The need for data democratization in the digital enterprise | Straight Talk

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This article is by Featured Blogger Naveen Joshi from his Blog Page. Republished with the author’s permission.   

Data democratization is enabling individuals in organizations to access data without any bottlenecks to make data-based decisions. And this is helping organizations get closer to their vision of becoming fully data-driven. 

Digital enterprises require a tremendous amount of data. This data helps them to improve their performance and simplify their daily operations. But often, data is only accessible to business leaders, managers, and data analysts. But business leaders and data scientists might not usually know what insights employees require. Hence, the access of data to only a few individuals in the entire organization restricts businesses from using it optimally. And this restriction holds back companies from becoming complete data-driven organizations. 

Data democratization refers to data being easily accessible to all employees in an organization without any barriers. Data democratization can help businesses achieve their vision of becoming data-driven. 

The vision of data-driven organizations 

Data is an essential part of every organization today as it can be used to enhance business operations. The use of any digital technologies, be it AI, BI, or big data analytics, requires data input to operate efficiently. The good part for businesses is that with IoT and big data, there is an abundance of data easily available to them. And businesses want to use this data for decision-making. That’s because data-driven decision making has various benefits. With data-driven decisions, companies can make confident decisions, be it for developing marketing strategies or making infrastructural changes, based on the generated insights. It can also help businesses to become more proactive. For instance, predictive analytics will help identify business opportunities before a competitor. But, despite an urge to become data-driven, most organizations are not even close to achieving the vision. And that’s because of the hurdles they face in becoming a data-driven organization. 

The barriers to the vision 

According to a survey, 31% of respondents say that they have created a data-driven organization. The remaining 69% are way behind in their road to data democratization. That’s because most of organizations provide access to data only to business leaders, data analysts, or the IT department. These individuals can get insights relevant to their functions, but these insights might not be helpful to the other employees. Based on the function and hierarchical level of employees in an organization, they require different types of insights. For example, a business leader might require insights such as increment or decrement in sales. A salesperson, on the other hand, might require insights such as why the sales have seen growth or decline. Further, in the case of growth, they might want to know how to act in order to maintain it. And in the case of decline, they might want to get actionable insights for increasing sales. 

Another hurdle that comes in the path is data analysts’ scarcity. Businesses want to leverage every bit of data, but there are not many people with analytical skills. This has exponentially increased the demand for data scientists against their availability. And this growth in demand has led to increased costs for hiring data analysts. Hence, not every organization can afford to hire a complete team of data scientists to analyze every bit of data. This has further led to reduced data insights. By making data available and accessible to everyone in a company, data democratization can help them overcome these barriers and become a data-driven organization. 

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The helping hand from data democratization 

Data democratization can be the trampoline to help businesses jump over the hurdles on their way to become a data-driven organization. 

To compensate for the shortage of data analysts 

Demand for data scientists is much higher than their availability. According to a survey, the USA alone faces a shortage of 151,717 people with data science skills. Data democratization enables every individual to access data and analyze it for better decisions. And with every employee leveraging data for their benefits, data scientists can focus on more complicated analytics and generating deeper insights. Data democratization will also enable large organizations to operate with a few data analysts. This will reduce the demand for data analysts and bring it close to availability. Businesses can also integrate NLP into their analytics tools to further simplify data interpretation. NLP will convert data generated insights into plain text. This will help employees to understand complicated data structures. Also, they will be able to communicate with data in natural language and dive deeper into insights. 

To provide insights to employees 

Since every department will have access to data, they can understand data and generate insights based on their requirements. Considering the same sales example mentioned above, the sales team will be able to get actionable insights to maintain and increase the number of sales. Data democratization will also reduce the amount of dark data. Dark data is the data that is collected by enterprise systems but remains unused. Business leaders might prefer generating consumer-focused insights. For instance, they might want to know how to retain customers or enhance their experience with the company. And that’s because they fall short in time to focus much on other insights. But, this results in an unknown bias. Focusing on consumer insights keeps the non-customer data such as networking data and log files data at the bay. And this increases the amount of dark data. Since every individual will have access to data, they would generate consumer insights by themselves. And this will provide some time to business leaders to not just focus on consumer data, but also generate insights from dark data. For instance, they can generate insights from networking data to know how they are using their resources. And such insights can help them save costs or optimally use the internet and network resources. 

To enhance real-time decision making 

Often employees have to rely on managers and team leaders to get data-driven insights. And the business leaders would generate insights when they feel the need for the same. And on top of that, these insights might not be even of much use to end-users. With data democratization in place, employees would be able to generate insights for themselves. 

The capability of accessing data at any time will assist employees in making real-time decisions based on data. For instance, retail sales employees can view the historical purchase of all the customers. With access to all historical data, employees will be able to provide real-time recommendations to customers. For example, if a customer has purchased a shirt recently, then employees can recommend jeans to him or her. This will enhance the customer experience and make it more engaging. Real-time analytics will also help employees to offer promotions and incentives to consumers based on their loyalty towards the store. Suppose a customer has been buying from a retail store for more than one year and have made more than $500 worth of purchases. Employees can offer loyalty incentives to them based on the standards terms decided by the owners and managers. 

Data democratization brings various benefits to businesses like the ones mentioned above, but it comes with certain concerns. Data democratization enables non-technical employees to understand data. But, there are still chances of misinterpretation by employees. And these misinterpretations can lead to bad decisions. Hence, despite the simplicity that data democratization brings to data understanding, businesses should train their employees on how to interpret data. Businesses can conduct training sessions to educate their employees. They can also use AI to enable collaborative learning to help employees better understand how to interpret data.