Gartner predicts that 75% of enterprises will shift from piloting to operationalising AI by 2024, driving a 5X increase in streaming data and analytics infrastructures.
In the current COVID-19 reality, AI and machine learning are critical for providing vital insights about the virus and realigning supply and the supply chain to new demand patterns. Because of this “pre-COVID models based on historical data may no longer be valid”, reports Gartner.
FutureCIO spoke to Julian Quinn, senior vice president, APJ at Alteryx, on his take on how businesses could do better to achieve their data-driven initiatives and improve efficiency and enhance customer relationships.
In your view, do leadership at enterprises in Asia understand what data-driven means?
Julian Quinn: Data-driven is a strategic approach taken by organisations to drive insight-driven outcomes, based on data analysis and interpretation. APAC’s spending for Big Data and Analytics solutions is set to increase by US$41.9 billion by 2024, as investments in data and analytics are a key necessity in achieving business resiliency amidst the pandemic.
Almost 80% of business leaders in Singapore indicated that data is at the heart of the business and is a critical corporate asset. As regional and global enterprise leaders continue to recognise data as a strategic asset and deploy data analysis in every part of their organisation, cultivating a data-centric culture is permeated in leaderships.
In a company where you have a CIO and a Chief Data Officer, who owns the customer data? Who is accountable for what when it comes to customer data?
Julian Quinn: In any organisation, the CIO and CDO both play an equivalently crucial role in ensuring that data access is secure and delivering key business imperatives.
A CIO’s role encompasses managing and implementing successful IT systems in a company by owning data policies, access and applicability, while a CDO often oversees a range of data-related functions such as data management, ensuring data quality and creating data-directed strategies.
As companies gear towards constructing a unified data architecture to run their organisations, both have an equal share in owning customer data.
Data is recognised as a strategic asset to thrive in today’s landscape. With multiple functions competing to optimise processes with data, they and their teams, can empower organisations and their workforce in discovering new insights.
What skills are needed in-house to successfully acquire-manage-understand this data?
Julian Quinn: Traditionally, the in-house data teams possess relevant and unique skill sets, and technical knowledge in acquiring, managing, and understanding data. These multifaceted and challenging roles include expertise and capability in data mining and data visualisation. Armed with various tools to apply data science algorithms and ML, they are experts at leveraging data.
An organisation with a data literate workforce is well-positioned to make informed decisions as leading with insight-driven strategies are easily attainable. However, these skill sets should not be a roadblock for employees in realising new business acumens.
What should an enterprise do to ensure a sustainable and ethical use of customer data by its employees?
Julian Quinn: With huge amounts of data readily available and easily accessible, enterprises should have clear, transparent standards to ensure that customer data is treated and protected. When procuring, utilising and managing data with AI, ML and other analytic efforts, it is paramount to adhere to these key guiding principles.
The use of AI should fully comply with regulatory mandates that protect privacy and civil rights. Enterprises must be transparent and accountable for collecting and using data, taking steps to ensure that data inputs are bias-free.
Incorporating human judgement and informing decisions appropriately are essential to building a responsible AI framework, as historical data can dramatically affect a solution’s output.
At what point in an organization’s use of customer data does it become necessary to have a data science department as opposed to a business analytics team?
Julian Quinn: Business analytics teams and CIOs are responsible for data collection, management, and analysis. Their roles are usually limited to implementing data warehouses and BI systems. On the other hand, data science teams are formed to drive complex projects with the utilisation of statistical methods, ML algorithms and computer science. This team comprises individuals who typically have a variety of skills in areas of data modelling, business knowledge, and collaboration.
With the shortage of data scientists today, acquiring the analytic talents would be challenging and expensive to recruit for. Hiring data scientists adds unnecessary strain to any organisation’s top line, becoming a formidable barrier to entry in proliferating analytics for business outcomes. As such, some organisations are lagging in exploring the potential of data analytics and are slow adopters of big data analytics technology.
Evolution in data platforms eliminates the different point of solutions for data preparation, analytics, reporting and business process automation when introducing a data science team, which results in a disconnected experience with slow or misaligned outcomes.
Acting as the bridge between both teams, these platforms aggregate data, connect many disparate databases and data sources, and bring them together so that analytics can be done more intelligently.
What is your advice to leadership looking to tap data analytics to achieve both operational efficiency and enhance customer relationship?
Julian Quinn: Today’s data analytics platforms should converge all your greatest assets – data, people, and processes. Having these platforms that empower your workforce can help solve organisational problems with predictive insights and increase the overall performance and efficiency, as the decision-making process becomes faster and more reliable.
