How a departmental data workbench can speed integration, eliminate data silos and spread business intelligence across the enterprise.
Artificial intelligence and analytics have come a long way in a few short years, but the fact is, the majority of companies still use traditional reporting that derives from legacy systems and no- or low-code report generators. Every two or three years, IT reviews these reports, and the usual outcome is that 80% of reports go unused while 20% see major usage.
SEE: Electronic Data Disposal Policy (TechRepublic Premium)
The goal of report reviews is to identify which reports are used and to eliminate the rest; but a more essential exercise in reporting and data is to align both around each company business unit, which would enable IT to better understand business information needs from the eyes of the business.
Here's how that would work: The customer service department uses standard system reports that show how many service calls it has each month, how long it takes to complete each call and what the service issue was. Customer service also has standard reports that review manpower usage, budget allocations and spend, etc. Customer service uses an AI system to troubleshoot problems in real time in the field, and also an unstructured data repository of searchable product and parts schematics and manuals. For field logistics, the department uses a GIS mapping system that tracks each service vehicle by location.
SEE: How to break down data silos: 4 obstacles and solutions (TechRepublic)
Now this is the IT view: Customer service uses a mix of data and reporting that ranges from standard reports that process structured data in batch to real time and batch AI analytics systems that utilize both structured and unstructured data. This means that a data fabric must be knit that can enable access to structured data from legacy systems and unstructured data from schematics and GIS maps. As systems grow and Internet of Things data capability gets added, IT's job is to position integration so it can be as much plug-and-play as possible. This gives customer service flexible options for adding new systems and eliminates data silos.
The idea of a departmental data workbench
By understanding all of the data that customer service needs in order to do its job and to assess performance and trends, IT can begin to formulate a data fabric that is malleable, scalable and able to process any kind of data customer service needs. In the process, IT will also identify the systems and applications that customer service uses to process this data. The end result is a departmental "data workbench" that gives IT a holistic understanding of customer service as a business because the workbench aligns and catalogs all of the applications, systems and data that customer service uses.
SEE: Snowflake data warehouse platform: A cheat sheet (free PDF) (TechRepublic)
Why does a data workbench matter?
Approaching each business function with a data workbench approach that holistically integrates all departmental applications, systems and data, gives IT a better understanding of the business and its individual functions. Data and system integration is facilitated, and silos are eliminated.
SEE: 3 steps to build a data fabric to integrate all your data tools (TechRepublic)
To date, few IT organizations use a data workbench approach with their individual user departments, but hopefully more in the future will consider it because of its potential to better align data and applications with the business, diminish standalone and archaic systems and data, and spread business intelligence across the enterprise.
- Geospatial data is being used to help track pandemics and emergencies (TechRepublic)
- Akamai boosts traffic by 350% but keeps energy use flat thanks to edge computing (TechRepublic)
- How to become a data scientist: A cheat sheet (TechRepublic)
- Top 5 programming languages data admins should know (free PDF) (TechRepublic download)
- Data Encryption Policy (TechRepublic Premium)
- Volume, velocity, and variety: Understanding the three V's of big data (ZDNet)
- Big data: More must-read coverage (TechRepublic on Flipboard)