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Data – Spreads like Kudzu, but Smells like a Rose

By: , Posted on: March 30, 2015

business intelligence guidebookData is everywhere in the enterprise, from large legacy systems to departmental databases and spread­sheets. No one controls all of it, it’s often duplicated erratically across systems, and the quality spans a wide range. You might say it spreads like Kudzu, but despite the tendency for chaos, the bulk of data is the lifeblood of an enterprise. If it doesn’t smell like a rose – that is, if it’s not clean, current, comprehensive, and consistent – the enterprise is in trouble. And this is why the architecture of data is so important.

You may be more accustomed to hearing the term architecture applied to homes and buildings, but it’s an important term for data, too. And while data geeks may be the only ones delving deeply into this topic, data architecture does indirectly affect everyone in the enterprise.

The data architecture defines the data along with the schemas, integration, transformations, storage, and workflow required to enable the analytical requirements of the information architecture. A solid data architecture is a blueprint that helps align your company’s data with its business strategies. The data architecture guides how the data is collected, integrated, enhanced, stored, and delivered to busi­ness people who use it to do their jobs. It helps make data available, accurate, and complete so it can be used for business decision-making.

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As I discuss in Chapter 6 of my book, Business Intelligence Guidebook – From Data Integration to Analytics. data architecture is important for many reasons, including that it:

-Helps you gain a better understanding of the data

-Provides guidelines for managing data from initial capture in source systems to information consumption by business people

-Provides a structure upon which to develop and implement data governance

-Helps with enforcement of security and privacy

-Supports your business intelligence (BI) and data warehousing (DW) activities, particularly Big Data

Just as there are different styles of building architecture, there are different styles of data architecture.  These have grown and evolved over time. The major competing data architectures are:

-Enterprise Data Warehouse (EDW)

-Independent data marts

-Enterprise data bus architecture—as discussed in the books by Ralph Kimball [1]

-Hub-and-spoke, corporate information factory (CIF)– as discussed in the books by Bill Inmon [2]

-Analytical data architecture (ADA)

I recommend the Analytical Data Architecture (ADA) for my clients. This choice is an architecture representing the evolution of best and pragmatic practices for BI, data integration and data warehousing. This framework leverages current and emerging technologies in its implementation supporting structured, semi-structured and unstructured data. The framework includes:

-Data architecture – what are the various data structures that may be deployed

-Data integration – workflow and processes

-Data governance – where it applies and does not

-Business intelligence and analytics – styles and processes

Perhaps you want to learn the whole story on what the ADA is and why it’s important for your BI efforts.  Please join me at Enterprise Data World 2015 in Washington, DC March 30 and I’ll make it all clear to you in my half-day workshop, Designing & Implementing an Analytical Data Architecture. You’ll learn:

-Underlying foundational concepts

-Best and pragmatic practices

-Use cases supported

-High-level architectural design

-Where and when to apply architectural components

-How to avoid data and integration silos

I hope you’re already registered to attend, because this very popular conference is already sold out! If not, visit their website to learn how to attend by video.

  1.  Kimball R, Ross M. The data warehouse toolkit: the definitive guide to dimensional modeling. Wiley; 2013.
  2. Inmon WH. Building the data warehouse. Wiley; 1991.

Rick’s book Business Intelligence Guidebook – From Data Integration to Analytics is available for purchase on the Elsevier Store. Use discount code “STC215” at checkout to save up to 30% on your very own copy!

About the Author

rick shermanRick Sherman (@rpsherman) is the founder of Athena IT Solutions. Rick has over twenty years of data warehousing and decision support systems experience and has been an expert instructor and speaker at numerous data warehousing conferences and seminars. He also teaches at Northeastern University’s graduate school of engineering and at client sites. He is the author of Business Intelligence Guidebook – From Data Integration to Analytics.

Connect with Rick online here:

www.datadoghouse.com

https://www.linkedin.com/in/ricksherman

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