Analytics built for adoption
from 55 verified client reviews on Gartner Peer Insights — as of July 2026
Analytics investments often stall on adoption, not technology. We baseline your reporting estate, design for the people who have to use it, and run it – with a 150-person design practice and more than twenty years of enterprise reporting behind it.
4.9/5
Gartner Peer Insights, 55 reviews
150+
UI/UX and design experts focused on adoption
97.2%
Client retention
2004
Delivering enterprise reporting since
Definition
Business analytics is the practice of turning enterprise data into reports, dashboards and self-service tools that business users can act on directly. It spans enterprise reporting and BI, the semantic layer that gives numbers a shared definition, conversational and self-service access, and the design work that decides whether any of it gets used.
A complete analytics capability answers four questions: what decisions are we supporting, can people reach the numbers without help, do they trust the definitions behind them, and what is the estate costing us to run.
Analytics rarely fails on technology. It fails at the last mile — a report that is accurate, governed, on time, and opened by nobody. Adoption is a design problem long before it is a data problem.
Infocepts point of view
Business intelligence is the reporting and dashboard layer that shows what happened. Business analytics includes BI, and adds the design of how people interact with the data, natural-language access, the migration and consolidation of the tools involved, and the operating model that keeps the estate useful over time. BI is a layer; analytics is the whole capability.
A BI tool is software. An analytics capability is that software plus governed reporting, adoption-focused design, and a modernization path off whatever came before. The gap between the two is why organizations can own three licensed platforms and still export to spreadsheets.
The problem
Not from a shortage of reports. From a surplus of them, built without anyone asking who would open them on a Tuesday morning.
01
Large report estates where only a fraction is opened. Nobody can retire anything because nobody knows which ones matter to whom.
02
Three dashboards, three margin numbers. Meetings spent reconciling figures instead of acting on them, and trust eroding each time.
03
Overlapping licenses and a platform nearing end of life. Migration keeps getting deferred because the true report inventory is unknown.
04
Tools marketed as self-service that still need a specialist to answer a simple question. The backlog moves, it doesn’t shrink.
Where the cause sits upstream — in ungoverned or fragmented data rather than in the reporting layer — that is a data and AI strategy and data modernization question, and the baseline will say so.
Services
Four named, separately scoped engagements — so you can compare us directly against any other bidder. Many clients start with the baseline, which makes the right one obvious.
01
Governed reporting and BI built on two decades of delivery, with the manual, error-prone reporting work automated out. Aligned to the decisions your stakeholders are actually making.
02
Analytics designed as a product with an owner and an audience, by 150+ design experts working on data-driven applications. Interfaces that make complex data straightforward — an accurate report nobody can read is not a delivered report.
03
Voice, text and touch access to your data using NLP and machine learning, so self-service is genuinely self-service — and surfaces the questions people were not asking.
04
Consolidating redundant tools or modernizing legacy ones, with rationalization first so you migrate what matters. Repeatable approach, accelerators, predictable cost.
Embedded capacity is one of three delivery models. Our specialists work inside your team, to your process and tooling — to absorb a backlog, cover a skills gap like semantic modeling or UX, or hold delivery steady through a hiring cycle.
Many engagements begin with the baseline, because a usage-ranked report inventory helps settle the migrate-or-redesign question early. Thirty minutes with a practice lead will tell you whether that is true for you.
We work in the tools you already run — Power BI, Tableau, Strategy, Looker, Databricks and Snowflake. We recommend switching only when the baseline shows a clear cost or capability reason, most often overlapping licenses or a platform reaching end of life.
The baseline
Every engagement starts by measuring what you already run. Four measures anchor it, and ten scored areas explain them.
M1Active usersWho opens analytics, how often, and which functions never do
M2Report usageWhat is used, what is duplicated, and what is safe to retire
M3Time to insightHow long a new question takes to answer, end to end
M4Cost of the estateLicenses, platforms, and the support effort behind them
Ten areas, scored 1–5Maturity scale1 Ad hoc2 Repeatable3 Defined4 Managed5 Optimized
AD·01Adoption
How many people across which functions actually open analytics, and how that trends.
2.0
AD·02Adoption
Whether a business user can answer a new question without raising a ticket.
2.0
AD·03Adoption
Whether the people the reporting was built for changed how they decide.
2.0
DS·01Design
Whether analytics is designed for a task and an audience, or assembled from available fields.
3.0
DS·02Design
Whether core metrics carry one agreed definition across every tool that reports them.
2.0
ES·01Estate
How many platforms are in use, where they overlap, and what the overlap costs.
4.0
ES·02Estate
What exists, what is duplicated, and what can be retired without anyone noticing.
4.0
ES·03Estate
Whether the data underneath is reliable enough to support self-service safely.
3.0
DL·01Delivery
How long a new question takes from asked to answered, and where the queue forms.
3.0
DL·02Delivery
How analytics is staffed, supported and funded once it is live.
3.0
What the output looks like
All ten areas on one view, so the argument makes itself. The profile below is an illustrative sample, not client data.
Sample analytics adoption scoreIllustrative
Illustrative sample profile · scored 1–5 · not client data
2.8Overall adoption score out of 5 · illustrative
This is a common shape. A well-run estate scored at 4 feeding adoption scored at 2 means the problem was never the platform — it is that nobody designed for the person expected to use it. Buying another tool moves the estate score, not the adoption score.
A scored view of all ten areas, a usage-ranked report inventory, an estate cost view, and a sequenced plan for redesign, migration or consolidation.
Alongside your BI team rather than pausing delivery, and scoped to the size of your estate.
Scoped and agreed up front, so you know what the baseline covers, and what it costs, before you commit.
The method
Five stages, run by one accountable team. The last one is the stage many analytics engagements treat as somebody else’s job.
Stage 01
Measure usage, inventory, time to insight and estate cost. Score the ten areas and establish what is actually being used.
Stage 02
Our 150-person design practice works out what each audience needs to decide, then designs the data product around that — not around the available fields.
Stage 03
Semantic layer, reporting, self-service and conversational access — built in the tools you already run, against definitions everyone has agreed.
Stage 04
Where the baseline justifies it, we consolidate or modernize using a repeatable approach and our own Flash Migrate accelerators — retiring what nobody uses rather than porting it.
Stage 05
We run it and we drive usage — enablement, support through D&A operations, and re-measurement against the baseline we started from.
Many analytics engagements end at go-live, after Stage 03. The dashboards are delivered, the invoice clears, and whether anyone opens them becomes an internal problem. Stages 04 and 05 are where the adoption number actually moves — and they are the reason we baseline usage before we build anything.
The decision
If you are comparing analytics vendors, this is the difference that shows up in the usage logs a year later.
| Dimension | Project-based dashboard delivery | Infocepts: designed and run for adoption |
|---|---|---|
| Where it starts | Often a requirements list of reports to build. | A measured baseline of what exists and who uses it. |
| What gets migrated | Often everything, because usage data is not there to show what is safe to retire. | What usage data justifies. The rest is retired, not ported. |
| Who designs it | Typically the BI developer building it, between other tickets. | A 150-person design practice working on data products specifically. |
| Definition of done | Typically go-live: the dashboard exists and passes UAT. | Usage against the baseline. Delivered and unopened is not done. |
| Where it ends | Typically handover to your team, plus a support SLA. | We run it, drive adoption, and re-measure against the baseline. |
| How success is measured | Typically delivery milestones and tickets closed. | Active users, report usage, time to insight, estate cost — and the business metric in scope. |
Industries
Industry matters on an analytics engagement because the data sources, the KPI definitions and the regulatory constraints differ — and the semantic layer has to reflect that from the start.
Decisions that turn over weekly, on data arriving faster than definitions can settle.
Reporting that has to survive an audit, across systems never designed to agree.
Audience and revenue signals that move hourly, across platforms that rarely share a definition.
Customer, risk and compliance decisions that rest on data which has to be explainable and controlled.
Plant data that never stops arriving, and decisions measured in shifts rather than quarters.
Proof
4.9
Gartner®
Gartner Peer Insights
Highest rated in the category in Gartner’s Voice of the Customer report for Data and Analytics Service Providers, 2022
Databricks Silver Partner · Snowflake Cortex Code Preferred Partner 2026
Recognized in the HFS Challenger Series 2025 for driving scalable enterprise AI
150+ UI/UX and design experts · delivering enterprise reporting since 2004
Retail case study
Cut the manual effort behind executive reporting by 30 to 50%.
Retail case study
300+ business users across 43 stores, a 9.5% rise in store profits within a year and $1.5M a year in savings.
Retail case study
Sub-second response, used by 1,000+ employees across teams and countries to track KPIs in real time.
See all case studies across retail, CPG, financial services, media, industrials and life sciences.
The Retail Health Index Scorecard developed by Infocepts revolutionized the way we analyzed and utilized our data. The solution’s real-time insights, presented intuitively, enabled us to monitor key metrics, uncover hidden patterns, and respond promptly.
Global Head, Data & AILeading luxury retailer
Scoped and agreed before work starts
Runs alongside your BI team, not instead of it
A usage-ranked report inventory and a sequenced plan
Start here
Many organizations find they need less new reporting than they thought, and far more design than they budgeted for. The baseline tells you which.
Or read a case study before you decide.
Questions
Twelve questions leaders ask before commissioning business analytics work.
Last reviewed October 2026 by the Infocepts Business Analytics practice.
Business analytics services cover the design, build, modernization and ongoing operation of an organization’s analytics and BI estate. At Infocepts this spans four capabilities: enterprise reporting and BI, UI/UX and data product design, conversational analytics, and analytics migration and tool consolidation. The service line includes assessment, delivery and adoption enablement.
Business intelligence usually refers to the reporting and dashboarding layer that shows what happened. Business analytics is broader: it includes BI, but also the design of how people interact with data, natural language access, the migration and consolidation of the tools involved, and the operating model that keeps the estate useful over time. Infocepts delivers both under one service line.
Four measures anchor it — active users, report usage, time to insight and cost of the estate — explained by ten areas scored 1 to 5 across adoption, design, estate and delivery. The output is a usage-ranked report inventory, an estate cost view and a sequenced plan.
We work in the tools you already run, including Power BI, Tableau, Strategy, Looker, Databricks and Snowflake. Switching is a recommendation we make only when the baseline shows a clear cost or capability reason — most often overlapping tool licenses or a legacy platform reaching end of life. If a migration is warranted, it is a defined engagement with its own assessment stage.
Rationalization first. The baseline ranks every report by actual usage, so the migration scope is what people use rather than everything that exists — which is usually a fraction of the inventory. We then migrate in waves using repeatable accelerators, running old and new in parallel for the reports that matter until usage has moved across.
Conversational analytics lets business users query data through voice, text and touch rather than by navigating a dashboard, using NLP and machine learning over a governed semantic layer. It works where the definitions underneath are agreed — which is why we assess the semantic layer before recommending it, not after.
DecideBetter is Infocepts’ decision intelligence approach. It determines which business decisions carry the most value and what data, analytics and AI those decisions require. Business Analytics is the service line that then designs and builds that analytics layer and keeps it adopted. The approach sets the priority; the service line does the work.
By treating it as a design problem rather than a licensing one. Adoption improves when a specific audience can complete a specific task without help, which means designing around the decision rather than the data model, agreeing definitions so nobody has to reconcile two numbers, and retiring the reports that create noise. We measure it before and after against the same baseline.
Yes. Embedded capacity is one of three delivery models. Infocepts specialists work inside your team, to your process and tooling, on a defined capacity rather than a defined project. Teams typically use this to absorb a backlog, cover a skills gap such as semantic modeling or UX, or hold delivery steady through a hiring cycle.
Retail and consumer products, life sciences, media and entertainment, financial services, and manufacturing. Industry matters on a service engagement because the data sources, the KPI definitions and the regulatory constraints differ, and the semantic layer has to reflect that from the start.
On usage and on the business metric behind it, not on delivery milestones alone. Every engagement sets a baseline at assessment covering active users, report usage, time to insight and cost of the estate, and reports against it. Where a business metric is in scope — margin, forecast accuracy or working capital — that is tracked alongside.
Request an Analytics Estate Baseline below, or take thirty minutes with a practice lead first if you would rather scope it. Either way you get a written summary of what the baseline would cover for your organization before any agreement is signed.
Insights
The Enterprise AI & Data Transformation Partner. Delivering measurable business impact globally.
#1 on Gartner Peer Insights · 97.2% client retention
