Data Democratization
Unlock the full potential of data
The ultimate goal of every organization is to base strategies, tactics and decisions on insights that are not available to competition in order to create a competitive advantage. Achieving this is only possible when everybody and every system in an organization has access to the right data to create value added insights. Or in other words “data democratization”:
“Everybody has access to data and there are no gatekeepers that create a bottleneck at the gateway to the data. The goal is to have anybody use data at any time to make decisions with no barriers to access or understanding.”
- Bernard Marr
Challenges
What if we tell you that this ultimate goal is achievable for everybody who puts the right effort in growing the data maturity.
Do you recognize these challenges on your way to data democratization?
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What pipelines and platforms need to be set-up to enable self service analytics
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Which dashboards and data visualization tools integrate best with the current tech stack
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What is the most optimal way to store & model my data: data warehouse vs data lake, data vaulting
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How should I set-up data governance, access & security
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Which training is required to improve data literacy of data & analytics teams and business
These are exactly the challenges we can tackle together so you can benefit from the advantages of data democratization: easily discover and access data, better and faster decision making and enforced security & compliance.
The 5 Dimensions of Data Democratisation
Democratizing data within your organization requires action in 5 dimensions. The objective of Data Minded is that our customers achieve their objective in a pragmatic and cost efficient way. Our clients can count on our expertise in every phase of their journey.
1. Self Service Analytics
2. Dashboards & Data Visualisation
According to Gartner, Self-Service Analytics is a form of business intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support.
Accomplishing this requires years of careful planning and a complete rethinking the enterprise data systems and architecture. Actually, it’s broader than merely a choice of tools. Processes, people and how you think about a product will need to adapt as well.
Data Minded can help you identify and visualize the areas that will help you reach a functional level of self-serviceness. We do this typically using our data maturity framework as a guideline. This can serve as a basis for your data strategy for the coming years.
Creating value from data, necessitates converting raw data into data insights that are understandable and actionable to everyone. While other data management tools may make data more accessible, dashboards and data visualization tools (e.g. Tableau, PowerBI, Data Studio) facilitate interpretation.
Data Minded helps to select the data platform that will feed the visualization tools. The number of vendors and platforms you could consider is huge. Based on what we saw at previous clients, Data Minded is in a unique position to form a neutral recommendation and accelerate the implementation and adoption.
3. Data Storage & Modelling
Multiple options exist to structure your data to facilitate analytics i.e. a data warehouse, a data lake or a hybrid form called a data lakehouse. Each has their own pros and cons.
https://www.fivetran.com/blog/what-is-a-data-lakehouse
There is not one version of the truth and organizational needs change when higher levels of data maturity are achieved. Often not only the data structure is changed but also the underlying infrastructure could be impacted when combined with a cloud migration.
Much debate has been had about data modeling in the modern data space. Traditional dimensional (star schema) modelling allows for fast and simple queries and is well suited for typical reporting needs. On the other hand, it isn’t all that flexible. Techniques like data vaulting add an additional layer of complexity which might sometimes be worthwhile.
4. Data Governance, Access & Security
It is essential to make sure the data managed by your organization is protected and easy to access. Even more so when dealing with client data. Optimally managing enterprise data offers different advantages:
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Access to high-quality data to improve the quality of analyses
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Secure and compliant with regard to regulation
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Consolidate data across multiple sources for increased efficiency
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Easily identify data dependencies and users of data sources
Data Minded helps to make the right governance and security choices on every level of your tech stack. Data Minded doesn’t only ensure you have the right tools (e.g. Collibra, Datahub, Great Expectations, AWS IAM, etc.) and fit with your overall architecture, but also implement the right processes.
Fundamentally, Data Minded believes in a decentralized data landscape following data mesh principles. Each team delivers its own set of data products which can be consumed by other teams in the organization. There is no one global data model for the entire organisation. Instead, each department models the data to their needs. New data products can be created to reuse those models. Eg: A customer360 view or a dimensional model of finance data.
Data Minded has already implemented data warehouses and data lakes in multiple organizations. Before implementing changes, a deepdive is performed on the current challenges and needs. By looking at the bigger picture Data Minded guides organizations through the phases of design, development and implementation.
5. Data Literacy
Having the tools, and processes is one thing. Adoption throughout the organization can only happen if people are proficient with them.
Different roles require different levels of knowledge and experience. Data Minded helps data engineers, data scientists, and data analysts learn the correct fundamentals and grasp more advanced concepts through our Data Minded Academy.