Analysts typically spend between 60-80% of their time finding, gathering, and prepping data. If that’s you, it means you’re spending fewer than four out of every 10 working hours actually building models and gleaning insights.
And, since you do much of that work in spreadsheets, it remains hidden from your fellow analysts — as theirs is hidden from you. So everyone is forced to waste additional time, accidentally duplicating data searches and building information assets that already exist.
Add in data tools that require time-consuming contributions from trained specialists, forcing you to work extra hours or turn in reports late, and things can get costly for your sanity. (Incidentally, they happen to be expensive for your company, too: IDC found that for every 100 employees, the fragmented state of data intelligence and analytics adds $1.7 million in costs.)
Fortunately, a new breed of self-service analytics solutions has arrived. From a single platform, they can search a wide variety of different sources, then access and blend the data themselves, all in seconds. The best also provide an assortment of user-friendly tools, and let you share analyses and data models with colleagues.
Fall in Love with Your Job Again with These Five Best Practices
When it comes to serving up a unified analytics experience that will help you crush your workday, one self-service solution stands out. The Alteryx self-service platform takes a revolutionary approach to business analytics, serving as an end-to-end solution that empowers you to break data barriers, deliver insights, and experience the thrill of getting to the answer much faster than you ever could before.
With Alteryx, these five best practices are no sweat.
Best Practice No. 1: Don’t pull your hair out when pulling your data.
You need to access every important byte of data to make sure you and your organization make informed decisions. Data comes from every corner of the world, through different types of sources and formats like social media, mobile applications, and the cloud. To accommodate this data deluge, new types of databases have been designed to store unstructured data — as well as new flavors of structured data, such as Big Data.
To be effective, analysts need the flexibility to access all data sources, regardless of data type, and getting a full analysis requires pulling data from multiple sources. As noted in a recent Harvard Business Review article, most analytics projects use data from an average of five different sources, and data models that rely on data from as many as 15 different sources are not uncommon.
Most analytics projects use data from an average of five different sources, and data models that rely on data from as many as 15 different sources are not uncommon.
The Alteryx Platform allows you to access any number of sources from a single portal without having to write SQL code or custom scripts — or wait around for help from your IT and data scientist colleagues. Search results are ranked and qualified based on how often the data is used and whether or not it’s certified, so you can quickly sort through a large number of potential sources and not waste time running down dead ends.
Best Practice No. 2: Stop prepping. You were born to solve.
Once data from multiple sources has been extracted, it needs to be scrubbed, deduped, and merged. To manage this themselves without having to rely on others’ expertise, many analysts turn to Excel. But using spreadsheets to blend data is a frustrating, labor-intensive process prone to errors.
That’s why it’s time to pivot away from those spreadsheets! The Alteryx Platform works with virtually any type of data — including unstructured data and Big Data — whether it comes from internal or external sources. Once the data you need is located, you can quickly eliminate nulls and duplicate entries, group by common variables, identify unique values, and easily join data from multiple sources using drag-and-drop tools.
Mark Thompson, Director of Data Services at auto insurer The General, knows his way around spreadsheets — and he knows what they’re excellent at and what they’re not. “When I use Alteryx, I feel like a lumberjack who just discovered the chainsaw. I've been an Excel power user for 25 years and I look at it and go, 'Excel just got lapped, bad."
No matter which application or database the data originally came from, the user interface in Alteryx remains the same, shortening the learning curve. Oh, and did we mention that once you’ve set up the process to blend your data, the need to further manipulate that data manually is virtually eliminated?
Kenneth Van Wanrooij, a pre-season marketplace operations manager at Nike, agrees: “I think Alteryx fits very well with Nike's tagline 'Just Do It,' because with Alteryx you can just blend it."
Best Practice No. 3: Be a rebel without code.
To answer more complex, forward-looking questions, you’re often forced to rely on specialists with advanced coding skills. Your company’s data scientists dream in R and can do logic regressions in their sleep — but the queue for their help is long.
When you’re on deadline, waiting on someone else is stressful, and by the time you get to the front of the line your data might be stale. Yet these hold-ups have become increasingly common as the demand for data-driven business insights grows faster than the available pool of data science talent.
Alteryx blows away this issue by eliminating the need to learn specialized languages like R and Python to perform advanced analytics. The platform’s self-service tools allow business analysts to drag and drop data into a code-free predictive tool to answer questions like who’s most likely to respond to a marketing campaign or be a financial risk. This lightens the burden on your organization’s data scientists, so they can focus on the massive projects they were hired for, and gives you more opportunities to answer urgent, high-value questions.
Best Practice No. 4: Repeatable workflows rock. Repeatable workflows rock.
The typical analyst wastes an average of 10 hours a week redoing data searches and re-creating reports that they or others have done before. Is this time you can afford to lose? Didn’t think so. And neither can your organization: IDC finds that the annual cost of time spent repeating data management work exceeds $5 million for a company with 10,000 or more employees.
The Alteryx Platform stores project workflows in a centralized location that’s accessible to everyone in your organization. The work that you and your colleagues do can be readily searched and shared, which makes it easy to reuse both data and analytics models that have already been built. Stop duplicating efforts. Stop duplicating efforts. Repeatable workflows rock. Repeatable workflows rock. (See what we did there?)
Alteryx will also automatically refresh your data each time it’s updated at the original source. Sit back and consider that for a second: No more painfully recreating your manual reports every month, week, or quarter. They’re automatically updated for you, so you can spend time on the awesome stuff.
As Avinash Kaushik, Digital Marketing Evangelist at Google, says, “No company hires anyone called a Reporting Squirrel. Everyone hires what they believe are Analysis Ninjas. It is the work the employee does that makes them a Squirrel or a Ninja. Reporting Squirrels spend 75% or more of their time in data production activities. Analysis Ninjas spend 75% or more of their time in analysis that delivers actionable insights.”
Are you a Reporting Squirrel or an Analysis Ninja?
Are you a Reporting Squirrel or an Analysis Ninja? Reporting Squirrels: Spend 75% or more of their time in data production activities
Analysis Ninjas: Spend 75% or more of their time in analysis that delivers actionable insights
Best Practice No. 5: Experience the thrill of solving.
Today, many analysts still use more than 10 different tools to do their jobs. Having to work with a hodgepodge like this means you’re leaning precariously in the “reporting squirrel” direction, spending far more time gathering and prepping data than analyzing it. Indeed, studies estimate that analysts typically spend as much as 80 percent of their time gathering and preparing data, leaving only 20 percent for, well, analysis.
The Alteryx Platform turns that 80/20 ratio on its head. Its search capabilities and data integration tools mean you only have to spend 20 percent of your time locating and blending data — leaving you the lion’s share of your work week to build data models and extract insights. (At 80 percent of your time spent analyzing, you’re a better “Analysis Ninja” than the 75-percenter Google’s Avinash Kaushik described above!)
With Alteryx you don’t just get your time back for analysis, you also get to make the most of that time. Pre-built drag-and-drop, code-free tools let you perform statistical analyses like linear regressions, create forecasting models such as ARIMA, and conduct Monte Carlo simulations, among others. Additional tools allow you to use the location points in your data to perform location-based calculations, including drive-time, trade-area, and spatial-matching analyses. All of this output can be mapped and geographically visualized.
Other functions let you create custom reports that include data tables, charts, images, and maps. You can present these as PDF files, HTML, or Word documents, or as interactive formats that allow decision-makers to zoom in on the data in a variety of ways.
We’ve covered saving time, becoming incredibly efficient, and falling in love with your work again, but there’s another hidden benefit of the Alteryx Platform: It makes you look like a genius to your business stakeholders. You’re helping them solve more and more complex questions and creating incredible value for the business. Goodbye, spreadsheet hell. Hello, job satisfaction.
Dawn Rinehart, Total Cost of Ownership Manager at Daimler Trucks North America, sums it up nicely:
It has been an amazing transition for my team to start using Alteryx — to go from manual, tedious, cumbersome processes where we still didn't have insight into the data to a world where we have self-service analytics.
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