Many areas in your business need their own metrics. Be it to analyze sales, to plan replenishments, to manage stocks or to monitor business-specific productivity indicators, the possibilities offered by the transactional system (OLTP) are often limited and too rigid.

An extract of the data from the ERP is necessary to take full advantage of the information, and very often the data also needs to be integrated with data from different processes to provide a meaningful picture.

Too often, this work is made in Excel sheets, wasting time every month of business executives, instead of using a professional solution which runs on an automated basis. Also, the analytic possibilities from a DWH are without comparison.

Data Warehouses can be a quite complex process. The literature on the subject lists a lot of possible data stores, and particularities in the change of data to handle. In practice, not all have a practical meaning in your situation.

Data Warehouses are not as much an IT project as we would like to think. In order to have the solution answer real business needs, and not only providing a technically perfect solution for copying data, input from the business is needed. The users need to have a very active role in the project. CIS uses rapid prototyping, in order to get feedback from the users as soon as possible. If the project is headed in the wrong direction, we need to know it now, and not after 6 month of developing off the track.

After only a few weeks, you should be able to get a feeling of what the final will look like. We start small, but with a solution capable of accommodating growth.

The generic approach

Generic Data Warehouses exist for most ERP’s, with shorter adoption cycles than custom built DWH. You basically only need to install and configure the solution, to see if it matches your needs. The risk, and cost, of such implementations is much smaller than a custom development.

The caveat is that customization of generic DWH can be more difficult, as well as updates. The solution often focuses on the ERP system in general, and not your specific implementation. But depending on your situation, there might be a match.

Custom development

On the opposite, while more costly, custom development ensures that your expectations are met, that no unneeded complexity is added, and that you keep ownership of the product.

The competence of CIS’s partners is of great value, as we have experience in creating Data Warehouses, and will bring you the needed knowledge.

The failure rates of Data Warehouse projects are plain scary. Not a majority of projects fail, a large majority do fail.

Ralph Kimball, in his book "the Data Warehouse toolkit", list the following pitfalls

• Pitfall 10. Become overly enamored with technology and data rather than focusing on the business’s requirements and goals.
• Pitfall 9. Fail to embrace or recruit an influential, accessible, and reasonable management visionary as the business sponsor of the data warehouse.
• Pitfall 8. Tackle a galactic multiyear project rather than pursuing more manageable, while still compelling, iterative development efforts.
• Pitfall 7. Allocate energy to construct a normalized data structure, yet run out of budget before building a viable presentation area based on dimensional models.
• Pitfall 6. Pay more attention to backroom operational performance and ease of development than to front-room query performance and ease of use.
• Pitfall 5. Make the supposedly queryable data in the presentation area overly complex. Database designers who prefer a more complex presentation should spend a year supporting business users; they’d develop a much better appreciation for the need to seek simpler solutions.
• Pitfall 4. Populate dimensional models on a standalone basis without regard to a data architecture that ties them together using shared, conformed dimensions.
• Pitfall 3. Load only summarized data into the presentation area’s dimensional structures.
• Pitfall 2. Presume that the business, its requirements and analytics, and the underlying data and the supporting technology are static.
• Pitfall 1. Neglect to acknowledge that data warehouse success is tied directly to user acceptance. If the users haven’t accepted the data warehouse as a foundation for improved decision making, then your efforts have been exercises in futility.

CIS helps you minimizing the risk by:

• using generic solutions that have made their proofs in similar situations, if applicable.
• use prototypes to provide a basis for discussion as soon as possible.
• use prototypes to get user feedback as soon as possible.
• bringing in dept expertise on the technologies used.
• having been on DWH projects before, but still listening to why yours is different.
• pragmatic approach: screening of requirements, data modeling and step by step implementation.

CIS also takes great consideration for the needs expressed by the users. This makes our data warehouses not just replicas of the underlying system, but a true presentation layer for the data the users is asking for. To create 'useful' data warehouses, the involvement of and the support by management is absolutely required.