Analytics that fit your business, your team, your data
Most analytics programs do not fail in the tool. They fail in the gap between what the business asked for and what the team actually got. DI Squared has spent since 2008 closing that gap. We build dashboards, semantic models, and self-service environments people use, on Qlik, Power BI, Tableau, and Looker, grounded in a documented strategy and engineered for the long term.
Vendor-neutral across Qlik, Power BI, Tableau, Looker
- We design and build analytics: dashboards, semantic models, self-service environments
- Platforms: Qlik (primary), Power BI, Tableau, Looker
- Collaboration and writeback capabilities: planning, forecasting, approvals, and operational workflows directly within analytics experience
- Method: Discover, Map, Navigate, Adjust
- Outcomes: faster reporting cycles, fewer rebuilds, analytics the team actually adopts
- First engagement is usually a two- to six-week Discover
Qlik Solution Partner of the Year
What does a data analytics consultant do
A data analytics consultant translates business questions into a working analytics environment: the dashboards leaders read on Monday, the semantic model that keeps the numbers consistent, the self-service tools the operations team can run on their own. The work spans design (what should this look like), engineering (where does the data come from and how do we trust it), and adoption (does the team actually use it).
DI Squared comes at the work from a particular angle. Our consultants have shipped on the business side, not only the engineering side. We sit with the people who will use the analytics before we build them. We document the metric definitions before we put them in a tile. And we plan for the leadership change that will arrive in eighteen months, because it always does, and we want the work to survive it.
That orientation is why so many of our analytics engagements grow into longer relationships. We are not building a deliverable. We are building a capability the business can run on.
What we build
Analytics work falls into four overlapping deliverable categories. Executive dashboards (the leadership view, with the metrics that drive the quarterly review). Operational analytics (the working dashboards for finance, supply chain, customer ops, and the rest of the running business). Self-service environments (semantic models and governed datasets that let business users build their own answers). And embedded analytics (analytics inside the products and portals the business already runs).
The right mix depends on the maturity of the data behind the dashboards, which is why we always start with a Discover. There is no point building a self-service environment on top of pipelines no one trusts. We will tell you that before we scope the build, not after.
Platforms we work in
DI Squared delivers analytics and business intelligence solutions across Qlik, Microsoft Power BI, Tableau, Looker, and other leading enterprise BI platforms. We help organizations build modern data and analytics environments using technologies such as Snowflake, Databricks, dbt, Fivetran, Microsoft Azure, Amazon Web Services (AWS), and Google Cloud. While our expertise spans today’s leading platforms, we’re also proud to be a Qlik Elite Solution Provider and a three-time Qlik Solution Partner of the Year, reflecting nearly two decades of leadership in enterprise analytics.
We do not recommend a platform on day one. We recommend a platform after Discover, because the right answer depends on the data shape, the team’s skills, the licensing footprint already in place, and the kind of analytics the business actually needs. Vendor-neutral, but fluent.
How an analytics engagement runs
Discover (two to four weeks).
We assess the current analytics environment, the data underneath it, the team that runs it, and the decisions it is supposed to support. We surface the gap between what is in production and what the business actually relies on. The deliverable is a written diagnosis, not a slide deck.
Map (four to eight weeks, often run in parallel with late Discover).
We document the analytics strategy: the metric definitions, the report inventory, the platform recommendation, the adoption plan, and the prioritized build sequence. This is the document the CFO can fund from and the analytics team can build from.
Navigate (varies by scope).
We build. Dashboards, semantic models, self-service environments, embedded analytics. We staff alongside your team, not in a sealed room, so the capability transfers as the work ships.
Adjust (ongoing).
Quarterly or as priorities change. We review what is being used, what has gone stale, what the new leadership team needs, and what the next build should be. Most clients stay with us through Adjust, because the work is never done.
Where we have done this work
Analytics work is industry-shaped. A utilities outage dashboard, a healthcare quality measure, a manufacturing yield report, and a retail margin dashboard look almost nothing like each other in the data layer. Our consultants have shipped in utilities (our deepest delivery muscle), healthcare and life sciences, manufacturing, retail and distribution, financial services, and energy. From life science to lifestyle.
See industries for industry-specific notes and case studies for engagement detail.
Data Analytics Consulting, answered.
Q: How is data analytics consulting different from a BI tool implementation?
A: A tool implementation gets the platform installed and connected. Analytics consulting gets the platform actually used. The difference shows up in the design work, the metric documentation, the adoption planning, and the change management. A tool partner is finished at go-live. An analytics consultant is partway through.
Q: Do I need a data warehouse before I can do analytics?
A: Not always, but usually. A small business can run useful analytics on direct database connections or a single data extract. Anything mid-market and above benefits from a warehouse or lakehouse underneath. If the engineering layer is the gap, we will say so in Discover and either scope the engineering work ourselves or coordinate it with your platform team.
Q: How long does it take to build a dashboard?
A: A single dashboard on clean data, with documented definitions, can ship in two to four weeks. A dashboard on disputed data, with definitions that need to be agreed first, can take three months. Most of the work is upstream of the tile. We will scope honestly.
Q: Can you work with our in-house analytics team?
A: Yes. Most of our engagements are co-delivered with an in-house team. We add capacity for the build, senior judgment for the design choices, and an outside perspective for the strategy work. The objective is always to leave the in-house team better equipped, not dependent on us.
Q: Which platform should we use: Qlik, Power BI, Tableau, or Looker?
A: It depends on the data shape, the existing license footprint, the team’s skills, and the kinds of analysis the business needs. We recommend after Discover, not before. Our Qlik bench is the deepest, but we will recommend Power BI or Tableau or Looker if that is the right answer for your situation.
Q: What is included in an Adjust engagement?
A: A standing review cadence (usually quarterly), with content that adapts to what you need: dashboard usage analysis, refactoring stale reports, onboarding new leadership to the analytics environment, planning the next wave of build. Adjust is sized to the client’s pace, not ours.
Analytics that the team actually uses
Tell us what you are trying to achieve with your data. We will diagnose the current state, document the strategy, and build the analytics surface alongside your team. Vendor-neutral. Operator-grounded. Since 2008.