How augmented analytics can empower data-driven enterprises
By Jacques du Preez, CEO at Intellinexus
One of the most promising data and analytics trends for modern enterprises is the growing use of augmented analytics.
As a subset of data analytics, augmented analytics uses natural language processing, machine learning and artificial intelligence to automate and enhance data analytics, data sharing, business intelligence and insight discovery.
The global augmented analytics market is set to grow by a compound annual growth rate of nearly 30% over the coming years to reach $62.6 billion by 2028.
Augmented analytics helps organisations deal with the complexity and scale of data that has become typical in digitally transformed modern enterprises. One of its most important applications is in helping data and analytics team prepare, manage and analyse data so that reports can be delivered to business leaders to guide decision-making.
This doesn't replace the need for data scientists, who have traditionally been tasked with the main task of turning data into actionable insights. As the name suggests, augmented analytics is meant to enhance human ability, not replace it.
At its core, augmented analytics simply takes over repetitive tasks related to data collection and processing, freeing up time for data scientists to focus on more strategic tasks.
For example, data scientists may develop more innovative data sources, or spend time interpreting data insights at a more strategic level. This can enhance the value that the organisation gets from its data and analytics and even help identify new revenue streams or business models based on the data insights.
Broadening user base of analytics
Augmented analytics is used in a broad range of business applications today. In data preparation, augmented analytics can help automate the extract, transform and load (ETL) process so data scientists and business users spend more of their time gaining insights from the data than they do processing it.
It is a useful tool in retaining corporate knowledge, especially in key technical positions. For example, a manufacturer using augmented analytics can embed learnings from more experienced engineers that are then used to augment the decision-making capabilities of more junior team members. This ensures learnings are retained and helps maintain continuity.
One of the most promising benefits of augmented analytics is its ability to enable business users that don't have deep technical or analytical skills to still use data insights in their roles. Some industry analysts have even predicted that companies will shift away from requiring candidates to have statistical or mathematical backgrounds perform business intelligence tasks: the tech will be able to provide sufficient support to allow users to simply derive insights and apply data to decision-making.
This lowering of the barriers to adoption can create more comfort with data and analytics tools among non-technical users and deliver greater business benefits across the organisation.
In many industries, the use of augmented analytics is already commonplace.
Augmenting customer experiences and offers
The use of augmented analytics in the retail sector, for example, is already at a high level of maturity. The tech is used in basket analysis to understand what products are purchased and what is the relationship between those products. For example, if a shopper purchases a new TV and Blu-ray player, the augmented analytics could guide recommendations for surround sound units, wall mounting brackets or even a new Xbox.
Augmented analytics also help retailers with demand forecasting to understand which products need stock replenishment and which ones are not selling well, pointing to products that can be removed or replaced.
In financial services, augmented analytics is used to better identify which additional financial products are best suited to a customer based on a broad range of attributes, including income, age, existing policies and gender.
An insurance company may, for example, offer a tailored medical aid package to a customer with existing retirement annuity and life insurance cover, as they'd have a good view into that customer's personal details and would know what is affordable and appropriate to that customer.
In the telecoms sector, where operators have vast amounts of customer data due to the extent to which people typically use their phones, augmented analytics could help telcos design more tailored connectivity packages for customers based on accurate data.
As organisations' use of augmented analytics matures, more industries will start unlocking the vast benefits of having powerful data insights at the fingertips of more business users. For companies seeking the power of data-driven decision-making capabilities, augmented analytics is a vital cog in the broader analytics machine.