British businesses, and of course businesses all around the globe, are facing the biggest challenges of a generation. For example, geopolitical uncertainty is driving supply cost pressure, and the long-term effects of COVID-19 are still being felt in conjunction with a global digital skills shortage.
At the same time, due to digital acceleration also driven by COVID-19, there is more data and more potential in AI than ever and businesses worldwide are investing billions to unlock it, bringing data at the heart of new business models and technologies.
Many large organisations worldwide have appointed a Chief Data Office (CDO) to oversee and handle data as an asset, recognising its strategic value for the business. A Qlik research predicts that the CDO will be a stable role within organisations by the next decade, and also revealed that 76% of employees are investing their own time and 58% their own money to improve their data (HRNews, 2022).
AI investments worldwide have grown at a stunning rate of 20.7% in 2021 according to the IDC. 56% of organisations still have a low level of data quality (Experian, 2022) and are grappling with legacy data, technology landscape and skill shortage, e.g. many banks struggle to achieve a single customer view, many utility companies struggle to benchmark and drive-down OPEX, many retail and consumer product companies struggle to predict and manage supply costs.
Amidst all of this, the challenge can seem daunting and it can be difficult to know where to begin. Use this resource developed by Infosys to kick-start your data transformation.
Discover ten principles of a robust data strategy
1. Inspire your business with a vision
Begin with an exciting business vision for an Intelligent Enterprise which places data, analytics, and AI at the heart of how business is done. Align compelling long-term business goals to the vision to inform the definition of an achievable roadmap, start small and grow. Initially, focus on identifying what trusted data, insight, and technology are required to deliver against the roadmap to ultimately achieve the business goals.
2. Understand where you are now
Data, analytics, and AI maturity vary greatly between companies. Delivering data, analytics, and AI strategy will always require new capabilities to be built, such as data management, automation, and handling big data. You will need to establish what your existing capabilities are, in terms of people, process, technology, and the way your business currently works with data, analytics, and AI, in order to understand the scope, timescale, and investment required to be successful.
3. Prove value as you go and scale
Boardroom discussions on data can lack clarity and evidence of return on investment. Signing off the budget for a new CRM or Enterprise resource planning (ERP) system, for example, can feel easier for businesses because they appear more tangible. What you get for investing in data might appear less clear. It is therefore critical to start from your business goals and align on the clear KPI targets that will be achieved as a result of investment in data, analytics, and AI. Prioritise initiatives that are easy to execute, with demonstrable positive benefits, this will help to build the case to scale.
4. Your data is a valuable asset in its own right
There is more to the value of your data than just insight to grow the business, drive down costs, or improve environmental, social, and governance responsibilities. Data can enable entirely new business models. Some retailers, for example, are exploring the possibility of running their business at a near cost (low profit) to gather data on consumer behaviour that can be shared with other businesses at a great profit.
5. Be use case driven
Most established firms are grappling with a technology landscape that limits their ability to make progress on data, analytics, and AI. Evolving your business to keep pace with new developments in data and related technologies may seem daunting. And it can be difficult to know where to begin. Start with your business goals; define the relevant use cases; and use value to your business as the key measure to inform what you do, and when. Getting this process right is critical to developing a manageable and understandable data, analytics, and AI strategy.
6. Automate where you can
Companies in many sectors still rely on manual workarounds, adjustments, processing, and old ways of working, with data and legacy technologies that can impose significant workloads on the business. This ‘technical debt’ is a long-term challenge itself. Automation can help businesses overcome many of these challenges. Routine customer service, for example, can be delivered by AI, or data quality checks can be automated by adopting low/no-code solutions reducing the dependency on technical developers to progress with the delivery of benefits through automation. Whilst data, analytics, and AI maturity may constrain the extent to which you can automate, always keep an eye out for where it can be done.
7. Execute with agility
Being value and use-case-driven lends itself to an agile execution approach. What does this mean in practice? Begin by understanding what capabilities and products your business needs when it comes to data, analytics, and AI, and then what you will need to do to deliver them. Organise the work in manageable chunks, deliver benefits often and reiterate to continuously improve the solutions. For organisations more used to waterfall delivery approaches, consider the role of agile coaches to help with the transitions toward a more agile approach to the delivery that accelerates the realisation of value from any investments made into new products and capabilities.
8. Build a diverse, multi-skilled team
Most organisations are not fully aware of the enormous gap they have in skills such as Machine Learning, AI, data engineering, architecture, and data quality. Part of figuring out where your business sits now is benchmarking the current skills it has, identifying the gaps, and then developing the right recruitment and development plan to address them.
9. People matter most
Many organisations will fall into the trap of seeing technology as the silver bullet solution to data, analytics, and AI opportunities and challenges. Tools can absolutely support and automate things like data quality checks and customer service, but these technologies will require new ways of work, new processes, and even new roles. Data, analytics, and AI cut across the entire business and so require collaboration across business functions and markets if delivery of data and AI-driven transformation is to be achieved. Giving your people the opportunity to lead the development of new ways of working and to offer feedback on the ongoing strategy, will help to drive ownership and engagement across your business.
10. Be ethical
Regulations and policies define the boundaries about the use of data, but organisations must go a step further with the value-added ethical element of the "What should we do" to include the moral implication to it. They must add a layer of control actions, accountability, and responsibility to their governance framework to mitigate, if not eliminate, the risk of unethical and unfair use of data, and keep evolving it.
Infosys Consulting is a global management consulting firm helping some of the world’s most recognizable brands transform and innovate. We are industry experts that lead complex change agendas driven by disruptive technology. With offices in 20 countries and backed by the power of the global Infosys, we help our clients to win market share through the creation of value and lasting competitive advantage. Visit us. Or to get in touch with David, Partner, EMEA Head of AI and Automation and Richard, Associate Partner, EMEA Head of Data and Analytics.