Data is the heartbeat of digitally transformed businesses – and by the end of this year, 60% of GDP globally will be digitised
However, harnessing data is often still a time-consuming process which can hamper agility, and ultimately business success.
Problems with legacy analytics solutions
Whilst organisations are making significant investments in digital transformation, commonly used legacy BI and complex analytics solutions were not built with dynamic environments in mind and the real-time insights they need. The complex data preparation required by them reduces the speed and accuracy of essential business data analysis.
Siloed data sets further complicate matters, with a quarter of enterprises reporting more than 50 data silos in their company. The complex work of accessing, aggregating, and unifying these data buckets has typically been left to specialist data scientists or engineers to gain the necessary insights and then communicate them back to relevant teams. However, this is a time-consuming procedure that leads to insights quickly becoming outdated.
Historical solutions also lack the scalability to handle the large volumes of data which modern businesses now need to ingest in order to deliver the digital experiences audiences expect. Without real-time capabilities, businesses are stuck with stale data whose value is limited to spotting historic trends and doesn’t allow for agile decision-making, putting them at risk of being unable to innovate quickly enough. Thankfully, technology is creating solutions to these problems.
Solving problems with legacy analytics solutions
Continuous Intelligence (CI) is a concept which leverages advanced AI technology to ingest large volumes of real-time data across the business then perform complex analysis on it to produce valuable insights at pace, enabling the 360-degree customer view digitally transformed businesses need with no code analytics. It also allows for data visualisation, creating insights that are immediately actionable.
CI can be achieved by processing data under a single pane of glass and expanding its usability with a conversational, and easy to use UI to increase decision making abilities across a business. By using real-time data processing and analysis – alongside more streamlined systems – companies can ask tailored questions and receive quick feedback, allowing for informed business decisions based on current, rather than old, data.
Despite these clear advantages, not all businesses are leveraging CI, instead continuing to rely on historic and static data. Gartner predicted that CI would be incorporated into more than half of major new business systems by 2022. Whilst data from last year showed that significant strides had been made towards this, many brands are still in the dark about how CI can positively impact their business.
Becoming intelligent continuously
Achieving true continuous intelligence brings challenges, including scalability, privacy, and the ability of business users to make real-time business decisions which can deter businesses from adoption, but there are solutions.
Keeping data within a public or private cloud can protect its integrity and security. Privacy risks can also be mitigated by implementing firewalls which can simultaneously strengthen GDPR regulation compliance. Brands should also minimise visualisation tools relying on complex ETL or integrations to process data. Whilst graphic representations can assist users, the additional processing time can massively impact the ability to gather data in real-time, going against the very purpose of CI.
Continuous Intelligence in Practice
To illustrate CI in practice, take a one-stop car buying website. Analytics plays a key role in enabling the business to provide valuable content to consumers and providing data to marketers, dealers, and manufacturers who want to understand customers and their car buying decision-making.
As the business grows with buyer vehicle searches becoming more sophisticated, the product team needs to understand behaviour across the customer journey which is becoming more complex. To do this, they are relying on data analysts to run custom reports for them which require additional ETLs and back end modelling, but this is a lengthy process that means product owners are waiting too long for answers.
By adopting a Continuous Intelligence approach that enables them to analyse large pools of time-series vehicle and customer data across multiple channels, data analysts and product managers can quickly iterate on questions about groups of users and product features to understand how people are using the site and provide the content they want. Analysts are then able to apply their time to strategic work and proactively identifying opportunities.
This is just one example of how businesses that recognise the shortcomings of legacy analytics frameworks and who harness the power of CI will reap benefits becoming an agile enterprise that is able to deliver the customer experience users expect.
About the Author
Tony Ayaz is CEO at Scuba Analytics. Scuba is a continuous intelligence platform designed to unify customer experience management. Scuba empowers BI and data science teams to make real-time business decisions within a single view across allbusiness silos. Explore @ scuba.io We’re hiring! Join our amazing team.
