In this special guest feature, Rob Woollen, CEO and Co-Founder of Sigma Computing, asks why rely solely on data analysts when you have a full team of experts that know their domain? Rob has over 20 years of experience building distributed and cloud systems.
He spent more than six years at Salesforce.
com, serving as the CTO for the Salesforce Platform and Work.
com, as well as Senior Vice President of Platform Product Management.
Rob holds a Bachelor of Science degree in Computer Science from Princeton University.
Data is the foundation of modern business.
Whether you’re part of the sales team, analyzing why one region outperformed the others; in marketing, asking why the asset conversion rate is so high; or even on the product team, looking to determine why your new feature isn’t resonating with customers — putting the right data into the hands of the right decision-makers can mean the difference between momentous growth, or missed opportunities.
This makes accessing that data easily and quickly of paramount importance—a task that has become the holy grail of business intelligence (BI) analysts.
To solve this quandary, businesses have started encouraging new waves of “citizen data analysts” — a step in the right direction — but without supplying the tools necessary for those domain experts to succeed.
Many of whom often turn to potentially unsafe practices, like extracting, downloading, and analyzing data in a spreadsheet.
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display(div-gpt-ad-1439400881943-0); }); More often though, analysts are creating limited dashboards, searches, and queries based on what they think each team needs rather than trusting domain experts to dig into the data required to elevate their specific business objectives.
Dazzled by a nice user interface, business leaders may not even realize that they are still beholden to the data team to write modeling code in order for them to do anything remotely new or exploratory.
These so-called “modern” analytics tools still require someone to code modeling changes for even the most basic of follow up questions, essentially coding an entirely new query.
This can take hours, depending on the complexity, so these requests are often ignored or denied due to bandwidth and time constraints.
And for a team looking to find the unique data insights that will help differentiate them from their competitors, a reliance on the forethought of the data team can be limiting, to say the least.
What is needed is a platform that allows domain experts to analyze data as they see fit, providing complete business oversight and empowerment.
Not a model that effectively supports the dumbification of BI, namely canned dashboards, static reports, and trendy natural language tools that only support the most simple queries.
This raises a fundamental question: why rely on predefined, predetermined views and data tools, instead of using true self-service analytics? Shouldn’t any technology enable the business and data teams to play on the same field? Right now, the disconnect is all about the approach and barriers inherent within leading solutions.
Data queries are written in SQL.
And BI tools are complicated, typically taking months to master.
The ability to speak SQL and write these queries allows data team members to ask questions directly from the data and retrieve insights.
BI experts can do similar things, but often hit roadblocks when the tool can’t answer segmented, specific follow up questions, leading them to still turn to the data team to update reports, fix dashboards, or share extracts.
The ‘rub’ here is that few business team members know SQL or their BI tool well enough to access the data themselves, hence why adoption rates for BI tools remains at only 35%, according to Gartner.
By eliminating the language barrier between the domain experts and the data, in marrying the visual front-end of the data with SQL back-end, a new approach to business intelligence is introduced, allowing for 100% participation in the iterative data conversation.
While many paradigms have attempted to bridge this gap, many claiming to be “self-service” BI, what they lack is the ability to provide true freedom to the business expert—meaning no requirement of extensive training, and the ability to explore data using a familiar and comfortable interface.
While some tools available have solved parts of the equation, they still require SQL knowledge.
Pre-built business interfaces and dashboards are also based on predefined questions, and even when presented in mildly adaptive visual interfaces, provide little depth or flexibility.
In turn, domain experts are unable to explore and further investigate the data in their own way.
By utilizing a cloud-built analytics tool that is inspired by the granddaddy of all business analytics interfaces — the spreadsheet — domain experts are enriched with capabilities far beyond a simple spreadsheet program or a static dashboard or report.
Through a truly flexible medium, in which domain experts can analyze data without having to know complex BI tools, SQL, or worse, a whole new proprietary coding language, the expertise of the business team member can shine without the risk of being lost in translation.
The programming shackles can finally come off, giving domain experts and decision-makers the freedom to ask the questions they want and retrieve the data that may make a difference.
This freedom does not have to be granted at the expense of control.
There must be a partnership between the data team and the business experts that allows everyone within the organization to explore and analyze the data that is relevant to their needs, safely and securely.
It remains the data team’s responsibility to shape the data so that it is accessible to the rest of the organization and modeled in such a way that others can easily build off of the foundation the data team has laid for them.
The data team must also continue to enforce and maintain a single source of truth for all of an organization’s cloud data, creating trustworthy data sets and eliminating dangerous extracts from circulation.
Freedom must always be balanced with the right amount of control.
In contrast, technologies that provide limited freedom or depth can hold these experts back from making the most of the data they have.
It’s counter-intuitive.
True technological advancement features an inverse ratio of capability to comprehension.
Meaning, as a technology grows in power, the results produced should become more comprehensive.
It’s hard to pin that essential rule of innovation on the current direction in which BI tools have evolved.
Why rely solely on data analysts when you have a full team of experts that know their domain? Instead of simply sitting back and hoping for a eureka moment for your business, it’s time to level out the playing field for empowered data discovery — across all business teams.
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