From Data Chaos to Clarity: Blueprint To Building Scalable Data Architectures To Gain Clarity & Edge In The Market
This Is For
1
Businesses in tech, software and others industries as from logistics, gaming, manufacturing, renewable doing at least $500,000 in annual revenue.
2
Organization having data scattered all around unable to grasp the big picture in unified view.
3
Organization relying on off-the-shelf solutions as sheets, excel or relying on architerture with low performance as long load time, high usage, maitanance and constant problems.
4
Organisations unable to gain actionable insight that produce business outcomes and rely on surface level metrics
At The End You Will Receive
Scalable Data Architecture Blueprint
A comprehensive step-by-step guide with visual diagrams, tool recommendations, and best practices for building a scalable data infrastructure.
Comparison Matrix
Comparison of top tools (e.g., Snowflake vs. BigQuery, Fivetran vs. custom ETL). You’re going to receive a template and the exact steps of how to pick the correct tools aligning with your organisational needs.
Already Made Data Architecture
You can just utilize the architecture we recommend based on +30 implementations.
Architecture Checklist
A checklist for auditing current data systems, tools, and architecture.
Data Architecture Diagram
Diagram template that can be used to map out your own data stack and architecture.
Challenges & Mistakes to Avoid
List of challenges and mistakes to avoid.

This would be worth over $15,000 if gone to any other analytics, bi agency consulting firm. So pay attention!
Who Am I
Ali Šifrar
CEO & Founder @aztela
  • Responsible for driving over $10m+ in pure cash collected with data, BI and insights.
  • Not traditional route started as sales rep, then marketing and became Revenue Operation's Director before moving into data engineering & analytics. This turned in our biggest competitive edge.
  • Deployed end to end over +20 analytics infrastructures and created +30 analytics solutions, dashboards with 100% adoption success rate.
  • AZTELA has top 0.01% of data talent in the world because we can bridge the gap between data and business.
  • Founded AZTELA helping business utilise data to fullest to generate more actionable insights, growth, profits using our proven frameworks.
Why Should You Listen
Case 1
DealFuel.io
Number 1 software to land top 1% sales roles in the B2B SaaS & Technology space.
Challange
  • Dispersed data across the enterprise, making it difficult to comprehend its overall scope.
  • Dependence on pre-made tools, sheets, which caused a loss of over 100 hours.
  • The inability to identify bottlenecks and insights into sales team performance to enhance efficacy, sales, and revenue.
  • An unscalable infrastructure, which caused a lack of trust in data and the insights derived from it.
Solution
  • Conducted Data Strategy Assessment
  • Developed Data Roadmap
  • Steak holder interviews
  • Technology Assessment
  • Deployed Scalable Analytics Infrastructure.
  • Python scripts (custom ETL) to extract data from CRMs & Finance sources
  • The Hetzner server for storing the data collection scripts
  • Airflow for workflow management
  • Google BigQuery as a data warehouse
  • Looker for data visualisation
  • Provided actionable insights
  • Custom analytics dashboards for their executives (board members) and their sales departments.
Result
  • Generated over $10M in sales, increasing MRR from few thousends to $800K by gaining insights into their sales department and identifying process pitfalls.
  • Saved over 50 hours per week for executives by eliminating manual processes such as searching for data, analyzing it, and presenting it, through automated reporting.
  • Increased sales team performance, consistently achieving quota attainment.
  • Built a reliance on data through clearly defined metrics, definitions, and governance practices.
Reviews
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Our Work & Social Proof

Check page filled with our examples, testemonials and reviews

Let's Start The Blueprint
Common Pain Points
1
Messy Data Silos
Organizations struggle with data scattered across multiple tools, from CRMs to marketing platforms, creating isolated pockets of information without a central source of truth. This fragmentation leads to inconsistencies and hinders comprehensive analysis.
2
Manual Processes
Reliance on manual data entry, cleaning, and consolidation introduces human errors and inefficiencies, consuming valuable time and resources that could be better allocated to strategic initiatives.
3
Unscalable Systems
Legacy architectures or poorly designed pipelines buckle under the weight of growing data volumes and complexity, leading to system crashes, slow processing times, and inability to incorporate new data sources.
4
Lack of Real-Time Insights
Outdated batch processing methods result in delayed reporting, preventing organizations from making timely, data-driven decisions crucial in today's fast-paced business environment.
5
No Actionable Insights
You are unable to uncover true bottlenecks, insights to drive business outcomes.
The Power of Scalable Data Architectures
1
Centralized Data Hub:
Integration of all data sources into a unified platform for easy access and analysis.
2
Efficiency Gains:
Automation of data cleaning, transformation, and aggregation processes.
3
Real-Time Insights:
Faster, more reliable analytics for data-driven decision-making.
4
Cost Optimization:
Reduction in storage, processing, and manpower costs by streamlining workflows.
5
Future-Proof Systems:
Flexibility to adapt to new data sources, tools, or increased data volumes.
6
AI Foundation
Prerequisite for AI because AI and machine learning models depend on high-quality, structured, and accessible data.
Introduction To Data Architecture
In this slide down below we breakdown each component based on why is it important, their use cases, recommendations and so on.

Data Architecture provides a blueprint for organizing, integrating, and managing data within an organization.
It encompasses the design principles, standards, and guidelines for ensuring data is stored, accessed, and analyzed effectively.
A well-designed data architecture enables organizations to unlock the full potential of their data and drive informed decision-making.
  1. Data Sources Definition: Where the data originates.
  1. Examples in the Context: CRM (e.g., Salesforce), product analytics (e.g., Mixpanel, Amplitude), marketing platforms (e.g., Google Ads, HubSpot), databases, or application logs.
  1. Data Transporation: Tools or processes to extract, transform, and load (ETL) or extract, load, and transform (ELT) data from various sources into a central repository.
  1. Examples in the Context: Fivetran, Stitch, Airbyte.
  1. Purpose: Automates the movement of data from source systems to a data warehouse or data lake.
  1. Data Warehouse: Centralised systems for storing and managing data.
  1. Examples in the Context: Cloud-based data warehouses like Snowflake, Google BigQuery, or Amazon Redshift.
  1. Data Transformation Layer:
  1. Tools and processes for cleaning, aggregating, and transforming raw data into usable formats. Examples in the Context:
  1. Batch processing: dbt (Data Build Tool).
  1. Real-time processing: Apache Kafka, Google Dataflow.
  1. Purpose: Prepares data for analysis by ensuring consistency, quality, and alignment with business needs.
  1. Data Orchestration: Tools to schedule, monitor, and manage workflows in the data pipeline.
  1. Examples in the Context: Apache Airflow, Prefect, Dagster.
  1. Purpose: Ensures all parts of the pipeline run in a coordinated and reliable way.
  1. Visualization: Tools for querying, analyzing, and visualizing data to derive insights.
  1. Examples in the Context: Tableau, Looker, Power BI.
Mistakes To Avoide
We've seen multiple mistakes organisation do and later regret but this three ones are definitely on the top.

Please don't over focus on tools & technology. This part shouldn't be complicate and should be done last step before ending data strategy and delivering analytics roadmap & prioritisation matrix. To generate revenue, ROI with data the thing that is responsible 80% of it is before you even touch any tool, or development.
1
Not Conducting Strategy Assesment
No conducting in-depth strategy assessment before focusing on technology & tools assessment.
Check previous video about this and HERE is more in-depth macro process about.
2
Over Focusing On Tech & Tools
Focusing on technology, fancy tools thinking they are going to solve your problem.
3
Ignoring Data Quality
  • Dashboards show incorrect or inconsistent metrics.
  • Analysts waste time fixing errors in datasets before analysis.
  • Data anomalies lead to poor decision-making.
4
Overcomplicating
If you start overcomplicating adding unnecessary processes, tools that are not priority and are good down the road.
Chances of implementing, adopting and becoming a data driven company became slimmer. Summary prirotize accordingly, start small but dream big.
5
Performance Pitfalls
Ignoring performance as long ingestion times from ETL to warehouse, long hours of loading data to analytics dashboards and increse in load time. Make sure to consistently maintenance, review and set notification alerts systems.
Practical Framework for Implementation
Let's start the step-by-step framework NOW!

As shown in the previous step this please don't start this initiative without assessing and conducting strategy assessment which is true challenges, pains, goals, desire, steak holder alignments, interview of the end users in department. This all belongs under data strategy development which is crucial for success and ROI.
1. Conduct Strategy Assessment
Uncover true challenges, pains, goals, desire, steakholder & enduser interviews in key department's. At the end analysing all the findings and providing the roadmap with the priorities.
This is foundation so we are able to align data with business to product business outcome.
Here is more in-depth macro process about

19:50

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Key Steps to Take Before Starting Your Data Analytics Initiative

Want to create custom analytics dashboards, turn data into revenue? - https://social-proof-mockups-nml50go.gamma.site/ Breakdown the exact key steps to take to turn data into revenue before you start your data analytics initiatives. We implemented +25 data analytics infrastructure, BI, and enabled companies to turn raw data into actionable insights. Many organizations rush into development creating dashboards, trackers, advance solutions all to result in low adoption and no ROI. We understand that to bridge a gap between data and business magic happens before development even starts. Thats why we often say the strategy is 80% of success CONNECT WITH ME LinkedIn: Ali Z. Twitter: @grandmaszter Book a FREE data assessment call: https://calendly.com/alizu/15-minute-... Transcript

2. Audit Current Infrastructure
Begin by comprehensively mapping out all existing data sources, evaluating current pipelines, tools, technology, processes, people, challenges, goals, data quality…
This step involves identifying bottlenecks, inefficiencies, and areas ripe for improvement, providing a clear picture of your data landscape. ​

miro.com

AZTELA: Data Architecture Diagram (Copy It)

3. Gap Analysis
Based on the conducted strategy assessment you understand whole concept of your company from the goals to challenge's to current infrastructure state.
Enables you to identify where you are currently missing, lacking in terms infrastructure to achieve your desired state. Next steps is picking correct tools and comparing to pick best that suits your needs.
4. Tool Comparison
Tool Matrix Comparison
Here is a template please copy it, it's gone help you to pick correct tools & technology based on your current situation.

Google Docs

AZTELA: Tech & Technology Matrix (Copy It)

Comparison
Here are the comparison between top 4 providers at each stage. This is gone help you compare each vendor to pick the ones that suits your needs.
Data Warehouses
ETLs
BI
5. Design Data Architecture Diagram
Utilize down below template to map out Data Architecture.
Identify your data sources and types, note the tools you would need to effectively extract & ingest, store & replicate, transform, house, and analyse your data.
Drag and drop icons like in previous examples or utilise text to map out your architecture.
Follow the instructions!!!

miro.com

AZTELA: Data Architecture Diagram (Copy It)

6. Ensuring Governance and Security

Not necessary especially if you just starting out. For governance you can also utilize existing solution tools like spreadsheets, Notion or your project management systems where you store your documentations.
Data Governance and Security Layer Definition: Processes and tools for managing data access, quality, and compliance.
Purpose: Ensures data is secure, accurate, and compliant with regulations like GDPR or CCPA.
Examples: Data Dictionary, Business Glossary, Simple Data Contracts.
7. Iterating and Optimizing Your Data Architecture
Regular Reviews
Schedule quarterly architecture reviews to assess performance, identify bottlenecks, and align with evolving business needs. Use metrics like query response times, data freshness, and system uptime to guide optimization efforts.
Stakeholder Feedback
Establish channels for continuous feedback from data consumers across the organization. This insight helps prioritize improvements and ensures the architecture evolves to meet real business demands.
Technology Updates
Stay informed about advancements in data technologies and assess their potential impact on your architecture. Consider proof-of-concept projects for promising new tools or methodologies to drive continuous improvement.
Performance Tuning
Implement ongoing performance tuning practices, including query optimization, index management, and resource allocation adjustments. Use monitoring tools to identify and address performance issues proactively.
Data Quality Audits
Make sure to set correct audits, logics making sure to get alerted if the data quality drops before it gets ruined.
BONUS: Already Made Data Stack For The Ones Starting

Still conduct the neccessery step from the above.
If you are a company in tech sector or other and don't know what to pick. We made it easier for you and just do the following below.
You can choose between off-the-shelf or custom ETL both works amazing. If gone utilize custom would need also VPS to host the python scripts. For event data as product, operational analytics utilise Kafka or Segment. If tech organization would recommend Segment.
DBT or DataForm for transformation. Utilize DataForm only if you utilising BigQuery as a Data Warehouse.
For BI can't go wrong with any of two below. Could add looker but very limited in the features.
And to finalize everything airflow as orchestration to keep everything running as well oiled machine.
You can access the already made stack for your from our recommendation in the MIRO board which is shared to you. Here is also the link and the picture of diagram.

miro.com

AZTELA: Data Architecture Diagram (Copy It)

Assets
AZTELA: Tool & Technology Matrix & Comparsion (Template)

Google Docs

AZTELA: Tool & Technology Matrix & Comparison (Template)

AZTELA: Data Architecture Diagram (Template)

miro.com

AZTELA: Data Architecture Diagram (Template)

Our Offer To You:
We will turn your raw data into actionable insights to increse revenue, retention, EBITDA and gain you edge in the market.
By implementing custom analytics ecosystem end-to-end and acting as your data partner making sure that data constantly evolves to your goals to give you a competitive edge.
Guaranteeing 100% Adoption, Increse Data Driven Decision, Revenue, Retention, EBITDA & Having Insights Weren't Aware That Existed Before.

Book a FREE Data Strategy Assessment Call With Our Team →

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Exploration Call - Ali

Please only proceed with booking a call if you're genuinely interested in exploring our services further. We value your time and ours, and want to ensure that each interaction is meaningful and beneficial for both parties involved.In this call, we will delve into your needs, goals, and priorities to

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Custom Data Analytics Dashboards and Business Intelligence Solutions That Are Responsible For Increase Client Retention, Profits, Optimize Profits and Reduce Costs. Implementing Data Warehouses, ETLs, Data architecture, Predictive Analytics & AI.

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Our goal is to bridge the gap between business and data. We create data analytics solutions, BI, and dashboards that transform raw data into actionable insights. This allows you to increase revenue, profits, and LTV, reduce churn, and gain a competitive edge in the market.

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