What is Data Quality (DQ)?
Data quality is a perception or an assessment of data's fitness to serve its purpose in a given context. It is an ongoing and measurable effort.
Dimesions of data quality include:
||What is it?
||How correct is the data? Does it represent or measure what it is intended to represent or measure?
||Is the data available for access? How easily and quickly is the data retrievable?
||Is the data available can be regarded as true and credible?
||Is any data missed? Is all the data sufficient in breadth & depth for the task in hand?
||Is the data available presented in the same format? Is it represented compactly and is the volume of data available enough for the task in hand?
|Ease of Manipulation
||Is the data available easy to manipulate and apply to different tasks?
||Are the data languages, units, symbols etc. are clearly defined and easily interpretable?
|Relevancy / Timeliness
||Is the data up-to date? How applicable and helpful is the data to task on hand?
||Is the data restricted appropriately for confidentiality & privacy?
||How easily comprehendible is the data that is available?
||Is the data available beneficial and provides advantages from its use?
Within an organization, acceptable data quality is crucial to operational and transactional processes and to the reliability of business analytics / business intelligence (BI) reporting.
Data quality is affected by the way data is entered, stored and managed. Data quality assurance and management is the process of verifying the reliability and effectiveness of data.
Data Quality assurance and maintenance requires “Scrubbing” of data periodically – that is,
- Update of Data
- Standardizing Data (Consistent representation)
- De-duplicating Data (Eliminating duplicates)
to create a single view of data, even if it is stored in multiple systems.
Who needs Data Quality?
Quality of Data is of supreme importance for any business enterprise as inaccurate, inconsistent and poor quality data can affect business decisions in a huge way.
- Any enterprise who wants to avoid wrong business decisions, delayed decisions etc. due to poor quality of data.
- Data Quality is a prerequisite for all those enterprises who implement DWH or BI initiatives.
- Any organization in a data-intensive field like Banking, Insurance, Retailing, Telecommunications, and Transportation etc. would necessarily require Data Quality initiative.
- Any particular area or department
Why Data Quality?
- Low Data Quality leads to Operational ineffiency
- Low Data Quality constrains Decision making.
- Low Data Quality leads to high costs attributed to error detection, rework, delays in Customer service etc.
- Poor Data Quality leads to lowered ability to compete in the market.
- Data Quality is pivotal point for Knowledge Management in Organizations.
When Data Quality?
- When poor data quality increases the cost of doing businesses.
- When Low Quality data affects current revenues.
- When cost of error detection, correction etc. increases.
- When data quality problems that affect customers incur costs for fixing and compensation to customers.
- When data are not accessible or timely availability of data is constrained, leading to delay in decision making process.
- When data are deemed untrustworthy by managers for decision making.
Advantages of Good Data Quality
- Good Quality data enhances New Customer Acquisition and reduces Customer attrition.
- Good Quality data means more successes with Marketing efforts
- Good Data Quality enhances Data Warehouse utility.
- Good data increases ROI on IT Investment by limiting downtime and failed processes.
What do we do in Data Quality?
- Complete implementation of Data Quality Solution - Need Identification to Strategy to Implementation.
- Data Quality Assessment
- Data Cleansing Solutions
- CONSULTANCY & MANAGEMENT
- Project Management.
- Test Management.
- Subject Matter Expert Consultancy.
Who do we cater to in Data Quality?
- Any Organization that wants to implement Data Quality Initiative in order to stay ahead of competition and avoid impacts of poor data quality affecting their businesses.