If you work for a company that lacks an enterprise data strategy, you probably already know what kind of problems can result. Ad hoc reporting is difficult, if not impossible. The incredible system complexity gets in the way of power users. Valuable resources are wasted on manually re-keying data between systems. Enterprise management reporting is inaccurate – if it can even occur. And even simple report changes require heavy support from IT staff or expensive outsourcing. Not to mention sharing data outside of the organizational walls. This, for some companies, could be virtually impossible.
For the past 40 years, companies have moved readily through the different stages of data access. These stages are as follows:
- Data Entry—Users are able to enter data into a system
- Data Access—Users are able to view data within their own systems through the use of simple queries
- Strategic Data Access—Users are able to view data through the use of specialized query generators to help make decisions about running their business
- Data Sharing—Users share data with customers and suppliers
- Data Collaboration—Users collaborate throughout the supply chain by strategically using and viewing data on a real-time basis. (CPFR and ECR are just a few uses of the Data collaboration model)
Even now many companies are mired in Stage 3 and Stage 4, and have yet to realize the benefits of true data collaboration. As such they are victims to the following weaknesses:
- Data development is expensive (costs are not shared)
- The metadata (data about the data) is not consistent, because different users have different specifications
- Costs are duplicated both internally and externally. For example, failure to unify and implement data models within a single enterprise leads to duplication of effort and expensive customization of reporting.
The collaborative model has one common metadata set, where users can overlay their specific requirements for the data. Even though the benefits are obvious there are some weaknesses that must be discussed:
- Requirements take longer to identify and maintain
- Integrating different resources can be complex. Goals and priorities between organizational functions and crossing corporate boundaries are complicated.
- Most organizations focus on data use and have very little time for data sharing. Face the facts. The sales department has the need to report off of sales data (wrong or right) with little regards to the needs of the financial groups.
These weaknesses may be noticeable, but with the right tools, they are virtually invisible and certainly not a detriment to doing business. Management teams must be able to support the value chain. To view the value chain, management must be able to dismantle the boxes of traditional thinking, and push the strategic thinking and toolsets that will allow businesses to support a Collaborative Data Model.
When the ERP models came about, companies started focusing their attention internally. But where companies have not paid sufficient attention is the information flow between and across trading partners. ERP is internally focused and ensures all departments talk the same language. But what about their customers or suppliers? What ensures that all parties talk the same language there? Traditional ERP systems do not cross inter-company boundaries so it cannot help – and so we come to Data Collaboration. ERP is synonymous with execution, and Data Collaboration is synonymous with strategy – the provision for the materials and processes to meet the needs of the customer when an order arrives. In other words, a new strategy requires the anticipation of the customer’s needs, the anticipated needs of one’s own organization, and those of the supplier base. Therefore true data collaboration must include the ability to access and report off the customer’s data to increase the effectiveness of activities such as Demand Forecasting. By forecasting a customer’s demand accurately, an organization can take steps to go beyond simple ERP-focused asset efficiencies and move to exploiting asset effectiveness. The discussion of the benefits of data collaboration could be vast, but here is a list:
- Lower cost per company and functional group
- Lower aggregate costs
- Improved consistency of information
- Improved consistency of plans and decisions based on shared data
- Quicker Project start-up
- Lower Operating Costs
- Improved quality and currency of data
- More robust data model
- Higher data accuracy
- Improved relationships both internally and externally