Business Intelligence
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