BUSINESS INTELLIGENCE -
VOLTAIRE CONSULTANTS BUSINESS

Data Warehousing Modern day business characteristics are as follows:
	Just in time production.
	Economies of scale.
	Customer binding and loyalty programs.
	The classic value chain has changed from a production driven process to Supply Chain Management and Demand Planning.
	Providing Information that is proven to be correct and complete from start (legacy system) till end (end-user).
	Tendency to customization.
	Use of ERP and CRM packages.
	Management decisions based on information stored in company computer systems.
	Information seen as a competitive resource.
	Knowlegde management.
	Internet.
	Subject to globalisation.

All these factors pose new demands on the modern day management of the corporation. The ability to successfully make critical business decisions depends more and more on the availability of relevant gleaned information and the use of information technology. Companies and organizations generally have stored significant amounts of information in their ERP and operational legacy systems. However, this all needs to be formatted and aggregated into meaningful information about customers, suppliers and production processes. Paradox in many companies is: "The more information systems there are, the more difficult it appears to obtain the right information". Two reasons for this are:
	Corporate production systems are not designed to meet the needs of decision making.
	These production systems contain specific categories of data (sales, financial and product), that are not easily combined.

More and more organisations have therefore decided to implement a Data Warehouse in order  to obviate this paradox. The Data Warehouse is set up to facilitate Customer Management processes (e.g. Database Marketing, Relationship Marketing) or for Performance Management goals - Balanced Scorecard. These elements form the basis for an integral Business Intelligence strategy. The Data Warehouse is no more than a prerequisite (an infrastructure) for improving decision making effectiveness. But increasingly it appears to be a necessary prerequisite.

Data Mining and Analytical CRM
Standard reporting techniques do not always provide relevant insights into an organisation’s customers. Obviously you are interested in maximizing revenue, equally you may wish to identify any potential risks your customers or prospects may pose. Data Mining can provide valuable previously unknown insights into customers and prospects as well as providing relevant contexts and cross referencing. Typically Data Mining has no preconceived goal. Data analysis simply provides new insights via statistical sampling and methods into customer behaviour, etc. SAS can perform customer segmentation, identify risks and cross-selling opportunities.

Customer Loyalty plays a large role in some sectors. Is this still warranted? Instead of a Loyalty Card, should we not turn our attention to Behavioural Segmentation? Campaign Management: how do you measure the effectiveness of such? Is the campaign a good idea given future strategies? Is the Database Marketing effort relevant in the overall scheme of things?

What is any given customer worth to date? I.e. what is their “Nett Present Value”? Should you target more effort at her/him? Is it a platinum, gold of silver customer? What are your future expectations of this customer in terms of revenue? Is a long terms relationship condusive to the company? In other words what is the expecte “Life Time Value” of the client? Where should your marketing efforts be focused for the highest return on investment? How should you address customer retention and churn? All these questions are relevant today. In a nutshell, follow your customers.

In summary you can perform the following with customers:
	Segmentation from different perspectives e.g. customer value, products sold.
	Cross-sell and deep-sell / up-sell them.
	Credit scores assignation.
	Assess response chances.

Reconciliation
The rollout of a new Data Warehouse or management information system is a task often underestimated. Delivery is arduous enough. Accepting the new reporting figures can cause some consternation within the relevant departments and upper management. This is generally  a consequence of old and new figures not matching. How big should the deviations be? And what is the explanation for these differences? Have misconceptions crept into the new Data Warehouse or were the old figures not actually that reliable? Comparison of the old and new figures requires often significant effort when there are differences. Knowledge of existing legacy systems and their inbuilt assumptions are not always evident. The system integrator will want to sign off the project,  the client on the other hand will want assurances. In short a very difficult situation.

In such cases the challenge is to explain any differences to all parties in a neutral and timely fashion allowing fixes to proceed or the organization to adopt a new reporting paradigma. This process is quite complex and should not be underestimated. All this requires a special program with experienced personnel. Voltaire Consultants has gained significant exposure to these problems during the implementation of numerous data warehouses. They can offers a neutral and balanced QA approach starting earlier rather than later, resulting in a more timely delivery and realistic customer expectations due to bottlenecks being identified early on. 

Reassuring correctness and completeness
An increasing tendency can be seen towards internal and external requirements being posed on the distribution of information being complete and reliable. Accountants want to be proven that all information distributed inside and outside the company, or serving as input for other systems, is in fact accurate and correct. 
These requirements put heavy demands on the design and operational use of your Data Warehouse system. Not only because the information residing in the legacy systems are stored in a very different way than it is stored in the Data Warehouse or presented in the reports. Also, data can be lost during load, scheduled routines can partly fail, network connections can be down, etc. Moreover, proving the correctness of information being calculated/derived or deducted from other information can be quite tedious.
Voltaire Consultants has gained a lot of knowledge with building extensive control mechanisms that reassure the completeness and correctness of information according to accountants standards and internal or external regulations (like Sarbanes-Oxley (SOx) and Basel).

Other Expertise
Voltaire Consultants has considerable experience in the implementation of the Operational Data Store (ODS). Organizations and companies with similar information stored across disparate systems as a result of mergers and acquisitons, require an integral overview of such data which is consumed, standardized and passed on to down-stream systems such as the Corporate Data Warehouse, CRM and other  applications. Voltaire Consultants chooses expressly for the data oriented approach for the ODS.

We also have experience in implementing Balanced Score Card systems. The challenge here is to develope a representative and usable measuring system that will satisfy the organisation as well as shareholders.

Revenue Assurance tasks can be carried out so as to detect any Revenue Leakage. First class understanding and knowledge of the relevant sector and business processes as well the software usage are a must in order to be successful. Voltaire Consultants fulfills these requirements with respect to the telecomsector.

Operations Research / Forecasting and Simulation solutions are services that Voltaire Consultants can provide. We are also experts in high volume / high performance scaleable solutions and understand the complexities of Very Large Database design.