In this special guest feature, Ram Venkatesh, CTO at Cloudera, discusses how leveraging data to delight customers, improve decision making, and increase operational efficiency is now possible for companies that commit to becoming data-driven.
The long sought nirvana of enterprise marketers has been knowing their customers better than their customers know themselves. The idea is to know what the customer truly wants and needs, and delight them with the right experience.
The ever-increasing amount of data that enterprises have to work with, coupled with the vast analytical powers of today’s machine learning (ML) capabilities, makes this goal suddenly attainable.
Some enterprises are using hybrid data platforms that span public clouds and on-premises data centers. The hybrid model means they can collect and mine a wide variety of data to deliver meaningful business value and dominate their respective industries. Here’s one example:
Deutsche Telecom delivers telecom services to 150 million global customers, and preventing network fraud is a major challenge for the company. To better identify fraud patterns, the company’s analysts needed a way to capture and analyze a greater volume of data. They worked on a system to improve not only fraud detection but also customer relationship management, network quality, and operational efficiency through machine learning.
Through more targeted use of its data and AI, the company now finds network problems before customers even notice them and detects fraud patterns and threats in real time before they can affect the business. As a result, losses from fraud have dropped as much as 20 percent.
Data-driven companies like Deutsche Telekom have special characteristics that are clear indicators of a next-generation approach to digital business. When data is being used to its fullest extent to analyze all aspects of a company’s operations, the enterprise redefines itself as a data-driven organization. When fresh new information comes into a system in real time, with the right tools, leadership can make faster decisions to react to market changes, pivot faster when supply chains falter, react immediately to inevitable power and system outages and truly understand customers better. All of these factors weigh heavily on the success of products and services in the market.
Challenges Remain
Data should be well-organized and well-maintained, but most companies are exactly the opposite. Data is everywhere and enterprises may have data across multiple databases, siloed operational data stores, analytics tools, machine data, or web applications — and these days, data may be within company walls or public clouds. Companies that want to control data and not be controlled by it must know where the data is, without centralizing and confining it.
Data can empower more mid-level employees to make decisions, taking much of the burden off C-level leaders. Executives often use data to communicate the rationale behind their decisions and to motivate action. Data should empower everyone to make decisions without having to consult managers three levels up, whether it’s showing churn rates to explain additional spend on customer services versus marketing or showing revenues relative to competitors to explain increased spend on sales.
Only about 12 percent of data in a typical organization was analyzed in 2020, according to a study by Experian. The rest isn’t touched at all often because the teams that store it and the groups that need it are in different parts of the organization. Data-driven organizations break down the barriers of data silos and let staff access useful data across divisional boundaries.
Data quality is extremely important. Enterprises often handle terabytes and petabytes of data, with data scientists running Apache Hadoop clusters with data analytics, and see this as giving them a competitive advantage. However, many of them do not have big data in terms of complexity or volume; most data management systems actually have data diluted with incorrect, outdated, or irrelevant data. This invariably hurts business efficiency.
Leveraging data to delight customers, improve decision making, and increase operational efficiency is now possible for companies that commit to becoming data-driven. Like Deutsche Telecom, the key is to use a hybrid data platform to better control and leverage all data and ensure data quality, so analytics teams can deliver truly meaningful business value.
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