Today’s digital transformation journey is fraught with disruptive bumps such as sustainability, data-hungry AI tools and headline-grabbing data breaches…
In an era when ESG goals and sustainability issues are high on the priorities of governments and enterprises in Asia Pacific, most organizations’ business transformation strategies need tweaking.
Digital transformation is a never-ending journey, as technology and innovations never stop; in the midst of this journey, how we manage, process, store, use and share data becomes critical.
DigiconAsia sought out some insights from Chua Chee Pin, Area Vice President, Greater China, Korea, Japan and ASEAN, Commvault:
Organizations have had to consider simplification and security when it came to their data. However, sustainability has increasingly been added to the mix. In light of this, what can organizations do to ensure that their business continues to grow without straying from their goals, while playing their part for the environment?
Chua: In recent years, with Environment, Social and Governance (ESG) added into the mix, organizations must pay more attention to sustainability and how they can ensure business continuity while playing their part for the environment. This can start with organizations conducting sustainability assessments where comprehensive checks are performed to understand the organization’s environmental impact across its operations, supply chain and products.
Dark data, one of the key attributors towards increased carbon footprint, is often used once and then forgotten, like the duplicate images you have saved in your drive, outdated spreadsheets from years ago, geolocation data, and old financial statements, among others. This unwanted information is tethered to reality by the energy used to store it.
Therefore, unless organizations train their employees on good data habits, there will be 91ZB of dark data in five years – over four times the volume we have today, leading to unsustainable data processing practices. Needless to say, this calls for an acute need for organizations to eliminate this dark data pollution and adopt a greener approach to data management to save energy and ultimately our planet.
While companies are making the shift to cloud to mitigate the environmental impact, it is still going to be a long time before any organization becomes a 100% cloud company. Therefore, data centers cannot just yet be completely disregarded. One of the key steps businesses can integrate in their approach towards creating a green technological landscape is to reduce the amount of Redundant, Obsolete, or Trivial (ROT) data that they store. AI algorithms can be leveraged here to analyze data types to ascertain usability and put it to work to benefit the company while removing the irrelevant data.
To ensure that their business continues to grow while playing their part for the environment, organizations can also collaborate with stakeholders — customers, partners and industry associations to join forces on sustainability initiatives. It will be more effective when everyone is working towards a common goal. They will be able to share best practices, collaborate on research and resolve sustainability challenges together. At the end, embracing sustainability not only helps reduce environmental impact but also enhances reputation, attracts customers who value sustainability, fosters innovation, and creates long-term value for the organization and planet.
One of the challenges of driving a culture of innovation is that it needs to be built. How would you recommend business leaders drive a culture of innovation in a sustainable way?
Chua: Fostering such a culture helps organizations move through innovation cycles, explore new possibilities, and remain competitive in today’s rapidly changing landscape. It is also known to attract talent and encourage employees to think creatively, explore new ideas, and embrace change — which is crucial for survival and long-term success in a dynamic business environment.
The first step towards driving a culture of innovation is to foster a growth mindset. Leaders need to encourage a curious mindset within the organization where the employees are motivated to learn, adapt, and embrace new ideas. For example, with organizations moving their data from off-prem to on-prem, employees must be willing to learn and accept the new platforms and how their data is now stored. In addition, employees should also be encouraged to challenge the status quo and take calculated risks for the ultimate goal of data protection.
Business leaders should also establish channels for open communication and collaboration across teams and hierarchies, and foster cross-functional collaboration, knowledge sharing, and idea generation. Brainstorming sessions or innovation challenges should be added to facilitate collaboration and ideation with rewards added to attract participants. For example, organizations who wanted to enhance data protection worked with third party vendors to manage and protect their data on all levels, and also reward employees who practice good digital hygiene.
Data plays a pivotal role in the process of digital transformation and has been identified as a priority for many business leaders, but most organizations are unable to fully utilize it. What are some better ways organizations can derive value and actionable insights?
Chua: While data has become a priority for organizations, many business leaders are unsure about what to do with the massive amount of data, or how to classify them for use. In worst-case scenarios, they are uncertain of how much data they even have.
To derive value and actionable insights, organizations need to first establish strong data quality and governance practices to ensure the accuracy, consistency, and reliability of data. This also includes data cleansing, validation, and verification processes to maintain the data integrity of the company. Data governance frameworks should also be added, which define the roles, responsibilities, and processes for data management to ensure that no gaps are made.
Next, to offer a bird’s eye view of all the data, organizations can integrate information from diverse sources and consolidate them into one centralized data warehouse. This offers business leaders a holistic view of the data to analyze and draw insights for business innovation. While integrating data, they need to ensure that the processes are efficient, scalable, and well-documented with no stones left unturned. For organizations that do not have these specializations, they can garner the help of third-party vendors to consolidate, filter, and analyze data.
Organizations can employ advanced analytics techniques and machine learning algorithms to extract insights from data, while reducing the risks of human error. This can include predictive modelling, clustering, regression, and anomaly detection. Business leaders should utilize tools that help enable sophisticated analysis and automate repetitive tasks.
Conducting risk analyses help organizations identify and secure sensitive files in the environment, thus preventing cyber exposure and potential data exfiltration. It can also provide organizations with insights to drive informative actions for better data utilization. Using the approaches mentioned above, organizations can unlock the value hidden in their data and derive actionable insights to make informed decisions for their business.
Lastly, beyond technology, fostering a data culture across the organization is an integral part of building a culture of innovation. An organization which adopts data culture across business functions and hierarchies and uses its data in its decision-making process will better appreciate its relevance and benefits. Leaders and human resources play an important role in creating such a data culture.
While there are many tools and professionals to help organizations gather data, we need to admit that value judgement exists. What is value judgement in data analysis and how can organizations navigate through this bias when dealing with their own data?
Value judgement in data analysis is a trending topic. It refers to evaluating whether something is good or bad, based on subjective opinions, biases or preconceived notions. Whenever humans are involved, we can expect biases to come into play — from personal beliefs, cultural influences or organizational biases.
To avoid plummeting into this prejudice when dealing with data, organizations need to set clear objectives for the data analysis and the specific questions that they are seeking to answer. For example, some enterprises have decided to leverage on AI and data software for data analysis to reduce the likelihood of human bias influencing the interpretation of data.
Next, organizations can employ rigorous and well-defined methodologies for data collection, processing, and analysis. This includes ensuring data quality, employing statistical techniques, and adhering to established best practices. Transparent methodologies can help to minimize the risk of bias and provide a foundation for data-driven decision-making.
Lastly, organizations can consider partnering with a third-party vendor who can offer a fair and non-biased view in data analysis. Getting a point of view from vendors allows organizations to tap into specific skillsets, experience and innovations that may not be readily available within the company.