Managing Data as an Asset: Improve Data Quality & Analyses to Drive Insights and Accelerate Action
In today’s technology era, data assets are considered to be some of the most valuable in helping companies to improve decision making, better serve customers, reduce costs, and ultimately, increase revenue and profits. Does your company actively manage, enrich, and analyze its data and treat it like a precious asset?
In reality, most companies have failed to extract any significant value from big data investments. Primarily, this is due to lack of insight into the linkage between data and functional performance.
Common Data Governance Challenges
HANDLING LARGE AMOUNTS OF DATA IS DIFFICULT
INTEGRATION AND DATA TRANSFORMATION IS STILL A CHALLENGE
DATA SILOS STILL EXIST AND WE HAVE NOT YET ACHIEVED FULL DATA DEMOCRATIZATION
DATA QUALITY ISSUES PERSIST AND DATA NEEDS ENRICHMENT TO EFFECTIVELY DRIVE DECISION MAKING
THERE’S A SHORTAGE OF SKILLED PEOPLE (DATA SCIENTISTS, ETC.)
REAL-TIME CAN BE COMPLEX – KNOW WHEN IT’S REALLY NEEDED
We Believe That Certain Core Principles Drive Successful Data Governance:
- Recognize that data is one of your company’s most important assets — if put to use properly!
- Data ownership and accountability must be clearly defined.
- Data for the sake of data is almost meaningless. The “right data” must be collected, and in most cases, the data must be cleansed, transformed, and/or enriched to support meaningful insights.
- Value is achieved by understanding linkages between data, asset risk, and functional performance. Data has little value unless it is linked to a decision that will create value!
- Wherever possible, leverage diagnostic and prescriptive data analytics to better understand where you are today and to provide data-backed insights and decision support.
- Harness predictive analytics, including statistical algorithms and machine learning techniques, to improve your identification and understanding of the likelihood of future outcomes or events.
Specific benefits derived from better data governance, cleansing, and enrichment:
Why InfraShield Labs?
We like to add value as a strategic partner – an external data analytics expert and consultancy with broad industry experience in helping utilities adapt and drive sustainable change. By combining common data sets with advanced analytical tools and methodologies, InfraShield Labs acts in a data enrichment capacity – auditing, collecting, correcting and creating data as needed by each critical decision or use case, always at a faster path to value and at a much lower cost than alternative Big Data software solutions.
Because we are known for our lightweight operational and predictive analytic applications and benchmarking services, we understand the importance of “good” data. We have expanded our service offerings to help companies achieve better data governance, with the initial focus on data cleansing and enrichment.
With Any InfraShield Labs Data Governance Project, You Can Expect To:
Our digital solutions team will work to identify outliers, anomalies and suspect data, and wherever possible automate the process of data cleanup through alerts, which highlight suspect events from the prior day and send them to operations for immediate validation and correction. We also focus on data enrichment initiatives to fill in data gaps or create entirely new datasets, such as collecting transmission structure elevations, generating substation fence shape files, and creating a spans database.
REDUCE COST & EFFORT OF DATA MANAGEMENT
Most utilities either have dedicated (and costly) resources focused on data validation and clean up or simply wait for data anomalies to be discovered and corrected by analysts in the course of their work. The former is very costly in O&M dollars, and not always reliable. The latter is far less effective, as correcting bad data, sometimes weeks or months after the error was made, is often impossible, leading to even more costly bad decisions. InfraShield Labs data solutions support comprehensive and timely data governance, with automatic data anomaly detection, alerts, and suggestions for corrections.
We will help you to deploy automated data validation and cleanup of outage, asset, and customer data. This could range from routine periodic validation tests of essential data sets, to daily validation tests and notifications to the associated initiator of each data record, to short and mid-term outlier identification, to longer-term analysis and anomalous pattern identification. The sooner data is corrected or refined and able to be deployed in prescriptive and predictive analyses/applications, the faster AND better data-driven decision-making can be made. We will help you use key data insights to take action!
Project Result Highlight
Sample visualization of a client transmission line – showing each structure and its sequenced path, overlaid with an OH line shape file. This is used primarily for checking for data quality issues. Sample data with span length are shown as well.
InfraShield Labs built a comprehensive database of 148,000 transmission line spans in less than 3 weeks and delivered it to the client for upload as a new layer in GIS. Establishment of this asset class was a significant step forward for this utility, allowing more effective asset risk management, as well as development of condition assessments, lifecycle strategies and risk profiles for each transmission span. We were able to:
• Document conductor risk (i.e., Asset criticality and condition) on each span
• Assign to each span ROW clearance attributes, structure heights, span midpoint elevations, conductor mid span sag as function of circuit load and ambient temperature/wind speed, etc., To measure veg management risk
• Link asset condition and vegetation exposure to consequence (river and highway crossings, schools, etc.)
• The client realized that they could not adopted an effective risk-based approach to prioritization of inspections and veg management without data on this critical missing
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