In Enterprise Data Management we are confronted with major complexities: the integration of Data Warehouses, Big Data solutions, new channels, new stakeholders, and complexities in master data distribution, threaten to overwhelm effective planning and cost control. As a consequence we often see massive waste and increased risks:
Major technology replacements are brought forward. For example, losing 2 years from an expected 10 year life of an asset increases amortisation costs by 20%.
Major transformations increase in duration and cost in unexplained ways (work estimate overruns are often linked to hidden, unexpected complexity)
Operating expense structures creep up with no ability to cap or control them (IT departments work long hours, but seem overwhelmed with work)
These are very often caused by immature data supply management:
- Data Stores - the data is in too many places
- Data Movement - the data is handled too many times
- Data Management - Ineffective control over data inventory
There are five common symptoms:
- When data is all over the place, it is very difficult to remember where it all is even using snazzy technology
- Data stores and interfaces are built in ad-hoc scrambles, then all the budget is spent keeping the system alive over the next 20 years.
- Incremental improvements result in exponential increases in complexity.
- Satellite systems multiply complexity and risk of cost blowouts.
- Interactions at the boundary of the organisation are ad hoc; electronic markets can create chaos.
Business problems that involve moving lots of goods around are called Supply Chains. For postal, courier, retailing, transportation, and manufacturing organisations, managing the movement of goods is one of the key determinants of success, profitability and sustainability. They have dedicated departments to supply chain management and the leader it usually a senior executive.
We can learn from these organisations to help us manage data supply chains. Think of data storage, movement and transformation as warehousing, transportation and packing and unpacking. Where is the senior executive who is managing the data supply chain for maximum reliability, quality and efficiency?
Modern organisations manage supply chains for strategic and competitive advantage. Can we imagine our data storage, movement and transformation as a competitive advantage? Can we imagine inventory systems that tell us where all our data is at any point? Can we set limitations on the diversity of data being supplied and can we imagine saying “no” where it is outside agreed parameters?.
By examining what good supply chain managers do and applying these principles to Data Supply Chains, these insights can assist the practical questions on how to diagnose, plan and execute an improvement strategy.