Accelerate data democratization in your organization
To become data-driven, an organization must make data readily available and enable business users within the organization to leverage it. We refer to this process as data democratization. We can help democratize data in your organization and put the right information into the right people’s hands at the right time, empowering them to extract information, make decisions, and uncover opportunities directly.
Why Organizations Fail to Turn Data Assets Into Business Value
Discover and realize the value of your data assets with the analytics engineer and the modern data stack
Challenges in becoming a data-driven organization
While the potential of data is widely recognized, many organizations struggle with using their data assets efficiently. Challenges include:
- Divide between IT and Business. Many organizations delegate the preparation of datasets to a central, relatively small team of IT or data professionals, while business domain knowledge is spread across the entire company. The disparity between data and business expertise can lead to high costs in discovering, creating, trusting, and eventually using high-quality data. As such, IT or data teams can become a bottleneck when organizations want to scale the development of data products, and data availability is limited to business end-users.
- Lack of Data Literacy and Awareness. As the access to and usage of data assets is limited, practice and application do not reach the average business end-user. Not all employees are aware of the potential and possibilities of working with data. Consequently, the business domains do not acquire the necessary experience, skills, and literacy to use data in their day-to-day work.
- No Data Management or Quality Assurance. Visibility in data handling is limited within the organization. It is unclear who owns data sources or assures data quality, and data quality issues compromise trust in data assets.
These challenges all converge into one overarching obstacle: data and information do not reach the right people at the right time to enable data-driven decision-making.
“We empower our data analysts with engineering capabilities. Our goal is to enable the power of data democratization by automating the most time-consuming parts of the analytics workflow. The introduction of the analytics engineer is a crucial step.”
“Data democratization increases the usage of data assets, uncovers new opportunities with data, and improves existing data operations. All this expedites decision making and facilitates the creation of business value.”
“We’ve transformed the way we work at Funda, shifting our focus more towards understanding our customers to drive product and business innovations. It would have been impossible to do that without a reset in how we technically collect, share, and explore data and how we make it accessible for analysis. In just a few years, we’ve moved away from traditional centralized and closed business intelligence to an open, explorative, and collaborative approach to data analysis and customer research. It has positively influenced how we present arguments and make decisions across the entire organization.”
“The data platform is the engine room for our democratization. It not only centralizes technical resources but also consolidates innovation.”
“The biggest bottleneck used to be data availability. Now it’s the quality and useability of data.”
“The analytics engineer makes it possible for people to use data to generate value. Whether consumers need new tooling to access new datasets or require training on how to self-service from data consumption points, the analytics engineer meets their needs.”
“Scaling up the benefits of data and analytics capabilities through the modern data stack also shifts new responsibilities to the platform team. Where previously data consumers with domain knowledge ensured data quality on demand through manual wrangling, today’s automated pipelines require savvy monitoring and new business processes for governance.”
“A modern data platform should be simple, scalable, and not limit the end user’s analytics needs. When it follows these principles, it can be an essential tool for value creation within any business.”
Availability and Self-Service
Enablers of Data Democratization
- Increasing Availability of Data and Information
As the demand for data and information grows, organizations need to facilitate its discovery, usage, and distribution in a scalable, secure way to increase availability to users. We help our clients by building a modern data stack, creating data as a product, and setting up data governance programs to promote company-wide availability and usage of data assets. - Introducing Self-Service Analytics
As companies strive to become more data-driven, the demand for information grows, potentially overloading IT, BI, or data engineering and creating a bottleneck. To overcome these challenges, organizations can expand the skillsets of information users, enabling them to use the data directly. GoDataDriven can improve data literacy across your organization and establish a dedicated support structure to enable self-service analytics.
Why Organizations Fail to Turn Data Assets Into Business Value
Discover and realize the value of your data assets with the analytics engineer and the modern data stack
Ending of the Traditional Data Warehouse
The traditional data warehouse is moving to the cloud and a new stack of tools, often driven by open source initiatives have changed the playing field beyond recognition. Until recently, data was “owned” by IT departments. Business units used data to make business decisions, but they always had to go through the IT department to get the data.
The goal of data democratization is to have anybody use data at any time to make decisions with no barriers to access or understanding. Data needs to be generally available and a modern data warehouse enables that data democratization. The end of the traditional data warehouse is near, ready to be replaced by more relevant architectures that can be deployed on the cloud within the blink of an eye.
Lowering Technical Barriers
The Modern Data Stack
Organizations can reduce the complexity of setting up and tuning a data platform by choosing a set of tools that together cover the main functions of a data platform. In this way, they can create a fit-for-purpose solution that is easy to implement, maintain, and leverage. We refer to this set of tools as the modern data stack.
As a company we have partnered with many key players in the ecosystem. Below, we give suggestions for solutions to start using in your organization.
- Cloud Data Platform: Azure, AWS, GCP
- Modern data warehouse: Amazon Redshift, Google BigQuery, Snowflake, DataBricks
- Data Ingestion: Fivetran, Stitch
- Transformation and Orchestration: Apache Airflow, DBT
- Business Intelligence: PowerBI, Tableau, Looker
We help you make the best selection possible based on the unique setup at your organization.
Become data-driven
Introduce Self-Service Analytics
The emergence and prevalence of data affect many business domains and roles. Today, 75% of the global workforce needs data in their daily work, yet only 21% are fully confident in their data literacy skills. This means that there is a significant knowledge gap hindering people’s use of data for efficient decision-making.
Introducing self-service analytics enables (business) users to extract information from data themselves. To successfully introduce Self-Service Analytics, it is very important to improve data literacy in your organization to create synergies between the data and business domains. This enables professionals to contribute to analytics use cases and embeds data analytics in the business processes.
One of our core principles is sharing knowledge. Our consultants will train in-house talent ‘on the job’ in making data available to the organization and leverage it for business purposes. In addition, the GoDataDriven Academy offers dedicated learning journeys for all business users to improve data literacy across your organization.
Become data-aware
Implement Data Observability and Restore Trust in Data
Many organizations fail their data-driven journey because of data downtime: the period when data is missing, incomplete, inaccurate or incorrect. Data observability is the practice that assures data quality and brings stakeholders’ trust back to data.
Prerequisite for Data Democratization
The Analytics Engineer
The recent developments in technology, the rise of the modern data stack, and the prevalence of self-service analytics have disrupted the field of data and analytics. Out of these game-changing events, a new professional role has emerged in the data field: the analytics engineer.
An analytics engineer’s responsibilities include:
- Producing high-quality datasets for reporting, machine learning modeling, and operational workflows
- Automation, version control, monitoring, and testing of data pipelines.
- Improving data observability and maintaining data definitions and documentation.
- Enabling analysts and business users to leverage tooling for self-service analytics.
Our analytics engineers will help train talent in your organization to leverage modern cloud tooling and introduce modern analytics principles within your teams.
Realise the value of your data today
The Road to Data Democratization
The road to data democratization begins with defining your company’s current maturity and future ambitions. We can help you to create a data strategy and build a concrete roadmap to establish the right enablers for data democratization. We also have extensive experience in raising data literacy and awareness across business domains. Specifically, we help organizations to design and execute their data strategies along three tracks:
Case Studies
“What I like about working with GoDataDriven, is that I don’t have to worry about evaluating people’s skillset. I know you have already done that. They went through the process, and you made sure that everything is of high quality. Knowing you can depend on that is very reassuring to me.”
“Our customers are, in general, technologically advanced companies that recognize the advantages of having a cloud platform. They understand it doesn’t compromise security but, on the contrary, increases it and allows a solution to be accessible everywhere without complex and expensive on-premises infrastructures.”
“I’m curious to see what our data-driven future will look like. By using data, we now know how to stay relevant for individual visitors. I’d like to go beyond predicting theater occupancy and introduce smart applications to optimize the number of visitors. But that’s not all. Data also helps us better understand the member life cycle and retain members.” – Rick Stammes, Business Analyst at Pathé.
Are you interested in Data Democratization?
Contact Bram Ochsendorf (lead data scientist at GoDataDriven) to learn more about data democratization, self-service analytics, the modern data stack, data governance, and analytics engineering.