By: Samantha Diersen
A current and emerging trend in analytics is Data Socialization. Data socialization is the process of making data easily accessible to and understandable by regular people in an organization (Joshi, 2017). This is a shift from previous practices in analytics, and streamlines the process.
Previously, specific departments or people within an organization handled the collection and analysis of data to glean insights. Employees in these departments would have field specific education and language, as well as a strong understanding of statistics. The general insulation of the departments encouraged the use of these terms, but created a ‘language barrier’ when it came to dealing with outside departments or supervisors. When statistics are not properly explained in terms that all parties understand, it inhibits mutual understanding. Consequently, confusion results when employees specializing in the collection and analysis of data attempt to present new or updated findings to supervisors or outside departments (Sharma, 2017).
The process of shifting to data socialization and removing the barrier in organizations started with the idea of data democratization. Data democratization is ability of data to be available and “accessible to the average end user” (Rouse, 2017, para. 1). This distributes data throughout the business for individuals to manipulate and gain insights from it. Advancements in virtualization and cloud storage made data democratization possible. This allows applications to store and manipulate data without the technical details like formatting, and to share it in real time (Rouse, 2017).
Data socialization takes user demand and advancements, and expands on the concept of data democratization. The sharing culture of employees created the demand, and the advancements in storage, sharing and computation created the ability of data socialization (Hans, 2017). Users wanted the ability “to search for, reuse, and share managed data” across the company (Joshi, 2017).
With the desire and advancements in mind, there are a couple reasons why data socialization works. First, it simplifies analytics creation for the everyday user. These everyday users are not as tech-savvy as those employed or educated primarily in a data, statistics or analytics heavy field. However, with data socialization platforms, they “can not only access this data and perform analytics, but also learn from their colleagues by sharing interesting resources or results” (Hans, 2017, para. 6). Second, data socialization involves the idea of “the right data to the right person…at the right time” (Joshi, 2017, para. 10). The right person, a key player, can speed up the creation and distribution of insights in an organization (Hans, 2017, para. 6). This allows data and analysis to be used at every point in the decision making process, and thus makes the process more efficient and employees more productive (Marr, 2017).
Data socialization software can take different forms. One involves dedicated platforms for creating and sharing data and analyses. The second involves a set of multiple platforms that are tied together by an organization. In this situation, one platform might be used for data sharing and analyses, while another would be used for creation and storage (Hans, 2017).
There are advantages and disadvantages for both methods.
Having a dedicated platform makes the system user friendly. This means that creation, storage and sharing of data, analytics and insights can be done via a single platform. Dedicated systems allow for faster interaction between people and data. All-in-one platforms have a search function, allowing for all users to search to find data, charts and previously explored and created analytics and insights. Users can access all the information in one place, removing the need to login to different services. Dedicated platforms that use data socialization are created to pull extra information, such as “ratings, comments, and discussions” (Pannaman, 2017, para. 3). These platforms, however are more expensive than the other option (G2Crowd, n.d.).
On the other hand, combining services can be cheaper than dedicated platforms. This is because these services serve only one function (sharing, creating or sharing). For instance, Microsoft SharePoint is available at different price ranges, and is useful for different sized businesses (Microsoft, 2017). However, as the services are not streamlined, access to past and future data and analyses may be slowed. Methods that require manual data entry are also prone to human error, such as wrongly entered age, price or number of items (Developer, 2017).
The trend of data socialization will lead to improvements to understanding and speed in analytics. With access given to all users, employees can view previously made analytics and insights, enabling them to learn from past work. Easy accessibility also speeds up the review process and quickens the transfer of information from the creators/analysists to the decision makers. With access to data and analytics and ability to create analytics and insights given to all users, the task of analyzing data is broken down, speeding up the process for individuals.
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