Every organisation that works with data faces the same pressure: turning raw information into results people can trust. The data, deadlines and audiences differ from one sector to the next. Analytics Horizon works with market research agencies, health and public health teams, government programmes, think tanks, corporate teams, NGOs, academic researchers and universities. Below, you will see the challenges each group faces and how we solve them. Our services include data cleaning, statistical analysis, survey and qualitative analysis, text analytics, and Power BI and Tableau dashboards. Not sure where you fit? Visit our Home page overview or contact us.
Agencies and consultancies that run quantitative and qualitative studies and need white-label or overflow support.
Fieldwork ends and the clock starts. Open-ended answers need coding, tables need checking, and dashboards are due before the client call. Agencies often run several studies at once, so analysts are stretched thin. Hiring for a busy period is costly, and quality can slip when everyone is rushing. Clients still expect clean data and findings they can trust, often with very little notice. A short delay can strain a client relationship, so dependable support matters. The right partner adds capacity quickly and keeps quality steady.
We work behind your team, white-label or as overflow support. We begin with data cleaning and preparation, then run survey data analysis and Likert scale analysis for your data tables. Our team handles qualitative coding of open-ended responses and builds Power BI or Tableau dashboards for client delivery. We work across time zones, so projects are often turned around overnight. You keep the client relationship, and we stay in the background. Every project follows your templates, naming rules and quality checks, so our work fits straight into your reports.
Hospitals, medical researchers, pharmaceutical teams and public health programmes that need dependable biostatistics and qualitative analysis.
Health data is rarely tidy. Patient records have gaps, KAP surveys use many scales, and interview transcripts run to hundreds of pages. Clinicians and programme staff have little time for analysis, yet reviewers, ethics committees and funders expect sound methods. One wrong test or an undocumented step can delay a paper or weaken a programme report. Careful, well-documented analysis protects your work. Many teams also work across several sites and languages, which adds more variation to the data. A clear, repeatable process keeps results consistent from the first dataset to the final report.
We prepare your data first, with missing-data and outlier handling. Then we run hypothesis testing, regression and reliability analysis in SPSS and STATA. KAP surveys go through survey data analysis. Patient interviews are coded with thematic analysis in NVivo, so the numbers and the stories sit together in one clear report. Every step is documented, so reviewers and co-authors can follow it. We also prepare clear tables, figures and short method notes for papers, ethics reports and funder updates. Your team gets results that are easy to check and ready to submit.
Public agencies, and the consultancies supporting national transformation programmes, that must measure results and report them clearly.
Public programmes are judged on results, yet the data often sits in different departments and formats. Citizen and customer satisfaction surveys produce large files, and KPIs are tracked by hand in spreadsheets. Leaders need a clear view of performance. Slow reporting makes it hard to adjust a programme while it is still running. Public teams need evidence they can share with ministries and partners. Timely, reliable reporting also builds trust with citizens, donors and decision makers.
We analyse citizen and customer satisfaction surveys through survey data analysis and clear results reporting. We build KPI dashboards in Power BI, Tableau or Excel, so leaders can follow performance in one place. Programme monitoring dashboards track delivery against targets, and programme evaluation data support each review. Clear tables and charts make reports easy to share with ministries, donors and partner agencies. Each dashboard is built around the indicators your leaders already use, so adoption is quick. We document every step, which keeps reporting consistent from one review cycle to the next.
Think tanks and policy institutes that rely on public opinion data, policy surveys and large document collections.
Policy research draws on many sources. Opinion polls, policy surveys, reports, speeches and legislation all need to be read and compared. Doing this by hand takes months, and small errors in weighting or coding can weaken a paper. Researchers need analysis that is transparent, repeatable and strong enough to stand up to peers and policymakers. Time is short and the stakes are high. Clear methods also help a paper earn trust and reach the right audience.
We analyse survey and opinion data with hypothesis testing and regression in SPSS and STATA. For large document collections we use NLP and text analytics, including document analysis and topic modelling. Mixed-methods analysis brings numbers and narrative together, so your findings are clear, documented and ready to support evidence-based policy recommendations. Every method is explained in plain language, and your team receives the tables behind each result. We document every step, so findings can be repeated, reviewed and updated as new data arrives. Policy briefs, reports and presentations then rest on solid evidence.
Teams in banking, telecom, retail, FMCG and services companies that track customer satisfaction, NPS and employee engagement.
Companies collect satisfaction scores, NPS and engagement surveys every quarter, but the results rarely tell a clear story. Scores move up or down with no explanation. Reports differ between teams, and leaders ask what is really driving the change. Without sound analysis, it is hard to decide where to invest time and budget. Managers want practical answers, not another long report. Teams also struggle to compare results across branches, regions and survey rounds. Without a shared method, small differences are easy to misread. A clear, consistent approach turns scattered scores into decisions you can defend.
We start with survey data analysis for customer satisfaction, NPS and employee engagement studies. Regression modelling in SPSS and STATA shows what drives your scores. We then build Power BI or Tableau dashboards with agreed KPI reporting, so leadership can follow trends by branch, region or team and decide with confidence. Short, plain-language summaries explain what changed and what to do next. We keep every report consistent from one survey wave to the next, so progress is easy to track. Your managers see what moved, why it moved and where to act first.
Monitoring, evaluation and learning teams, programme managers and country offices in NGOs, foundations and development organisations.
Baseline, midline and endline surveys, household questionnaires, interviews and focus groups produce a large amount of data, but donor reporting deadlines leave little time to analyse it properly. Small teams struggle to clean responses, test whether change is real, and present indicators in a way that funders, partners and communities can understand and trust, especially when programmes run across several districts or countries. A reliable analysis plan makes each reporting cycle faster and easier.
We analyse your survey responses through survey data analysis, field and household survey analysis and programme evaluation data for baseline, midline and endline reporting. Statistical analysis in SPSS and STATA tests whether changes are significant, NVivo and thematic analysis bring out what communities say, and programme monitoring dashboards track indicators for funders and partners throughout the programme cycle, not just at the end. Clear, documented results help you answer donor questions with confidence.
Master’s and PhD students, early-career researchers, and research teams preparing theses, journal papers and grant applications.
You know your research question but are unsure which statistical test, coding approach or software suits your design. Deadlines for theses, journal submissions and grant applications add pressure, and reviewers and examiners quickly notice weak methods, unexplained choices or unclear results tables, which can lead to long revisions, delays or rejection, especially when you are working alone without a statistician to check your approach. Clear, well-supported analysis gives you more confidence at every stage, from proposal to defence.
For quantitative work we run hypothesis testing, correlation and regression, reliability and factor analysis and structural equation modelling in SPSS and STATA or R, Python, MATLAB and Excel. For qualitative work we support thematic analysis, grounded theory and coding in NVivo, MAXQDA or ATLAS.ti, with clear results tables, plain-language interpretation and write-up guidance that matches your methodology chapter and satisfies reviewers. We explain each result in plain language, so you can defend your choices to supervisors and reviewers. You stay in control of your study, and we make the analysis clear.
Research centres, institutes and university departments that run several studies at once and need consistent, documented analysis.
Different projects use different tools and different standards for data handling, so datasets arrive with missing values, inconsistent coding and no documentation. This makes results hard to reproduce or audit, slows collaboration between researchers, and creates risk when departments must justify findings to funders, ethics committees or external reviewers, particularly when staff, students or software versions change between studies and projects. A shared standard makes every study easier to review and reuse.
We apply data validation, missing-data and outlier handling and codebooks and documentation so every dataset follows one standard. We support large survey and mixed-methods studies and deliver analysis in the software your department already uses, from SPSS and STATA to NVivo, MAXQDA and ATLAS.ti. Document analysis and topic modelling help with large archives and open-ended responses, while every step is documented so another team member can repeat it. Your team receives clean datasets, clear documentation and tables that are easy to audit. New staff and students can then pick up any project without starting from scratch.
Not sure which group you fit?
Let’s talk. Tell us about your data and what you need to deliver, and we will recommend the right service.