Intelligent Information Management (IIM) | Digital Transformation
Digital Transformation requires that organizations meet a new generation of process challenges. Holistic customer journeys that embrace multiple sub-systems and processes, often owned by different departments, require a different way of looking at process problems.
Looking to specific industries, consider some of the following (per Michael Croal from Cornerstone Advisors).
Healthcare:
- Patient Tracking: Admission to Discharge
- Patient Billing: Service/Supply to Bill Satisfaction
- Patient Surgery: Diagnoses to Follow-up
Energy:
- New Production: Land Acquisition to 1st Barrel
- Refining: Raw Material to Final Product
Insurance:
- Claims Adjustment: Initial Contact to Final Resolution
- New Policy: Request for Quote to Renewal
Banking:
- New Customer/New Account: Sale to 1st Anniversary
- Consumer Loan Delivery: Application to Lien Perfection
- Mortgage Loan Delivery: Lead to Investor Sale
- Commercial Loan Delivery: Expression of Interest to Annual Review
- Collections: Past Due to Current
- Loan Servicing: Boarding to Collateral Release
The next generation of business problems require a 360-degree view of information and access to this information – both data and content, in geometrically increasing volumes, and regardless of where it is stored. Organizations can no longer afford to look at data management and content management disciplines in isolation.
While all of the above processes are vastly different, their core characteristics say a lot about why the past bifurcation between data management and content management is becoming blurred and strained.
- BOTH Data and content are needed to solve these problems.
- The scale of the data and content challenge is increasing geometrically and will increase even more rapidly with the internet of things.
- The data and content needed to solve these problems do not reside in a single repository or system but are scattered throughout the organization.
The data and content to solve these problems – assuming you can find it -- in many cases does not inherently contain sufficient metadata to make these information assets either understood or actionable. Traditional ETL (Extract, Transform, Load) approaches focus on bringing big data and content into a common data warehouse. These are proving to be too expensive, too complex, and too slow. Traditional relational/SQL databases are straining to meet big data demands, and new semantic, NoSQL approaches are arising. And the world of content management is being transformed by concepts and disciplines from this new world of data.
