The reversal from information scarcity to information abundance and the shift from the primacy of entities to the primacy of interactions has resulted in an increased burden for the data involved in those interactions to be trustworthy.
If service and results are substandard or wrong, the reputation of the company can more quickly than ever before become tarnished because of the rapid, easy and uncontrollable dissemination of information on the internet.
Because of the increased volume of customer facing business now conducted over the web, the risk of populating and perpetuating incorrect data is substantially greater, while its accuracy is more critical.
Using digital data in reliable and responsible ways is an integral part of any company’s data strategy and requires significantly more focus on data governance. Data governance should ensure accurate data definitions and dissemination. However, it must also limit the potential for misuse of personal data and the risk of undermining public trust.
Techniques, methods and tools are essential to safeguard fair, accurate and confidential use of data that is findable, accessible, interoperable and reusable—and they should be applied universally throughout the enterprise.
The responsibility of ensuring a successful strategy and the means to deliver a data governance program falls to the office of the CDO or equivalent Data Leader. The office of the CDO, essentially, is the largest stakeholder in the governance of a company’s data.
The question that needs to ask is: ‘Does our existing data management infrastructure meet the needs of what the new requirements are for this digital age?’
The Critical Importance of Data Governance
Information governance is still maturing at the majority of companies, with several forming data governance councils or benefitting from appointing a CDO. Companies must define and implement information policies, definitions, and rules that best support efficient business processes. They must also have trust in the metadata itself as well as the resulting reporting.
Data is at the core of digital transformation, but before considering new technologies, companies must analyze their current infrastructure to understand the state of their data. Without accurate data, companies can encounter loss and suffer from a lack of brand value. Leveraging accurate data, however, can lead to drastic improvements in revenue and overall business growth.
DBP (Data for Business Performance) Institute cites the following statistics across business enterprises:
- An average user spends 2 hours a day looking for the right data (Mckinsey).
- Just 3% of the data in a business enterprise meets quality standards (Harvard Business Review).
- Bad data costs 12% of the company’s revenue (Experian Data Quality).
- 27% of data in the world’s top companies is flawed (Gartner).
- 60-73% of data in an organization is never successfully used for any strategic purpose (Forrester).
- 84% of digital transformation initiatives in business enterprises fail (Forbes).
These are alarming statistics—and the reality is that almost all large companies have data silos that contain inconsistent data and classifications.
The 2020 State of Data Governance and Automation report includes other key findings:
- 25% of respondents say length of project/delivery time is the most significant challenge, followed by data quality/accuracy at 24%, time to value at 16%, and reliance on developer and other technical resources at 13%.
- 70% of respondents spend 10 or more hours per week on time-consuming data-related activities. Searching for data being the biggest culprit.
And discovering data that is incorrect increases the time spent even more.
Because of this reality, data governance has become one of the core elements of an overall data management strategy. When implemented, it provides greater efficiency, better quality and compliance and enables improved decision-making and business performance.
Gaining Control of Enterprise Metadata
Successful data governance is built upon a strong standards-based framework. A key goal is to ensure that all data is defined in a consistent and standardized way. While policy can guide the overall standardization process, efficient and usable design technology, methodologies and enforceable best practices are also required.
Data modeling tools and data modeling methodologies are well proven and are generally the way in which organizations accomplish effective data design. The challenge enterprises struggle with is implementing critical standards and best practices that are effective and easy to use.
Enterprises that have established these critical standards and best practices have seen increased trust in their data, providing the business with the confidence to leverage their data to the best of its ability, and deliver the controls that are key to a successful data governance program.
The CMMI website states, “52% of C-Suite executives have dismissed data because they couldn’t understand it.”
The fault lies in the absence of a well thought out data strategy. This includes a sufficient data governance program that features the tools necessary to implement a reliable data management infrastructure to deliver trustworthy metadata.
With so much at stake, the challenges have increased significantly in our Digital Society. In reality, the office of the CDO is more essential than ever before. It is the critical group required to ‘oversee’ our data management efforts. Rigorous data governance and management is the critical foundation to enable companies to thrive in this ever-growing data environment.
