We commonly see key problem areas such as:
- Ingestion and content: Bad data collection, inadequate quality checks, and lack of system integration.
- Architecture & storage: Errors in database setup and storage processes result in unusable or mismatched data, such as missing customer IDs or unreliable provisioning & billing records.
- Model & reporting risk: Analytic research and reporting conducted on suspect data will lead to untrustworthy operational and strategic decisions.
The risks of not addressing these areas are high and move beyond monetary cost to adverse impact on customers, operational efficiencies, delays and rework.
Perfection versus reality
While it is rare to find the perfect data scenario, insisting on certain minimum criteria will help your organization on its data journey. At a bare minimum, companies need:
- Reliable customer and financial data
- Accurate inventory transactions history (e.g. shipments, provisioning, activations), ensuring inventory is properly tracked and managed
To achieve this, data must be transparent and sufficiently accurate to avoid material distortion of analytic models. It also should not have biases that could lead to incorrect decision making or skewed reporting (e.g., incomplete information on a particular billing area).
Where to begin
A comprehensive audit of customer and billing data is a suggested start point, cleaning up any outstanding issues:
- Document and report any processes that led to incorrect entry of customer data such as name, address, and device information.
- Ensure the billing cycle is secure by auditing payment history, customer-to-account links, work order history, and service types.
- Validate that devices are correctly recorded in inventory and provisioning.
When essential data is at an acceptable level of quality, the second step is to ensure data quality standards prevent any recurrence of data quality issues. Within enterprise Operations, data quality standards exist that apply across all business areas, but certain specific parts of an organization can benefit from tailored governance processes specific to their needs. Consider these areas:
- Supply Chain Lifecycle
- Customer Billing Operations
- Operational Services Support
- Business Services Support
Be sure to build adequate quality checks into your data processes and require sufficient documentation (such as standardized data quality reports). Both will save huge headaches down the road.
Communications, Media and Technology companies will benefit from “cleaning” existing data quality issues, ensuring appropriate policies and procedures are put in place to enforce data reliability. These improvements in data quality can reduce financial and operational risks, allowing an organization to implement accurate business insights that will improve the bottom line and drive growth.
In today’s digital world, that is competitive advantage.
1 Accenture: Intelligent operations goal requires data backbone
2 How to Stop Data Quality Undermining Your Business - Smarter With Gartner
3 Accenture Technology Vision, 2018