Implementing Self-Service Analytics: Succeed Where Many Others Fail

using self-service analytics
using self-service analytics

Organizations are now inundated with data. This is supposed to be a good thing for enabling business intelligence (BI), but when a figure like “5%” can also be written as “five percent” and miswritten as “0.5,” we can see that the data must first be good in and of itself. We explore this and other BI enablement challenges in this post — and show how solutions that are built on self-service analytics address them all.

When implemented and governed properly, BI and self-service analytics can:

•    Improve decision-making and operational efficiency.
•    Enable data-driven growth strategies.
•    Achieve peak financial performance and ROI
•    Improve scalability and future readiness of business processes.
•    Enhance customer insights and satisfaction. 

Given these benefits, it is no surprise that actualizing BI’s potential is a top goal of many enterprises. Four large blockers get in the way of this goal.  

BI and self-service analytics challenge 1: Implementing data integration and governance

Data silos form because of geographical separation and departmental segmentation within an organization as well as the absence of an integrated data strategy. This leads to inconsistent data that takes time and effort to standardize. 

Real-world example: Unifying data across multiple systems in a retail chain

A retail company has multiple stores across different regions, each with its own point-of-sale system, inventory management system, and customer loyalty program. The company wants to gain a holistic view of its sales performance, customer behavior, and inventory levels across all stores, but the data is siloed and inconsistent. The company needs a solution that can aggregate the data from various sources, standardize the data formats and definitions, and provide a secure and centralized data platform that can be accessed by different business users for analysis and reporting.

Hurdles to solving this problem

Clients cite the following pain points when they try to do away with their data silos and integrate their data:

•    Complex integration processes: Data analysts must navigate the complexities of integrating data from various sources. Dealing with legacy systems and different data formats is especially difficult since these often require custom solutions and extensive manual effort. 

•    Data quality concerns: Ensuring that the aggregated data is high-quality involves cleansing, deduplication, and validation processes that are both time-consuming and resource-intensive. 

•    Limitations in real-time data processing: For data visualization to reflect the most current information, the BI solution must have real-time data-processing capabilities. The solution must also be powerful enough to quickly process large volumes of data. Real-time data processing is more difficult to achieve when the BI solution is used with systems that are not designed for real-time updates. 

•    Security and compliance issues: Balancing the need for easy data access with stringent security requirements and compliance with data protection regulations further complicates data integration efforts.

•    Lack of data governance: Organizations that lack data governance, metadata management, data marketplaces, and catalogs struggle with data trustworthiness, security, and discoverability, which complicates scalability and compliance. This absence leads to inefficient use of resources, reduced data literacy, and increased barriers to collaboration. Implementing robust governance and metadata management is essential for fostering trust and enabling effective self-service data environments.

How businesses are trying — and failing — to overcome this challenge

Firms use a hybrid architecture — a combination of on-premises and cloud-based data integration platforms such as Amazon Web Services (AWS), Google Cloud, or Microsoft Azure Data Factory — to integrate data from various sources. Reporting is also done from disparate sources, for example SAP Business Warehouse (BW) and Azure Data Lake Storage (ADLS). Hybrid architectures are serviceable when they’re still small and relatively simple. However, over time, they grow in size and complexity, making them unwieldly to use effectively.

implementing self-service analytics

BI and self-service analytics challenge 2: Demonstrating the value of analytics investments

Convincing stakeholders of the value of BI and analytics investments is practically a high-stakes mission. This includes demonstrating the ROI of BI projects, which can sometimes have intangible benefits or long-term payoffs that are hard to quantify in the short term. Managers need effective methods for calculating and presenting the value realized by BI initiatives to justify ongoing and future investments in data and analytics.

Real-world example: Assessing the impact and benefits of BI on manufacturing

A manufacturing company invested in a BI solution that provides dashboards and reports on key performance indicators (KPIs), such as production efficiency, product quality, and customer satisfaction. The company wants to measure the impact of the BI solution on its business outcomes, such as revenue, profitability, and market share. However, the company faces difficulties in isolating the effects of the BI solution from other factors, such as market conditions, competitor actions, and operational changes. 

Hurdles to solving this problem

Clients cite the following pain points when they try to convince stakeholders of the value of BI investments:

•    Quantifying intangible benefits: The intangible benefits of BI projects, such as improved decision-making or customer satisfaction, are critical but hard to measure. 

•    Long-term ROI projection: The long-term value of BI investments can be difficult to see when short-term benefits are few and small or when initial costs are high. 

•    Data to support ROI claims: Collecting and presenting data that convincingly demonstrates ROI requires comprehensive tracking and analysis that might not be readily available. 

How businesses are trying – and failing – to overcome this challenge

Most companies go to data service providers to overcome this challenge. However, many lack a framework for calculating the tangible benefits of BI investments.

BI and self-service analytics challenge 3: Fostering data literacy in the organization  

Across many industries, there’s a high demand for professionals who are skilled in BI as well as data and analytics. Enterprises need to find, retain, and upskill talent through custom-tailored training programs. Firms also need to build a culture of continuous learning and innovation, where non-IT business users are trained in using AI-powered, self-service BI solutions to gain insights without the help of the IT team.

Real-world example: Implementing self-service analytics in an organization with limited resources

A nonprofit organization has a small and busy team that uses data to monitor and evaluate its programs and activities. The organization wants to empower its staff to use BI as well data and analytics tools so it can rely less on external consultants or IT support. However, the organization lacks the resources and time to train its staff on such tools, so it needs a self-service solution that is easy to use, intuitive, and can guide the staff through the data analysis and visualization processes.

Hurdles to solving this problem

Clients cite the following pain points when they try to shore up BI as well as data and analytics skills within their respective organizations:

•    Competitive talent market: Demand for skilled BI and analytics professionals often outstrips supply, making the market highly competitive. Recruiting, training, and retaining talent are costly endeavors. 

•    Tailored training programs: To be effective, training programs for upskilling employees must be tailored to their various skill levels and learning styles. 

•    Resistance to change: Cultivating a culture that supports continuous learning and innovation constitutes an organizational change that will be met with resistance or lack of engagement from staff. 

•    Unpreparedness for the future: Due to the rapid evolution of generative AI and large language models, it is easy for firms to fall behind and incur technical debt. 

How businesses are trying — and failing — to overcome this challenge

Before becoming our clients, firms used e-learning platforms like Udemy and obtained services like Data Literacy as a Service to have staff gain BI skills. While better than nothing, virtual learning sessions can’t provide the hands-on, instructor-led experiences that mirror real-world applications of technology.

Moreover, generic online courses cannot adequately align with the organization's technology stack or address the specific needs of employees. Employees require tailored courses that allow them to balance upskilling with their day-to-day responsibilities. Last but not least, virtual training leaves a noticeable gap in on-the-job training opportunities, which are crucial for applying new skills in practical, work-related contexts.

Lingaro’s proactive approach to self-service analytics

To help organizations address the problem of technical debt, Lingaro embraces emerging technologies for them. We ensure that our solutions evolve at the pace of change, thus making organizations agile and proactive. This approach to self-service analytics in particular is not just for enabling better decision-making, but also for transforming how every team interacts with data. Lingaro also helps companies by preparing their infrastructure and organizational culture to use these advanced tools, ensuring that they stay ahead in a data-driven future.


BI and self-service analytics challenge 4: Aligning analytics with business objectives and strategies

C-level executives must ensure that BI initiatives and data visualization efforts are fully aligned with business objectives and strategies. Initiatives and efforts must directly support the company's goals, whether it's revenue growth, customer satisfaction, or operational efficiency. The executives need to develop strategies for embedding BI capabilities into business processes to enhance their decision-making and operational workflows. They also need to align their BI tools with business use cases.

Real-world example: Boosting a travel company's customer loyalty with integrated business intelligence

A travel company wants to increase its customer loyalty and retention. Its strategy is to provide personalized and relevant offers and recommendations to its customers. The company has a BI solution that can analyze customer data, such as preferences, behavior, and feedback, and generate insights and suggestions for improving customer satisfaction and loyalty. The BI solution could be integrated into business processes for marketing, sales, and customer service, which would increase the reliability of the BI insights and recommendations and creates a feedback loop that can measure and improve the effectiveness of the BI solution. However, department leaders hesitate to implement the BI solution for fear that it will slow down and disrupt their processes.

Hurdles to solving this problem

Clients cite the following pain points when they try to align BI initiatives with business objectives: 

•    Understanding business needs: Deep business acumen and ongoing communication with stakeholders are required to bridge the gap between technical BI capabilities and the strategic business objectives they’re meant to support. 

•    Adapting BI tools to business processes: Customizing BI tools and analytics to fit specific business processes and use cases is a technically complex process and requires ongoing adjustments as business needs evolve. 

•    Stakeholder engagement: Consistent engagement and buy-in from stakeholders across the organization is essential for aligning BI initiatives but can be challenging due to differing priorities and perspectives.

How businesses are trying — and failing — to overcome this challenge

Companies mistakenly let departments align BI investments with their objectives instead of with the organization’s. Others use solutions that often overlook the necessity for a use-case driven approach to BI, which is essential for ensuring that BI efforts are directly contributing to the organization's strategic objectives and delivering measurable value. Even when a use-case-driven approach is employed, a significant disconnect or rift can become apparent between overarching business strategies and their translation into actionable BI use cases, which define BI initiatives. This gap means that BI initiatives do not fully support or align with the organization's strategic goals.

Lingaro’s approach to self-service analytics

In addition to focusing on the technological solutions, Lingaro also applies its business expertise to address the needs of the enterprise. Given this approach, Lingaro built the following solutions:

Data Omnilence 

Data Omnilence is Lingaro’s data architecture framework that simultaneously addresses the first and fourth challenges (data integration and accessibility, alignment of BI with business objectives and strategies). Data Omnilence does this by bringing data strategy, AI technology enablement, and business value realization together. Unlike solutions that apply a technology-driven approach, Data Omnilence pushes forth a business-driven approach. In the Omnilence framework, architectures are customized for clients, BI investments are aligned to business use cases, data silos are reduced, and the client's architecture is simplified to make it scalable, secure, and integrated.

Lingaro Forecasting Tool

Self-service strategy 

Lingaro’s self-service strategy offering enables business users to use data to make data-driven decisions, arrive at business insights, and come up with effective solutions more quickly and more easily. Enabling self-service data consumption allows business users to create value from data on their own, with little to no reliance on technical resources like data experts.

Lingaro’s self-service framework is designed to ensure that the organization’s processes, people, and technologies are fully primed for transformative self-service excellence. We begin with a comprehensive evaluation that ensures the following:

•    Well-governed, high quality data that is ready for self-service
•    A technology infrastructure that includes robust tools like data catalogs, marketplaces, detailed data dictionaries, automated lineage tracing, metrics stores, and thorough documentation
•    A skilled team that can utilize these resources 
•    Tailored reskilling programs to bridge skills gap and elevate the employees to data-savvy business analysts, ensuring that everyone can make the most of the company’s BI capabilities

From there, we design a road map for building upon strengths and bridging capability gaps, achieving quick wins and long-term improvements, and scaling solutions up and throughout the entire organization.

Business value realization

Beyond technology excellence, Lingaro boasts a strong understanding of its clients’ businesses and the challenges they face, especially in industries like consumer-packaged goods (CPG). Lingaro exhibits this through its business value realization offering. It addresses the second challenge detailed earlier (demonstrating the value of BI investments) by enabling the following:

•    Business validation (change management)
•    Communication
•    User experience
•    Learning
•    Measurement

Furthermore, this offering allows clients to employ Lingaro’s data visualization team. 

Data literacy

Lingaro’s data literacy offerings address the third challenge (developing BI and data analytics skills). Powered by our AI-enhanced brAIn solution and a targeted accelerator model, these offerings help our clients obtain BI knowledge through a combination of custom-tailored scheduled classroom sessions, webinars, train-the-trainer programs, and short videos. The educational materials used here are developed and crafted by ALCHEMY., Lingaro’s creative agency, to help ensure adoption. 

The brAIn solution can also empower organizations to organize, search, and use existing knowledge to gain great returns. We ensure that a company becomes data literate and that their employees will be able to apply what they’ve learned to create reports effectively, generate insights through AI-powered self-service, and convert insights into data-driven decisions.

The brAIn solution

When data sources are integrated under one roof and people are literate on all the analytics reporting tools that are available to them, business users can really start using analytics efficiently and effectively. However, while BI helps solve many enterprise challenges, it also poses challenges of its own. Having a data and analytics partner like Lingaro will help you overcome them and maximize BI’s benefit to your business.

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