We continue the following five steps to continue the November 2023 blog, focusing primarily on IT, data, and systems.
Forced by the developments related to cybercrime, digitisation, data transformation and automation, many international businesses have started the journey to ramp up their enterprise data and IT governance efforts. Unfortunately, the many pitfalls and the success rate of these initiatives are both alarming and disappointing. In this blog, we identify some of the difficulties and consequences.
Many companies are involved with AI, big data and IoT to ensure competitive advantages from their digitisation journey but face tough fallouts due to cost issues and the massive amount of data that must be structured and controlled. In this situation, never lose sight of the ultimate goal: maintain competitiveness, meet customer (stakeholder) demands, and comply with the hard and soft laws. In response to this struggle, focus on the architect programs and systems to enable sustainability, offer structural credibility and finally take the decisions that focus on data and IT business integrity and integration.
6. Practicing poor data profiling and data mining
Data is unpredictable due to the constant change, requiring flexibility in the datasets. Therefore, in-depth data profiling from the start of the data transformation and digitisation journey will ensure fewer resources to update and clean the data later. Data profiling and structuring data sets must be a priority to comply with data privacy and develop state-of-the-art application integration. After ensuring that the datasets are clean focus then on that the processes are never stored in silos. Management must deliver the message that the structure in the datasets starts from the bottom of the data, and silos are not tolerated.
7. Collected unnecessary data
ROT (redundant, obsolete, or trivial) is digital documentation that an organisation retains even though the documented information has no business or legal value. The multiple requirements of data privacy and related global regulations (SFDR, CPRA, CDPA, EU-US DPF, CTDPA, UCPA, ADPPA, CSRD, DSA, DMA, OMG, the list increases…?) is the critical features of data governance and data retirement strategy. Besides the regulatory deletion and the data subject right to be forgotten, data at some point should be recycled as a routine to avoid issues related to the need for extra cycles to ensure ‘every data’ is in order.
8. Piling up unmaintained applications
If an app has flaws or grey areas continue working on its complexity so that the software at some point does not fail to deliver, and avoid to redevelop it from scratch. Ensure that the applications are well maintained and updated on time without fail.
9. Meet the compliance target
Data governance is needed to meet the regulator’s requirements, but avoid any shortcuts in a complex scenario. Business benefits must be in focus when satisfying the regulator’s demands and achieving long-term goals to get value out of compliance activities.
10. Relying solely on the IT team
The IT team normally handles and manage data governance initiatives, however consider other business processes to maintain a well-balanced approach. This will give depth to data applications as business can choose a better path of digital transformation to maintain relevancy and current trends. Above all recognise the importance of big data as an asset due to its ability to decide growth trajectory and ability to offer a competitive advantage and transforming data into consistent, correct and comprehensive information so that the corporate asset has rules, regulations, policies, and procedures.
Learn more at The 2023 Data Protection Day on the 30th of January 2023: Register here: https://us06web.zoom.us/webinar/register/WN_majQe97sQoau_OJj8rhFPg