Integration with Enterprise Systems

The Business Problem: Bridging Operational and Business Systems

Utilities run on a web of interconnected systems. Operational technology platforms manage real-time grid functions, while enterprise systems handle planning, maintenance, regulatory compliance, and customer interactions. Geographic Information Systems (GIS) track infrastructure locations, Supervisory Control and Data Acquisition (SCADA) systems deliver telemetry from substations and feeders, and Enterprise Asset Management (EAM) systems log inspections, repairs, and equipment data.

The challenge is that these systems rarely communicate effectively. An operator might see an alarm in SCADA but need to cross-reference GIS to locate the asset and EAM to find its maintenance history. This manual stitching slows response times and increases the risk of errors. Asset planners must export data from multiple platforms to build a clear picture of equipment condition, while vegetation management teams lack direct visibility into grid telemetry that could inform trimming priorities.

The result is inefficiency. Field crews waste time reconciling conflicting records, planners work from incomplete information, and decision-making is delayed. Without seamless integration across these platforms, utilities cannot fully capitalize on the data they already collect.

The Analytics Solution: Unifying GIS, SCADA, and EAM

Integrating enterprise systems transforms fragmented workflows into connected, data-driven operations. GIS data grounds assets spatially, enabling visual context for SCADA alarms or inspection reports. SCADA feeds provide real-time performance metrics linked directly to equipment records in EAM. Together, they create a unified view where condition, performance, and location converge.

For example, when a transformer’s SCADA telemetry shows abnormal temperature, integration with EAM can instantly display its inspection history and outstanding work orders. GIS overlays reveal nearby feeders or customers who might be affected if the transformer is taken offline. This holistic view accelerates diagnosis and response.

Integration also enables predictive and prescriptive analytics. Linking outage history from OMS with vegetation data in GIS can highlight circuits most prone to storm-related failures. Combining SCADA performance metrics with EAM records supports risk-based maintenance prioritization, while geospatial analysis informs where to deploy sensors or reinforce infrastructure.

Operational Benefits

By breaking down data silos, integration reduces manual effort, eliminates redundant data entry, and speeds decision-making. Field crews access accurate asset locations and histories from mobile devices, minimizing truck rolls and repeat visits. Planners visualize system health geospatially, overlaying environmental risk factors with asset condition to target investments more precisely.

This connected environment is essential for real-time analytics. Outage prediction models rely on GIS for feeder topology, SCADA for operational context, and EAM for asset conditions. Integrating these systems ensures analytics outputs are actionable and tied directly to operational workflows.

Transition to the Demo

In this chapter’s demo, we will integrate GIS, SCADA, and EAM datasets to:

Visualize asset locations on feeder maps and overlay SCADA telemetry.
Link alarms to equipment condition histories from EAM.
Create a unified data layer suitable for predictive analytics and operational dashboards.

By the end of this example, we will demonstrate how integration streamlines workflows, enhances situational awareness, and provides the foundation for advanced analytics that cut across operational and enterprise domains.

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Code

"""
Chapter 19: Integration with Enterprise Systems (GIS, SCADA, EAM)
Combine GIS (asset maps), SCADA telemetry, and EAM records for integrated analytics.
"""

import pandas as pd
import geopandas as gpd
from shapely.geometry import Point
from kafka import KafkaConsumer
import json
import threading

# --- GIS Integration ---
def load_feeder_map(shapefile="data/gis/feeders.shp"):
    """
    Load GIS feeder data as a GeoDataFrame.
    """
    gdf = gpd.read_file(shapefile)
    print(f"Loaded feeder GIS map with {len(gdf)} features.")
    return gdf

def plot_assets_on_map(feeder_map, assets_df):
    """
    Plot asset locations (transformers) on feeder map.
    """
    assets_gdf = gpd.GeoDataFrame(
        assets_df,
        geometry=gpd.points_from_xy(assets_df["Longitude"], assets_df["Latitude"]),
        crs="EPSG:4326"
    )
    base = feeder_map.plot(color="lightgray", figsize=(8, 8))
    assets_gdf.plot(ax=base, color="red", markersize=50)
    print("Plotted assets on feeder map.")

# --- EAM Integration ---
def load_eam_data():
    """
    Load synthetic asset data from EAM.
    """
    return pd.DataFrame({
        "AssetID": ["TX-101", "TX-102", "TX-103"],
        "Latitude": [30.25, 30.28, 30.26],
        "Longitude": [-97.72, -97.75, -97.70],
        "Age_Years": [15, 20, 8],
        "Condition": ["Fair", "Poor", "Good"]
    })

# --- SCADA Streaming Integration ---
def start_scada_stream(topic="scada_stream", bootstrap="localhost:9092"):
    """
    Simulate SCADA stream consumption.
    """
    consumer = KafkaConsumer(
        topic,
        bootstrap_servers=bootstrap,
        value_deserializer=lambda x: json.loads(x.decode("utf-8"))
    )
    print("Listening to SCADA telemetry stream...")
    for msg in consumer:
        data = msg.value
        print(f"[SCADA Update] Transformer={data['TransformerID']} Temp={data['Temperature_C']}C Vibration={data['Vibration_g']}g")

if __name__ == "__main__":
    feeders = load_feeder_map()
    eam_assets = load_eam_data()
    plot_assets_on_map(feeders, eam_assets)

    # Start SCADA streaming in background
    threading.Thread(target=start_scada_stream, daemon=True).start()