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DESCRIPTION:Click for Latest Location Information: http://edw2024.dataversi
 ty.net/sessionPop.cfm?confid=159&proposalid=14945\n\nAs we all know, data i
 s a new fuel and we also know we should not use bad-grade fuel in our 
 machines, similarly, clean data is very critical to any organization a
 nd bad data should not be consumed. In fact, the life of the data is more t
 han the life of the fuel, fuel can be consumed once and we should be recycl
 ing the same fuel that was consumed for any other purpose as it can damage 
 the machine. Data on the other hand can be re-consumed many times with diff
 erent perspectives.\n\n	Overview of Data Governance and Data Quality\n
 Data Quality Management Framework\n	Data Quality Maturity Level\n
 Proactive Data Quality in an Agile Environment\n
 Detection of Data Anomalies and Its Remediation\n\nData is at the heart of 
 almost every modern enterprise. Information drives sales, enables customer 
 insight, and generates growth through repeat business. It is also an essent
 ial component of good customer service, with few organizations managing to 
 offer a differentiated service without good data quality\n\nTo understand t
 he role that robotic process automation can play in improving data quality,
  it is helpful to understand some of the root causes of poor-quality data. 
 Though numerous, these reasons often include:\n\n	Simple human error.\n
 Inadequate training and/or poor process adherence by users, particularly wh
 ere organizations have to respond in an agile manner to seasonal business p
 atterns, and where the use of temporary staff is commonplace\n
 The existence of multiple systems with potentially overlapping data and a l
 ack of referential integrity between records across the systems\n
 Business processes containing many manual steps, often within outdated or u
 nintuitive systems designed for a different set of requirements, thus intro
 ducing numerous opportunities for human error\n
 System workarounds and reuse of data fields intended for another purpose (s
 uch as a notes field being used for mobile phone numbers), often with poor 
 data definition and formatting - and with limited consistency and adherence
  by users Infrequently used data and lack of opportunity to maintain or upd
 ate it\n
 Inappropriate incentivization or performance measurements of staff activity
 , leading to rushed or poor-quality work\n
 Equally, the lack of incentivization of operational staff to improve data q
 uality problems, even where the problems are immediately apparent and the o
 pportunity is present\n
 Incomplete levels of integration between systems\n\n\n\n\n\n\nPreventing da
 ta problems through validation robotic automation can help to reduce the in
 cidence of bad data by identifying and intercepting poor data quality at th
 e source before it enters business systems.\n\nThe validation features, des
 cribed in more detail below, allow for a multitude of mechanisms, including
 :\n\n
 Rules-based validation of input data, checking input formats, data lengths,
  data types, etc.\n
 Transformation of data into the correct format – e.g., translation of
  dates from European format dd/mm/yyyy to US format mm/dd/yyyy\n
 Verifying the presence (or absence) of data\n
 Verifying low-level attributes – e.g. length, character set, data che
 cksums (e.g. MD5), etc.\n
 Complex pattern matching and transformation according to definitions are ex
 pressible using wildcards and regular expressions\n\nFrom a workflow and op
 erational standpoint, software robots allow operational teams to leverage t
 he Pareto principle: Robots can clear the bulk of the workload whilst ident
 ifying and referring data exceptions to human teams. This elevates the role
  of the operational agents from performing mundane repetitive tasks to high
 er-value activities with greater job satisfaction and increased returns for
  the employer.\n\nData governance is a system by which the entities (Orgs, 
 Functions, Data, etc.) are structured, directed, and controlled for decisio
 n-making, accountability, authority, and compliance.\n\n\n\n\n
DTSTART:20240327T140000
SUMMARY:Data Governance and Data Quality in an Agile Environment
DTEND:20240327T144459
LOCATION: See Description
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