Isn’t 2023 the year of Democratization?
It definitely is. Demand for democratizing data will continue to rise during 2023, requiring businesses to move away from the traditional top-down approach to data governance.
Instead, the focus will be on getting data into as many (approved) hands as necessary. Rather than expecting human expertise to seek out the data (often through manual and lengthy processes and bottlenecks), compliant data will become more accessible and available on demand.
It will mean business intelligence becomes more oriented toward self-service— rather than being the preserve of IT. Corporate culture will also change, with employees increasingly incorporating data into decisions and collaborations.
From generating enriched data visualizations to building applications, the rise in low code has shown what can be achieved by non-technical users.
Democratizing structured and unstructured data is a natural evolution of the process where usability gets priority, reducing the complexity and rigidity of traditional data governance processes.
To sum up, this means
- Encourage staff to feel at ease asking questions about statistics.
- Make the necessary tools available so that everyone can work with data.
- Consider data democratization an ongoing effort that can even call for a shift in organizational culture.
Data Democratization roadblocks
The top five data democratization challenges that businesses will have to solve in 2023 if they want to adopt new data platform strategies and thereby cut costs and complexity.
1.Where is my access to the data?
I don’t have access to the data which I need. They are not yet in a data warehouse but in data silos. To be effective in the analytical process, the users need direct access to transactional databases, data lakes, data warehouses, etc.A solution to this might be a so-called semantical layer of the data.
2.Missing the necessary tools to analyze the data
The goal behind the Democratization of Data is the distribution of analytical work to the domain experts. However, to succeed in doing this, the domain experts need the necessary tools to perform those actions without any programming or Machine Learning knowledge. No-, Low- and Full-code tools are necessary to make analytical processes possible for any profile.
3.Versions of Truth
A side effect of democratizing data is that any user can analyze data, potentially leading to redundant work, as we could do the same thing multiple times. Hence, the necessary data analytics tools need to be able to share services between business units and make it visible to all the users what currently exists to be effective in their daily work and not rebuild existing solutions.
4.Lack of Semantic Layers
An easier way for people to access data without IT support is through a semantic layer, a business representation of the data.
Despite being excellent at protecting users from the underlying complexity of data, semantic layers intend to represent the data in one database at a time. To connect to and engage with numerous data sources spread across various regions, today’s users want a semantic layer that is more widely available. This concept is called “Data Virtualization.”
For the implementation of successful data democratization, employees must possess a specific data skillset. One such program is Data Citizen where employees explore and comprehend data within their parameters. The program has two levels-Data stewards (study and analysis) and Data Citizen (ML model development). Post completion, mentorship programs running already help industrialize the solutions in production.
Data and analytics transformation cannot happen for an organization in a few select pockets or silos. Regardless of skill level, everyone should be able to participate in the data analytics process and drive value.
Technical knowledge leaders must act as mentors to knowledge workers with domain expertise and guide them through the analytics process. This collaboration between technical and domain experts will help organizations achieve breakthroughs with their data faster.
Further, insights are critical for the continuous improvements of the business. It is vital to track business metrics like profitability, revenue, operational expenses, sales, attrition, etc. These metrics help in monitoring operations and making data-driven decisions to perform better. It is up to businesses to decide why they need specific metrics and which ones are valuable.
Eventually, analytics needs to be easy. Organizations should invest in technologies that move away from being highly dependent on writing code.
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