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Case study

Enabling Digital Transformation for a Leading Multi-Sector Manufacturing Conglomerate5.0 rating5 Stars Rated On Clutch

Industry

Manufacturing & Industrial Engineering

Services Used

Azure Data Engineering, Business Intelligence (BI), Data Analytics, Data Warehousing, Digital Transformation, Power BI

Region

India

Engagement Model

End to End Project Lifecycle Management

Function/Department

Financial Planning and Analysis (FP&A), Manufacturing & Production, Strategy and Planning

About Client

  • A joint venture between a global manufacturer based in the USA and leading Indian industrial and services conglomerate that caters to industries like Automotive, Defense & Aerospace, Energy, Transportation, Oil & Gas and more.
  • They wanted to gain better insights from the vast data they had collected, fix their stagnant revenue curve and achieve the targets for the FY.

Problem STATEMENT

Our client had difficulty gauging the revenue and operational performance due to various underlying factors.
  • Multiple sources of unprocessed and large data with no common source of data.
  • Lack of a central data warehouse for the massive amount of data.
  • Absence of a comprehensive reporting system to keep track of all KPIs at 360-degree level.
  • Recurring concerns about the security of the enterprise’s data.

Solution

Scope
Our team of experts carefully broke down the problem into components. Solutions for each of these components were brainstormed, and a proper flow of work:
  • Source all data and store it in an object storage solution for the cloud.
  • Analyze the multiple types of data.
  • Combine the data from multiple sources and build a consistent data model in the cloud.
  • Securing the model and system from cyber attacks.
  • Create dashboards (as per the operations and key segments like Orders, Sales, Inventory, P&L, etc.) to visualize and gain better insights from the data.
Challenges Involved
With every problem, there are numerous challenges that need to be taken care of. Listed below are some of the important aspects that we had to consider:
  • Multiple types of data result in compatibility issues. Hence we had to take the data warehouse approach.
  • The quality of data was of huge concern and we had to clean them, perform transformations, etc. For business logic, we had to be precise with calculations to ensure data sanity.
  • Managing heavy workloads on data gateways due to the large amounts of data. This was done by categorizing report workloads in live connection and DirectQuery respectively.
  • Ensuring that the data model is up-to-date and scheduling of data refreshes as per requirement.
  • Developing and maintaining efficient and optimized data pipelines for data ingestion in every step.
  • Keeping the overall solution cost-effective for the client.
The DataToBiz Solution
The steps that we followed to execute our solution were as follows:
  • Data Sourcing: The data was acquired from multiple sources and in different formats like flat files (Excel and JSON), legacy on-premise SQL, SharePoint files and Microsoft OneDrive files.
  • Data Ingestion: The massive amounts of unstructured data was then stored in Azure Blob storage. This was then converted into a structured format so that it can be fed to the data warehousing layer.
  • Data Storage: To analyze multiple data formats (.xls, .csv, .json, etc.) we leveraged the Azure Synapse Analytics which is used for intelligent workload management.
  • Building the Data Model: Next, with the help of a very handy tool- Azure Analysis Services, we built the data model on the cloud. This highly compatible platform as a service was used to combine and analyze multiple data sources and metrics.
  • Protection Against Cyber Attacks: Azure Active Directory, an enterprise identity service was implemented to authenticate users and add conditional access. This would ensure that there is no threat to our client’s data.
  • Data Visualization: We created a customized dashboard to track all the KPIs that are needed for better insights. Power BI was used being the leading data visualization tool in the industry. It can be connected to multiple data sources and proves to be the ideal solution for the management to check the metrics and make data-driven decisions.

Business Impact

  • Streamlined operations and the pace of the cycle improved by 17%
  • 42% of over due money cleared, approx. 20% above 181 days
  • Delay in orders reduced from 502 to 368 units.

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Frequently Asked Questions(FAQs)

How do I know if a solution like this is right for my business?

A solution like this is a good fit if your teams spend more time collecting and reconciling data than acting on it, if reports from different departments do not match, or if decisions depend on delayed or manual reporting. It also fits when your data is growing in volume or sources, and your current setup struggles to keep up.

DataToBiz starts with a discovery call to review your goals, data sources, and reporting gaps. You get a clear view of the expected outcomes, effort, and timeline before any commitment.

Can DataToBiz customize the solution for my industry and current data setup?

Yes. Every solution DataToBiz delivers is configured around your industry, business processes, and existing data environment. The team assesses your current systems, data quality, and reporting needs first, then designs a solution that works with what you already have instead of replacing it. This applies across industries such as manufacturing, retail, healthcare, finance, and logistics, and across setups ranging from legacy databases to cloud data platforms.

Which tools, platforms, and data sources does DataToBiz work with?

DataToBiz works across the major analytics, cloud, and data engineering stacks. For BI and visualization, this includes Power BI, Tableau, and Looker. For cloud platforms, it includes Microsoft Azure, AWS, and Google Cloud. For data warehousing and processing, it includes Snowflake, Databricks, and SQL-based databases. Data sources include ERP and CRM systems, spreadsheets, APIs, IoT and sensor data, and third-party applications.

If your stack includes a tool not listed here, the team can assess compatibility during discovery.

How long does a project like this take with DataToBiz?

Most projects with DataToBiz take from a few weeks to a few months. The timeline depends on how ready your data infrastructure is for the initiative and how many systems need to connect. Smaller solutions go live quickly, while larger programs roll out in phases so you see value early. After an initial discovery call, you receive a timeline with clear milestones.

How does DataToBiz keep my data secure and compliant during the entire engagement?

Security and governance is part of the plan from the first conversation. DataToBiz works within your security policies and follows the regulations that apply to your industry. The project usually Access stays limited to the people who need it, and confidentiality terms cover the engagement from kickoff to handover.

What support and results can I expect after the solution goes live?

The work does not stop at go-live. Your team gets a proper handover and guidance on using the solution, and DataToBiz stays available for questions and improvements. The business impact of the project is measured against the goals set at the start, so you can see exactly what changed for your company.

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