Suddenly, the data makes more sense because you are looking at it in buckets.
Step 3: Knowledge – Spotting Patterns
Once you’ve organized your data, you can start spotting patterns. This is where your data turns into knowledge. It’s like noticing that every time you put out a curtain display ad, more people end up on your website. Patterns are what turn data into something that you can actually use.
You may discover that customers who see your display ads are landing on your website and buying more often than those who come through other channels like organic search. Now you’re onto something!
Step 4: Insights – Digging Deeper Into the Why
Alright, you’ve got your patterns, but now it’s time to take it a step further. Insights are where you dig into the why behind those patterns. Why are certain customers responding to certain ads or offers? When you can answer that, you’re moving toward actionable insights.
Maybe you find that families with young kids are way more likely to become repeat customers after clicking on Ad A. That’s a huge insight! It tells you something important about your audience that you can use to shape your marketing strategies.
Step 5: Wisdom–Telling the Data Story
Now it’s time to put all of those pieces together and tell the story your data is revealing. This is where your data turns into wisdom–understanding the bigger picture. You can now see how everything is connected, from the initial ad click to the final sale, and what it means for your business.
For example, picture that you now know that families with young kids are a key audience, and they love your messaging in Ad A. That’s a golden nugget of wisdom you can take straight to the bank (literally!).
Step 6: Impact–Taking Action That Moves the Needle
Finally, the big moment. This is where all your data work pays off. The impact comes from taking what you’ve learned and actually applying it to your business strategy. It’s about making changes that lead to real results, like increased sales, better customer experiences, or more targeted marketing.
Imagine you decided to shift your marketing strategy to focus more on families with young kids. You create more ads like Ad A, tweak your messaging to speak directly to an audience, and watch your sales soar. That’s how you drive impact!
Data isn’t Just Numbers–It’s your business GPS
There’s no shortage of data, but the real value lies in how you use it. More and more companies are investing in data infrastructure solutions to support decision making at a faster rate than ever before. To make a lasting impact on your business, don’t just collect data–proactively organize it, analyze it for patterns, extract insights, and make informed decisions based on the data your story tells.
Remember, the key to unlocking the power of your data is taking a problem-solving approach at each stage–moving from raw information to insights and, finally, to impactful action that drives growth.
Have you ever wondered how big companies keep track of all their data and then actually use it to make decisions? Imagine owning thousands of books in a library without any shelves or labels, and you needed to know how many stories had a protagonist named "Matthew"—that would be very time consuming to answer, right? Large companies face a similar challenge with their data. That's where a data warehouse and analytical software come in! You can read more about how data improves outcomes for growing businesses here. But before you get started, you need to know: What is the true cost of building a modern data infrastructure in-house?
A complete data infrastructure will detail the complete journey of data from its raw state to the procured insight that is used to eventually drive business impact. This includes the transformation pipeline that moves data from its source to the target data warehouse, the actual data warehouse that houses all the data and enables users to query data to perform analyses and finally the visualization software that automates recurring reports via dashboards.
Each of four components outlined above usually live in different software. Additionally, they require a team of data architects and engineers to tie in all the pieces together and provide ongoing maintenance and support. The cost for each will depend significantly on which software you choose and how much data is involved. A typical all-in-cost for a mid-market company ranges from $25k-$500k per year for the software plus resources and an additional $400k-$800k for salary and benefits of a professional data team to support and maintain the solution.
That cost may seem high at first, and it is. Let's break it down to get a more true cost for a company like yours.
Transforming raw data from its original source (i.e. accounting software) to cleaned data in the target data warehouse happens through a transformation pipeline, often referred to as ETL, or Extract, Transform, Load.
Data sources can include business software (i.e. Quickbooks or other specialized accounting software), databases (i.e. PostgreSQL, MySQL or other database), Google Sheets, CSV uploads and even unstructured text data (PDFs, word documents, Notion, etc). See here for an ever growing list of data sources that Go Fig supports.
Go Fig offers a No-Code Workflow builder with plug-n-play functions to clean and transform data into a format that is useful and suitable for analysis and reporting. Datasets that are not clean can potentially be dangerous, resulting in errors that can lead to misguided decision-making that creates risk for the business. According to Gartner, such bad data cost companies an average of $15 million in lost revenue in 2017-- significantly more than costs of building a robust data infrastructure.
All-in Cost: between $5k to over $50k per year depending on the plan you choose and the volume of jobs. Many ETL solutions offer a variable pricing model that starts off with a free or low introductory rate, but scales up rapidly as you begin to use it more. This keeps you locked in on elevated prices that can jump unpredictably on any given month with particularly heavy usage.
To keep costs lower and predictable month to month, you may want to consider an ETL solution with a fixed pricing model that keeps rates consistent, and only increases when you choose a higher tiered plan.
Go Fig, for example, charges a fixed monthly or annual rate for unlimited workload for any of the tiered plans offered and only increases if you exceed the storage limit for each plan. This allows you to know exactly how much the plan will cost and you will have months to plan for a price hike, if at all.
A data warehouse is like a giant, super-organized library for a company's information. It stores post-transformed data from lots of places, like websites, sales records, and customer lists, and stores it all in one location. This is also the place where people in the company go to access their data for creating reports and doing analysis on company performance.
Companies can store data on their own servers (called on-premises) or using an external cloud-based solution. Deciding between on-premise vs cloud solution is wholly dependent on the company. On-premise requires physical storage space, upfront investment in time and resources, and ongoing maintenance, but they also give the company full control of their data.
All-in Cost:
Starting out, a company will likely find it more cost efficient with a cloud-based data warehouse. Once a company's data infrastructure reaches a peak stage of maturity, it could migrate its infrastructure to its own servers to obtain more cost savings.
All data warehousing solutions offer a platform for analysts to write SQL queries to pull the data they need to perform specific analyses, such as explaining the main drivers of recent sales trends or investigating an opportunity to improve marketing outcomes. This operation has typically been reserved for professional data analysts with a strong understanding of relational databases and technical experience writing code to get accurate data. With the advent of LLMs and AI, it has become possible for others to also query accurate data to answer such questions.
Go Fig is an example of a company that is leveraging LLMs like ChatGPT to equip C-Suite leaders and frontline employees alike with this analytics capability. Our proprietary Harvest-1 foundational model is built to understand the intent of each individual user and translates requests into a simple and understandable No-Code Workflow and Fig that can be validated or modified further.
Completing the chain of the data lifecycle are dashboards, which are elegant visual representations of the data prepared in a consumable format for humans to understand and digest insights from data so they can make informed decisions that drive the business forward. Data visualization software that prepare these dashboards are often referred to as Business Intelligence, or BI tools.
Without a dashboarding software, data that lives in a data warehouse can be pretty meaningless, so this software is a critical piece to the puzzle in order to get value from your investment in data. Analysis that depends on a human to manually query data and update a static excel spreadsheet, as valuable as that may be, is slow and time-consuming. With dashboards, any type of report or data manipulation you would perform in Excel could be available in a dashboard, updated every time the underlying data warehouse is updated. Imagine saving 2 hours every Monday from your intern who manually updates your weekly sales report, and having that same report updated every day of the week.
Extending beyond visualization, more advanced BI tools offer features to proactively monitor data and send alerts proactively. For example, Go Fig can send an alert when sales by 12pm on a particular day are below the acceptable threshold, signaling that there could be a severe issue with the sales team that requires your attention immediately. Creating an alert is simple, simply determine thresholds for a pre-defined metric, and we'll check it every time new data is pulled in!
All-in Cost: the average business intelligence solution costs $3k per year, but can cost upwards of $10k per year for more advanced solutions
As highlighted above, the cost of bad data is higher than the cost to build a robust data infrastructure. Building a team of qualified and experienced data and software professionals is critical to accomplish this goal. There are four main roles that are typically required to set up and maintain such a data infrastructure:
All-in Cost: the average salary for each of these roles exceeds $100k per year. Midmarket companies will require at least 1 of each role, for a minimum $400k per year but can likely reach up to $800k.
That being said, some of the more advanced software solutions can simplify the setup and maintenance of data infrastructure. Go Fig, for example, manages data storage for you, offers a simple, drag-and-drop ETL solution with a user-friendly visual interface, AI-powered analytics and dashboarding, as well as managed service add-ons. Choosing Go Fig as your all-in-one solution could potentially save you a lot on both software and staffing.
Let's look at some pretend companies to see how much a data warehouse might cost them.
Costs:
Software Cost: $9k (ETL) + $1k (data warehouse) + $3k (Visualization) = $13k per year
Total Cost: $13k per year
Estimated cost using Go Fig's Self-Service Premium plan: $4,500 per year (savings of $8,500!)
Costs:
Software Cost: $25k (ETL) + $20k (data warehouse) + $15k (Visualization) = $60k per year
Staffing Cost: four full-time data professional at $500k per year
Total Cost: $560k per year
Estimated cost using Go Fig's Enterprise plan with fractional data services: $285k per year (savings of $275k!)
As we can see above, the all-in cost of building a robust data solution can be steep, starting with $10k per year for just storage and software and easily exceeding $500k for larger companies who need to hire out a professional data team for more complex solutions. Companies choose to invest in data solutions, however, because the return is significantly higher than the cost:
Data infrastructure does not need to be as complex as it once was. Go Fig allows companies to bring their data warehouse, ETL and visualization solutions all in one place. Go Fig is a powerful platform that is simple to use, yet offers customizations you cannot find in other platforms. It is specific to your company and you, so you don't need to be overwhelmed by all the clutter.
It turns out that making it extremely simple for C-Suite leaders to centralize and access their own data, you can significantly reduce the cost of building and maintaining your own data infrastructure. Go figure! Schedule a demo today to see how Go Fig can work for your unique business needs and objectives.
