Finance reporting and forecasting. Monthly close acceleration, rolling forecasts, variance analysis, scenario modeling. The CFO and FP&A teams are usually the most reliable executive sponsors for an analytics engagement.
DATA ANALYTICS CONSULTING
Data Analytics Use Cases by Industry and Function
Data analytics use cases are the specific decisions an analytics capability is built to support. A use case is not a dashboard; it is a question, a user, a decision cadence, and the data that connects them. This guide covers the use cases DI Squared sees most often across utilities, healthcare, manufacturing, retail, financial services, and energy, plus the cross-functional patterns (finance, operations, customer, workforce) that show up in every industry.
Recognized byQlik Elite Solution Provider
Vendor-neutral across Qlik, Power BI, Tableau, Looker
- A use case is a specific decision, not a generic capability.
- Cross-functional use cases (finance, operations, customer) cut across every industry.
- Industry-specific use cases compound when paired with the cross-functional ones.
- DI Squared prioritizes use cases in Map, sequencing the highest-value ones first in Navigate.
200+Companies guided since 2008
3x
Qlik Solution Partner of the Year
Qlik Solution Partner of the Year
What are common data analytics use cases?
Common data analytics use cases group into two layers. The first is the cross-functional layer that exists in nearly every organization: financial reporting and forecasting, operational performance, customer analytics, workforce analytics, and risk and compliance. The second is the industry layer, where the use cases reflect the specific physics of the business: outage management in utilities, patient flow in healthcare, throughput and yield in manufacturing, assortment and inventory in retail, portfolio performance in financial services, and asset performance in energy.
The most effective analytics roadmaps mix one or two cross-functional use cases with one or two industry-specific ones. The cross-functional work builds the foundation. The industry work builds the differentiation.
At a glance
- Cross-functional layer: finance, operations, customer, workforce, risk.
- Industry layer: utilities, healthcare, manufacturing, retail, financial services, energy.
- A use case is defined by a question, a user, a cadence, and the data behind it.
- Roadmaps that mix cross-functional and industry use cases tend to land best.
- DI Squared’s deepest industry muscle is utilities, with active delivery across all six verticals.
Cross-functional analytics use cases
The use cases below show up across nearly every client, regardless of industry. They form the foundation that the more specialized use cases sit on top of.
Industry-specific data analytics use cases
The use cases below are where the industry context sharpens the analytics work. DI Squared has delivered against each of them.
Utilities.
Outage prediction and restoration analytics, asset health and maintenance prioritization, customer service performance, regulatory reporting, work management analytics, distributed energy resource integration. Utilities is DI Squared’s deepest delivery muscle.
Healthcare and life sciences.
Patient flow and capacity, clinical quality measures, denial management, claims analytics, commercial pipeline analytics, real-world evidence reporting.
Manufacturing.
Throughput and yield, downtime root cause, quality and defect analytics, supply chain visibility, demand forecasting, S&OP analytics.
Retail and distribution.
Assortment performance, inventory and replenishment, store and channel productivity, promotion lift, basket analytics.
Financial services.
Portfolio performance, risk-weighted asset analytics, fraud detection, client profitability, regulatory and capital reporting.
Energy.
Asset performance and reliability, production accounting, HSE analytics, market and trading analytics, ESG and emissions reporting.
Across industries, DI Squared applies proven data and analytics use cases while tailoring each solution to the unique goals, processes, and capabilities of the organization.
How to prioritize analytics use cases
A useful prioritization filter has four questions.
- Value: does the decision the use case supports change a meaningful business outcome?
- Feasibility: is the data available, accessible, and clean enough?
- Ownership: is there a named business owner who will use the output?
- Time horizon: can the use case land in the next 6 to 12 weeks?
Use cases that score well on all four become the first wave. Use cases that fail on ownership are deferred until a sponsor emerges. Use cases that fail on feasibility get a separate data engineering workstream.
The trap is starting with the most technically interesting use case rather than the most strategically important. A capable consultancy pushes back on that pattern in week one.
Use case sequencing through Discover, Map, Navigate, Adjust
DI Squared treats use case selection as a first-class output of the engagement, not an assumption.
1
Discover.
Discover surfaces the candidate use cases through stakeholder interviews and environment review.
2
Map.
Map prioritizes them using the four-question filter above and produces a sequenced roadmap.
3
Navigate.
Navigate delivers the first wave, with explicit attention to adoption and handover.
4
Adjust.
Adjust revisits the roadmap each quarter, retiring or reprioritizing use cases as the business changes.
The result is a capability that grows in step with the organization, not a one-time project that ages quickly.
Frequently asked
Data Analytics Use Cases, answered.
Q: What is a data analytics use case?
A use case is a specific business decision, supported by a specific data product, used by a specific person or team, on a specific cadence. “Sales analytics” is not a use case; “weekly territory pipeline review by the regional sales VP, using a CRM-sourced pipeline dashboard” is. The specificity is what makes the use case implementable.
Q: How many use cases should a first engagement target?
Most successful first engagements scope two to four use cases. One is too narrow to justify the investment; five or more usually fragments the work and dilutes adoption. The sweet spot is enough use cases to demonstrate the platform and the partnership without overcommitting either.
Q: Which use cases tend to deliver value fastest?
Finance reporting and operational performance use cases usually deliver value fastest because the data, the sponsors, and the decision cadence are already in place. Customer and predictive use cases tend to take longer because they require more data integration and statistical work. There are exceptions in every industry.
Q: Do analytics use cases require a cloud data platform?
Not always, but most do at any meaningful scale. Snowflake, Databricks, and the cloud-native services on Azure, AWS, and GCP have become the default substrate for cross-functional analytics. Smaller, departmental use cases can still run effectively on tools like Power BI or Qlik against operational extracts.
Q: How do industry-specific use cases differ from generic analytics?
Industry-specific use cases reflect domain physics. An outage management use case in utilities depends on understanding feeders, restoration sequencing, and regulatory reporting in a way that a generic operational dashboard does not. Industry fluency on the consulting side compresses the requirements work and avoids common modeling mistakes.
Pick the right use cases. Skip the wrong ones.
Bring two or three candidate use cases to a 30-minute strategy call. We will walk through the four-question filter with you and flag the ones worth pursuing first.