The Definitive Guide to Self-Service Analytics: Benefits, Design, and Emerging Trends

The Current Scenario of Data Requisition

Suppose the CMO of your company needs data of unique visits on the sales page of the company’s mobile app, unique recurrent website traffic on products page, and a 360-degree view of the clients' engagement of a particular region before his urgent client meeting in that geography.  

Similarly, the CIO needs access data to analyse a high-security data breach at a client’s server, and the Statutory Auditor needs offshore resource data to identify the scope of cost savings before a major vendor’s contract renews. Where do these business users go for such information?
All of them reach out to the Data team with urgent marked emails.
In response, the Data team puts on hold their own critical and strategic tasks to burn their hours on routine data requests. Once the initial reports are shared, they often need further tweaks, even overhauls, as the communications are often lost in translation between the end users and technical teams. Even basic differences like the way business defines a metric can blow up into massive rework.
So, the business users again knock on the doors of the Data team at odd hours, and the team gets on with the work again until the business requirements are met. But by then, the decision-makers had already made urgent decisions powered by guts instead of data . Delayed response to the data breach can cost the company its reputation and SLA breaches, unavailability of timely data reduced the cost savings by the Auditor, and the CMO couldn’t upsell on the existing client relationship.
On the next day, the cycle starts all over again, many times for the same data in the same department, just for the lack of function specific Self Service Analytics Solution.
In short, users rely heavily on the Data experts to retrieve and process data. This results in delayed decision-making, opportunities for cost and resource efficiencies get delayed or go unnoticed, data professionals spend time on mundane data retrievals, and data driven decisions with right metrics at fingertips becomes a pipe dream.
Data Requisition with Self Service Analytics
Imagine a data solution where a business user asks for data in natural language. Instantly, it displays pure actionable data with intuitive charts and graphs and an easily customizable interface. As easy as dispensing coffee.
Imagine if every business user could have a personalized solution and make data-driven decisions. And over that, they can share the knowledge across the users, so that not every team invents the wheel and can rather work collaboratively.
Wouldn’t that enable smart decision-making, reduce business and IT man-hours, and make the company more agile?
So, what is Self Service Analytics?
Simply put, Self Service Analytics is a stack of data solutions that empower business users, with or without data expertise, to access, manipulate, and visualize data without depending on Data Scientists and IT teams. To make it real, a typical data architecture will entail collating data sources into an integrated base table, this works as semantic table which is a consolidated table with relevant details. Then, it’s layered with views or visualisation for ease of consumption by business users. For a business user, semantic layer becomes the single-source-of-truth for all the relevant metrics needed in a unified form. This would mean collaborating with data teams, while defining the requirements, sourcing metrics from right data tables, aligning on KPI logics. Once semantic layer is deployed, data can be accessed by a BI tool, one of the options to consume the business metrics.
According to the technical preference of the user, custom solutions are developed – right from plain English statements to check relevant details to preset SQL queries and further to full-fledged technical queries. This makes Self Service Solution a personalized experience independent of the technical knowledge of the user. In essence, it enables the democratization of the data to derive insights at the user level. Self Service Analytics can provide a single source of truth to the users by accessing functionwide data from various sources including unstructured big data, web data, cloud platforms, spreadsheets, and social media. On ground business users are armed with real-time data access to drive action and innovation. Through intuitive data visualization, users can identify trends and relationships and explore data as never before. Individual analyses can be shared across the teams to foster progressive work methodology instead of losing valuable manhours on redundant analyses.
Misconception - Self Service Analytics not a Silver Bullet for every data need
Self Service Analytics brings the data to the fore, but it does not replace the essential skills of Data Scientists and Data Analysts. Sophisticated data manipulation and interpretation will still depend upon the skill of the user and would be best complemented by the competence of Data experts.
Self Service Analytics does not magically improve data quality or improve upon the security layers and organization’s data governance. These considerations will still need utmost caution and effort from the company. In the absence of correct and complete information, the analytics solution cannot enable critical decision-making.
Further, the change management to Self Service Analytics would entail training, empathetic nudging, and concurrent support from data team to let the users extract the most out of the Solution.
Recommendations to Build Self Service Solution: The Four Pillars of Data Democracy
Key to success of a self-service solution lies in the extent to which business users embrace the data solution. Driving adoption needs users to be open to change, receptive to new mechanism and tools to consume relevant metrics. Personalised solution is the name of the game as no one solution fits all. 

Overcoming these problems become the building blocks for a self-serve solution.

1. Start with the end: Onboarding and Training the end user

The user's desired outcome serves as the guiding principle for the Data team for developing the Solution and establishing a clear direction. 
Initiating the process entails gaining an initial understanding of business requirements, followed by comprehensive documentation, effective communication, and obtaining confirmation from the end user regarding the data needs - essential key performance indicators (KPIs), visualization requirements, and querying mechanisms. This initial phase sets the foundation for constructing the Self-Service Analytics Solution.
Engaging with stakeholders involves ongoing and continuous communication throughout the development journey, rather than being a one-time activity. This collaborative agreement ensures that the resulting solution is easily accessible, understandable, and straightforward to train on.
The user assumes an integral role within the solution, actively driving its functionality, rather than remaining a distant stakeholder.
2. Solution Designing
The Engineering team collaborates closely with the end user to define the following technical aspects:
a. The determination of relevant data tables necessary for constructing new data models. This involves analysing the data requirements and selecting appropriate tables or sources that will be incorporated into the solution.
b. The establishment of logical frameworks and semantic layers that enable the generation of outputs in alignment with the predefined key performance indicators (KPIs). This step involves implementing logical rules and calculations to transform raw data into meaningful insights.
c. The specification of the frequency and scope of the output generated by the solution need to be agreed upon. This includes determining the desired intervals or timeframes for data updates and the extent of data coverage required for accurate analysis.
Finally, the Self-Service Analytics solution are meticulously designed to facilitate right metrics or data with efficient mechanism to query, complemented with visualization for easy consumption. These data solutions serve as user-friendly interfaces that allow end users to interact with the solution, explore data, and gain valuable insights through visually appealing and informative displays.
3. Business Orientation 
With the intent of moving away from the Black Box Concept, the Self Service Analytics Solution is oriented toward the business. Technical jargon is minimized on the dashboard, natural language processing features are introduced (caveated with BI tool of choice) to save the users from learning technical syntaxes, and intelligence and intuitiveness are introduced in the UI in collaboration with the Business User.
4. Method of Consumption and Improvisation
Depending upon the technical skills and preference of the user, top use cases are developed separately, or a set of visualizations can be preset as ‘favourites’, and an interface is provided with a crisp and clean personalized view to consume metrics.
For more technically inclined users, access to data can be provided in a Sandbox environment. This gives the Business Users opportunities to explore and analyze the data with quick turnaround time, play on the what-if scenarios, uncover the hidden trends and relationships, and discover valuable intelligence or automation cases that can be permanently added to data or Business intelligence layer.
Emerging Trends in Self Service Analytics
In the coming years, Self Service Analytics will become a necessary solution to augment the emerging functionalities including:

1. Augmented Analytics

Augmented Analytics is an emerging trend within the realm of Self Service Analytics. This cutting-edge approach harnesses the power of artificial intelligence (AI) and machine learning to enhance the analytics process. With Augmented Analytics, tasks such as data cleaning, basic and advanced analysis, and even suggesting insights with commentary are automated, saving valuable time and effort for users. Additionally, the data and trends are intelligently visualized, providing intuitive and actionable insights. By leveraging these AI-driven capabilities, Augmented Analytics empowers users throughout the business hierarchy, from entry-level employees to top-level executives, to confidently make data-driven decisions, fostering a culture of informed decision-making and propelling the organization towards success.

2. Explore data visually

Call it the orthodox way but knowing right visualisation to tell the story is quintessential for making users self serve metrics. Advanced data visualization and exploration will be the next milestone for Self Service Analytics. With compelling charts, graphs, and maps, data exploration will become intuitive and serve as a go-to analysis for a wide range of users, just like a Google search.

3. Data Preparation for business needs

The field of self-service analytics is witnessing yet another emerging trend – the integration of data preparation tailored to specific business needs. This innovative approach enables users to transform raw data into refined and analysis-ready formats. By embedding robust principles of data governance, this process ensures that the data is thoroughly cleansed, sorted, and secured, complying with relevant regulations and privacy standards.

Furthermore, users can access and manipulate the data according to their specific requirements and authorizations, all without the need for IT involvement. This self-service data preparation capability empowers users to swiftly and independently prepare data for analysis, promoting agility and efficiency in decision-making, and accelerating the overall analytics workflow.

4. Embedded Analytics

The future of self-service analytics lies in its integration with various software, applications, and platforms. This means that self-service capabilities will be embedded directly into existing tools such as customer relationship management (CRM), management tools, and business intelligence (BI) platforms.

By eliminating the need for external tools, users can seamlessly access and manipulate data, as well as visualize insights, without having to switch between different applications. This streamlined approach optimizes processes and adds value to products by providing a unified experience.

From a business perspective, this integration saves valuable time, resources, and bandwidth for users, enabling them to focus on deriving meaningful business intelligence.

5. Data Storytelling

The implementation of a Self Service Analytics Solution is poised to revolutionize the art of Data Storytelling. By providing users with intuitive tools and access to vast data repositories, this solution empowers them to unearth profound insights, unveil hidden trends, and uncover intricate relationships within the data.

Armed with this capability, users can craft captivating and persuasive narratives to effectively communicate their findings to the board of stakeholders. With the ability to present compelling and vivid stories, this analytics solution amplifies the impact of data-driven decision-making, fostering a deeper understanding and engagement among stakeholders, ultimately driving organizational growth and success.

Case in Point: From Data to Insights in Marketing

Marketing is an intelligence-driven department that is flooded with data from infinite devices to ever-increasing platforms. Marketing struggles to use such quantities of data and make informed decisions quickly. 

The result is an incorrect target audience, disengagement with the customers, an incorrect choice of communication channels, and generic boring messaging to the client. 

The opportunities for cross selling and upselling vanish just for the lack of a 360-degree view of the customer.

Self Service Analytics tool steps in to save the day. 

The marketing department can run a query or ask in natural language to access the real-time intelligence of the customers. With such insight, they can laser focus their target audience, test the platforms on which the audience interacts and use precisely tailored messaging with impact.

The results of such campaigns can close the open engagements, give the efforts a logical conclusion, and propel marketing efforts in the right direction.

Parting Words

In the tech industry, it’s clear that Self Service Analytics is going to revolutionize the ways of working of the business functions and users that were hitherto challenged by the lack of data expertise. Now, all the sources of data from data lakes to overflowing spreadsheets can be tamed into a Self Service Solution to provide one source of truth for a function and bring agility to the decision making. From the Analytics Solution, the data can be accessed, analysed, visualized, and distributed in a seamless flow. Data Experts can work on sophisticated problems and the Business users can save valuable man hours by fostering a collaborative work environment that thrives on shared knowledge and individual innovation.

The Self Service technology is already on its way to augmented intelligence. The question is when your company will get on this exciting journey and make the most out of your own Self Service Analytics Solution.

Posted on : January 02, 2024

Category : Data Engineering

Tags : Self-service analytics data governance business intelligence data democratization data visualization artificial intelligence machine learning data storytelling technology adoption.

About the Authors

Sourabh Arora

The author is a Director (Finance) in Eucloid. For any queries, reach out to us at:

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Tarun Dudeja

The author is a Director (Delivery) in Eucloid. For any queries, reach out to us at:

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