Data Governance: the key driver of digital transformation

Data Governance the key driver of digital transformation
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According to an IDC report, organisations lack knowledge in practical data governance activities, with 30% of efforts on data-related activities unsuccessful. As the amount of data continues to rapidly increase, organisations must address this issue if they are looking to remain competitive and drive digital transformation, as this is underpinned by effective management of data.

Governing with data should be a focus of attention within all organisations as data governance and enablement initiatives revolve around the overall management, extraction, usability, and integration of data in an enterprise. While there are many data management and data platforms available, data enablement requires governance as the foundation.

Enablement focuses on leveraging high-quality data with the help of data intelligence tools so that insights are readily available and can be utilised to enable growth. Hence, it is vital for organisations to lead with data platforms for digital governance and transformation. An organisation must also remain compliant with new regulations under the General Data Protection Regulation (GDPR) and the Australian Privacy Principles; thus, balancing transformation with privacy regulations and data protection.

To understand how data management software enables the cycle of digital transformation and better data governance in organisations, let us delve into how digital transformation can be implemented.

Data intelligence software capabilities

Data Intelligence Software is practical to gain insights considering that 80% of the time organisations spend is on data discovery, preparation, and protection, while only 20% on actual analytics and insights.

Data Intelligence Software answers all the W’s of data, including:

➢ Where is it, and where did it come from?
➢ What does it mean?
➢ Who can access it?
➢ When was it last validated?
➢ Why does it exist?
➢ How can it be consumed?
➢ What are the relationships inherent in data?

Data intelligence software leverages the data based on extracting and harnessing metadata in addition to data cataloguing, master data definition and control, data profiling, and data stewardship software. Best known as traditional metadata management software, data lineage software focuses on tracing the series of data and is focused on locating the lineage of data used in data integration and BI reporting. Harnessing the power of metadata that is used for analytics can be powerful for strategic and operational decision making.

Although intelligence solutions deliver functionality and support data enablement through governance by giving users access to self-service and quality data management, data intelligence software cannot enforce data security. After the introduction of the GDPR in the EU, strict requirements on data have come into effect to protect personal data and privacy. Non-compliance with GDPR will result in fines up to 4% of an organisations annual revenue. Therefore, developing a data governance plan, deploying data security software to enforce security policies and protecting data to avoid breaches.

What is data governance
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What is data governance?

Data governance is strategic and involves managing the operation of data activities across the organisation. Governance focuses on cultivating a culture where data can be utilised most effectively. Management must be implemented through data intelligence software, corporate policies, strategies and organisational architecture.

Data enablement involves direct management of the actual data. To achieve data enablement, organisations need data that is trustworthy, timely and readily available. Data intelligence software supports enablement through governance as it answers the 5 W’s of data. When data enablement is implemented, organisations become more efficient and capable of innovation in data governance.

Many organisations with failed data governance attempts can point to outdated or absent data intelligence, captured and maintained manually. The IDC survey found that most organisations used manual methods of cataloguing data, not only is this inefficient, but there are also risks of inaccuracy. As such, data intelligence software reduces the risk of error, as manual-activities and data-related risks are minimised.

Additionally, as people power data intelligence software, the software can only provide context accordingly. An increase in governance will empower organisations internally, increase productivity and knowledge in data-related activities. Complying with regulatory measures becomes more efficient, and data integrity improves.

According to the results of IDC’s Data Integration and Integrity End-User Survey, organisations lack knowledge of data intelligence and software needed to undertake data governance activities with only 20% of time spent on data output of actual value. Data Intelligence Software reverses the 80-20 rule, in turn, reducing unproductive efforts, helping organisations better manage and govern with data to overcome the challenges of digital transformation. As such, it is critical for both business and IT professionals to collaborate in the process of deploying data intelligence software.

Data enablement program

Data is the pinnacle of digital transformation; establishing a data enablement program starts with planning, people, process, and then data and technology. In this age of digital transformation, data enablement alone is insufficient; governance requires people who are empowered and have an understanding of data intelligence. This can be implemented through program management of people, data, and processes which support data enablement initiatives.

The process of data enablement will include setting principles and guidelines, defining standards and objectives, implementing controls, and defining issue resolutions. Organisations approaching data enablement should develop an agile roadmap, considering business and other market conditions as well. A collaborative effort between business and IT is required to achieve data enablement, literacy and ethics across the organisation.

Key takeaways

Data intelligence is not new; organisations should perform an inventory of currently deployed technology, identifying what technology is needed to support the objectives of the program. If an organisation has a data integration software but has not implemented any data intelligence software, there may be opportunities to leverage existing data. By defining the program objectives, which should also be aligned with business objectives, enterprises can improve outcomes and work to achieve digital transformation.

To help your organisation achieve digital transformation, download the IDC report for more information on data governance and enablement initiatives.

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