For Business Intelligence which type of solution is best will ultimately depend on individual business circumstances. The solution type assessment that has been discussed in this series of articles will aid that decision.

We are in the age of careful spending. So companies need to know that each dollar spent is going to provide business value. It is important to develop a consolidated global view irrespective of operational software used. Consolidating data into a common platform is not always easy. The Data Warehouse must always be the single source of the truth for corporate information. This must be based on an open and flexible architecture that is extendible. To be clear, whether the data warehouse database is a bespoke design or implements a packaged solution there is still a large amount of work to be completed. There will always be the need to build a project team and acquire data for the data warehouse.

When ready made solutions provide best results

Generally ready-made solutions provide best results when they offer a high degree of integration for existing operational applications. If using a corporate-wide ERP or CRM solution that provides the backbone tying all corporate applications together then it is possible to utilise the associated data warehouse as the corporate Business Intelligence backbone. The preference for a pre-built database can be strengthened if all of the applications identified can be provided by the same vendor.

Ready-made solutions are becoming increasingly complex as the solution providers learn and adapt their data models based on real business scenarios, these will continue to evolve into the future.

What custom solutions can provide

Custom-made data warehouses are capable of supporting any application that is required to run against it. The primary concern will always be the amount of time necessary to develop any solution. Even where packaged Analytic applications are to be used is it generally necessary to tweak the metadata to enable them to function smoothly against the database. Often these Analytic applications can best be supported through a custom data mart, fed by the data warehouse – the single source of truth.

When custom solutions provide best results

Custom solutions provide the best results when there is a high degree of complexity in the corporate legacy architecture, even if there is an intention trim and simplify the architecture. Custom solutions are also best when the solution itself is likely to be unique in nature, e.g. when your corporation is genuinely the only corporation in the world doing this – however this situation is increasingly rare in today’s business climate.

Five years ago the combination of complex business processes and a complex architecture guaranteed that customised solutions were necessary. Today this is less clear cut. look at some industry models and see whether it is possible to adapt them before making the final decision.

Leveraging the Solution Type Assessment

In the earlier articles the author talked about a Solution Type Assessment. Through this process it is possible to build a score based on firstly mission criticality, and secondly the complexity rating for your organisation. These will act as a weighting factor for the importance of each item covered in the decision process.the weighting factor should be agreed before starting the assessment. In weighting there is a tendency for business to show a low weighting factor for IT issues, this MUST be avoided as the technical obstacles do need to be considered.

The Complexity to Provide column relates to the ability of the current data processing facilities to provide such data in a succinct and meaningful way. It becomes complex if data is stored in many different systems across the world, and in different languages. It is also complex if many types of data storage system are used (e.g. non-relational data stores and relational data bases can provide parts of the data).

Overall taking a solution type assessment will ensure the necessary due diligence considerations are covered. Of course capability has to assessed alongside the overall project costs. Customised solutions can be implemented over a longer period of time whereas ready-made solutions will generally be implemented altogether, even if some data comes on-line at a later date.

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This series will be taking a break for a few weeks, but when it returns the next article will focus on: “Staffing your BI Solution”

Previous:

Part 1: Financing the Business Intelligence Project (Buy or Build?)

Part 2: BI Project Decisions: Ready Made Database Options

Part 3: The Corporate Architecture and Complexity of Data Links

Part 4: Financing the BI Project: Uniqueness Factors (or USP)

Part 5: Starting your BI Project: Suitable Analytical Applications

In this context the adoption of analytical applications includes general reporting requirements (e.g. monthly sales reporting) as well as specialist applications (for example Activity Based Costing). This requires a projection of future need, but is still essential to determining the necessity of a pre-built Data Warehouse. Some applications will be tied to a packaged solution, whilst other options will have general availability.

Indeed when you are defining a ‘greenfield’ business intelligence implementation with a corporation that has few skills in the area it can be difficult to think about advanced analytics. The problem here is that some individuals will hail from other corporations that are analytically rich and these individuals want to launch into advanced analytical applications, when the traditional groundwork has not been laid out (e.g. a long history for analytics and a knowledgeable analytical team in the business).

Having recently worked with a growing mid-sized corporation who had recently appointed a new CFO to assist in their growth, she of-course came to the corporation bursting with ideas that had assisted her former employer to success, yet in the new environment there was no analytical capability and to make matters worse there were two accounting systems. She had a challenge on her hands, none the least being how she could implement the cost analysis modules she required.

The solution assessment  requires ratings for all analytical applications. It is essential to indicate all applications that are intended to be used in the future, even if this picture is currently uncertain. for example:

◊ Monthly financial reporting

◊ Ad-hoc queries

◊Customer Profitability

◊ Product Profitability

◊ Activity Based Costing

◊ Balanced Scorecard

◊ Inventory Analysis

◊ Business modelling

Identifying basic applications is as important the more advanced. It is essential to focus on those elements that are mission critical right now as well as its complexity to achieve. Complexity here is based on a number of factors including the immediate availability of this data. Where data components are not available today then the complexity goes up as the corporation has to consider how to obtain this data.

As an observation the reader will need to analyse the range of applications that are provided in association with any pre-built Data Warehouse in order to assess its suitability. Be aware also that many applications are based on data marts, these are focused data sets that should take data from the existing data warehouse database and restructure it to suit the analytical application being implemented. This mart should never be confused with the role of the data warehouse.

Next: “Which Solution is Best?”

Previous:

Part 1: Financing the Business Intelligence Project (Buy or Build?)

Part 2: BI Project Decisions: Ready Made Database Options

Part 3: The Corporate Architecture and Complexity of Data Links

Part 4: Financing the BI Project: Uniqueness Factors (or USP)

1. A full data model, usually linked closely with an operational application. SAP, Peoplesoft, Siebel and other software suppliers provide Data Warehouses linked with their ERP and CRM applications.
2. An industry ‘template’ that provides a basic model Data Warehouse relating to the industry sector that the business is trading in.

Remember this section is talking purely about the supporting database, the Data Warehouse, rather that the overall BI solution.

Additionally there are Cloud based BI, or BI as a Service, solutions that tend to have their own internal database, most are linked to cloud based solutions. Generally you should consider these to be included with the first choice here as they are normally related to other operational applications. However some cloud based solutions are not extendible, which may be a factor in the decision making.

This is a continuing article based upon: Financing the Business Intelligence Project (Buy or Build?)…

Full Data Warehouse Model:

Many software companies claim to provide pre-built, fully configurable and extensible Data Warehousing solutions. Few, if any, actually provide an ‘out-of-the-box’ solution for the Data Warehouse that is not linked to another application. Most are linked with other operational software, such as Enterprise Resource Planning or Customer Relationship Management, where the business will have committed a significant investment such technology and associated change programmes.

Solutions are likely to be tailored to industry specific business analysis needs and will probably include pre-configured ETL components that may include extraction software for other commonly used software packages. This can act as a considerable advantage as it can reduce Data Integration efforts.

The advantage offered is that where a business uses the supplier’s solution it can have a ready-made business intelligence offering ‘for a few dollars per head more’. The final solution is still customisable, but is linked into key enterprise systems. This is an excellent goal, but whether it is achievable is a question that must be factored into the decision process.

Industry Templates:

Bill Inmon, used to be a great believer in the use of industry templates, or generic data models. Other companies also provide customisable data models, focused on specific marketplaces. The general aim of these templates is to act as a teaser for a subsequent engagement of the supplier’s consultancy services in order to have that organisation complete the full Data Warehouse and Business Intelligence implementation.

Templates or generic data models are therefore an aid, or kick-start, to custom development rather than a full blown ready-made solution. They are discussed here because generic data models are sometimes seen as a solution rather than a step-up on the development process. The template, or generic data model, can provide a boost to the initial Data Warehouse development project. They can be used to:

♦  Provide a foundation that can be built upon

♦  Identify major data classes

♦  Identify common relationships

♦  Identify some common attributes within the data

♦  Identify some common keys and unique identifiers

♦  Get the modelling exercise off to a fast start

In other words, the generic data model is a good starting point for the creation of a corporate data model, upon which the Data Warehouse will be founded. Using the template or generic data model as the corporate data model is inevitably a mistake. They do not alleviate the need to think about the design of any solution; they simply provide a way to jump-start the data modelling exercise.

The template or generic data model is not really a ready-made solution it is really a short-cut to providing a custom solution. Such a model will not include any pre-configured ETL components so these factors have to enter into any decision made.

The Decision:

At the end of the day having a customisable model is important because the Business Intelligence capability should be able to map onto the organisation’s USP. These are factors that you need to use in order to make your purchase decision. The best models are visible and expansible.

It is easy to assume that there is a ready made solution available and today’s choices are becoming increasingly complex, because vendors are learning the lessons from work they have completed for customers in the past and they have therefore extended their models to include a large array of new capabilities. Some vendors have simply extended default models over time others make these capabilities available only for extra cost. Make sure you know all the cost factors before making any decision.

The Next Decision Factor: Your Corporate Architecture and the complexity of data