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Data & Analytics.

Turning operational and market data into decisions: BI dashboards, competitive research, the pipelines that make data usable, and the governance that keeps it trustworthy.

Data analytics dashboards and reporting
Data Analytics & BI Market Research & Insights Big Data Engineering Data Strategy & Governance

Data only creates value when someone can trust it and act on it. DashMindsIQ builds the dashboards, research, pipelines, and governance frameworks that turn raw operational and market data into numbers your teams actually rely on to make decisions.

Service 01

Data Analytics & Business Intelligence

KPI dashboards, reporting, Tableau, Power BI, Looker.
What this includes
  • KPI dashboardsA KPI dashboard is only useful when the metrics it shows are the ones that drive decisions, defined consistently, sourced from a single authoritative data model, and updated at a frequency that matches how often decisions are made. We design KPI frameworks before building dashboards, ensuring that the numbers on screen mean the same thing to every person who reads them and connect to the business outcomes they are meant to track.
  • ReportingOperational reporting gives teams the detailed, filterable views of business data they need to do their jobs: sales pipelines by region, inventory positions by SKU, support ticket volumes by category and agent. We build reporting layers on top of structured data models so that adding a new report or filter does not require an engineering change, and so that reports load quickly even as the underlying data grows.
  • Tableau, Power BI, LookerWe work across the three major enterprise BI platforms, Tableau, Microsoft Power BI, and Looker, and select the right tool based on your existing infrastructure, your team's technical profile, and your budget. We handle the full deployment: connecting to data sources, building semantic models, designing the visualisation layer, and training your team to extend and maintain dashboards independently.
Service 02

Market Research & Insights

Competitive analysis, consumer research, industry benchmarking.
What this includes
  • Competitive analysisCompetitive analysis maps the landscape you are operating in: who the relevant players are, how they position themselves, what they charge, where their products are strong, and where they are vulnerable. We produce competitive analysis that is current, specific, and structured for decision-making, not a list of competitor names and logos, but a clear picture of where your differentiation is genuine and where the gaps in your positioning need to be addressed.
  • Consumer researchConsumer research answers the questions that internal assumptions cannot: what your customers actually value, what is frustrating them about current solutions, how they make purchasing decisions, and what would make them choose you over an alternative. We conduct primary research through surveys, depth interviews, and usability sessions, and complement it with secondary data to give a complete picture of your target audience.
  • Industry benchmarkingIndustry benchmarking compares your performance, pricing, product coverage, or operational metrics against sector norms, giving you a factual basis for investment decisions and a credible story for investors, partners, or board members who want to know how you compare. We design benchmarking studies that use consistent methodologies across the comparison set, so the conclusions hold up to scrutiny.
Service 03

Big Data Engineering

Data pipelines, warehousing, Snowflake, dbt, Apache Spark.
What this includes
  • Data pipelinesData pipelines move data from where it is generated to where it needs to be used, reliably, at scale, and with the transformations and quality checks that make downstream consumption trustworthy. We build batch and streaming pipelines using Apache Airflow, Prefect, Kafka, and cloud-native services, with monitoring, alerting, and lineage tracking so that when a pipeline fails, you know immediately and can trace the impact downstream.
  • WarehousingA data warehouse is a purpose-built store for analytical workloads, structured to support fast, complex queries across large datasets that would cripple a transactional database. We design warehouse schemas (dimensional modelling, OBT, or hybrid approaches depending on your query patterns), manage the infrastructure, and establish the governance policies that keep the warehouse useful as the number of data sources and consumers grows.
  • Snowflake & dbtSnowflake is our primary cloud data warehouse for clients who need elastic scaling, multi-cluster concurrency, and clean separation between storage and compute costs. We pair it with dbt for transformation logic: version-controlled SQL models, automated testing of data quality assertions, and documentation that makes the data model navigable to analysts who did not build it.
  • Apache SparkApache Spark handles the data processing workloads that are too large, too complex, or too time-sensitive for a standard data warehouse to manage, large-scale batch transformations, streaming analytics, and machine learning feature engineering on data volumes measured in terabytes. We deploy Spark on Databricks or cloud-managed services (EMR, Dataproc) and design jobs for efficiency, minimising shuffle, partitioning data appropriately, and controlling compute costs.
Service 04

Data Strategy & Governance

Data architecture, quality frameworks, compliance-aligned data ops.
What this includes
  • Data architectureData architecture is the set of decisions that determines how data flows through your organisation, from source systems to storage layers to consumers, and how those flows are structured, secured, and maintained over time. We design data architectures that fit your current data volumes, your team's technical capabilities, and your growth trajectory, with a clear path from where you are today to the target state without requiring a big-bang migration.
  • Quality frameworksData quality problems, duplicate records, inconsistent definitions, stale values, missing fields, silently undermine analytics, reporting, and AI systems that depend on clean data. We build data quality frameworks that define what good data looks like for each domain, implement automated checks at ingestion and transformation stages, surface quality metrics in dashboards that data owners can act on, and create ownership accountability for resolution.
  • Compliance-aligned data opsData operations in regulated industries, finance, healthcare, insurance, and any business handling personal data under GDPR or India's DPDP Act, require more than technical controls. They require documented data lineage, retention and deletion policies that are actually enforced, access controls that can be audited, and breach detection and response procedures. We design data operations processes that satisfy these requirements and hold up under regulatory scrutiny.
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