20-01-2020 Door: Dave Wells

Four Steps to a Modern Data Management Architecture

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Data architecture is a challenging and sometimes confusing field. It can be confusing because data architecture means different things to different people and there are many kinds and levels of data architecture - enterprise architecture, technical architecture, etc. In this article, the focus is data management architecture with attention to the processes, data stores, data flows, etc. needed to collect, organize, harmonize, and utilize data to business advantage.

Nearly every organization today is facing the need to rethink and refresh their data management architecture. Data and technology advances of the past decade bring new opportunities and new complexities to data management, yet most organizations continue to work with turn-of-the-century architecture from the BI era. Patching new components onto the surface of obsolete architecture—a band aid and duct tape approach—is not sustainable and won’t readily adapt to changes yet to come. Still, many avoid stepping up to modern data management architecture because it is complex and difficult. The goal with this article is to provide guidance that helps to manage the complexities and minimize the difficulties.

Start with Business Capabilities
The first responsibility of data management is to enable the business to do the things that they need to do to get maximum value from their data. Defining data management architecture doesn’t begin with data, or even with goals like “cloud first” or “streaming first.” Those technical goals must be subordinate to business goals. Begin by working with business stakeholders to develop a list of data-dependent business capabilities. Make them tangible by identifying the kinds of data deliverables that enable those capabilities. Start with the reference list shown in the table below. Refine and customize to represent the needs of your business.

Business Capability          Enabled With             
Inform about … scheduled reports
ad hoc reports
self-service reporting
Inquire about … managed query
ad hoc query
Analyze behavior of … OLAP
self-service analytics
Track … against goals scorecards
Monitor current state of … dashboards
Send/receive alerts about … event monitoring
automated messaging
Examine alternatives for … analytic models
Simulate behavior of … simulation models
Explore patterns and trends of … data mining models
Discover hidden insights of … data mining models
Predict future state of … predictive models
Recommend decisions for … prescriptive models
Automate decisions for … prescriptive models
AI/ML models


Refine and customize by brainstorming to add capabilities not shown here, to change terminology to match language common to your organization, and to remove any capabilities that you don’t need now and don’t anticipate as future needs.

Explore Business Requirements
Good architecture is a tool that helps you to meet business requirements. It is impractical to undertake exhaustive and detailed business requirements analysis as part of architectural definition. You’ll get bogged down in requirements details and find it difficult to get back to working on architecture. Instead, work with representative groups of users to collect a few sample requirements for each business capability. For example:
Inquire about order status.
Inquire about employee compensation.
Analyze the behavior of marketing campaigns.
Track customer loyalty programs against goals.
Simulate behavior of P&L for new product launch at various price points.
Recommend decisions for discount offers to customers.
Automate decisions for next best upsell offer to customers.

Itemize Data Capabilities
Although not the place to begin, technical capabilities such as cloud capable are an important part of architectural definition.  Work with technical stakeholders to develop the list of essential data capabilities. Identify for each capability the architectural components that are needed to support that capability. Start with the reference list shown in the table below. Refine and customize to represent the needs of your organization.

Data Capability

Enabled With

Support all data use cases data consumption layer of architecture
Support all data latencies batch data capture & ingestion
changed data capture (CDC)
data stream processing

Support hybrid data ecosystem microservices architecture
containerization
cross-platform orchestration
Sustain legacy data warehouse value legacy warehouse ingestion into data lake
Easy access for all data consumers data access layer of architecture
data catalog
Work with all data types data source layer of architecture
data source connectors
SQL and NoSQL databases
Scalable and elastic cloud platforms
Smart and agile data pipelines data fabric & pipeline automation technology
DataOps tools and techniques


This reference table illustrates examples of needed data capabilities. You are certain to have new and different needs from those listed here. Two good resources to help you brainstorm data capabilities are Wayne Eckerson’s articles Ten Characteristics of a Modern Data Architecture and Ten Things Companies Want from a Modern Data Architecture.

Adapt a Reference Architecture
Now that you’ve expressed architectural requirements as business capabilities and data capabilities it is time to create a diagram that visually represents the architecture. This can be quite an intimidating task if you start with a blank page. A better approach is to work from a reference architecture and adapt it to support your list of needed capabilities. A reference architecture is a template that represents best practices and provides a starting place for architectural definition. A quick web search finds many reference data architectures. Remember that the focus here is data management architecture so be sure that the reference architecture that you choose represents data management best practices. Of course, I recommend Eckerson Group’s reference data management architecture (see figure 1).


FIgure_1.png


Figure 1 – Eckerson Group Reference Data Management Architecture.

Remember that reference architecture is a template—a starting place from which you’ll adapt to create the architecture that best matches your organization’s needs. As you prepare to adapt I suggest reading (or rereading) my article about Modernizing Data Management Architecture.  Then adapt by mapping architecture components to your lists of business and data capabilities. Remove any components that you don’t need and add any components that are needed. Adjust terminology to match the language used in your organization.

Finally, revisit your collection of example business requirements. Walk each example through the architecture to test that the data, the processing, and the use case are all supported by the architecture. Continue to adjust the architecture iteratively until all of the example business requirements are supported without compromising data capabilities such as low latency, large data volumes, high throughput, etc.

Dave Wells will present two keynotes during the Datawarehousing & Business Intelligence Summit:
'Cloud Data Warehousing: Planning for Data Warehouse Migration' on March 25th and
'Modernizing Data Governance for the Age of Self-Service Analytics' on March 26th.
Furthermore he will present an unique post-conference workshop: Cloud Data Warehousing on March 30th en 31st.

Dave Wells

Dave Wells is the Data Management Practice Director at Eckerson Group, a data analytics research and consulting organization. He is an internationally recognized thought leader in data management, a frequent speaker at industry conferences, and a contributing author to industry publications. Dave brings a unique perspective to data management based on several decades of working with data in both technical and business roles. He works at the intersection of information management and business management, where real value is derived from data assets.

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