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3 Key Challenges When It Comes to Democratizing Data

Data Basics, Scaling AI Lauren Anderson

As the volume of data needs for a given company grows, so does the need to enable more efficient access and frictionless self-service data capabilities for various business groups within that company. To make quick and accurate decisions around key initiatives like creating better customer experiences, finding new internal efficiencies, or reducing customer churn, lines of business need to be able to quickly access and derive insights from data. And, when it comes to building an inclusive AI strategy, data democratization is one of the necessary first steps. 

What Is Democratized Data?

Put simply, when data is democratized, it’s systemized in such a way that it’s available when and where it is needed most, and the use of data and AI becomes everyday behavior for everyone across the organization — a concept that our team at Dataiku aptly calls Everyday AI. All areas of the business, regardless of technical skill, are able to quickly and securely access data so that they can gain those necessary insights without the heavy involvement of IT or central data teams. By democratizing data, centralized data teams are able to dedicate more time to strategic projects versus ad-hoc requests, IT teams can deliver more value to stakeholders while reducing involvement in data access management and, ultimately, companies are able to foster a culture of data-driven decision making and innovation. 

two women discussing data

What Are the Top Challenges When It Comes to Democratizing Data?

The concept sounds great, right? But, obviously, it’s easier said than done. Democratizing data nearly always requires major shifts in ways of working. Here are some of the most common challenges we’ve seen along the way to data democratization:

  • Establishing and Enforcing Data Governance - Data governance is the approach that your company has to give you access to the "good" data meaning the most relevant and clean data for your analytics while being compliant with security and data policies. Not having it can have a drastic impact and decisions about how data systems are managed will fall to the default “owners” of the system, of whom there are often several, leading to inconsistencies in data availability, integrity, and access. This can have drastic downstream impacts in data quality, create confusion and inefficiencies, open up the company to unnecessary risk, and can quickly get out of hand as data requests continue to grow. 
  • Education and Adoption - Perhaps the biggest challenge when it comes to democratizing data is ensuring that those accessing the data understand how to get value from it. After all, why does it matter if different teams have access to the data if they can’t do anything meaningful with it? If coding is necessary to get to usable data, but the majority of the company can’t do that, they’ll still have the same process bottleneck issues as before. 
  • Ensuring Data Quality - Whether due to downstream impact of absence of data governance practices or from lack of understanding, poor data quality can lead to mistrust in data and its ability to be useful for the business. If all users aren’t able to easily and quickly clean, wrangle, and transform data for their purposes, it’s likely that data will just sit on a shelf — or worse, negatively impact the business. 

From Data Democratization to Everyday AI

Using Dataiku, many companies have been able to overcome the challenges associated with democratizing data. For example, the FP&A team at Standard Chartered Bank was able to improve its analyst productivity by a factor of thirty while scaling the use of data across the company. They use Dataiku to pull in enterprise-level data connected to a centralized, homogeneous pool with product owners for every dataset and defined governance, which connects out to the entire organization. This is just one example from hundreds of stories from our customers

Dataiku was designed to make the use of data and AI everyday behavior for everyone across the organization, regardless of skill set. With built-in data governance features, easy to use, visual tools for less technical users, and full code options for those that prefer code, everyone is able to work in the same, centralized place to make data democratization — and Everyday AI, a reality.

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