Digital transformation systems and data issues (Article 2)

What systems and data issues can digital transformation resolve or reduce? This article describes multiple issues that digital transformation can attack.

In the previous article, I introduced the digital transformation concept and its benefits for the oil and gas industry.

Digital transformation can help many companies struggling with various systems and data issues that increase costs and risk while reducing net income. Many companies are successful despite these issues. However, the continuing pressure for cost reductions and competition for capital means these issues have escalated into significant impediments.

The typical systems and data issues include the following.

Too many paper-based, manual processes

Some companies continue to rely heavily on different paper-based, manual processes. Examples include:

1. Contracts.
2. Warranty claims.
3. Consumer coupons.
4. Regulatory submissions.
5. Performance reports for management.

No integration among IT systems

Some companies operate with limited or no integration among their IT systems. Examples include:

1. Financial systems don’t receive enough data from inventory systems.
2. Reporting systems rely heavily on Excel to integrate data.
3. Product return processes don’t see sales data.
4. Manufacturing systems don’t see operating cost data.
5. Forecasting data is not readily available to other departments.
6. Logistics staff rely on public data sources for internal data.

Collaboration struggles

Some companies struggle with employee and partner collaboration. Examples include:

1. Multiple employees can’t work with the same data concurrently.
2. Employees can’t share project data with partners.
3. Electronic connectivity with product and service vendors is limited to email and attachments.
4. Systems don’t support the languages used by staff.

Haphazard data management

Some companies work with haphazard data management practices. Examples include:

1. Data has gaps and inconsistencies that require cleanup before real analytical work can begin.
2. Data quality varies from vendor to vendor, such as component and material vendors.
3. Data quality varies from application to application and from region to region.
4. Data model differences across software packages thwart data stewardship efforts.

Overlapping systems

Some companies operate too much software with overlapping functionality, which is often the result of failure to prune after mergers. Examples include:

1. Multiple ERP systems.
2. Multiple electronic document management systems.
3. Multiple manufacturing systems.
4. Microsoft 365 and Google Docs.
5. OneDrive and iCloud.
6. Numerous brands of data visualization software.

Not modernizing IT systems

Some companies repeatedly defer modernizing IT systems, falsely believing the costs exceed the benefits. Examples include:

1. Failing to rationalize overlapping software packages.
2. Deferring software package version upgrades.
3. Not retiring systems with few end-users.
4. Delaying application integration proposals.

The next article in this series explores applications that offer significant benefits when implementing digital transformation.