Enterprise data preparation that supports the next business decision
Customer information arrives from several systems. Finance and operations use different definitions. Reporting teams spend their time explaining why two totals disagree. Before a new dashboard or AI feature can help, the underlying data needs a dependable route from its sources to the people using it.
Fristine Infotech delivers enterprise data preparation, integration and reporting work. We start with the business use and the records it requires, then define the preparation, validation and operating process. The output should be something a responsible business owner can understand and accept.
Daily ETL supporting Customer 360 at Motilal Oswal
Fristine delivered daily extract, transform and load activity for Motilal Oswal Financial Services, supporting the preparation and loading of information into Customer 360. The work involved high volume financial data and an ongoing requirement to prepare it each day.
Published coverage of Zoho’s September 2026 announcement names Fristine in the implementation context. Our case overview stays at the level of the confirmed delivery role. Internal sources, transaction details and architecture remain confidential, and the wider programme’s scale is not presented as a separate result achieved solely by our workstream.
Daily data preparation for Customer 360
Motilal Oswal Financial Services · ETL delivered by Fristine Infotech
A recurring enterprise data responsibility
High level delivery scope · No internal architecture or transaction details
No financial return or processing speed claim is made
Define what Customer 360 needs to show
A consolidated view depends on more than collecting records. Teams need to agree what identifies the customer, how relationships are represented and which sources own particular information. Differences in timing and definitions should remain visible so users understand what the view can reliably tell them.
We begin with the intended use: service, relationship management, reporting or another defined business process. That establishes the relevant sources, required history and refresh expectations. It also helps separate the first useful release from information that can be added later.
Make recurring preparation an owned process
An ETL pipeline needs to work when a source changes, a record arrives late or an update fails. The scope should identify validation, exception handling and the people responsible for resolving issues. Reconciliation connects technical completion with the business meaning of the resulting information.
We assess the available access, source behaviour and update requirements before confirming the design. The proposal should state what is included in ongoing operation and what remains with the client or another provider. A flow that runs once is a demonstration; a recurring process needs ownership.
Dashboards around the relationship manager’s work
For an anonymous wealth management client, the team used Databricks to build four relationship manager dashboards in approximately six weeks. The views covered redemptions, net asset value, portfolio movement, total investments and maturing investments. No quantified efficiency or financial improvement was reported.
Built for relationship manager reporting, the dashboards were not a full CRM replacement. This engagement is separate from the Motilal Oswal daily ETL case.
Information for wealth management RMs
Anonymous client · Databricks used in the dashboard solution
Approximately six weeks from kickoff to go live
Delivered scope, not a quantified productivity or investment return claim
Information categories only · No customer holdings or balances shown
Choose the platform after defining the requirement
The right tool depends on the sources, transformation needs, access model and operating skills available. Databricks or another platform should earn its place against the actual workload. A limited reporting requirement may not justify a broader platform programme.
We consider the ongoing cost alongside the initial implementation. Refresh frequency, query patterns and development environments affect what must be operated. The design should make those assumptions visible so the organisation can assess the complete commitment.
Zoho DataPrep for recurring preparation work
Assess Zoho DataPrep for connecting supported sources, cleaning and transforming records, and moving prepared data into an agreed destination. Its visual pipelines make the flow inspectable. Fit depends on source support, transformation needs and the people who will maintain it.
Start with one output and its owner. Agree mappings, required fields, duplicate handling and the checks that decide whether the result is ready. Test late records and changed source values, not only a clean sample. A flow that completes without an error can still produce information the business should not use.
Scheduling and monitoring belong in the scope. Define the required freshness, failure notification and recovery procedure for the selected plan and connectors. Confirm quotas, refresh options and data handling before choosing a design. Platform features support the process; they do not replace responsibility for definitions and acceptance.
Select the platform against the workload
Choose a platform from the workload
Databricks
Assess data engineering and analytical workloads that need an explicit approach to transformations, governance and reporting. Begin with the source to output checks and the team that will operate the result. Read our Databricks consulting guide for the delivery scope and confirmed reporting example.
Snowflake
Assess warehouse based analytics, migration and SQL reporting requirements against source dependencies, access and operating cost. Separate ingestion, transformation and consumption before sizing a first workload. Explore our Snowflake consulting approach for the questions to resolve before implementation.
Zoho DataPrep
Assess a focused preparation and movement requirement using supported sources and destinations. Assess the complete flow, including source reliability, transformations, destination support and the team responsible for maintenance. The business output, source reliability and operating ownership remain the basis for selection.
A focused Databricks engagement
For an anonymous wealth management client, we used Databricks to deliver four relationship manager dashboards in six weeks. The scope covered net asset value, redemptions, portfolio movement, total investments and maturing investments. This was a defined reporting engagement; broader platform work needs its own assessment.
Prepare a usable foundation for AI
AI applications need information they are permitted to use and can interpret in the intended context. A dataset that is adequate for a monthly management report may need additional work before it supports a customer facing answer. We assess source suitability, definitions and access against the proposed use.
The first release should include business validation, documentation and operating responsibilities. That gives your team a basis for extending the work, whether the next step is another report, a Customer 360 capability or a defined AI workflow.
Frequently asked questions
001Should we begin with a data platform or a business report?
Begin with the decision or operating process the data must support. That establishes the sources, definitions, users and refresh requirements. A platform decision can then be assessed against those needs and the organisation’s existing tools. Starting with a bounded output also gives the business something concrete to validate before expanding the programme.
002How do you approach inconsistent figures between systems?
Identify what each measure represents, which source owns the relevant records and how updates are applied. Differences may arise from definitions, timing or data quality rather than a reporting tool defect. We agree reconciliation checks with business owners so a new pipeline or dashboard does not simply reproduce the same disagreement in another place.
003What does an ETL engagement include?
A scope can cover source assessment, extraction, transformation, loading, validation and the operation of recurring data flows. The exact responsibilities depend on the requirement. It should identify failure handling, access and ownership as well as the normal path, with acceptance based on business validated outputs rather than only a technical job completion message.
004Can reporting start before a full CRM programme?
A focused reporting project may be feasible when the required information can be accessed and its definitions agreed. Our anonymous RM dashboard project addressed a need for users who did not have a CRM. That does not make a dashboard a replacement for every CRM process; the scope should identify what remains outside the reporting work.
005How do you handle confidential or regulated information?
We establish the data required, permitted access and contractual responsibilities during scoping, with the client’s security and privacy owners involved. The solution needs controls appropriate to the engagement and its processing arrangements. Platform selection alone does not establish compliance, and public case studies should avoid exposing protected records or internal architecture.
006How is a data project accepted?
Agree representative outputs and reconciliation checks with the people who own the business definitions. Test missing or late data, failed refreshes and permissions as well as normal results. The handover should identify owners, known limitations and operating procedures so users can raise issues and the support team can investigate them after the project is live.
007When is Zoho DataPrep worth evaluating?
Evaluate it for a defined preparation flow where supported connectors, transformations and destinations match the requirement. Review the operating team's skills, schedule and quality checks alongside licensing. A visual pipeline can help make work inspectable, but it does not resolve unclear business definitions or unreliable source ownership without an agreed process.
008How do we compare Databricks, Snowflake and DataPrep?
Compare them against the same business output, sources, transformations, access requirements and operating model. They have different scopes and should not be treated as interchangeable labels. Validate the first workload and total operating commitment. The smallest sufficient design may be preferable to a broader platform programme that the organisation does not yet need.
Review your delivery requirements
Discuss access, information handling and operational responsibilities as part of the proposed scope.
Discuss your enterprise data requirement
Share the output your team needs, the broad source categories and how current information falls short.
Discuss your requirementRelated: Motilal Oswal Data Preparation, Wealth Management RM Dashboards, Financial Services, Agentic AI Engineering.