The more an enterprise wants to know about itself and its business prospects, the more data it needs to collect and analyze. Additionally, the more data it collects and stores, the better its ability to know customers, to find new ones, and to provide more of what they want to buy.
Sounds simple, but a surprising majority of U.S. companies (about two-thirds, according to CIO.com) are only now getting tuned in to become fully functioning data-driven enterprises by starting new initiatives, scaling up systems, and changing cultures. The research indicates that only about 33 percent of companies are utilizing data by “tackling the technology” on a daily basis and showing an example for others.
Some enterprises inside that 33 percent group 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 turned to Cloudera Data Platform to improve not only fraud detection but also customer relationship management, network quality, and operational efficiency through machine learning and AI.
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 by 10% to 20%. Here’s the Deutsche Telekom case study if you want to learn more.
Common characteristics of data-driven companies
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 utilized 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 quicker when supply chains falter;
- react faster to inevitable power and system outages;
- better understand customers;
- and much more.
All of these factors weigh heavily on the success of products and services in the market. Here are some key data points that illustrate how the intelligent use of data and analytics redefines companies in 2021:
Data-driven companies know where all their data is located
Data should be well-organized and well-maintained—as in a library, where every book is stored in one place. In fact, most data-driven cultures are exactly the opposite. Data is everywhere; organizations 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. The key is knowing where the data is, not centralizing and confining it.
Data-driven companies ensure data flows freely inside an organization
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.
Data-driven companies utilize as much data as possible
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—though that portion could contain useful insights—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-driven companies keep data lean and clean
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.
An effective data management system can identify which data sets are afflicted with the factors noted above and make sure that real data garbage is marked or deleted. Experian Data Quality has reported that inaccurate data directly impacts the bottom line of 88 percent of organizations and affects up to 12 percent of revenues.
Data-driven companies offer technological freedom for fast insights
The main concern of people in data-driven businesses is the ability to get insights quickly, so they can better compete in their markets. Forcing analysts to learn and use IT-defined models and centrally specified tools slow down analysts and data scientists. In most data-driven enterprises, the person answering the question gets to pick the tools that are used; having a hybrid data platform enables that person to choose the right tool for the job yet access the same datasets easily and efficiently.
Enterprises undergoing a digital transformation should adopt a data management and analytics platform that empowers data-driven companies. Cloudera can help create an enterprise data cloud platform so your company can redefine what it means to be a data-driven enterprise.