Enterprise agility is the key to navigating disruption and building resilience, and it depends on the organisation’s ability to use data.
of G20 companies are using AI and analytics to monitor risk scenarios
G20 companies have continued to invest in digital solutions despite financial pressures
As market leaders and agile start-ups drive their growth by leveraging analytics, other organisations strive to catch up. In early 2020, 97% of business leaders were under pressure to integrate innovative technology into their business model, and more business leaders were involved in data and security at their firms (67%) than in marketing (62%) or finance (61%).
As our Q4 2020 FTI resilience data shows, the pandemic has been a catalyst for translating this pressure into action: Over 80% of G20 businesses have accelerated their digitisation since the crisis began. This drive has been enormously challenging for businesses, with over 60% saying that they have been unable to effectively ramp up from a digital perspective. The transition to a post-pandemic world therefore presents opportunities for firms which can leverage their data to drive innovation.
Data-driven businesses will also be well equipped to face future crisis scenarios. In an unstable, constantly changing business landscape, the ability to analyse threats in real time is the hallmark of resilient firms – over 90% of G20 businesses have felt forced to implement such measures as a direct result of the pandemic.
The competitive drive to collaborate in new digital ecosystems and supply chains makes the adoption of “digital first” business models a core enabler of resilience.
It is concerning, however, that almost 1 in 3 G20 companies have lost customer data or significant intellectual property as a result of cyber attacks since the start of the pandemic – already a greater figure than the entirety of 2019. Safeguarding data is critical to building resilience across business processes and to protect trust. Regulatory action goes some way to ensure industry-wide protection, but this brings its own risk for firms: over 80% of firms either have been investigated on data privacy, or expect to be in the next 12 months.
In addition, companies are under pressure to ensure that data and analytics are used in a way that not only complies with evolving legislation and regulation, but is also regarded as responsible and ethical by stakeholders – including customers and the general public, who will judge “fairness” more rapidly and potentially harshly than regulators and governments. To mitigate risk, a comprehensive view of the business and its environment is required.
Democratise data access. Maximising agility depends on pervasive use of real-time data to drive decision-making. This in turn requires actionable data to be available to front-line workers and support functions, not just to users of executive dashboards.
Convert data into understanding. Data analytics can convert raw data into signals that alert the business to new behaviours or patterns, identify risks such as unprotected personal data, and highlight problems and opportunities. With the help of AI, these signals can describe the past, predict likely outcomes, and prescribe appropriate courses of action. By consolidating disparate data sets and analysis methods into a single view, businesses can understand the bigger picture.
Use all relevant data sources. Looking beyond traditional financial data can transform a business’s understanding of its competitive environment and reputation. For example, clickstream data sheds light on the customer journey, and on barriers to completing a purchase. Social media conversations reveal customer and public perceptions, as well as evolving market needs and societal trends. Open Source websites provide a window to regulatory thinking and research. Employee chat exchanges can be monitored to help the business guard against misconduct.
Introduce effective governance. Data breaches and misuse cause untold reputational damage. Information governance is the way to manage such risks. Personal or high-value data can be protected and secured, with measures like data tagging and pseudonymisation enabling safe data exploitation. Data that no longer has value to the business can be identified and removed.