Spreadsheets are a mainstay in nearly every organization. In fact, they’re often the default, and no one questions their value or whether they’re the right tool for a given job … or if they’re used simply because they’re available.
According to the IDC info brief “The State of Data Science and Analytics,” 88% of data workers, approximately 47 million people worldwide, use spreadsheets for data activities.
It’s true that spreadsheets offer value in countless scenarios. With moderately-sized sets of data, spreadsheets are useful for information organization and basic data analysis.
But when the data gets messy or large, spreadsheets quickly become slow and cumbersome to work with — not to mention they’re prone to errors with copying and pasting and rogue formulas. That’s why self-service data analysis is on the rise.
The Rise of Self-Service Data Analytics
At its core, self-service data analytics places power in the hands of the data professional, whether an entry-level analyst or a data scientist with a statistics background. Platforms that enable modern analytics allow for the ability to democratize access to data and provide a playground for data professionals to analyze that data in. But don’t worry, IT pros: all that data is still secure and governed.
An end-to-end analytics solution unifies all data professionals in a way that spreadsheets can’t. Rather than having to choose between spreadsheets as a code-free option or learning SQL, R, or Python, self-service data analytics gives you the power of code without the learning curve.
Rather than having to choose between spreadsheets as a code-free option or learning SQL, R, or Python, self-service data analytics gives you the power of code without the learning curve.
They are the bridge between code-free professionals and coding professionals. So how does that look, and what are the benefits? (Don’t worry — your Excel skills will still come in handy!)
The Transition from Columns to Workflows
One of the hallmarks of a self-service data analytics platform is the workflow.
If Excel is known for gridded rows and columns, self-service data analytics platforms are known for their long-connected strings of functions and tools. And that long line — the common thread from one function to the next — is a critical factor in building transparency in your team and throughout your organization.
If Excel is known for gridded rows and columns, self-service data analytics platforms are known for their long-connected strings of functions and tools.
In Excel, there are a lot of things going on behind the cells. The functions and equations are written up and then disappear when you hit “Enter.” When you change one number in one cell, you set off an unseen chain reaction that automatically adjusts the rest of your table. It’s magic, and it’s easy for the magician to keep their process secret, whether they want to keep it secret or not.
Secrecy, of course, is the opposite of understanding and transparency, because when you can’t easily trace your work, it’s easy for mistakes to have a waterfall effect, compounding risk and the effect of human errors.
Secrecy, of course, is the opposite of understanding and transparency, because when you can’t easily trace your work, it’s easy for mistakes to have a waterfall effect, compounding risk and the effect of human errors.
With a workflow, anyone, including stakeholders without a true analytics background, can understand the basic idea of what is going on in your analysis. That means you can tell a clearer and more compelling story to non-technical people since they can see the workflow right there, threaded across your screen.
On the technical side, you can easily share workflows with people across the organization. Not only can your colleagues see what you’ve done, but they can replicate a similar process without reinventing the wheel, or simply build off of your previously created workflow rather than start from scratch themselves.
Workflows allow you to see what happened at each stage of the process and easily spot and fix errors without starting over from the beginning. Basically, they are transparency in action.
Repeatability … Repeatability
Many analysts spend a large part of their job preparing the same data in the same format. Think monthly or quarterly reporting. It’s time-consuming but very important for business functions and happens at regular, predictable cadences.
True, spreadsheets allow for some simple automation for basic repetition. But if you work entirely in Excel, you end up doing the same tweaking, cleaning, and wrangling repeatedly.
That’s not the good kind of repeatability. With a workflow in a self-service data analytics environment, since you’ve already built out the logic, you can schedule repeatable workflows to run automatically. If you have quarterly, monthly, or weekly reporting, rather than going through the same tedious cleaning and analysis each time, you can schedule the workflow to pull the necessary data and crunch through everything while you’re asleep.
If you have quarterly, monthly, or weekly reporting, rather than going through the same tedious cleaning and analysis each time, you can schedule the workflow to pull the necessary data and crunch through everything while you’re asleep.
Plus, with the right platform, you can turn a workflow into an analytic app that becomes sharable throughout the entire organization. Why make everyone else reinvent the wheel if you already created an awesome workflow? Alternatively, why should you have to reinvent the wheel if a colleague created something valuable that you could use? Remember, good artists borrow, but great artists steal. It’s all about reducing tedious tasks, reusing awesome workflows, and recycling valuable processes.
Scalability — Grow with Big Data
In the age of Big Data, you need technology and processes that can grow with your ever-expanding datasets. For many data professionals, that’s currently not the case. The more data you plug into a spreadsheet, the slower it processes until the dreaded spinning wheel of death appears and the spreadsheet crashes your computer or runs out of rows. This is the moment when you question your career, your software choices, and even your sanity. (You know what they say, the definition of insanity is doing the same thing over and over and expecting different results.)
The self-service workflow model allows you to scale and grow your analytics processes way beyond the 1 million Excel row limit. While the workflows may grow more complex and large, their capacity to process enormous sets of data doesn’t diminish — most self-service platforms allow you to process data in a separate server so it’s super fast. Goodbye, spinning wheel of spreadsheet death!
In the age of Big Data, you need technology and processes that can grow with your ever-expanding data-sets. For many data professionals, that’s currently not the case.
Translate Spreadsheets to Self-Service
Although self-service data analytics is a new way of working with and analyzing data, it doesn’t mean that you have to throw out all your hard-earned Excel knowledge. The formats might be different, but much of the analytical logic is the same.
For example, you’re probably pretty good at IF statements in Excel. Great news! You can take that knowledge and easily apply it to a self-service platform. The same goes for many other functions like pivoting data, appending data, and VLOOKUPs. You can perform all those functions in a self-service environment — but with the self-service take.
Rather than writing out formulas, you drag and drop tools and then reformat within the tools. It’s like taking the best of both worlds — Excel and workflows — and having it in one environment.
Stay PUT.
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Check out our full cookbook, “7 Steps to Successful Data Blending for Excel,” and get some great recipes for transferring your spreadsheet into more robust, repeatable self-service analytics.
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