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Every time we book a hotel, a flight, call our bank or buy a meal on a delivery platform, we are generating data. The last decade has seen significant growth for the analytics market, driven by a shift away from analysing data “in memory”, which can dramatically accelerate time to answer, to cloud-based analytics. According to recent Aberdeen research, the average company is seeing the volume of their data growing at a rate that exceeds 50% per year, managing an average of 33 unique data sources used for analysis. It’s easy to imagine the complexity that businesses need to face to handle and analyse multiple types of data especially when this comes from different sources.
There has been a complex evolution in terms of data management. It started with local storage, evolved towards data warehouses until getting to today’s data lake concept which improves capabilities for companies to store, manage and analyse the growing amount of data generated each day.
Sophisticated data lake infrastructure can help exploit the influx of new data types using existing legacy data systems by merging them together into a single system. This means that data teams can move faster as they are able to use data without needing to access multiple platforms. They have the most complete and up-to-date data available for data science, machine learning, and business analytics projects while translating analytics into substantial ROI in the form of business growth and profit boost.
All kinds of organisations benefit from managing and analysing such an amount of data, however, many, due to their size, often don’t generate enough to that lets them derive tangible insights. The solution lies in leveraging vendor’s external data lakes in scalable, cost-efficient way.
Cloud technology as the data analytics enabler
One area where businesses may not be creating enough data internally is in customer experience (CX). One of the great ironies of this time is that the faster technology progresses, the harder it can become for many businesses to keep up with the competition. Simultaneously, consumers are becoming so-used to instant gratification – think ordering a taxi or food delivery – that they become frustrated by the friction created when brands cannot deliver a seamless experience.
To stay competitive organisations, need to benefit from the latest data-enabled insights but may not have the number of individual contacts to produce enough data for useful analytics. Leveraging data lake houses allow SMEs to take advantage of third-party structured and unstructured data in a cost-effective way.
CX software vendors own a volume of data – voice calls, chats and interactions - far larger than an individual business. This means that a business can take advantage of millions of customer calls being analysed rather than, say, a thousand over the course of a year.
Vendor data lakes brings together and anonymises customer, agent, and interaction data from digital and voice channels. It allows customers to easily adopt AI - with pre-built models that are natively embedded into applications – in turn transforming their business and improving their customer’s experience. One benefit of this is how organisations can take engage with sentiment analysis whereby we find out if digital channel users are happier than customers who use phone support, or if customers are more inclined to send an angry email than an angry text message, and
so on. Sentiment analysis capabilities can sift through digital customer data from contacts and determine the CX quality. These capabilities unite to create a frictionless consumer reality that helps shape positive opinions and delineates between the brands that we love and those that we feel are wasting our time.
Organisations that use third-party cloud services can rely on shared data produced from triangulations between partner data and stored information, which are easily and constantly updated on the cloud. This means having the chance to leverage an incredible, constantly updated data basin, without the need for spending time and resources to analyse data sources while saving costs. If businesses are going to stay competitive in the data decade they need to look beyond their own datasets and utilise those companies that have the advantage of scale. Cloud computing gives access to billions of interactions that they otherwise would not. Which, in the context, of customer service, creates a smart self-service that works.