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Data consulting and analytics

Market data your team can trust, trace and keep using after the project ends

Many teams hold market data in scattered spreadsheets with unclear sources and inconsistent definitions. Credence Research helps design and build market databases and models, and analyzes market and customer data with documented methods, so the numbers behind your decisions can be traced and updated.

Discuss your data projectSee what the outputs include

How a market data project runs, and where quality problems usually start

1Define the questionConsistent definitions
2Plan the data modelFit to future use
3Source and collectSource rights and gaps
4Clean and validateDuplicates and errors
5Analyze and reportStatistical validity

Marked items are where market datasets most often fail. Each is addressed and documented during the project.

What does data consulting mean here?

In this service, data consulting means planning and building structured market datasets, such as market size models, company databases or pricing trackers, with the definitions, sources and assumptions needed to maintain them. Data analytics means applying statistical methods to market, survey or sales data and reporting the results.

Situations that lead teams to data consulting

Most requests start when a team relies on market data that no one can fully explain or update.

Building a market model to keep

You want a market size model your team can update each year rather than a one-time report.

Reconciling conflicting numbers

Different teams use different market figures, and leadership wants one version.

Creating a company or product database

You need a structured record of competitors, customers or products in a market.

Analyzing survey or sales data

You have data but need statistical analysis to find the patterns that matter.

Setting up dashboards

Leadership wants market indicators in a format they can review regularly.

Start with what you already know

Most teams already have internal sales data, purchased reports and ad hoc spreadsheets. What is usually missing is a shared definition of the market, a record of where each number came from and a process for updating it.

Questions a data project can answer

Structure

  • How should the market be segmented so different teams use the same definitions?
  • Which fields and levels of detail will the data need to support future decisions?
  • How should the model handle revisions?

Sources

  • Which sources can supply each data point, and what are their limits?
  • Which data can we license or reuse?
  • Where are the gaps, and how should they be estimated?

Quality

  • Which records are duplicated, outdated or inconsistent?
  • How do our numbers compare with independent sources?
  • How confident can we be in each estimate?

Analysis

  • Which factors are associated with sales or adoption?
  • Are differences between groups statistically meaningful?
  • What trends appear over time?

What your data can and cannot show

Data projects are only as good as their inputs. The table shows common gaps and how they are handled.

Common data quality gaps in market datasets
IssueWhat is often availableWhat often goes wrongHow it is handled
DefinitionsFigures from several reportsFigures combined despite different scopesDocumented definitions and reconciliation
SourcesPublic and purchased dataSources not recorded, or reuse rights unclearSource log with dates and usage terms
CoverageData for large companies and countriesMissing smaller players or regionsEstimates labeled as estimates, with method
AccuracyRaw recordsDuplicates, errors and outdated entriesCleaning rules and validation checks
InterpretationCorrelations in the dataCorrelation read as causationAppropriate statistical tests and stated limits

Web scraping and AI tools can collect and classify large volumes of public data quickly. Their output still needs source records, deduplication and validation, and conclusions drawn from it should state what the data can and cannot support.

How evidence gaps translate into research

This example is illustrative. It shows how a study could be designed and does not describe a specific client engagement.

A single market model for a product division

Illustrative example
Business question
A specialty materials division uses three different market figures in its planning documents and wants one model it can update annually.
Evidence gap
The figures come from reports with different definitions, and nobody recorded how internal adjustments were made.
Research method
Definition workshop with the client team, source reconciliation, bottom-up rebuild of key segments and validation against independent data.
Deliverable
A market model with a data dictionary, source log and update guide.
Decision supported
Consistent planning figures and an annual update process the team can run.

Our research approach

Our process covers requirement analysis, database planning, data collection and database design, followed by analysis where needed. Statistical methods include regression and correlation analysis, t-tests, chi-square tests and time series analysis.

Methods, what they are used for and their limits
MethodPurposeLimitation
Requirement analysisDefine the questions the data must answerScope changes later add cost
Data model designStructure fields, segments and hierarchiesNeeds agreement across teams
Data collectionGather data from documented sourcesCoverage limited by availability and licensing
Cleaning and validationRemove duplicates and errors, check against independent sourcesSome errors cannot be detected
Statistical analysisTest relationships and trendsResults depend on data quality and sample size
Reporting and dashboardsPresent results for regular reviewDashboards need owners to stay current

We document sources and assumptions for every dataset we build, so your team can check and update them.

What you receive

Deliverables depend on scope, and not every study includes every output. We agree on outputs during scoping.

  • Data dictionary

    Definitions, segments and field descriptions.

    Use it to keep teams consistent.

  • Database or model

    The structured dataset or market model in an agreed format.

    Use it to run planning and analysis from one source.

  • Source and validation log

    Where each data point came from, when and how it was checked.

    Use it to trace and defend the numbers.

  • Analysis report

    Statistical findings with methods and limitations.

    Use it to act on patterns in your data.

  • Dashboard

    Key indicators in a format for regular review.

    Use it to monitor the market over time.

How an engagement works

  1. 1

    ScopingWe agree on the questions, users, definitions and outputs.

  2. 2

    DesignWe propose the data model and sources for your review.

  3. 3

    BuildWe collect, clean and validate the data.

  4. 4

    Analysis and handoffWe deliver the dataset, documentation and any analysis, and walk your team through updating it.

Published report or custom research?

If you need a one-time view of a market, a published market research report may be enough. Data consulting is more useful when several teams rely on the same numbers, the data must be updated or you need to analyze your own data.

Is a data project right for your needs?

  • Several teams use market data that should be consistent.
  • You need to update the data regularly.
  • You cannot trace where current figures came from.
  • You have data that needs statistical analysis.

If most of these apply, a scoping conversation is likely to be productive. If not, a published report or a short discussion about scope may be a better first step.

Discuss your data project

Frequently asked questions

Discuss your data project

Tell us about the data you have, the decisions it supports and what you need.

What happens next

A member of our research team reviews your brief and contacts you to discuss scope and approach. If existing research can answer your question, we will point you to it.

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