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Beyond Bi
Data Quality: turning insight into action
 Consider
an enterprise platform for profiling, cleansing, augmenting, integrating and
monitoring data to create consistent and reliable information, says George
Varghese
Organisations depend on data. This is an unstated truth. However, its significance
and the impact of its availability and quality are often ignored. Unfortunately,
data quality rarely gets the attention it deserves. No one wants to consciously
admit that their business decisions are based on inaccurate or incomplete data.
It transpires that the majority of organisations have made little effort to
determine the severity of data quality issues and its impact on their businesses.
Many organisations are not even aware of their data quality problems. Business
users may not know their data is inaccurate until something goes wrong. When
corporate projects fail to produce expected returns, the problem can often be
traced to redundancies and inconsistencies in data.
Ensuring data quality
Data is a key strategic asset, so ensuring its quality is imperative. Data quality
not only provides enterprises with a ‘single & true’ vision of
the customer, but also helps decision-makers get the high-quality information
needed to determine how best to allocate resources, manage costs, and do more.
Inaccurate and unreliable data affects all organisations. And with data volumes
constantly on the rise, it’s no wonder that improving data quality has
become a concern for most. There could be many reasons why the quality of the
data that organisations collect and analyse is so poor.
Organisations collect data from various sources: legacy databases, external
providers, the Web, etc. Due to the massive amount of data variety and sources,
quality is often compromised.
These could arise from data entry errors, faulty data acquired from an outside
source, and combining good data with outdated records. Integration of systems
with different data standards could also be impediment to streamlining quality
data. But none is more compelling than the simple fact that organisations rely
on many different data sources to obtain a holistic view of the business.
With isolated technology platforms across various departments,
and disparate data stored across several servers and silos, organisations today
deal with similar pain areas. It is therefore important for them to integrate
and augment diverse resources within an existing environment, to turn vast amounts
of data from any source and across channels into the usable knowledge they need
to make better decisions.
Data Quality Solutions
What is needed therefore to drive growth is a Data Quality Solution (DQS) that
easily integrates and leverages existing computing and operational environments
across all platform and storage facilities while improving data quality through
the entire IT process. It is imperative that data is cleansed before loading
it into the data warehouse so that further downstream analyses and decisions
are based on reliable information.
A DQS also provides the ability to analyse and assess the quality of data across
the enterprise. Apart from exponentially improving the quality of the data,
a DQS enables organisations to bring together a 360-degree view of themselves,
their suppliers and customers. It also provides them with the ability to cleanse
and eliminate duplicate data quickly and effectively—which translates into
faster return on investment.
An essential component of a DQS is its relevance to various markets and their
nuances. Even geographic locations have their unique characteristics that must
be considered when putting in place a DQS. For example, a DQS designed for the
US market may not be able to compute distinctive Indian names, addresses, phone/mobile
numbers, cities, states, districts, postal codes, etc.
It is therefore necessary that the DQS should have an Indian knowledge base
with Indian characteristics for ‘names,’ ‘addresses,’ ‘e-mail,’
‘organisations,’ ‘phone/mobile numbers,’ ‘city,’
‘state/province,’ ‘postal code,’ ‘global definitions
for names, addresses, Web site, date, text and account number,’ that are
specially customised to operate on Indian data.
It is however important to note that the chosen environment for data quality
must be low-risk, easy to build and manage, and flexible to change with evolving
business needs.
Treating data right
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By addressing data quality at the source, data cleansing
becomes a proactive rather than a reactive process.
This will go a long way in establishing a framework that brings together
a 360-degree view of the organisation,its suppliers and customers
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Data is a vital resource, and it should be treated as a key
strategic asset in order to obtain a reliable and accurate view of your enterprise.
Ignoring data quality is costly, and it affects the organisation adversely.
Data quality is a continuous process. By addressing data quality at the source,
data cleansing becomes a proactive rather than a reactive process. This will
not only help in exponentially improving the quality of the data across the
enterprise, but will go a long way in establishing a framework that brings together
a 360-degree view of the organisation, its suppliers and customers.
Poor data quality often has a greater effect on companies than they realise.
In most cases, the costs associated with poor data quality are not only ignored
but subsumed into the overall category of the cost of doing business. Yet, real
costs are associated with nonconforming data—and they add up. However,
a business can eliminate these significant costs by instituting a data quality
improvement programme that implements a data quality solution as a core component
of its business intelligence strategy.
The author is Head, Pharma & ITeS, Marketing & Alliances,
SAS Institute (India)
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