What if We Were ALL Wrong about Data Democratization?

by   |   May 1, 2017 5:30 am   |   3 Comments

Roman Stanek, Founder and CEO, GoodData

Roman Stanek, Founder and CEO, GoodData

The basic concept of data democratization sounds idyllic. The volume of raw data that businesses of all types are generating as part of their daily operations has grown exponentially, so the idea of giving employees throughout the organization the ability to derive insights and value from this information is extremely attractive. However, I believe the principle that giving everyone in the enterprise access to more raw data creates a competitive advantage is flawed. As the flood of data continues to increase, the ability of legacy-analytical tools to provide clear insights is rapidly degrading. And simply trying to cram more information into a dashboard only compounds the problem by presenting too many signals for most business users to fully understand and derive meaningful insights from.

The basic premise of data democratization is that putting more data in the hands of more people leads to those people making better business decisions. This sounds good, but the emphasis over the past few years on delivering so-called “self-service analytics” has made it difficult for business users to keep up with the changes to analytics and predictive monitoring in addition to their core job duties. Giving these users access to as much data as possible has become harmful rather than helpful, as humans are naturally subject to biases and making errors, especially when they are overloaded with an abundance of unfiltered data.

Not every business user needs access to self-service analytics tools; they don’t have the time nor expertise to parse through and make sense of all the data available to them. And more importantly, I would argue that they should not have to. We don’t need more data in the hands of more people; we need better insights into data in the hands of more people.

“By 2018, most business users will have access to self-service tools, but the fact remains that there’s too much data for the average business user to know where to start,” notes Anne Moxie of Nucleus Research. “What we are starting to see is a new generation of BI where mundane daily tasks are automated while flagging more significant anomalies for employees to focus on.”

Achieving this type of automation requires a new generation of analytical tools that take advantage of advances in machine learning and predictive analytics to draw real, actionable insights from complex data sets, and surface them where and when they are needed most. It’s inefficient to have employees spending time analyzing data and making mundane decisions when massive increases in computing power have evolved machine learning to the point where these types of insights can be extracted from complex data sets in seconds.

I’m not arguing that roles throughout organizations shouldn’t be data driven, but the expectation that every employee should also be their own data scientist isn’t reasonable, and from everything I’ve seen, it isn’t happening. Most workers are part of what we might call the production environment within a business. They have core job tasks to complete each day, and the notion that hundreds of employees have the time, inclination and expertise to slice and dice data on a regular basis isn’t sustainable.

The idea behind data democratization isn’t a bad one. Fulfilling the promise of Big Data requires getting the right information to the right people at the right time, in a way that they can use it. Truly achieving this, and making full use of the mountain of structured and unstructured data that your organization generates, requires re-thinking our business tools. What is needed is a new generation of analytical systems that automatically take the heavy lifting out of data analysis to give employees the right insights they need to focus on to complete their core responsibilities, make better decisions and drive more impactful business actions.


Roman Stanek is CEO and founder of GoodData. Stanek is a passionate entrepreneur and industry thought leader with over 20 years of high-tech experience. His latest venture, GoodData, was founded in 2007 with the mission to disrupt the business intelligence space and monetize big data. Prior to GoodData, Roman was Founder and CEO of NetBeans, the leading Java development environment (acquired by Sun Microsystems in 1999) and Systinet, a leading SOA governance platform (acquired by Mercury Interactive, later Hewlett Packard, in 2006).


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  1. Raj Mukherjee
    Posted May 2, 2017 at 5:02 am | Permalink

    Roman I don’t think self service analytics or data democratisation needs any more tools there is plenty in the market already. The problem is that enterprises only tend to invest in tools and not on people and better data management processes and that ultimately leads to suboptimal outcomes. If you look at companies like amazon,google they have the right model to make data democratisation work at all level across their business because they invest in people and have processes to treat data and insight as a primary product not just an after thought .

  2. Sergey Makarevich
    Posted May 3, 2017 at 6:12 am | Permalink

    I believe a good point is being made that the over-hyped expectation of better access to more data automatically and quickly resulting in more insights is unreasonable for exactly the reason Roman provides.
    Nevertheless, providing such access allows those who indeed has inclination and facility and willingness to get their hands dirty to actually go and apply their business knowledge directly to data analysis. Which is likely to end up being of significant importance and discernible value to the company, despite the fact that there will be only a small portion of all who have this new data access would do it. The benefit may be not as revolutionary as some hoped, but worthwhile the effort nevertheless.
    Of course, this is no estimation, just a hunch of a trained economist.

  3. Shandor
    Posted May 3, 2017 at 2:02 pm | Permalink

    Roman Stanek correctly asserts that legacy data tools aren’t up to the job anymore, but were they ever adequate? Of course not. And they’re still bad. Raw data has existed all through history but software’s very new, ‘cos relational databases were invented in the 1970’s and the original spreadsheet VisiCalc came about in the early 1980’s. Roman’s partly right that more raw data in front of more eyes isn’t the answer, but this begs the question of why data tools aren’t hugely more capable in 2017 than they were in 2007 for the task of presenting cleaned up, actionable views of information.
    Where’s the equivalent of the Tesla car, in the data visualization & management field? It’s mired down with legacy dinosaurs plodding through corporate meeting rooms, bellowing about security as they strive to keep their consultants employed. This stems from when Moore’s Law provided developers with ever increasing computational power from the 1980’s till now, but monopolists like Bill Gates employed the tactic of Embrace Extend Extinguish (EEE) to destroy competitors and expand control over the informational world and ensure prosperity for their own workers at the expense of innovation. The same goes for today’s SAP, Teradata, Oracle, and other legacy dinosaurs who create work for themselves.
    But wait, there’s hope: the bane of monopolists is springboarding fresh from Moore’s Law. Today’s Cloud promises unlimited computation & storage for the masses—a price & performance revolution in data democratization that can’t be stopped by the Old Guard. So what’s the holdup with Cloud in the business world? Mostly BI (Businessman Inertia) dependent on legacy dinosaurs who profit from keeping prices high and skillsets proprietary. They don’t want the data scientist’s job to be too easy, because then they can charge six hundred dollars per hour for their consultant’s time to recreate the wheel in every customer’s information infrastructure and get paid to box-watch “secure” on-premises hardware.
    Yet, more and more software tools are appearing with power to obsolesce those legacy dinosaurs. Data wrangling and presentation are becoming more and more automated, leaving smart work to smart people, as hinted at by Anne Moxie in the article. I heard one lady say at a demo: “Alteryx frees data scientists from having to be programmers!” And that’s just one program. What I nickname the “AWSome system” is putting SQL, NoSQL, IoT, serverless Lambda, elastic compute, monster storage, and ever-improving services & security in the Cloud at mind-bogglingly cheap prices. It’s time for boss tools like Snowflake EDW, Alteryx, to partner with a merger (of a few dozen visualizer companies like Tableau, Qlik, Birst, Sisense, DataWatch, Datameer, Pyramid, Panorama, a new one pops up every week, ad nauseum) and drag data analysis kicking and screaming into the modern business world. We knowledge workers will thank them as our insights come fast and furious.

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