Streamlined Data Analytics: Precision Solutions for Better Insights

Streamlined Data Analytics

We helped a digital marketing agency consolidate and analyze data from multiple sources to generate actionable insights for their clients. Our delivery used a combination of data warehousing, ETL tools, and APIs to streamline the data integration process. The result was an automated system that collects and stores data in a data lake and utilizes BI for easy visualization and daily updates, providing valuable data insights which support the client's business decisions.

1.5 mln

DB entries

4+

integrated sources

About the client

LaFleur marketing is a digital marketing partner that creates innovative, data-driven marketing strategies and assets for law firms, healthcare organizations, technology, and growing businesses.

Tech stack

Python

SQL

Redshift

AWS Lambda

Cloudwatch

The client's needs

The company provides audience analysis services to assist clients in enhancing their messaging, as well as launching marketing campaigns for market entry. LaFleur utilizes a variety of visualization tools and data from various sources to achieve this.

The project's primary objective is to consolidate and analyze data from multiple sources (such as Treez, Google Analytics, LeafLink, SproutCRM, etc.) to generate actionable insights for the clients.

Challenges & solutions

Challenge

A lot of valuable data was stored in a way that was hard to analyze. The client wanted to collect all data in one place, update it daily, and aggregate it in a way that is easy to use for visualization and reporting.

Solution

We helped achieve a streamlined data integration process that allows for efficient data collection, transformation, and analysis by combining data warehousing, ETL tools, and APIs. We built a data warehouse that serves as a central repository for all of LaFleur's data, allowing for easy access and analysis.

To extract and transform data from various sources, ETL tools were used. These tools helped to automate the process of collecting data, ensuring that the data is clean, consistent, and transformed into the required format.

APIs were utilized to connect to the client's existing systems and extract data in real-time, providing up-to-date information that can be useful for reporting and visualization purposes.

Challenge

The client had multiple databases that were stored in separate locations, which required manual review to access the data. However, the data stored in these databases was not useful unless it could be analyzed and interpreted effectively. One particular issue with the Redshift database was the presence of duplicate IDs for data entries such as transactions. This caused difficulties in updating the database and using it as a reliable reporting system.

Solution

To address the issue of duplicate IDs in the Redshift database, a custom logic was developed to store and update the data. This approach allowed for the retention of the original IDs, which could be matched and linked back to the corresponding transactions.

Queries were created to group the data in a way that enables the client to view visualizations in Domo which align with their specific reporting requirements. These visualizations enable the client to gain insights into the data that they are interested in, facilitating effective decision-making.

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Luxury Retailer AI Demand Forecasting Implementation

Results

The client was provided with an automated system that collects data from multiple sources, including Treez, Google Analytics, LeafLink, and SproutCRM. The data is stored in Domo, which is a Business Intelligence platform that enables easy visualization of the data. The system is designed to update every day, ensuring that the client has access to up-to-date information.

The project succeeded in leveraging value from previously underutilized data, delivering valuable insights about the client's business.

This process is ongoing and continuously provides the client with the necessary information to make informed business decisions.

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Streamlined Data Analytics first slider image

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Their communication was great, and their ability to work within our time zone was very much appreciated.

Charlie White

Senior Software Developer Team Lead LaFleur Marketing, digital marketing agency

How we provide data integration solutions

Step 1 of 5

Free consultation

It's a good time to get info about each other, share values and discuss your project in detail. We will advise you on a solution and try to help to understand if we are a perfect match for you.

Step 2 of 5

Discovering and feasibility analysis

One of our core values is flexibility, hence we work with either one page high level requirements or with a full pack of tech docs.

At this stage, we need to ensure that we understand the full scope of the project. We receive from you or perform a set of interviews and prepare the following documents: integration pipeline (which data we should get and where to upload), process logic (how system should work); use cases and acceptance criteria; solution architecture. Ultimately we make a project plan which we strictly follow.

Step 3 of 5

Solution development

At this stage, we build ETL pipelines and necessary APIs to automate the process. We attract our DevOps team to build the most efficient and scalable solution. Ending up with unit tests and quality assurance tests to ensure that the solution is working properly. Focus on Results is one of our core values as well.

Step 4 of 5

Solution delivery

After quality assurance tests are completed, we deliver solutions to the client. Though we have over 15 years of expertise in data engineering, we are expecting client’s participation in the project. While developing the integration system, we provide midterm results so you can always see where we are and provide us with feedback. By the way, a high-level of communication is also our core value.

Step 5 of 5

Support and continuous improvement

We understand how crucial the solutions that we code for our clients are! Our goal is to build long-term relations, so we provide guarantees and support agreements. What is more, we are always happy to assist with further developments and statistics show that for us, 97% of our clients return to us with new projects.

Success stories

Check out a few case studies that show why DATAFOREST will meet your business needs.

All Success Stories

Optimise e-commerce with modern data management solutions

An e-commerce business uses reports from multiple platforms to inform its operations but has been storing data manually in various formats, which causes inefficiencies and inconsistencies. To optimize their analytical capabilities and drive decision-making, the client required an automated process for regular collection, processing, and consolidation of their data into a unified data warehouse. We streamlined the process of their critical metrics data into a centralized data repository. The final solution helps the client to quickly and accurately assess their business's performance, optimize their operations, and stay ahead of the competition in the dynamic e-commerce landscape.

450k

DB entries daily

10+

sources integrations

Lesley D.

Product Owner E-commerce business

View case study

E-commerce Data Management case image preview

We are extremely satisfied with the automated and streamlined process that DATAFOREST has provided for us.

Data-driven marketing

We created a solution that helped optimize the customer base to get the most out of the customer data. This solution notifies the client about the services/goods, which they would likely buy, according to the gathered information.

20%

sales growth

200%

traffic boost

Jeremy Groves

CEO ThinkDigital, Digital and Marketing Agency

View case study

They developed solutions that brought value to our business.

Client Identification

The client wanted to provide the highest quality service to its customers. To achieve this, they needed to find the best way to collect information about customer preferences and build an optimal tracking system for customer behavior. To solve this challenge, we built a recommendation and customer behavior tracking system using advanced analytics, Face Recognition, Computer Vision, and AI technologies. This system helped the club staff to build customer loyalty and create a top-notch experience for their customers.

customer retention boost

25%

profit growth

Christopher Loss

CEO Dayrize Co, Restaurant chain

View case study

The team has met all requirements. DATAFOREST produces high-quality deliverables on time and at excellent value.

Supply chain dashboard

The client needed to optimize the work of employees by building a data source integration and reporting system to use at different management levels. Ultimately, we developed a system that unifies relevant data from all sources and stores them in a structured form, which saves more than 900 hours of manual work monthly.

900h+

manual work reduced

100+