Data Democratization: Cultural Building Block for Data-Driven Organizations

15th November 2022
Data Democratization: Cultural Building Block for Data-Driven Organizations
Image Credit: Reto Scheiwiller from Pixabay

By Manish Kumar, Data Engineering Leader, CDK Global India
November 15, 2022: Ten years ago, when the term “Data Democratization” surfaced, everyone wondered what they are trying to achieve, how this will bring any difference, who will be benefited, and moreover, will it bring job loss for Data Analysts, Engineers, etc.
What is Data Democratization?
At an early age, access to data autonomously was defined as Data Democratization but in the past many years, the definition has evolved as a continuous process to empower everyone in the organization to have access to data easily, securely with high confidence in its accuracy which helps them make an informed decision.
A recent Google Cloud and Harvard Business Review survey of industry leaders showed that 97% of organization-wide access to data and analytics is critical to the success of their business. 

Why is it one of the high-priority needs for every organization –

  • Messy data which is hard to interpret
  • Trust in data available to people
  • Who will access the data, does he/she have permission to access it?
  • Reliance on Data Analyst/experts who are fewer in number increasing the response time
  • Skills gap to use a sophisticated analytics tool to find answers to data-related questions
  • And most important “Culture to adopt data” to make informed decisions

What role  is Data Democratization playing  to solve data-related concerns?
Organize and prepare Data – Data is collected in multiple formats through thousands of different systems. An enormous investment has been done in this area to design tools and platforms which can collect, curate, and organize data. Tools like Kafka, StraemSets, Talend, Apache Airflow, and Informatica are some examples. These tools are capable to collect data from sources using easy to deploy connectors, process them in real time and load into target storage systems in a much more organized manner.
Trust Building – Trust was a problem since data is being managed mostly by data experts who may or may not have complete business context. Now, organizations are investing in platforms like Power BI, and Tableau which helps end users access the data in the format they like with all the business logics/rules in place by the individual themselves.
Data Security – As we democratize data, security becomes a much larger concern since it contains lot of sensitive information. Nowadays, database to data visualization tools are coming with out-of-the-box data security module which allows administrators to apply checks and permission for data access. Users can only extract or visualize data to which they are having permission, sensitive data like SSN, Date of birth, personal ID number, etc. can be masked to avoid any misuse or security breach.  
Data Experts Dependency – Data experts/engineers are now investing more time in enabling systems to allow users to access the data with lots of flexibility than working on individual requests. This helps in two ways, engineers and analyst are engaged in challenging work and users are now no more dependent on these experts to gather the data they need.
Technical expertise is not a prerequisite – Platforms like Power BI, Qlik, and Tableau provide simpler and easy-to-navigate environments. These tools allow users to drag and drop attributes, apply business rules and filters and finally visualize as per individual needs. Options like share with others, annotate, and discussion board makes the platform much more useful.
Promote culture to adopt data – Organizational culture is a big driver to adopt data for informed decisions. Data Democratization provides processes, tools, and platforms which makes people comfortable to access the data.
Data Literacy: Need of the hour
While Data democratization brings lot of power and independence to end users, it also brings some risks, like data access breaches, and wrong decisions due to incomplete data or knowledge of it. To gain maximum benefit, one big aspect organizations need to invest in is Data Literacy. It’s not about how to use tools and analyse reports but the ability to interpret and gain insights from available data. For example, a team which is working on marketing data may not have much exposure to data produced by manufacturing. It’s essential that teams are educated on data, and domain along with tools and platform to gain the maximum benefits.
We now understand how data democratization is changing the world and empowering humans to make decisions faster and independently. Additionally, it raises another set of challenges like data availability, scalability, continuous delivery, etc.
Data Cloud advancements are solving these problems to a big extent. Lots of organizations are transitioning over cloud from on-premise to address the scenarios like data mart, data leak, real-time streaming, machine learning, and processing terabytes of data.
Organizations used on-premise vertical scalable infrastructure for many years to manage their applications and data. As we advance in Data Democratization, the demand to have a high throughput and scaled solutions have increased, to provide access to information in much faster and more reliable ways. Let’s double click on use cases which Data Cloud helps with –
Big Data – Big data is defined as 3Vs (Volume, Variety, and Veracity). Organizations are managing and processing data with the size of terabytes to exabytes which require huge processing and storage capabilities, data contents vary from tabular data to video files, comments, large objects, etc. Cloud delivery model like IAAS and PAAS helps manage Big Data scenario very well with optimal investments.
Scale – Elastic scaling helps an organization to deploy solutions where the demand for resources varies depending upon time, geo, events, etc. Support for multiple languages and serverless architecture is a big relief in terms of setting up data processing tasks at any scale.
DataOps – Being agile is another key here since demand and requirements are changing very frequently. Auto-scaling and serverless architecture help in solving the agility within infrastructure demand but agility in features and availability of data bring the concept of DataOps. Tools like StraemSets,, RightData, etc. provide a complete set of features Agile, DevOps, Integration, Data Quality, and Simplicity help engineers and business people access the required data in much faster and more agile ways.
Data Democratization is critical and needed in today’s fast-changing world to make an ecosystem where an individual can take informed decisions much faster and more accurately. Data Cloud can play a big role in the success whereas Data literacy can be an impediment.
 Manish Kumar is a global leader for Data Engineering product who  has experience of more than 20 years in the field of Data Engineering, Visualization, Cloud and Big Data Engineering.
 CDK Global is a leading US-based  provider of retail technology and software as a service (SaaS) solutions that help dealers and auto manufacturers run their businesses