This blog was written based on a collaborative webinar conducted by Hevo Data and Danu Consulting- “Data Bytes and Insights: Building a Modern Data Stack from the Ground Up”, furthering Hevo’s partnership with Danu consulting. The webinar explored how to build a robust modern data stack that will act as a foundation towards more advanced data science applications like AI and ML. If you are interested in knowing more, visit our YouTube channel now!

The Foundation for Good Data Science

The general scope of data science is very broad. The hot topics in data science today are all related to ML and AI. However, this is only the tip of the iceberg- the aspirational state of data science. There is a lot that needs to go on in the background for ML and AI within an organization to be successful. 

What do we need to have first? 

We need to have a solid foundation to build up to AI capabilities within an organization. Some key questions to be answered to evaluate this foundation could be- 

  1. Do we have access to the data we need? 
  2. How is the required data accessed? 
  3. Do we have good data governance? 
  4. Do we have the infrastructure in place to implement all our required projects?
  5. Can we view and understand data easily? 
  6. How can an ML/AI model go into production? 
Partnership with Danu Consulting: Foundation of ML & AI

1. Digitalization, Access, and Control

The first thing to understand is how data is captured within your system. This may be done through a variety of methods, from manual entry into spreadsheets to complex database systems. It’s important to consider which method will allow easiest and clearest access to data within the data stack. 

Next, you need to find out who has the ultimate source of truth. Formation of data silos can be a huge issue within organizations, with data from different teams displaying completely different numbers, making it very complicated to make data-driven decisions. It’s important to have a centralized source of truth that will act as the foundation for all data activities within the organization. 

Finally, it is important to consider how the data can be accessed. Even if the data is all captured and centralized in a common format, it is of no use unless it can be accessed easily by the necessary stakeholders. A complex and inaccessible database is of no use to the organization – data is most valuable when it is actively used to make decisions. 

2. Data Governance

Data governance is an iterative process between workflows, technologies and people. It is not achieved in one go but is a continuous process that needs to be continually improved. It involves a lot of change management and involves a lot of people. But with the right balance can become one of the biggest assets for the company.

Understanding by all involved stakeholders on the owner of data, the processes to be followed, the technology to be used and the control measures in place can ensure that data is safe, secure and traceable. 

Partnership with Danu Consulting: Data governance

The Benefits of Having a Cloud Infrastructure

There are a number of reasons why having a cloud infrastructure could prove to be beneficial for an organization’s data stack. With a good cloud analytics process, the benefits are multifold- far beyond just cost savings on the server! These include: 

  1. Being process focused: A cloud infrastructure would allow an organization to focus on the processes rather than the infrastructure. 
  2. Having an updated system: Being on the cloud means that an organization can always use the latest versions of tools and would not need to invest in purchasing their own infrastructure to keep up to date. 
  3. Integrating data: cloud systems allow organizations to integrate their data from different sources. 
  4. Enjoying Shorter time-to-market: with a cloud database, it is much easier to create endpoints for applications. 
  5. Having a better user experience: generally, cloud environments have a much better UX/CX which leads to a better user experience for all involved stakeholders. 
  6. Using a “sandbox” environment: cloud infrastructures often allow for the flexibility to experiment with queries, new analytics processes, products, etc. in a “sandbox” environment that can help the business hone in on what works best for them. 
  7. Lowering costs: The cost of cloud infrastructure for basic functions is often quite accessible, and can be easily scaled according to the growth and requirements of the organization. 
  8. Increased efficiency: Using serverless data warehouses would mean much faster queries and much more effective reporting. 

ELT: The Roads of the Cloud Data Infrastructure

Organizations often have a multitude of data sources like on-premise and cloud databases, social media platforms, digital platforms, excel files, and others. On the other hand, the data stack on the cloud would include a cloud data lake or data warehouse, from which dashboards, reports, and ML models can be created. 

How can these two separate aspects be integrated to bridge the disconnect and give a holistic data science process? The answer is through cloud ELT tools like Hevo Data. 

Using ELT (Extract, Load, Transform) we can extract data from data sources, load it into the data infrastructure, and then transform it in the way that is required. ELT tools act as the strong bridge between data sources and destinations, allowing seamless flow and control of data to enable advanced data science applications like AI, BI or ML. It allows data engineers to focus on the intricacies of these projects rather than the mundane building and maintenance activities involved with building data pipelines.

Partnership with Danu Consulting: lean analytics

Cloud ELT providers allow you to have a lean analytics model (Lean Analytics, Yoskovitz and Kroll), treating analytics like a process, allowing iterations on ideas, as they allow businesses to scale according to their data volumes. Dashboards demos can be built and validated by the stakeholders. Then gaps can be identified, and the dashboards can be launched into production using new ingested data. 

Advancements happen within days instead of months, allowing an amazing speed of execution. Hence, the value of such tools increases as an organization grows. These tools also help with access, governance and control, solving many of the basic blocks required for advanced data analytics and enabling accelerated success. 

Details About Partners

About Hevo Data: Hevo Data is an intuitive data pipeline platform that modern data analytics teams across 40+ countries rely on to fuel timely analytics and data-driven decisions. Hevo Data helps them reliably and effortlessly sync data from 150+ SaaS apps and other data sources to any cloud warehouse or data lake and turn it analytics-ready through intuitive models and workflows. Learn more about Hevo Data here:  

About Danu Consulting: Danu Consulting is a consulting firm specializing in big data and analytics strategies to support the growth and profitability of companies. Its solutions include data migration to the cloud, creation of BI dashboards, development of machine learning and AI algorithms, all adapted to the unique needs of each client. With over 15 clients and 50+ projects, Danu Consulting has the solution your company needs. Lear more about Danu Consulting at

Product Marketing Associate, Hevo Data

Maithili is a Product Marketing Associate with a keen interest in writing about the ways technology and data fluency can improve lives. She is on a constant mission to uncover and showcase the innovative ways data platforms like Hevo Data can bring businesses to a new level of data maturity.

Rodrigo Benavides is the CEO and Founder of Danu, a consulting group that helps companies get easy access and control of their data in order to innovate quicker and leaner. He has 8+ years of experience in data science and statistics, and has worked in data science roles in firms in financial services, food & beverage, and baby-development app companies. He holds an M.S. in Data Science and B.S. in Applied and Computational Mathematics and Statistics with a minor in Energy Studies from the University of Notre Dame.

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