Organizational IT departments have acted as data gatekeepers for many years. However, accessing the data needed to make data-driven choices has required many business divisions, including Sales and Marketing, to go via IT employees, which is time-consuming.
In addition, other firms continue to believe that data should be handled this way, despite other organizations looking for more effective ways to increase productivity.
As a recommended practice for discovering areas that have the potential to generate profits, areas that organizations didn’t aware they had previously, organizations that want to keep a competitive advantage in the market must harness insights from data.
Cost-cutting and fact-based decision-making that doesn’t only rely on intuition are two examples of this. Each company receives a tonne of data from all of its divisions, which it must analyze to learn how to operate better.
The capacity to analyze enormous volumes of data and the incorporation of new technologies that help non-technical people interpret the data may lead them to demand data democratization.
Importance & Purpose Of Democratizing Data
Making Seamless Remote Work Possible: Self-service is key to data democratization. As a result, work may be completed far more quickly than it otherwise could. For example, a tried-and-true strategy for speeding requests between departments in a conventional office setting would entail going down the hall to speak with your IT colleague. Unfortunately, that doesn’t function well in a virtual office setting, primarily when many people work flexible hours.
Increased Customer Satisfaction: Data democratization isn’t always about giving your staff more control. Sometimes the goal is to give customers more authority. For example, think about what occurs when you use a ride-sharing program on your smartphone to make a call. After choosing a ride, you’ll know exactly when it will come because you can check how close the closest drivers are in a matter of seconds.
Internal Process Simplification: Data democratization also encourages a deeper comprehension of the organization’s operational assets. Imagine, for instance, that the shipping department experiences an intolerably high number of deliveries that are delivered late due to incorrect address information and the sales tax. As a result, the compliance team has established a reliable geocoding and address verification system to ensure complete compliance with local and regional tax laws.
Risk & Challenges
Leaving Behind The Conventional Way Of Thinking: Data accessibility can be hampered by risk-averse company cultures and restrictive authority structures. They cannot keep up with emerging cloud-based data sources and growing data quantities.
Traditional business intelligence (BI) solutions were created for use by knowledgeable analysts and data scientists with the technical skills to glean insights from data. In addition to making business users more dependent on analysts for their standard information requirements, such a system takes valuable analyst time and attention away from difficult jobs and innovations.
Picking The Proper Data Stack: Different departmental users have various demands for data and therefore employ various procedures for gathering and storing data in various formats. For instance, sales teams employ CRMs and lead management software to find and follow up on new clients.
Marketing teams use automation solutions to share content, boost consumer interaction, and monitor social media and website traffic. To engage with customers and address their difficulties, customer support employees use helpdesk platforms.
Data can also be found in various formats, including logs, emails, spreadsheets, and service requests. As a result, data silos grow more entrenched over time, making it challenging to select a data stack that connects with numerous data sources.
