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Data Engineering and Customized Data Collection

Building the foundation for a data-driven culture

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Digital Data Tagging Services

Data in an enterprise are becoming increasingly siloed with each passing year with the rise in adoption of best of breed applications for specific use cases. Aggregating this data is the ideal way to make sense of this data holistically.

Data Collection
Specification
Data Architecture
Data Layer
Implementation
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Data Collection Specification

For a digital business, it is vital to understand the footprints their customers or visitors leave behind when they visit the site. However, based on our experience implementing tagging for over 100s of sites, we find that the nuances of tagging a site are not well understood or appreciated. The companies that understand these nuances also tend to understand their customers better and often achieve better marketing outcomes.

We partner with you to understand what data is essential to you, what data to capture, what data to avoid capturing, layout a data capture roadmap, and work with your engineering teams to implement the roadmap.

Data Architecture

Building an enterprise data warehouse has traditionally been an expensive undertaking. Over the last few years, costs have gone down significantly thanks to the advancement in public cloud technologies. Cloud technologies have also made it easier for analysts and business users alike to benefit from data warehouses without worrying about the management overhead. The first step in this journey is to create an infrastructure layer that allows you to capture data from disparate systems and consolidate that data into a cloud data warehouse.

It is time-consuming for internal resources to build data extraction scripts and maintain them over some time. Complexities and divergence in data sources makes it harder to build and maintain data extraction scripts internally. We have the experience of building 100s of data warehouses for companies large and small over the last two decades. This experience has given us a healthy perspective on how to approach the consolidation exercise. We can advise your team if they have questions with the approach they are considering, or get into the act and set up the data collection layer for teams that are constrained for time or may not have the necessary expertise.

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Data Layer Implementation

Once we understand your data capture requirements for mobile apps and web applications, we work with your engineering teams in the implementation of the data tagging specification. A data layer is a critical component of this exercise.

Marketing attribution issues often arise due to incorrect implementation of data captures on digital assets. Many customers of ours who underwent an audit of their data systems and set aside time and resources to implement our recommendations today have a better understanding of their marketing data and attribution. These customers have a better understanding of the drivers of growth and leverage insights from the data to optimize marketing spend and maximize business growth and profitability.

A reliable data foundation is imperative to achieve sustainable benefits, and it all starts with an audit. Contact us to get your systems audited today.

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Digital Engineering and Management

Data Engineering
and Custom ETL
Data Warehousing and
BI in the 2020s
Data Management

Data Engineering and Custom ETL

Data engineering spans a wide gamut of applications. The context in which we apply data engineering is to help you build a data warehouse or a data lake. Our product, Daton, is an automated data engineer with connections to over 80 sources and supports multiple cloud data warehouses. If there are data sources that are not supported yet, we are happy to prioritize adding support for these applications.

If you already have a data infrastructure in place and use products like Informatica, Oracle Data Integrator, Talend, open-source technologies like Airflow, Apache Nifi, or any other. We work with these tools and extend the use of these tools to increase coverage of applications presented in the data warehouse. Our end goal is to see every business taking advantage of their data, and our approach is to make decisions that maximize client success.

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Data Warehousing and BI in the 2020s

  • Are you one of the early adopters of business data warehousing?
  • Do you feel that the architecture design of the past is slowing you down?
  • Do you feel that the architecture design of the past has become too expensive to maintain?
  • Are you looking to modernize your data infrastructure to benefit from cloud technologies?
  • Are you looking to introduce more agility and experimentation into the system?
  • Are your analysts asking for more self-service capabilities?
  • Do you see multiple data warehousing instances springing up in your organization?

We help you audit your current infrastructure, rationalize your investment in technology and offer architecture, vendor selection guidance, and implementation services as you attempt to modernize your stack.

Data Management

Technology is changing at an unprecedented rate and with that bringing a degree of fluidity to data architecture and management that allows for rapid experimentation, prototyping, and innovation. We help you in every stage of your analytics journey but setting up systems and processes to capture, curate, store, and process data and preparing the ground for innovation to unleash.

The success of a data initiative requires the cohesive effort of resources with diverse skills in data engineering, data warehousing, data governance, business intelligence, data analysis, and data science. Small to medium-sized companies may try to blend all these roles into a couple of resources, and large companies may be battling with legacy issues to benefit from new technologies or from resource issues to kickstart new projects.

We consciously built Saras Analytics to provide a spectrum of services that can be invoked by companies at any stage of the data maturity.

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