Monetary Policy
What is the current BI Rate stance, and how has it changed?
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Indonesia Macro &
DATA ANALYSIS • EXCEL • POWER QUERY • VBA
Indonesia Macro &
Consumer Pulse Dashboard
An end-to-end analytical project that consolidates Indonesian macroeconomic and consumer indicators into a refreshable monthly monitoring dashboard.
01 / Overview
Turning fragmented economic data into one monitoring view.
Indonesian economic indicators are released through different datasets, frequencies, and publication schedules. Monitoring them together requires more than simply placing values in one spreadsheet.
I designed this project to simulate a recurring analytical workflow for a strategy or business analytics team that needs a concise view of monetary conditions, inflation, exchange-rate movements, consumer sentiment, and retail activity.
The result is a structured pipeline from raw source files to cleaned monthly datasets, analytical metrics, management commentary, and an automated Excel dashboard.
02 / Business Questions
What does management need to know?
Inflation
Are price pressures increasing or easing?
Currency
Is the rupiah strengthening or weakening against the US dollar?
Consumer Confidence
Are consumers still optimistic, and is confidence improving?
Retail Activity
Is retail activity expanding or contracting?
03 / Data
Five indicators, different reporting structures.
| Indicator | Producer | Raw Frequency | Monthly Treatment |
|---|---|---|---|
| BI Rate | Bank Indonesia | Policy decisions | Latest decision in each month |
| Inflation YoY | Bank Indonesia | Monthly | Standardized percentage |
| JISDOR | Bank Indonesia | Daily | Monthly average |
| Consumer Confidence | Bank Indonesia | Monthly | Published index value |
| Retail Sales Growth YoY | Bank Indonesia | Monthly | Published YoY growth |
04 / Workflow
A layered, refreshable analytical pipeline.
01
Raw Data
Source files preserved as the original reference.
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02
Power Query Staging
Cleaning, types, naming, and source-specific transformations.
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03
Monthly Master
All indicators merged using a standardized month key.
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04
Analytics
MoM changes, 12-month comparison, and interpretation logic.
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05
Dashboard
VBA-generated KPI cards, charts, and executive readout.
05 / Analytical Decisions
The important part was not just cleaning the data.
BI RATE
Latest decision, not monthly average.
When multiple policy decisions occurred within one month, the last decision was retained to represent the month-end policy stance.
JISDOR
Daily observations aggregated to a monthly average.
Partial September 2026 observations were excluded so the monthly figure would not mix complete and incomplete periods.
PUBLICATION LAG
Unavailable data stays unavailable.
IKK and retail data were not forward-filled. Each KPI retains its own latest published period.
INTERPRETATION
Direction and context are separated.
For example, positive JISDOR MoM means USD/IDR increased, which is interpreted as rupiah weakening.
06 / Output
Management-focused rather than chart-focused.
The dashboard combines five KPI cards with trend charts and an executive readout. Each KPI displays the latest available period instead of assuming every indicator has the same publication date.
Retail Sales Growth uses positive and negative bar colors to separate expansion from contraction. Consumer Confidence includes the 100-point optimism threshold, while the JISDOR chart explicitly follows the convention that a higher USD/IDR level represents a weaker rupiah.
VBA is used only as the automation and presentation layer. The underlying cleaning and transformations remain in Power Query so the workflow stays auditable and maintainable.
07 / Quality Assurance
Tested beyond whether the dashboard “looks right.”
80
monthly master rows
0
duplicate months
5
core indicators
PASS
final UAT
Testing covered source periods, row counts, missing values, publication lags, KPI reconciliation, chart output, refresh execution, dynamic commentary, and persistence of the VBA refresh workflow.
08 / What I Learned
A dashboard is only the visible end of the work.
This project reinforced the importance of defining each indicator before visualizing it. A technically correct chart can still be misleading if frequency, unit, publication lag, or direction of interpretation is handled incorrectly.
It also gave me practical experience separating the data pipeline, analytical logic, and presentation layer rather than trying to solve everything in one worksheet.
Project files