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.

Data Alone Does Not Change People’s Minds

On NPR’s Hidden Brain podcast, cognitive neuroscientist Tali Sharot discusses the role of data in changing people’s behavior.

From Data to Action

The goal of analytics is to have a positive impact on the performance of your organization. To have an impact, you usually need to convince people to change their behavior.

This is required whether you want to convince a CEO to adopt a new strategy, a manager to allocate resources differently, or a knowledge worker to change their processes.

That’s why data visualization and data storytelling have become key skill sets for modern analytics professionals.


Data is Not Enough

How do you convince people to change their behaviors? Many analysts fall into the trap of letting the data speak for itself.

On a recent episode of NPR’s Hidden Brain podcast, cognitive neuroscientists Tali Sharot explains that data alone won’t do the job.  (The podcast is embedded above.)

Most people are familiar with the concept of confirmation bias, where we tend to accept data that supports our existing opinions. Sharot suggests there are ways to override this kind of bias.

Some key takeaways:

• People evaluate new information based on what they already believe
• Strongly held false beliefs are difficult to change with data
• Fear tends to lead to inaction, rather than action
• Positive feedback or hope is a powerful motivator if you want to change peoples actions

This is a fascinating listen for anyone interested in telling stories with data. Not only does it offer suggestions on how to change people’s behavior, it also illustrates the power of tracking results and making them available to people.

I’ve pre-ordered Sharot’s upcoming book, The Influential Mind. You should too!

Recommended Podcast Apps

I have received a lot of positive feedback from people who enjoy listening to the podcasts I mention on this blog. Several people have asked me how to listen to podcasts.

You can, of course, simply click on the play button in the posts. But you can also subscribe to podcasts using a smartphone app. This lets you listen on the go, and also notifies you when new episodes are available.

Here are two apps I recommend if you use an iOS device:

• Castro Podcast Player is perfect if you are new to podcasts, or if you subscribe to a handful of podcasts.
• Overcast: Podcast Player is good for people who subscribe to a large number of podcasts. It is more complex, but allows you to set up multiple playlists and priorities.

Further Reading

• Read (or Listen to) Discussions of Analytic Models (9/28/2016)
• What Hollywood Can Teach Analytics Professionals: How to Tell Stories (12/23/15)

Source: NPR’s hidden brain podcast for March 13, 2017