The wave report is in on self-service analytics. Surf’s up! The conditions are right for anyone using data to streamline and enhance the core functions of their job, including finding and cleansing data, detecting and uncovering patterns, interpreting and preparing reports, and collaborating on results with colleagues. Forget wading in the waters for hours; it’s time to ride these revolutionary waves with self-service analytics.
Test the waters. Here’s an in-depth look at self-service data analytics, including a self-quiz to determine how close you are to cruising the ultimate waves and crushing any challenge in your path.
First Things First: Let’s Define Self-Service Analytics
According to Gartner, self-service analytics is a form of business intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal support. With more than than 60% of respondents saying they use some form of data analytics tools to generate insights for business decisions, who wouldn’t want to speed up that process and get to the good stuff faster?
With more than than 60% of respondents saying they use some form of data analytics tools to generate insights for business decisions, who wouldn’t want to speed up that process and get to the good stuff faster?
More than 80% of respondents expect their data analytics tools to be extremely important to them in the next two years, and if you’re in that category, keep reading.
Expect Huge Waves
Modern analytics provides far greater speed, ease of use, scalability, and other important capabilities than legacy solutions. Self-service platforms help analysts and managers analyze data with greater ease and sophistication, make better decisions, and even lower related costs. Could your organization catch some revolutionary waves?
Totally (insert best surfer’s voice here). Analytics software allows users to leverage data for business decisions that lead to better customer experience, more product and service innovation, optimized business processes, and ultimately, competitive diﬀerentiation. Jesse Luck at Southwest Airlines, for instance, reports moving from a reactive maintenance program to a predictive maintenance program using self-service analytics. AAA uses self-service analytics to understand what’s going on with members, processes, and industry trends.
It’s an issue if you miss an issue.
Get each new release of INPUT before the rest.
Quick Check: Where Do You Match Up?
Read through each section and tally up how many of these points you find yourself relating to on a daily basis.
Section 1: Wading in the Shallow End of Spreadsheets + Manual Reporting
Manually poring over spreadsheets, databases, and printed reports
Performing rudimentary data manipulation and reporting using solutions that weren’t built to access multiple data sources
Sharing reports via email
Some companies use analytics software that isn’t able to provide true self-service and thus can only be accessed by a few. Users ﬁnd themselves relying on a virtual army of data specialists and data scientists, often working under the auspices of a centralized analytics group, to prepare, blend, and analyze data — or even to do the actual reporting of what they believe are the key insights. According to the IDC info brief “The State of Data Science and Analytics,” workers are spending, on average, seven hours per week manually updating formulas, pivot tables, and cell and sheet references.
According to the IDC info brief “The State of Data Science and Analytics,” workers are spending, on average, seven hours per week manually updating formulas, pivot tables, and cell and sheet references.
And the problem with this is that they don’t really understand the insights the LOB needs. Meanwhile, other organizations find themselves beholden to IT to get the data in the first place before being able to put it into a spreadsheet.
Section 2: MOSTLY SWIMMING BEYOND THE BUOYS OF SPREADSHEETS
Seeing the value of data and of analytics reporting, but it’s not scalable
Experience with some self-service visualization tools and finding them somewhat helpful for eyeballing localized data compiled as charts and graphs in dashboards
Finding that visualization tools can’t handle much-needed data cleansing, preparation, and predictive analysis
One big challenge for business decision-makers and analysts is that, as a result of working with spreadsheets and other manual tools, they often glean information by “gut reaction.” Management by “gut” feels like the only reasonable alternative to delving into mountains of raw data across multiple storage areas and trying to make sense of it all.
One big challenge for business decision-makers and analysts is that, as a result of working with spreadsheets and other manual tools, they often glean information by “gut reaction.”
Section 3: Beyond the Shore and Riding the Waves of Self-Service Analytics
Implementing analytics as an end-to-end solution, not with half-steps, but with giant leaps
Deploying self-service analytics to both nontechnical managers and data analysts with solutions that both can easily use
Setting up workflows that are highly repeatable, so that initial setups can be used by others
— and be extremely scalable
— to process the enormous volumes of data from both inside and outside the organization that today’s managers and data analysts must analyze
Accessing data from multiple sources without needing help from IT
Self-Service Analytics at Its Peak
Modern solutions offer automation, ease of use, repeatability, and the ability to scale processes that include enormous volumes of data inside and outside your organization. Whether you find yourself with no sign of self-service analytics or somewhere between spreadsheets and a modern analytics platform, surf’s up! Time to catch the self-service wave.
Check out the full Harvard Business Review report “The Untapped Power of Self-Service Data Analytics.”
Chat with us at @alteryx and tell us where you’re at in your analytics journey.