Data Science

Data Quality is not a new issue.  What is new is the renewed focus that this issue is now receiving due to technological and regulatory changes.

The advent of the Internet and sophisticated data storage and analysis technologies have made Data Quality a highly visible issue for many organisations. Previously regarded by many as part of a back-office function, the processing of large volumes of data and the availability of timely and accurate data is now, in many cases, conducted in full view of an organisation’s stakeholders and is used in increasingly reduced timeframes to support business decision-making.    

The company's growth and acquisitions, as well as system changes, have resulted in inconsistent data, particularly in [insert appropriate process or area]. Some of this issue arises from the company's changing information needs as its business model evolves and becomes more sophisticated. The data in the company's systems is relied upon for management decision making and operations. As is, the quality of the company's information in this area is unknown, and without detailed analysis it is difficult for management to have confidence in the quality of decisions based on this information.

We recommend the company devote resources to analyzing and fixing, where appropriate, incomplete or erroneous data. There are specialists with more sophisticated tools who can analyze data and identify errors, gaps and potential cost savings for the company. Enhanced quality of data where management relies upon the data for decision making is essential to reduce the risk of erroneous decisions and to provide management an appropriate level of confidence in the information it receives.

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Data Quality Business Drivers and Challenges

Data Quality is not a new issue.  What is new is the renewed focus that this issue is now receiving due to technological and regulatory changes.

The advent of the Internet and sophisticated data storage and analysis technologies have made Data Quality a highly visible issue for many organisations.  Previously regarded by many as part of a back-office function, the processing of large volumes of data and the availability of timely and accurate data is now, in many cases, conducted in full view of an organisation’s stakeholders and is used in increasingly reduced timeframes to support business decision-making.

Coupled with this, regulations such as the Sarbanes-Oxley Act now mean that not only is the issue of Data Quality visible to an organisation’s customers, investors and suppliers but it is also being scrutinised by an increasing number of regulators.

The business drivers for a Data Quality initiative may include:

Governance and compliance initiative to meet current or future regulatory requirements

System implementation, e.g., ERP, CRM, Data Warehouse;

Use of new technologies e.g., Internet, XBRL to provide financial reporting;

Process improvement;

Supply chain initiatives;

Damage to reputation;

History of failed data conversion initiatives or information-related decisions;

Need to address Data Quality issues which are impacting business operations;

Merger, acquisition or reorganisation

Cost reduction.

These drivers impact the nature of the work that needs to be undertaken to address Data Quality for an organisation.

Some of the challenges that an organisation may need to address as part of a Data Quality initiative include:

Potential legal issues based on non-compliance or regulatory pressures;

Poor business decisions based on data duplication and inaccuracies;

Misleading data which leads to the questioning of the validity of financial and business reports;

Missed revenue and increased costs attributed to compromised data; and

In the past, Data Quality has not been viewed as a strategic issue by many organizations.  Today, however, many challenges specifically faced by CEOs, CFOs and CIOs are often impacted by the quality of data.

A recent survey conducted by PricewaterhouseCoopers with the assistance of the Gartner Group within the financial service sector found that:

Repetitive processes or poor data conversion due to inadequate system conversions or implementations.

Nearly half of the respondents estimated that their operational risk data is 50% accurate; and

50% of respondents had limited standard terminology to guide data collection processes;

40% of survey respondents could not measure the success of their Data Quality initiatives.

The internet, and information and communications technology more broadly, are fundamental enablers of the modern economy.

data-1

The spin-off benefit of this digitisation is data. The connected nature of our economy is producing a vast amount of data that helps us better understand how it works. During 2002, humans created five billion gigabytes of data – we now create that same amount every two days1. It’s estimated there are 4.4 zettabytes of data in existence, almost as many stars in the visible universe.2

The spin-off benefit of this digitisation is data. The connected nature of our economy is producing a vast amount of data that helps us better understand how it works. During 2002, humans created five billion gigabytes of data – we now create that same amount every two days1. It’s estimated there are 4.4 zettabytes of data in existence, almost as many stars in the visible universe.2

Data can be used to help businesses create new products and services that respond to customer needs faster than ever before.

Data-driven Goods and Services

   

Data can be used to help businesses create new products and services that respond to customer needs faster than ever before.

Economic Value From Working With Data

   

The benefits of data-driven innovation are rarely fully appropriated by the innovator themselves.

Spillover Benefits From Data-driven Innovation