Excellence in business intelligence solutions requires clean data that is accessible to systems and users throughout your organization. At Saama we use a variety of techniques that ensure the data management services are adequate to support the best BI solution we can build for your organization.
Data Management
Data Quality
The goal of data management is to make data accessible, usable, understandable and timely; The goal of a data quality initiative is to make that data correct. Data quality is the cornerstone of all business information, and Saama believes that it is particularly essential in the BI environment. Data that is correct will lead to good business decisions; data that is not correct cannot be used at all in BI solutions.
A decision maker will certainly know when data is late, incomprehensible, irrelevant, or when it cannot be accessed at all; but he may not know when it is incorrect. That is why incorrect data is not only useless—it is dangerous.
Saama pays enormous attention to data quality during the course of planning, implementing, and disseminating your BI solution. Your company's source data is critically examined for possible errors. Our analysis shows where data is missing, out of range, invalid, or without referential integrity. Saama reports those errors, fixes them at the source and rebuilds the data warehouse using the corrected data. We also propose how to fix the data during the extraction process so that the error does not re-occur.
Software tools are available that help ensure data quality. The tools examine data to see that it meets certain criteria, and some correct the data when it fails these tests. They avoid the necessity of manually assessing every scrap of information or manually building custom systems to do the job.
However, building an ETL process with impeccable data quality requires careful testing and analysis to make sure these processes are working, and Saama checks every step to make sure that your data is of the highest quality. Data is not only tested during the ETL phase, it is tested for accuracy where it is used in the BI solution we build, including the reporting and analysis applications.
The bottom line is that Saama makes an active commitment to ensuring the accuracy and quality of the data that goes into your BI solution, and that its quality is maintained throughout its use in the system.
Metadata Management
Information by itself is never enough. In order to evaluate the numbers in your data warehouse, you need to understand them, and that's where metadata comes into play. Metadata tells you how the information is defined, if the data are calculated, where they came from, how old they are, if the numbers have been cleaned for quality, filtered, or summarized, who is the information steward, and how the information was approved by its steward.
In short, metadata is data that documents data, and tells you how it was processed through the BI environment, from its source in a business process system, to its appearance in a BI report or use in an analytical model. That, in turn, will tell you how you can make appropriate use of the information, as well as when someone is using it inappropriately.
Gathering the metadata and storing it is a critical part of the main process of gathering, transforming and storing information that Saama does at the start of your BI solution project. Once the metadata is in place, you may not access it every day, but it will become a part of in how you think about the information you are using. You will of course need to review and revise the metadata when your business, and therefore your BI solution, changes in a way that impacts the way data are created for your system.
Metadata management has to be an integral part of your Business Intelligence in order for it to have substantial business value. Saama's consulting team works with your team to create a metadata strategy that is appropriate for your company, and that can be successfully integrated into your BI solution to provide you with a full set of current, accurate and available metadata.
Master Data Management
Business Intelligence analytics and reporting require data to be broken down into various categories and types. The categories used for these breakdowns are called Master Data, and their definition and management is known as Master Data Management, or MDM.
While some breakdown entities, such as date and geography, have well understood and agreed-upon definitions, others usually do not, and a Master Data Management system has to enable the creation and use of several different categorizations. Without a set of clear definitions and an MDM system to manage them, the value of your business information and your company's BI solution will be seriously reduced.
For example, the customer entity is often defined and used differently by different groups, and if these definitions are not understood and properly used, the information will have little value. For example, a customer may be someone who buys a product, but a customer may also be someone who replied to an advertisement, had a product repaired, called for technical support, or returned a gift item.
That makes it unclear how to respond when your CEO asks how many customers your company has. If you can say that there are 38,472 customers who have bought a product, 16,755 who have called for tech support, and 75,398 who have responded to a recent advertising campaign, you will have done your job correctly. If you can only say that there are 38,472 customers without qualifying what kind of customer they are and what they have done, you have not.
You can give unambiguous answers, and use unambiguous information in your reporting and analysis, only if you have an adequate MDM system in place as part of your BI solution. Saama not only understands that, our consultants will work closely with your team to make sure that your system is adequate, and that your definitions are not only clear, but can be supported in your ETL environment. Each of your business process systems will be thoroughly analyzed to ensure that the definitions your team agrees upon can be supported by the extraction process, and that the information arriving in your data warehouse conforms to the definitions.
Our goal is to make sure that when your dashboard or model says there are 38,472 customers, you know and can say what that means, and that you and your colleagues will use the information correctly in your work.
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