CIOs say that for translation of good intentions into a scalable strategy that is deeply integrated with business enterprise, a robust data-based policy is a must
Enterprise leaders say that balancing shareholder interest in the partner, social interests, community, and employees is the highest priority. As more organizations adopt the latest workplace measures to re-purpose supply chains, business operations, and protect employees to help countries manage the crisis, the need for environmental, social, and governance goals has increased.
Some enterprises have managed to accelerate their business growth by building on customer trust and loyalty by addressing social issues, but many other enterprises fail to do this. Such failure is generally attributed to elements ranging from inflexible corporate structures to regulatory barriers. CIOs believe that the highest barrier that most enterprises end up overlooking is the lack of data.
Enterprise leaders say that while organizations have years of experience in collecting and analyzing data for business operations, they often lack a set and defined data-driven policy for collation and translation of these insights. They could be driving results that are capable of providing solutions for issues like human rights, health, future of the workforce, and responsible innovation practices.
CIOs feel that organizations can comprehensively understand and combat these issues when only when they have the proper data and have the knowledge to deploy it. Lack of accurate data can end up misleading the enterprise and its strategies.
Due to a lack of data-rich, enterprises often struggle to promote effective regulatory and policy support. As a result, they are unable to quantify their progress with regards to the ESG goals. Ultimately, they cannot provide compelling results to stakeholders like potential investors, employees, shareholders, and customers. CIOs believe that an enterprise needs to follow important principles when building comprehensive data to meet the set goals.
Clear outlining of goal to initiating the data-discovery process
As an initiating step, enterprises need to clearly define the outcomes they wish to achieve the required performance indicators and then work in reverse to decide how to achieve the relevant goals. This procedure will help start with the data-discovery procedure. It includes identifying what kind of data is required and where they can obtain it.
Looking into data to create the coalition for change
CIOs point out that overcoming societal issues is not possible for an enterprise by itself. They need the partnership of an entire ecosystem, including nonprofit startups, academia, governments, and even rivals.
Organizations need first to build their core competency that can be used to achieve the social goals before they can build the ecosystem. They also need to understand how competence can collate with a bigger ecosystem to reach the expected outcomes.
Data will help identify the inter-dependencies. It includes both potential bottlenecks and synergies and helps them use either outside-in or inside-out approach towards the innovation operations.
Building a data-driven organization structure
CIOs believe that partnerships will help enterprises overcome some obstacles; however, they are still required to develop new expertise in data science. It includes analyzing not only the changing temporal and special aspects of data for developing dynamic solutions. They also need to know how to blend different data varieties like emotion, voice, vision, etc., for better insights.
Enterprises are also required to develop skills that can transform insights into results. They need to promote an incentive and corporate structure that holds project managers accountable for reaching the ESG goals. Only when an enterprise starts to assess the impact of each project on the set goals will they start considering more deeply how to track relevant data and analyze change.
Adopting the ethics first approach to data
Enterprises leaders say that it’s preferable to take values-oriented measures to analyze how data gets implemented in the innovation context. Data needs to be used to include and not exclude the masses. As technology evolves, organizations need to ensure that safeguards exist that can develop responsible software devoid of unintentional or intentional biases.