Dimensional Models: Now More Than Ever

Do new technologies and methods render the dimensional model obsolete?

The top question from readers of this blog continues to be: "Is the dimensional model still relevant?"

It is easy to understand why people ask this question:

• Our BI programs have expanded beyond data warehousing to include performance management, analytics, and governance functions. 
• Methods have evolved and streamlined, thanks to the application of agile principles. 
• Technologies have advanced to include NoSQL solutions, schema-less paradigms, and virtualization options. 

In a recent article for TDWI’s Upside, I discuss these changes to our data management processes, and their impacts on the dimensional model.

The conclusion: these pressures actually increase the importance of the dimensional model.

Here are a few points made in the article:

• Although NoSQL technologies are contributing to the evolution of data management platforms, they are not rendering relational storage extinct. It is still necessary to track key business metrics over time, and on this front relational storage reigns. In part, this explains why several big data initiatives seek to support relational processing on top of platforms such as Hadoop. Nonrelational technology is evolving to support relational; the future still contains stars.

• [A dimensional view of data] grows in importance as the underlying storage of data assets grows in complexity. The dimensional model is the business's entry point into the sprawling repositories of available data and the focal point that makes sense of it all.

• The dimensional model of a business process provides a representation of information needs that simultaneously drives the traditional facts and dimensions of a data mart, the key performance indicators of performance dashboards, the variables of analytics models, and the reference data managed by governance and MDM. In this light, the dimensional model becomes the nexus of a holistic approach managing BI, analytics, and governance programs.

As we move to treat information as a business asset, the dimensional model has become a critical success factor. Yet many organizations are spread so thin that this critical skill is often missing. Be sure that doesn’t apply to your business!

For the full discussion, check out the article:  Dimensional Models in the Big Data Era. (Chris Adamson, April 12, 2017, TDWI’s Upside.)



Learn More

Join Chris for three days of dimension modeling education in New York next month!

• TDWI New York Seminar, October 23-25.  Earn a certificate and 24 CPE credits.  Check out the sidebar of this blog for additional dates.  You can also bring my courses on site.

Read the full article on Upside:

• Dimensional Models in the Big Data Era. (Chris Adamson, April 12, 2017, TDWI’s Upside.)

Read Chris's book:

• Star Schema: The Complete Reference (McGraw-Hill, 2010)

In Praise of the Whiteboard

Modeling tools are great, but early stage modeling activities are best conducted on a whiteboard. It is better for collaborative development, and handles rapid change more effectively. And there are inexpensive solutions if one is not readily available.

When I lead seminars that cover modeling techniques, this question always comes up: “What’s the best modeling tool?” 

My answer: Start modeling activities on a whiteboard. Break out the software later, one the model has stabilized.

Using a whiteboard, you will get to a better solution, faster. 

I find this to be true across the spectrum of BI project types and modeling techniques. Examples  include:

• Dimensional models (OLAP projects)
• Strategy maps (Performance Management projects)
• Influence Diagrams (Business Analytics projects)
• Causal loop diagrams (Business Analytics projects)

There are two reasons a whiteboard works best for this kind of work: it supports collaboration, and it is better suited to the rapid changes common in early stage modeling. In short, a whiteboard is inherently agile.

Collaboration

The best models are produced by small groups, not individuals. Collaboration generates useful and creative ideas which reach beyond what a seasoned modeler can do alone. 

Each of the techniques listed above requires collaboration between business and technical personnel. And within either of these realms, a diversity of perspectives always produces better results. Brainstorming is the name of the game.

Use of a modeling tool quashes the creativity and spontaneity of brainstorming sessions. You may have experienced this yourself.

Imagine five people in a room, one person’s laptop connected to a projector. Four people call out ideas, but the facilitator with the laptop can only respond to one at a time. The session becomes frustrating to all participants, no matter how good the facilitator is. The team loses ideas, and participants lose enthusiasm.

Now imagine the same five people in front of a whiteboard, each holding a pen. Everyone is able to get their ideas onto the board. While this may seem like anarchy, it helps ensure that no ideas are lost and it keeps everyone engaged. The result is always a better model, developed faster.

Rapid change

The other reason to start on a white board is practical: it is easy to erase, change, redraw. And you will be doing a lot of these things if you are collaborating.

Imagine a group is sketching out a model, and decides to make a major change. Perhaps one fact table is to be split into two. Or a single input parameter is to be decomposed into four. If a modeling tool is in use, making the change will will require deletion of elements, addition of new elements, and perhaps a few dialog boxes, check boxes, and warning messages.  The tool gets in the way of the creative process.

Now imagine a whiteboard is in use. A couple of boxes are drawn, some lines erased, some new lines added. The free flow of ideas continues, uninterrupted. Once again, this is what you want.

Unimpeded collaboration produces better results, faster.

No white board? No problem.

Don’t let the lack of a whiteboard in your cube or project room stop you.

There are many brands of inexpensive whiteboard sheets that cling to the wall. These have the additional benefit of being easy to relocate if you are forced to move to another room.  Here is one example, available on Amazon.com:

There are also several kinds of whiteboard-style notebooks. These are useful if you are working alone or in a group of two. They provide the same benefit of being able to collaborate and quickly change your minds, but in a smaller format.  The one that I carry is called Wipebook:

I learned about these and similar solutions from clients and students in my seminars.

…and then the tool

All this is not to say that modeling tools are bad. To the contrary, they are essential. Once the ideas have been firmed up, a modeling tool is the next step.

Modeling tools allow you to gets ideas into a form that can be reviewed and revised. They support division of labor for doing required “grunt work” — such as filling in business definitions, technical characteristics, and other metadata. And they produce useful documentation for developers, maintainers, and consumers of your solutions.

But when you’re getting started, use a whiteboard!

Tapping Into Non-Relational Data

Modern BI and Analytics programs use non-reatlional data management for six key functions. You should be cognizant of these functions when you add non-relational technology to your data architecture.

Kinds of Non-Relational Storage

Most IT professionals are familiar with the RDBMS. Relational databases store data in tables that are defined in advance. The definition specifies the columns that comprise the table, their data types, and so forth. The design is referred to as a data model or schema.

Relational storage is immensely useful, but it is not the only game in town. There are several alternative types of data storage including:

• Key-value stores store data in associative arrays comprised of sort keys and associated data values. These flexible data structures can be distributed across nodes of commodity hardware, and are manipulated using distributed processing algorithms (map-reduce). Hadoop functions as a key-value store.

• Document stores track collections of documents that have self-defining structure, often represented in XML or JSON formats. A document store may be built on top of a key-value store. MongoDB is a document store.

• Graph databases store the connections between things as explicit data structures (similar to pointers). These are stored separately from the things they connect. This contrasts with the RDBMS approach, where relationship information is stored within the things being associated (keys within tables). Neo4j is a graph database. 

Reasons for Non-Relational Storage

In the age of big data, organizations are tapping into these forms of storage for a variety of reasons. Here are just a few:

• No model: not requiring a predefined schema enables storing new data that has yet to be explored and modeled.
• Low cost: the cost of data management can be significantly lower than RDBMS storage.
• Better fit: some business use cases are a natural fit to alternative paradigms.

But don’t simply add a box to your data architecture for non-relational data. You must plan for specific usage paradigms, and make sure your architecture and processes support them.

Uses for Non-Relational Storage

There are six major use cases for non-relational data in modern data architectures:

• Capture  Non-relational storage facilitates intake of raw data.  New data sets are captured in non-relational data stores, where they become “raw material” for various uses. A non-relational data store such as Hadoop can be used to capture data without having to model it first. This is often referred to as a data lake. I prefer to call it a landing zone.

• Explore  Non-relational storage facilitates exploration and discovery. Exploration is the search for value in new data sets. Exploration applies analytic methods to captured data, often combining it with existing enterprise data. The goal is to find value in the data, and to identify things that will be worth tracking on a regular basis. 

• Archive  Non-relational storage serves as an archive. Data for which immediate value has not been identified is moved to an archive. From here it can be fetched for future use. Archiving data helps ensure the data lake does not become the fabled “data swamp.” An alternative to archiving unused data is simply to purge it.

• Deploy  Non-relational storage supports production analytics. When value is found in data, it is transitioned to a production environment and processes are automated to keep it up to date. Deployments range from simple reports to complex analytic models.

• Augment  Non-relational storage serves as a staging area for the data warehouse. In many cases, the insights gained from exploration prove valuable enough to track on a regular basis. Augmentation is the process of adding elements to a relational data warehouse that come from non-relational sources or analytic processes. A credit score, for example, might be incorporated into a customer dimension.

• Extend:  Non-relational storage expands what can be maintained in the data warehouse. Sometimes there is lasting value in non-relational data, but is not appropriate to migrate it to relational storage. In such cases, the non-relational data is moved to a non-relational extension of the data warehouse. Applications can link relational and non-relational data. For example, non-relational XML documents may made available for “drill down” from a dimensional cube.

In addition to these six primary use cases, non relational platforms may serve several utility functions. These include staging, data standardization, cleansing, and so forth.

Learn More

Join me for my course Data Modeling in the Age of Big Data, offered exclusively through TDWI. At the time of this writing, it is offered next at TDWI Chicago on May 11, 2017. You can also bring this course to your site through TDWI Onsite Education. For more information contact me.