As data processing becomes more sophisticated and informs more business decisions, companies are increasingly investing in the analytics and reporting technology that allows marketers to better understand their ROI. To truly unlock the value of those tools, marketers must know how to make sense of the data.
While the volume of digital data created grows exponentially each year, it’s less about the quantity than it is about the knowledge. According to MIT Technology Review research, 99% of digital data is not used, analyzed or processed.
MIT also found that 80% of business decision-makers say they lack the skills internally to exploit their data.
Content produced in association with AT Internet.
Data science is having its moment in the sun
In 2012, Harvard Business Review called data scientist “the sexiest job of the 21st Century.” In the U.S., home to more than half the world’s data scientists, the number of data and analytics jobs will swell by 28% over the next two years.
Data scientists are an invaluable part of their organizations, finding patterns and extracting the insights from data that marketers ultimately use to create the best possible customer experience. A huge problem with data science, however, is a lack of talent. Though the number of jobs is expected to grow significantly, McKinsey still estimates a 60% greater demand than supply. As a result, data scientists are often generating reporting and analysis for everyone else in the organization.
The benefits and challenges of data democratization
Within a typical organization, there are very few people with the expertise to process and analyze data—and very few people who have access to it. Data democratization allows non-specialists to access and analyze data themselves, without assistance from the experts.
This process puts the data in more people’s hands, empowering them to make their own informed data-driven business decisions and encouraging collaboration. At the same time, it gives data scientists and digital analysts more time to focus on the high-value activities that make them such a commodity in the first place.
Data democratization has its risks, of course. Well-intentioned, but non-technical, “outsiders” may not necessarily know how to interpret data correctly, inadvertently resulting in inaccurate conclusions and unwise decisions. A bigger issue is that opening data up to more people creates a security risk, increasing the likelihood that sensitive data is leaked either internally or externally.
While data democratization is risky, implementing it isn’t nearly as dangerous to your business as failing to do so. According to PricewaterhouseCoopers France research, 20 to 35% of a company’s lost revenue is due to poor use of data.
One brand prioritizing data democratization is Walmart. The world’s largest retailer is in the process of building the world’s largest private cloud, which will process 2.5 petabytes of data (or enough to fill more than 163,000 iPhones) every hour. Walmart has also created an analytics hub called the Data Café, where employees bring their issues to analytics experts and solutions appear on touchscreen smartboards within minutes.
Putting data democratization into practice
In order to be beneficial, data democratization must be carefully managed and you must establish a strategic direction for your governance. Here are four steps to help you begin:
- Start at the top
Get support from senior leadership, the people with the power to make data democratization happen—and the influence to encourage the cultural change necessary to make it successful. In addition, buy-in from executive management can ensure that the project receives the necessary budget allocation and isn’t sidelined due to internal conflicts or pressure.
- Create an analytics team (if you don’t already have one)
The size of the team will depend on various factors, such as budget, the size of the company and the complexity of measurement needs. How the team will fit into the business’ overall ecosystem will determine the best organizational model. A centralized model involves an entire division dedicated to digital analytics, while resources are spread out among different departments within a decentralized model. Somewhere in between is a hybrid model, in which only certain tasks are outsourced to third-party teams.
- Educate your employees
Developing analytics expertise is a crucial element in data democratization. Failure to do so only exacerbates the risks of entrusting non-specialists with sensitive data. A Chief Data Officer role can help set the framework and determine a strategy for developing employees’ analytical skills within the organization. Meanwhile, internal analytics teams can serve as evangelists and trainers, sharing their knowledge and best practices with the rest of the staff.
- Invest in the right technology
Features should include the ability to easily share dashboards and reports; different levels of access rights that can be granted based on user profiles; customizable measurements; and a sophisticated, integrated API. The right technology makes it easier for the experts to share their knowledge, ultimately leading to more accountability and efficiency, better ROI and company culture, and most importantly, happier customers.
The best tool in the world is useless without the experience of qualified individuals who know how to implement it, use it optimally and extract insights that enable effective decisions. Data democratization allows those experts to share their knowledge, and makes it possible for employees across the company to generate their own data insights. This gives data experts more time to dedicate to the meatiest analytical tasks.
Data democratization can increase collaboration and enhance decision-making across the business, but only if it’s utilized correctly. When implemented strategically and with the right technology, democratizing data benefits everyone—right down to the customer.
To learn more about the benefits of data democratization, read AT Internet’s Democratizing Digital Analytics guide.