maximize adaptability through data management documentation … · maximize adaptability through...

MAXIMIZE ADAPTABILITYTHROUGH DATAMANAGEMENT

DOCUMENTATION

By Dan [email protected]

@kiwidankun@dqmatters

SESSION OBJECTIVES

1. Clarify the similarity and distinction between Business Analysis and Data Management domains

2. Identify which Data Management tools enable the Business Analyst to be efficient and highly adaptable

3. Identify who has the information you need to get the job done

This session will use the data management lens to identify key datamanagement resources that business analysts can use to ensure theyare agile and nimble.

(c) Dan Myers, DQMatters.com 2016 2

A FEW ASSUMPTIONS BEFORE WE GET STARTED

• This presentation is geared to provide a high-level overview of the data/informationmanagement domain and synergies, similarities, and differences with the business analysisdomain.

• I assume that you know business analysis and its variants (e.g. Strategic Planning, BusinessArchitecture/Model Analysis, Process Design, Systems Analysis)

• Different domains may claim ownership of some activity which is an area of overlap(similarity) between other domains, but I believe we can still learn a lot from from each other


DEFINITION OF BUSINESS ANALYSIS

What is Business Analysis?

Definition: “Business Analysis is the practice of enabling change in an organizational context, bydefining needs and recommending solutions that deliver value to stakeholders. The set of tasksand techniques that are used to perform business analysis are defined in A Guide to theBusiness Analysis Body of Knowledge® (BABOK®Guide).”


Definition: “A business analyst is someone who analyzes an organization or business domain (realor hypothetical) and documents its business or processes or systems, assessing the business modelor its integration with technology.” (Wikipedia, 10/2016)

DEFINITION OF DATA MANAGEMENT

From Data Administration and ManagementAssociation (DAMA): What is DataManagement?

“Data management (DM) is the businessfunction of planning for, controlling anddelivering data and information assets. Thisfunction includes: The disciplines ofdevelopment, execution, and supervision ofplans, policies, programs, projects, processes,practices and procedures that control, protect,deliver, and enhance the value of data andinformation assets.” DAMA DMBOK, p.4.DMBOK 2 download URL


SIMILARITY AND OVERLAP BETWEEN BUSINESSANALYSIS AND DATA MANAGEMENT?


Areas of Overlap: Both define needs and recommend solutions, document business processes, query andanalyze data, and data anomalies and manage projects…etc.)

Business Analyst Data Management RolesA Collect/document systems requirements Focus on data requirements, not functionalB Develop Business Models Data Modeler: Develop Logical and then Physical Data ModelsC Document processes, systems Architect: Document, Design and Govern Systems and DataD Query, analyze and create reports Data Tester: Test and ensure data at rest; reports are correctE Analyze data anomalies, document data

quality issues, report data qualitydefects

DBA: Administer data and access securityDQ Analyst: Assess and report DQ levels, conduct root causeanalysis, propose resolution

F Document DM requirements DM Analyst: Implement DQ rules, controls, define datagovernance policies and procedures

G Project Management Manage DQ remediation projects

OpenDiscussion

DM RESOURCES AVAILABLE TO YOU DURING THE SDLCWaterfall SDLC

Initiation

Requirements

Design

Build

Test

Train

Deploy

Maintain

MetadataManagement

Business Glossary

Glossary, Search,Lineage, Impact Analysis

Reuse of Models/Data/Definitions,Clarity afforded Developers

Technical metadata developed andstored in enterprise repository

Pseudo code for SystemsIntegration Testing, Clarity of Test

Cases

Definitions and lineage documentssupport training

Comprehensive communicationabout what and where new data

exists

Glossary and lineage facilitaterelease processes

Data Governance

Executive support throughgovernance committee

Stakeholder groups known androles and responsibilities

documented

Data Architecture standards exist &rules followed

Technical naming conventions &use of non-Production data to unit

test

Stakeholder groups defines UserAcceptance Testing andcommunication targets

Governance team explainsescalation processes for data

quality issue escalation

Stewardship; cross dept/silocommunication ensures successful

rollout

Ensures currency of documentationthrough governance audits and

committee involvement

Information/Data Quality

Correct Data=> CorrectQuestion=>Correct Project

Reusable DQ Rules for FormValidation, IT completeness

controls

Design for reuse, consistency andflexibility

Use of real-life test data ensuresrealistic unit testing

System Integration Testing includesuse of Dimensions of Data Quality

Training includes data qualityawareness components and fitness

for use discussion

Communication about appropriateuse of data & context provided

with release & DQ control reports

Data Quality Scorecards for keymeasures and dimensions over

time

The focus of this presentationis to explain what DataManagement resources areavailable to you as a BusinessAnalyst.

Resources can be indexed bythe phases of the SDLC andsubdivided into the domainsof Data Management, such asMetadata Management, DataGovernance and InformationQuality.


DEEP DIVE OF A FEW DATAMANAGEMENT TOOLSHere is a list of some of the tools used in DataManagement. Note that this is by no meanscomplete, but rather illustrative.

1. Metadata Managemento Metadata Repository

2. Data Qualityo Data Profiler

3. Data Modeling & Designo Data Modeling Tool


DataManagement

DataModeling& Design

DataArchitecture

Data Storage &Operations

Data Security

Data Integration&

InteroperabilityDocuments &

Content

Reference &Master Data

DataWarehousing &

BI

Metadata

Data Quality

DAMA, DMBOK2 Knowledge Area Wheel

IDENTIFYING THE PEOPLE YOU NEED


DataManagement

DataModeling& Design

DataArchitecture

Data Storage &Operations

Data Security

Data Integration&

InteroperabilityDocuments &

Content

Reference &Master Data

DataWarehousing &

BI

Metadata

Data Quality

Database Administrationteam, Data modelers

Enterprise, System, and DataArchitects

• DBAs• Off-site document storage team (e.g. Iron

Mountain)• Records Management dept• Legal dept

• Information Security team• Records Management dept• Legal dept

• DW, BI team• Data as a Service team (e.g. FTP, API,

web-services

• Sharepoint, Wiki, Document storageteam

• Metadata Repository, Metadatamanagement team

• Customer or Product domainmanagement team

• Sometimes in Marketing dept

• Data Warehousing;Business intelligencedepartment

• Data consumers, knowledgeworkers; data scientists

• Data Governance team• Metadata tool

administrator• Data stewards

• Data Quality score cards• DQ Center of Excellence• DM Council

METADATA REPOSITORY


PrivacyLabels (PII)

TechnicalMetadata

Tables &Columns

Data Marts,Warehouses

Data Lineage &Impact Analysis

BusinessMetadata

BusinessJargon

Acronyms

ReferenceCodes

DataGovernance Stewardship,

Ownership

Data QualityLevels

Original Purpose;Fitness for reuse

LearningTools

BusinessGlossary

Metadata Repository isbasically a digital card catalogwhere documentation is storedabout an organization’s data.

DATA LINEAGE EXAMPLES


DATA MODELING TOOLS

12

How can you easily communicate a concept? Usean illustration or model. Speed that up with a toolthat outputs your model in a familiar notation.• Data Modeling Styles

o Information Engineering (IE), IDEF1X,Object Role Modeling (ORM), UnifiedModeling Language (UML)

• Levels of Modeling (from DAMA DMBOK, Chapter 5)

o Conceptual Model- A conceptual data model is a visual, high-level perspective on a subject area ofimportance to the business. It contains only the basic and critical business entities within a given realm andfunction, with a description of each entity and the relationships between entities.

o Logical Model- A logical data model is a detailed representation of data requirements and the businessrules that govern data quality, usually in support of a specific usage context (application requirements). Alogical data model often begins as an extension of a conceptual data model, adding data attributes to eachentity.

o Physical Model- A physical data model optimizes the implementation of detailed data requirements andbusiness rules in light of technology constraints, application usage, performance requirements, andmodeling standards.

DATA QUALITY-


Data Profiling process of using software tools tocollect qualitative and quantitative informationabout the characteristics of a dataset (such as:count of cells null for a column,average/min/max/mode for numeric data,distinct list of categorical data).

Dimensions Of Data Quality

http://dimensionsofdataquality.com

Completeness Accuracy

Lineage

Timeliness Representation

Integrity

Currency

Consistency

PrecisionPrecision

Data Quality “The quality of data is defined bytwo related factors: how well it meets theexpectations of data consumers (how well it isable to serve the purposes of its intended use oruses) and how well it represents the objects,events, and concepts it is created to represent.” -

--Laura Sebastian-Coleman*

*Measuring data quality for ongoing improvement : a data qualityassessment framework, Sebastian-Coleman (2013), p. xxx (28)