Do you depend on a group of data analysts? Do the analysts on your team help you make clear-eyed business decisions and respond better to a dynamic marketplace?
If the answer is “yes” (and we suspect it is), then we have some good news for you. You have the power to equip your analysts with analytic building blocks so they can be far more productive than they are today, leveraging a platform that doesn’t force them to spend all day searching for data to answer a question or waste precious time mastering a different tool every time they need to pull data from a new source.
Research has shown that analysts spend between 60 and 80 percent of their time finding, gathering, and prepping data — which leaves them with fewer than four out of every 10 working hours to build models and glean insights. And since they do much of that work in spreadsheets, it remains hidden from their coworkers, forcing analysts all over your company to waste additional time accidentally duplicating data searches and building information assets that already exist … somewhere.
Analysts who can’t fall back on spreadsheets typically use several highly-specialized data tools. Often these tools require specific skills or coding ability, which means they’re dependent on other trained specialists — slowing the data gathering process even more. With so much time squandered, analysts are forced to work extra hours on grunt work, which is costly for your business. A recent survey by industry researcher IDC finds that, for every 100 employees, the fragmented state of data intelligence and analytics adds $1.7 million to a company’s costs.
But like we said, there’s good news: A new breed of self-service analytics building blocks is here to alter that equation. These data management solutions empower analysts to efficiently search many different data sources from a single platform, then access and blend the data themselves. The best of these platforms also provide an assortment of analytics building blocks and allow analysts to share their analyses and models with others in their organization.
Five Best Practices for Empowering Your Analysts
When it comes to serving up a unified analytics experience that will make your analysts far more productive, one self-service solution stands out. The self-service, Alteryx Analytic Process Automation (APA) PlatformTM platform takes a revolutionary approach to business analytics, serving as an end-to-end solution that empowers analysts to break data barriers and routinely deliver fresh insights.
These five best practices for empowering your analysts are easy to follow with Alteryx.
BEST PRACTICE NO. 1: Give analysts access to the maximum number of data sources.
Your analysts need to access every important byte of data to make sure you are making informed and holistic decisions. Data comes from every corner of the world and 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 these 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 APA Platform allows your analysts 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 their 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 they can quickly sort through a large number of potential sources and not waste time running down dead ends.
Your world gets exponentially better because: More inputs equals better outputs, and better outputs means you can be confident in making truly data-driven decisions.
BEST PRACTICE NO. 2: ELIMINATE MANUAL DATA PREP.
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 is time for them to pivot away from those spreadsheets! The Alteryx APA 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 your analysts need is located, they 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 the process to blend a data set has been set up, 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.”
“I think Alteryx fits very well with Nike’s tagline ‘Just Do It,‘ because with Alteryx you can just blend it.”
— Kenneth Van Wanrooij, a pre-season marketplace operations manager at Nike
Your world gets exponentially better because: Your analysts spend less time prepping and more time analyzing, so you get better and timelier reports.
BEST PRACTICE NO. 3: ELIMINATE THE NEED FOR CUSTOM CODING.
To answer more complex, forward-looking questions, analysts are 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 analysts are on deadline, waiting on someone else is stressful, and by the time they get to the front of the line, their 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 your analysts more opportunities to answer urgent questions of high import to the business.
Your world gets exponentially better because: You can ask increasingly complex questions and move from “What happened?” to “What should we do next?” and your analysts won’t break a sweat.
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 for them 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 APA Platform stores project workflows in a centralized location that’s accessible to everyone in your organization. The work that your analysts do can be readily searched and shared, which makes it easy to reuse both data and analytics models that have already been built. Help your analysts stop duplicating their efforts. Help your analysts stop duplicating their efforts. Repeatable workflows rock. Repeatable workflows rock. (See what we did there?)
Alteryx will also automatically refresh data each time it’s updated at the original source. Sit back and consider that for a second: No more painfully recreating manual reports every month, week, or quarter. They’re automatically updated for your analysts, so they 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?
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
Your world gets exponentially better because: Key reports are available on demand with fresh data, and your analysts are now busy working out your toughest problems instead of reinventing the report wheel.
BEST PRACTICE NO. 5: YOUR ANALYSTS WERE BORN TO SOLVE. LET THEM!
Today, many analysts still use more than 10 different tools to do their jobs. Having to work with a hodgepodge like this means they’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 APA Platform turns that 80/20 ratio on its head. Its search capabilities and data integration building blocks mean your analysts only have to spend 20 percent of their time locating and blending data — leaving them with the lion’s share of their work week to build models and extract insights.
With Alteryx your analysts don’t just get your time back for analysis, they also get to make the most of that time. Pre-built drag-and-drop, code-free building blocks let them perform statistical analyses like linear regressions, create forecasting models such as ARIMA, and conduct Monte Carlo simulations, among others. Additional building blocks allow them to use the location points in their 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 your analysts create custom reports that include data tables, charts, images, and maps. They can present these as PDF files, HTML or Word documents, or as interactive formats that allow you to zoom in on the data in a variety of ways.
Your world gets exponentially better because: Your analysts are freed to solve more and more complex questions and create incredible value for the business.
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.”