WP_Data Management Best Practices

W H I T E P A P E R

www.persistent.com

2.2 Enhancements to Reference Publication

Now, we turn onto the third point of our strategy, namely, how to enhance this reference publication. We believe we

can add value to this reference publication on two important dimensions:

—

Areas of interest to PSLdeveloped recently since the book was published, and

—

Our own experience as practitioners.

On the first point, during the last 8 years since this reference publication appeared, several tendencies have

materialized and developed enormously, which we want to cover explicitly:

Cloud deployments.

We mentioned this in the previous section (deployment type dimension). Until three

years ago, data warehousing was essentially an on-premises initiative. Since then, cloud computing has had

a tremendous impact on data warehousing and analytics, which we want to cover in this document.

Big data.

We mentioned

data lakes

as a new type of target environment associated with big data engines /

solutions. We will cover explicitly this topic in the big data section given its recent development (it is not

covered in the reference publication).

Self-service tools and agility.

This new set of tools and processes, democratizing access to data for users of

varying technical skills, will allow more agility in the delivery of analytics projects, and involve business users

more effectively to support data governance.

These are the three broad topics this paper is organized around on each area. We will describe the relevance of these

topics for our scope in section 3 below in greater detail.

Finally, as mentioned in the second point of our strategy, we want to concentrate on a handful of important transversal

topics (at the expense of others). After analyzing the current, internal knowledge of the technical scope at PSL, we

have chosen the following four topics of data management:

1. Technical architecture

2. Dimensional data modeling

3. Data quality

4. Nonfunctional aspects to successfully run in production, such as performance, scalability and security.

The technical architecture addresses the technology track. The next two ones are the most important aspects of the

data track in Kimball’s lifecycle. And the non-functional aspects are necessary for a successful deployment,

maintenance and growth, but they need to be considered from the start of any analytics project.

Concretely, we want to develop our best practices document as follows: each of these four transversal areas will be

expanded into chapters

through section

with the following content:

1. A brief presentation of the topic, followed by a summary of its treatment through the requirements gathering,

design, development, and deployment phases in the reference publication, including pointers to the chapters

and pages where the topic is developed in detail. This will serve as a high-level view of the reference

document from the vantage point of the topic in question.

2. Our value-add on each topic, both through