Connections that matter: How can enterprises adapt and adopt a data-driven mindset? – Times of India
In an all-pervasive digital world, consumers and businesses are generating tremendous volumes of useful data. Most of this data exists in unstructured, fragmented states. This though hasn’t stopped enterprises from leveraging data to create better business outcomes. Over 70% of the most valued companies in the world have established data-driven strategies at the core of their revenue growth and expansion roadmaps.
But what does it truly mean to be a data-driven enterprise? For some, it simply means using analytics platforms to unlock business insights, but that’s a narrow view. Being data-driven, in the truest sense means using every piece of information you can lay your hands on to better inform your view of your customer, your employees, your operations, and your success metrics.
What makes data-driven enterprises so competitive?
It’s a proven fact that data-driven enterprises grow faster (by as much as 30%) than legacy businesses. Companies that consistently leverage data to power their business decisions are often able to accelerate innovation and disruptive thinking. Plus, they’ve always got a finger on the customer pulse and can react more rapidly to evolving demand and customer behavior. In fact, the connected enterprise has capabilities that extend beyond what legacy companies can bring to bear.
These enterprises have the ability to use data and insights from sources that include IoT devices, help desk queries, workforce behavior patterns, workflow structures, and much, much more. They are also able to rapidly deploy and scale workforces that leverage AI and analytics tools to accelerate creative problem-solving. They use ML and AI platforms to virtually reproduce business scenarios and related outcomes with a high degree of accuracy. Additionally, they can build value networks that offer data transparency to all stakeholders while connecting every part of the value chain.
Creating new opportunities with a data driven connected enterprise
When Covid-19 shut down much of Europe, British Telecom Openreach needed a solution to keep their telecom infrastructure up and running without any downtime and to quickly respond to a rising flood of customer requests. But their existing processes were time-consuming and complex, with staff often using several applications to resolve a single customer query.
To tackle the issue, Openreach analysed its automation potential by mapping out every process and task in detail before launching a transformation initiative that automated 200+ tasks. By deploying an ML platform within its RPA infrastructure, the company also ensured that future opportunities for automation could be quickly identified and addressed. The results were staggering – Openreach reduced the average handling time of customer requests by over 60% and made 90% of their process documentation tasks redundant.
In much the same way, Mars Inc. used a combination of real-time data extraction and sharing tools to make their value networks more efficient, dramatically cutting down the trace time of their inventory and lessening the impact of product recalls.
These stories serve to demonstrate how automation and AI in a connected enterprise can create value in unexpected ways.
Making the shift from legacy to connected
When building a connected enterprise, it’s a good idea to use three foundation pillars as a rubric for success. Firstly, leaders should ask themselves, if they are using AI-driven cognitive operations to reap more value from their processes and optimise productivity. Secondly, if they deployed AI and ML tools in concert with their employees to help amplify their human potential and reduce repetitive tasks within the workforce? Lastly, if they are shifting from linear supply chains to interconnected value networks where every ecosystem participant can maximise value delivery.
These transformation goals that the above questions represent are not just ‘good to have’ – they underpin the competitive core that will differentiate successful enterprises from those that fail in the future.
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