By Derek Wang, Founder and CEO (An abbreviated version of this article first appeared in Forbes as "Four Steps to Data Democratization with Artificial Intelligence")
Silos of information have been an Achilles’ heel for businesses long before computers, databases and clouds came into the mix. But the amount of data companies are generating has turned those silos into behemoths casting long shadows across the entire organization. We’re finally at a point where technologies can start to turn the tides on these growing silos of data; with artificial intelligence and machine learning, companies have the tools to get data out and into the hands of the workers who need it to do their jobs more efficiently and intelligently. At least, that’s the hope for people like me who believe in democratizing data.
To borrow a phrase, data dies in darkness. It’s time to shine the light.
In an ideal world, simply supplying everyone in your organization access to all the data would solve the challenges caused by information silos. In reality, the work of data democratization is often messy. That’s okay, though. Technological, organizational, and cultural barriers exist in every workplace. When it comes to the question of data and what to do with it, one thing is clear: for most companies, the mess is already made, even if it's convincingly swept under the rug.
Let’s linger for a moment more on the vastness of data today, because it helps explain why it’s a problem that plagues almost every company. With each passing year, companies generate exponentially more data around the customer and employee experience, operations, and more. According to Splunk, 55% of the data in every organization is “dark data,” or “all the unknown and untapped data across your organization, generated by systems, devices and interactions.” The work of data democratization not only has to contend with the known data companies are already tapping into today, but also must turn on the light to expose the data that lies buried beneath the surface.
To clean up your company’s approach to data, you’ll need to get messy yourself as you dig into years of data.
Humans alone won’t be able to make heads or tails of all this data. Artificial intelligence and machine learning will be critical to this project. Thus, the work of data democratization is also the democratization of artificial intelligence.
Democratizing AI to Democratize Data
We’re just at the start of the democratization of AI. Most organizations are still struggling to make sense of traditional AI projects: 85% of these AI projects fail to deliver their promised value. The technology is slowly moving from the realm of data scientists, engineers, and developers into the hands of more business users. But, in most cases, it is still concentrated to a small percentage of the enterprise’s workforce.
True AI democratization—so critical to data democratization—will happen when low-to-no code AI platforms enable any business end user to easily apply and change AI models to their specific needs. That’s exactly what Stratifyd built with our Smart AI™ platform.
We’ve designed our AI solution to address the “long-tail” of AI deployment. By that, I mean we cover the entire addressable audience (all your employees), not just the short tail (the highly-skilled minority that are most often sought after as users). That’s critical because, as companies strive towards the democratization of data across the entire organization, all those workers will need the power of AI to do anything with that data. As the level of AI expertise decreases, you find more users. Businesses have two choices: either train a substantial portion of their workforce to elevate the level of AI expertise across the board or adopt AI technologies that lower the barriers to deployment.
We’re betting most companies want the latter.
The convergence of big data becoming everyone’s data and AI becoming a tool of the masses will have a profound impact on the way work gets done. Whereas “collaboration” often means getting members from other teams to share data or getting engineering help to extract insights from data, much of that work will be left behind. This will allow employees and teams to focus on collaborating in ways that are more often strategic than tactical.
The changes will be felt across the enterprise in ways big and small. Data wrangling is still the most time-consuming task for data scientists, despite advances in automation. What’s going to happen when the bulk of their time is focused on insights?
Of course, data scientists aren’t going to be the only beneficiaries. Take Contact Centers for example: they saw a 300% increase in call volume early on during the pandemic. What could they have done better with rapid insights into all those calls? As customers moved to entirely digital forms of interaction, what would CX teams liked to have learned from all that interaction data? What could have either team done if they access to the other’s insights in real time?
The work to get to the nirvana of strategic collaboration and employees empowered by data at their fingertips will not be simple or clean, but it is necessary and so, so worth it.
Why Data Democratization Matters More than Ever
We’re approaching a point where data can’t be ignored any longer. To start, there’s the competitive aspect of the data. Your competitors are adopting data-driven strategies and making data-driven decisions with AI-powered technologies. They’re taking advantage of the insights they have from across the entire customer experience. The gap between the haves and the have-nots will widen every year as less mature companies lag behind competitors in uncovering the hidden stories in their data.
That’s why the concept of Experience Analytics powered by AI continues to gain traction in and beyond CX teams. Experience Analytics help turn the mountains of unstructured data—chat logs, contact center phone calls, social media posts—into digestible insights. Experience Analytics, when it’s a unified, democratized layer sitting across an organization, will help turn that raw data into something useful and accessible to all.
Focus on Insights and Transparency, Not Just Access
How you go about the work of data democratization matters, though. If you come across a person dying from thirst, blasting them with a firehose will do more harm than good. On the other hand, a slow drip will do little to help. Or, if they do not know how to use the hose, the access to it is pointless. It is a real Goldilocks scenario to find the right balance as you roll out data democratization to your organization.
In other words, data alone is not enough. Data needs to be accessible and contextualized. Employees need to have the data but also understand why that data matters and what to do with it. That’s where AI and ML can help fill in the gaps by turning data into insights rapidly.
Just because it’s tough, just because it’s messy, doesn’t mean it’s not work worth doing. At the end of the day, your employees are desperate for insights that will help them do their job better, more efficiently.
As I’ve discussed already, AI that powers solutions like our Experience Analytics will be critically important. But there’s an inherent challenge for unaware buyers in the myriad tools that promise AI power. Too often, access to AI—and thus, more data—comes with the price of a lack of transparency.
Insights without transparency into how your AI-powered solutions are using data to come up with those insights aren’t sustainable over the long term. Data sets aren’t static because your customers’ experiences aren’t static; likewise, what you need to get from your data isn’t static either. As these things change, you need to be able to adjust your models on the fly, without slowing down. Your customers aren’t going to wait while you catch up.
Strategies towards Successful Democratization
So, how can you get buy-in for this initiative and then strategically roll out AI-powered data democratization without drowning your teams in a sea of senseless data? I’ve got a few tips for anyone looking to bring data democracy powered by AI to their organization.
Evaluate Your Blind Spots
Before getting started, it’s important to take an honest account of your company’s maturity when it comes to data practices. Do your teams spend their days manually compiling reports from disparate data sources? Or are you a fully data-driven organization, with automated insights driving every move you make? Odds are, you’re somewhere in between.
Finding out where your critical blind spots are will help point the way forward for data democratization. What insights are your teams missing in order to do their jobs better? What valuable data sources—like survey and Contact Center transcripts—are being underutilized? Where are the redundancies, or worse, where are there competing sets of data telling two different stories about the customer and employee experiences?
Be Precise to Start
There is no one-size-fits-all solution to democratizing data. Start with the low-hanging fruit: what groups or departments are starving for better access to data to do their jobs better? Customer Experience and Marketing teams are two of the organizations that are primed to benefit from greater access to data from across the enterprise. Once you solve the democratization formula in one of these areas, begin building the bridges to expand horizontally across the company.
With a precise start to show the ROI of data democratization (and to work out the process and technical kinks), you can help ensure broader buy-in and create a roadmap for success across the company.
Build from the Bottom Up
Empowering employees at every level with data and technology they’ve never had access to before requires special attention to the employee experience. Holistic projects like data democratization can only excel if you can optimize the employee experience with the right training and develop messaging that reinforces democratization as integral to the corporate culture.
That’s why it’s critical that executives allow data democratization to work from the bottom up. It’s a project that will reshape how many work day-to-day, and giving ownership to these stakeholders from the start will increase the likelihood of continued success. Top-down approaches to data democratization often fail because the people using the data and the technology don’t understand why things are being done the way they are.
This is especially true when you’re using traditional AI with a top-down approach. Traditional AI models are not transparent to the average user without a data science degree. That’s why our product is designed to help everyone, while giving them access to the logic behind the models they’re using.
Align with a Company-Wide Initiative
Democratizing data aligns with many company-wide initiatives when done right. Data governance and privacy compliance? Check. Keeping overhead costs down by removing frustrating silos and process redundancies? Check. Improving the customer and employee experience? Check and check.
By aligning data democratization with critical business goals and making a case for addressing it urgently, you can get buy-in for a project that can easily be the can that gets kicked down the road.
Find Smart AI to Support Your Democratization Efforts
Data democratization is a medicine for many corporate ills. But how you deliver that medicine makes all the difference. If the tools you’re going to rely on aren’t built for the people, your democratization efforts will quickly fade away.
As you kick off your efforts, find a Smart AI platform like Stratifyd that lets you personalize your data insights at the speed of your business without the need for lots of coding and IT resources. If you can democratize data alongside AI-powered analytics, you’ll see how it will lift up employees from the mess of disjointed data and empower them to do more.