Recent advances in technology such as Generative AI are enabling people to become self-reliant in gaining information, creating content, or designing visuals with minimal assistance or training. But when it comes to making important business decisions or taking quick actions, decision-makers struggle with complex analytics tools and are forced to rely on experts to make sense of data and gain insights. Request backlogs, long waiting times, obsolete information, and ultimately delays cost organizations in terms of missed opportunities and financial losses.
Self-service analytics can solve most of these challenges and make organizations data-driven to become future-ready. Let’s understand what is self-service analytics, why is it the future, and how organizations can reap benefits by adopting it early.
Self-service analytics is the ability provided by data analytics platforms to business users for accessing, analyzing, and interpreting enterprise data easily and instantly, with minimal support. With self-service analytics, anyone can ask questions in simple language to get instant insights with visualizations and summaries. It is useful for all types of users, especially non-technical business users who use insights on a daily basis to make decisions.
A self-service analytics platform offers an effective way to explore data and perform ad hoc analysis as and when insights are needed. With minimal training, users can start exploring data and gaining insights from the day they are onboarded.
In their top strategic technology trends, Gartner named Decision Intelligence as an important trend. It predicted that by 2023, more than 33% of large organizations will have analysts practicing decision intelligence. Decision intelligence makes insights more understandable, relevant, and actionable. And self-service analytics is the most effective enabler for non-technical business users for achieving decision intelligence.
When organizations extend analytics capabilities to their users, they empower them with insights to reach their goals better. Research by Harvard Business Review Analytic Services establishes that organizations can substantially improve business performance by giving frontline workers modern self-service analytics tools to enable fast and intelligent actions. As users become comfortable interacting with data, they also become confident in performing analysis themselves and relying on actual insights to make decisions.
Organizations that provide self-service analytics capabilities have a significant advantage over their competitors.
A simple unassuming search box can be very empowering. Search-driven analytics makes interacting with data more intuitive and effortless. Users do not need to learn syntax, create complex SQL queries, or struggle with spreadsheet formulae to get answers. They can simply use the search to enter questions such as “What was the customer churn last quarter?”, “Compare leads generated by channel A and channel B last month”, or “What was the growth in sales this year” and get instant answers. With self-service analytics, users become more confident and data-driven in their decision-making.
Whenever opportunities surface or challenges arise, decision-makers have to be ready to handle them immediately. Getting the right insights at the right time ensures their success. To convert opportunities or resolve problems in time, they need access to the latest and most accurate information. Self-service analytics helps decision-makers improve their speed of decisions by making insights accessible at any time.
Data can be present in various formats and scattered across multiple data stores. Departmental silos restrict decision-makers from getting a comprehensive view of the situation. For example, the customer preference for a particular product is changing but the sales team is focusing their efforts elsewhere due to a lack of this insight. Self-service analytics helps to break silos and integrate data from various sources, thus offering better visibility for users to access and search within all available data.
Self-service analytics eliminates the long process of requesting information, waiting for reports to be generated, and getting help interpreting the reports. By the time users receive insights, they are already old by days or weeks, which may lead to an inaccurate understanding. Self-service analytics offers users access to the latest available data, thus ensuring that they always have the latest insights at hand for making decisions. Along with answers, they also highlight analogies, anomalies, outliers, and clusters emerging in data, and provide personalized recommendations to expedite actions.
The process of seeking information is not limited to one question. It is through a series of iterative questions that users can dig deep to get to the root of the matter. With modern self-service analytics, users can keep asking questions until they get the granular insights they need to take effective actions. Detailed analysis such as root cause or why analysis, demand forecasting, churn calculations, and trend prediction can take weeks and require a lot of processing. However, self-service analytics reduces the process of extracting deep insights and performing advanced analysis to minutes. This helps surface hidden insights that users would have missed while looking at data or processing it manually.
Self-service analytics not only makes searching for insights intuitive but also makes consuming them easy. By using interactive visualizations and providing various chart options, insights are presented in an engaging and easy-to-understand manner. Instead of getting lost interpreting long data tables and large blocks of text, users can easily view data in a visual format and read short crisp summaries to understand the insights quickly. Bite-sized headlines on insights also ensure that users get focused insights and always stay updated with the latest happenings in their business.
Gartner predicts that leaders should prioritize data literacy and put in place strategies to address the scarcity of data and analytics talent. As data expands at a rapid pace and organizations realize the importance of data-driven decision-making, the demand for insights also increases. Data teams cannot scale quickly to meet this demand. Self-service analytics are effective in democratizing data, simplifying access to insights, and contributing to data literacy across the organization. It has the power to transform business users such as frontline workers, floor managers, customer representatives, accountants, or sales executives into business technologists who are able to interpret business conditions and take insight-driven decisions confidently.
Errors and biases can creep into human decisions, especially in high-pressure situations. With the limited capacity to process data, users face many risks while basing their decisions on incomplete information and faulty interpretations. Wrong decisions can result in heavy financial losses, wastage of resources, and lost opportunities. Self-service analytics helps minimize such risks by processing large volumes of data quickly to extract precise and accurate insights. It provides the much required support by enhancing decision-making abilities, eliminating guesswork, and reducing the possibility of human errors.
When business users become self-reliant in getting answers to their daily information needs, it reduces the pressure on IT and support teams. The Research by Harvard Business Review Analytic Services includes an example of how the data team of a global healthcare technology company saw their handling of user queries drop to 20% from 100%, when business users, who were provided with self-service analytics, started performing 80% of their own data analysis. This frees up data analysts who can devote their time and efforts to solving more complex problems and focusing on innovation.
With easy access to decision intelligence, personalized recommendations, and contextual insights, business users can focus on their work and actions better instead of struggling with analysis. Self-service analytics can boost speed, improve efficiency, and increase productivity in the workforce. With insights at their fingertips, users can become more agile in reacting to changing situations. They are better prepared to tackle challenges and can optimize their efforts where they are most required. Self-service analytics are instrumental in promoting a data-driven culture across the organization.
Traditional analytics platforms required huge investments in hardware, maintenance costs, and a dedicated army of technical staff to set them up and generate reports. With limited users and a time-consuming insight-generation process, organizations could hardly extract any value. However, modern self-service analytics platforms like MachEye are game-changers when it comes to increasing the adoption of analytics, reducing overheads, and providing maximum returns on analytics investment. Self-service analytics platforms offer quick deployments, and customized configurations, thus reducing costs. Their intuitive user interfaces and ability to provide instant insights save time, ensures faster actions, and make users self-reliant.
Self-service analytics platforms like MachEye will soon become an essential tool in every decision-maker’s toolkit. Retailers can identify top products, compare sales over different time periods, and identify what, why, and how sales changed. Marketers can track leads and assess campaign outcomes. Customer relationship managers can track various customer success metrics in real-time. By enabling their users to gain the what-why-how of business insights easily, organizations are poised better to future-proof themselves with self-service analytics.
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