Streamlined Data Analytics: Precision Engineering for In-Depth 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.

Data Engineering

1.5mln

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

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.

‍

Streamlined Data Analytics first slider image

Streamlined Data Analytics second slider image

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

Steps of providing
data scraping services

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. Receive from you or perform a set of interviews and prepare the following documents: list of features with detailed description and acceptance criteria; list of fields that need to be scraped, solution architecture. Ultimately we make a project plan which we strictly follow. We are a result-oriented company, and that is one of our core values as well.

Step 3 of 5

Solution development

At this stage, we develop the scraping engine core logic. We run multiple tests to ensure that the solution is working properly. We map the fields and run the scraping. While scraping, we keep the full raw data so the final model can be enlarged easily. Ultimately we store data in any database and run quality assurance tests.

Step 4 of 5

Data delivery

After quality assurance tests are completed, we deliver data and solutions to the client. Though we have over 15 years of expertise in data engineering, we expect client’s participation in the project. While developing and crawling data, 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.

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

Steps of providing web applications services

Step 1 of 7

Web development discovery

In the initial stage of the web-based development project, professional business analysts make detailed documentation of the project requirements and the approximate structure of the future web application. DATAFOREST is a custom web application development agency, guided by extensive experience in multiple industries. We give you detailed project documentation and then assemble the team according to your time and budget.

Step 2 of 7

UX and UI design

Based on your wishes, the needs of your target audience, and the best web application design and development practices, our UX and UI experts create an aesthetically pleasing and user-friendly interface for your app to satisfy even the most demanding users.

Step 3 of 7

Web-based application development

At DATAFOREST we are following the best programming design principles and approaches. Being a data engineering company, we build high load platforms, with a significant level of flexibility and result orientation. We keep our deadlines and follow SOC 2 compliance requirements.

Step 4 of 7

Integration

With DATAFOREST, integrating the application into your business won’t stop your processes for a minute. We provide seamless integration with your software infrastructure and ensure smooth operation in no time.

Step 5 of 7

Quality assurance

We use a multi-level quality assurance system to avoid any unforeseen issues. Working with DATAFOREST, you can be confident that your web app development service solutions arrive to the market polished and in full compliance with all certification requirements.

Step 6 of 7

24/7 support

Once a product is released to the market, it’s crucial to keep it running smoothly. That’s why our experts provide several models of post-release support to ensure application uptime and stable workflows, increasing user satisfaction.

Step 7 of 7

Web app continuous improvement

Every truly high-quality software product has to constantly evolve to keep up with the times. We understand this, and therefore we provide services for updating and refining our software, as well as introducing new features to meet the growing needs of your business and target audience.

The way we deal with your task and help achieve results

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.

In Data Science, there are numerous models and approaches, so at this stage we perform a set of interviews in order to define project objectives. We elaborate and discuss a set of hypotheses and assumptions. We create solution architecture, a project plan, and a list of insights or features that we have to achieve.

Step 3 of 5

Solution development

The work starts with data gathering, data cleaning and analysis. Feature engineering helps to determine your target variable and build several models for the initial review. Further modeling requires validating results and selecting models for the further development. Ultimately, we interpret the results. Nevertheless, data modeling is about a process that requires lots of back and forth iterations. We are result focused, as it’s one of our core values as well.

Step 4 of 5

Solution delivery

Data Science solutions can be a list of insights or a variety of different models that consume data and return results. Though we have over 15 years of expertise in data engineering, we expect client’s participation in the project. While modeling, 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

The way we deal with your issue and achieve result

Free consultation

Step 1 of 5

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.

Depending on project objectives, DevOps activity requires auditing the current approach, running metrics measurement, performing monitoring and checking logs. By having a set of interviews, we ensure that we understand the full scope of the project. Ultimately we make a project plan which we strictly follow. We are a result-oriented DevOps service provider company, and that is one of our core values as well.

Step 3 of 5

Solution development

At this stage, our certified DevOps engineers refine the product backlog. We deliver great results within digital transformation, cost optimization, CI/CD setup, containerization, and, last but not least, monitoring and logging. We are a result focused company – it’s one of our core values.

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 expect client’s participation in the project. By the way, a high-level of communication is also our core value.

Step 5 of 5

Support and continuous improvement

Success stories

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

All Success Stories

E-commerce Data Management

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+

system integrations