For some digital and communications services provider executives, the Big Data trend has been a big disappointment. Operators were entranced by the idea that rich data analysis can reveal targeted insights that drive more revenue, but not every telco has seen its analytics investments turn into real business results. That has created some noticeable Big Data frustrations.
Research firm Gartner tracks market enthusiasm for emerging technologies with its “Hype Cycle,” and last year, Big Data moved from the “peak of inflated expectations” to the “trough of disillusionment.” While that sounds bad at first glance, it really means that businesses are moving beyond the stage of unrestrained expectations and instead starting to ask practical questions about how Big Data can actually solve their problems.
This more realistic view of Big Data means that when a project falls short of expectations, results-oriented executives may be less forgiving of the entire premise. But, is a lack of ROI an indictment on data analytics as a whole, or is it more a reflection of poor execution?
At Comptel, we argue it is the latter. As my colleague, Malla Poikela, wrote in a recent piece for LinkedIn Pulse, the most common hallmarks of a poor-performing Big Data initiative include difficulties accounting for every new raw data source and then turning all of that data into real-time contextual decisions and actions.
Successful programs rely on relevant actionability. Relevance comes from identifying contexts in real-time data, implying specific needs and employing predictive analytics to optimise target selection for those needs. Actionability is achieved through an end-to-end, integrated, real-time process that connects data streams through analysis to action.
It’s not about Big Data. It’s about Intelligent Fast Data, and it’s the only way to treat information at a time when technology empowers consumers to make informed buying decisions faster than ever and complexity grows in multiple dimensions simultaneously.
What are the benefits? With better understanding of existing customers and their preferences, operators can cue up the personalised service offers that customers want at exactly the right time on any device. It’s real-time marketing, driven by in-the-moment analysis, which leads to instant revenue opportunities.
More generally, Intelligent Fast Data can be considered a process that constantly monitors various forms of digital demand and connects that demand with available digital supply, be it a subscriber needing faster bandwidth temporarily to watch a video on demand, a network requiring additional capacity from virtualized packet core functions or supplying a service desk with a data feed from temperature sensors in a connected home.
Here’s how operators can start to make the switch from Big Data to Intelligent Fast Data.
Think Beyond Rules-Based Parameters
One of the downfalls of traditional decision-making system implementations has been a sole reliance on rules-based infrastructure. This form of analytics provides recommendations based on a set of pre-determined rules, but the challenge is that such a system might not be very accurate and can become overly tedious to manage as the number of rules increases. Rules or logics are important decision-making capabilities, but just like in human decision-making, they often need to be supplemented with capabilities such as pattern matching, predictions and anomaly detection. Intelligent Fast Data enables just that: the embedding of machine-learning-driven advanced analytics capabilities into decision-making.
If Insurance, a property and casualty insurer, took this approach to revamp its insurance claim analysis. If stepped up its automation capabilities with an Intelligent Fast Data system, which automatically learns patterns of insurance claims and flags normal claims for automatic processing, while highlighting potentially fraudulent or anomalous claims for further inspection. With the Intelligent Fast Data system the company was able to further reduce manual claims processing work and triple its number of accurately processed claims.
By embracing Intelligent Fast Data (i.e. decision-making automation with embedded analytics), digital and communication services providers can speed up and enhance the process that turns their data streams through analysis and targeted actions into new revenue streams.
Eliminate ‘Data Wrangling’
Another obstacle that could be holding back your switch to Intelligent Fast Data is a phenomenon known as “data wrangling.” According to the New York Times, data scientists can spend 50 to 80 percent of their time and talent essentially prepping data for the analytics process. It’s busywork, and it means you could be taking far too long to turn customer data into action.
To eliminate time-consuming data cleansing and enable faster time to action, a flexible and agile data processing layer is required, particularly one with the ability to integrate information from any digital source, then automatically cleanse, normalise, enrich and transform the data into ready data products and actions, which are consequently delivered to the systems with specific demands. Such a data processing layer must have smart adaption capabilities so that is able to cope with changes in data streams and the addition of new ones without data wrangling.
Remove Purchasing Friction
Changing how you integrate and process data is step one to drawing more value from Intelligent Fast Data investments. However, operators also need to eliminate any potential roadblocks to realising revenue from the insights data provides. This sometimes requires creative solutions.
For example, Indosat, one of Indonesia’s top mobile operators, needed to find a way to monetise mobile revenue opportunities in a country with one major roadblock. Despite being home to more than 250 million residents, only 8 million people in Indonesia have credit cards. That’s 3.3 percent of the population and 7.7 percent of the country’s sizeable base of smartphone users.
Smartphone users in Indonesia can’t simply purchase apps and services on their phone from a stored credit card like consumers elsewhere. However, a creative solution – direct carrier billing for the Google Play store – enabled Indosat to offer its consumers the same purchasing experience smartphone users worldwide enjoy.
Removing this obstacle opened up a new revenue channel for Indosat, and as the operator collects customer app usage data, it will be able to refine this information into insights and actions that drive even more financial benefits.
Intelligent Fast Data, ultimately, allows operators to profit from a wealth of Big Data.
Want to learn more about how Intelligent Fast Data can help you draw more value from new and existing customer relationships? Download our new book, Operation Nexterday, for expert research and insights.