Manipulate and Mashup Data

In order for a BI software to deliver maximum performance and answer business questions, it needs to have flexibility in the data sources that it can access. InetSoft's StyleBI software lets users mashup disparate sources of data and create virtual private models, making it invaluable for BI uses such as corporate benchmarking, cost control, and risk management.

What makes InetSoft's data mashup tool so convenient for your business? With an easy to use drag and drop tool, complex queries can be built automatically without dealing with code. The new data mashups can be used to create cross functional dashboards that allow for an intuitive approach to understanding data relationships.

There's no longer a need to perform the old method of extracting, transforming, and loading data for operational use. InetSoft's data mashup tool can be used to create connections among disparate sources of data on the fly. End-users can create and modify their own data mashups, allowing a degree of self-service not possible with traditional BI tools. New data sources can quickly be added to your existing BI environment.

What Are the Ways to Manipulate and Mashup Data with StyleBI?

1. Direct Data Connections

StyleBI can connect to a wide range of sources: SQL databases, cloud services, spreadsheets, flat files, and even big data platforms like Hadoop. Once connected, you don’t need to extract everything into a warehouse if you don’t want to. Instead, you can leave data where it lives and create virtual mashups. That saves time and reduces the burden of keeping a central warehouse constantly up to date.

2. Joins Across Sources

One of the signature features is the ability to join data from totally different systems. For example, you could:

• Join sales transactions from an ERP with marketing leads from Salesforce.
• Combine IoT sensor data from a time-series database with maintenance logs in a relational system.

These joins don’t require writing SQL scripts by hand — they’re often done visually, though you can drop down into SQL or expressions if you prefer fine control.

3. Filters and Conditional Logic

StyleBI lets you filter data dynamically at both the source level and the mashup level. That means you can:

• Pull in only the last 90 days of data for performance.
• Apply conditional filters that can later be parameterized, so users can choose their region, product line, or time horizon on the fly.

This makes mashups much more flexible for end-user exploration.

4. Aggregations and Grouping

Another core function is grouping and summarizing data. You can:

• Roll up line-item data to the customer or product level.
• Compute averages, counts, or custom aggregates on the fly.
• Pivot and reshape data without going back to IT to create new database views.

The neat part is that StyleBI lets you define reusable transformations. So once you’ve built a useful aggregation, it can feed multiple dashboards and reports.

5. Calculated Fields

StyleBI supports user-defined expressions, letting you create calculated fields. These can be as simple as profit = revenue – cost, or as complex as weighted KPIs that draw on multiple columns and logic. Because calculations are reusable, you can define them centrally, which ensures everyone in the organization is working off the same metric definitions—a big deal when consistency matters.

6. Data Blending (Union / Append)

Sometimes you don’t just want to join data horizontally, you want to stack it vertically. StyleBI supports union operations, letting you bring together multiple datasets with the same structure. For instance:

• Append regional sales files from Europe, Asia, and North America into a single dataset.
• Combine monthly logs into one continuous dataset.

This avoids a lot of the messy copy-paste work that teams do manually in Excel.

7. Data Cleaning and Transformation

StyleBI includes options for transforming fields, such as:

• Normalizing date/time fields into consistent formats.
• Standardizing categorical values (e.g., “NY,” “New York,” and “N.Y.” into one).
• Removing duplicates or filling nulls with default values.

While it isn’t a full ETL tool, it provides enough cleaning capability to make reporting datasets trustworthy without sending everything back to IT.

8. Hierarchies and Drill Paths

A mashup isn’t just about the flat dataset — it’s also about building hierarchies. StyleBI lets you define drillable structures like Year → Quarter → Month → Day or Product Category → Product Line → SKU. These hierarchies make dashboards interactive and give end-users the ability to navigate from summary to detail without multiple reports.

9. Metadata Layer and Virtual Views

One of the cleverer aspects is the metadata layer. Analysts can create virtual views that behave like tables but are really mashups under the hood. This allows different business units to consume “pre-shaped” data without needing to know the technical sources behind it. This layer also enforces governance — you don’t end up with everyone reinventing the same KPI differently.

10. Parameterization

Mashups can include parameters that make them reusable and adaptable. For example, a sales pipeline mashup could have a parameter for region, fiscal year, or manager. Users can change parameters in dashboards without breaking the underlying mashup.

11. Integration of Real-Time and Batch Data

StyleBI supports streaming or near-real-time feeds alongside more traditional batch data. You can combine live sensor readings with yesterday’s financials in the same visualization, which is powerful for industries where “what just happened” is as important as historical context.

12. Embedded Scripting and Advanced Logic

For power users, there’s the ability to write more complex logic, scripts, or custom aggregations. This is especially helpful for:

• Statistical modeling
• Weighted scoring systems
• Predictive thresholds

While not a replacement for full-blown data science tools, it allows a surprising amount of sophistication right inside a BI layer.

Example Mashup Workflow in StyleBI

To make the abstract features of StyleBI more concrete, here’s a step-by-step example of how you might build a mashup workflow. Imagine a business that wants to understand the revenue-per-lead by joining Salesforce leads data with ERP invoice data. This is a classic case where marketing and finance systems don’t naturally talk to each other, but StyleBI bridges the gap.

Step 1: Connect to Data Sources

The first step is to connect StyleBI to the relevant systems:

• Salesforce: Pull in the Leads table with fields such as Lead ID, Source, Campaign, Date, and Owner.
• ERP System: Bring in Invoices, with Invoice ID, Lead ID (or Customer ID), Date, Amount, and Status.

Connections can be configured once and reused, so analysts don’t need to repeatedly reauthenticate or reconfigure sources.

Step 2: Perform the Join

In StyleBI’s mashup designer, create a join between the two datasets. The join key is typically Lead ID, but if that’s not consistent across systems, you might need a mapping table or transformation first.

The result is a unified dataset where each lead from Salesforce can be linked to one or more invoices from the ERP system.

Step 3: Apply Filters

Next, filter the data for:

• Invoices with Status = Paid, to avoid including bad debt.
• Leads created within the last 12 months, to focus on recent activity.

These filters help keep the dataset lean and relevant.

Step 4: Create Calculated Fields

With the mashup in place, define a few calculated fields:

• Total Revenue per Lead: SUM(Invoice Amount) grouped by Lead ID.
• Revenue per Campaign: SUM(Invoice Amount) grouped by Campaign.
• Average Revenue per Lead: (Total Revenue / Number of Leads).

These fields transform raw numbers into metrics that management actually cares about.

Step 5: Build Hierarchies

To make the analysis more interactive, set up hierarchies:

• Date → Quarter → Month → Week
• Campaign → Source → Lead

Executives can start at a high-level (quarterly revenue per campaign) and drill down to individual leads if they need details.

Step 6: Assemble the Dashboard

With the mashup dataset ready, design a dashboard:

• Bar Chart: Revenue per Campaign, filterable by date.
• Line Chart: Revenue trend over time, sliced by Source.
• KPI Tile: Average Revenue per Lead, updated daily.
• Drillable Table: Detailed view showing individual invoices linked to specific leads.

All visuals pull from the same mashup, ensuring consistency across every view.

Step 7: Parameterize for Flexibility

Add parameters for:

• Date range selection
• Sales region or territory
• Lead owner

Users can then adjust filters without modifying the underlying mashup.

Step 8: Automate and Share

Finally, set the mashup to refresh nightly and schedule reports to be emailed to stakeholders every Monday morning. Executives always have an up-to-date, accurate picture of marketing ROI and revenue contribution.