These days in traditional BI environments, IT departments and data specialists performing BI services handle the growing volumes of information, its heterogeneity, and a multitude of data management technologies, all while meeting the ever-increasing need for data-driven insights from business users. Even applying agile methodologies to integrate, curate and deliver data in short sprints, in most cases, the assigned teams cannot provide the needed insights on time. When this occurs too often at your workplace, you should consider implementing self-service business intelligence.
In this article, we’ll look into self-service business intelligence’s features and benefits and how it can make a difference for your business.
Self-service business intelligence: the essence
Self-service business intelligence (self-service BI) is a set of approved and supported processes, architectures and tools which enable business users to fulfill their data management and analytics tasks without relying on IT and data analytics teams.
In the case of traditional business intelligence systems, business users base their decisions on reports and dashboards curated by IT specialists and data analytics teams. If the necessary information is missing, a user makes a request and the dedicated team takes the process from there on. Firstly, they gather requirements, including what data is needed, how it should be processed and visualized, how often the insights should be delivered, what the information refresh rate should be, and so on. Then the tech team identifies the data sources and collects, aggregates, models and delivers data for analysis. When the report is ready, the user approves it or makes a further change request.
Although complicated, this process is a part of the daily routine in most data-oriented companies. Problems arise when requests to IT and data analytics teams become so frequent that a huge backlog of requests builds up. This is where the new approach to business intelligence–self-service BI–can make a huge difference for all involved in data-related processes and the company as a whole.
Self-service BI is not an alternative to traditional business intelligence but a complementary capability, allowing business users to interact with data and carry out analytics tasks themselves regardless of their tech expertise.
Traditional BI vs self-service BI
|Traditional BI||Self-service BI|
Challenges and constraints
Self-service BI users and their requirements
Traditionally, there are two self-service BI user groups – standard users (those who mostly consume the BI insights) and power users (advanced users who generate new BI insights for internal and external use). However, recently more and more business users are becoming skilled and acquiring data literacy, so the division between the two groups is blurring.
|Standard user||Power user|
Key features of self-service BI solutions
The functionality of self-service BI software naturally overlaps with those of traditional BI systems. Thus, we’ll focus on the very features that set self-service BI apart and make it a desirable capability to complement the traditional BI software with.
Data source connectors
The cornerstones of every BI architecture is an analytics data repository, which centralizes and maintains data for further processing. An enterprise data warehouse serves this purpose, and sometimes it may be complemented by data marts to streamline reporting for particular user groups. However, the data consolidated into a data warehouse may not be enough to meet the needs of business users, with its granularity or completeness insufficient.
In traditional BI scenarios, an IT team would have to build an integration point with a required data source, which would slow down the process. A self-service BI solution, in its turn, addresses the issue with the sets of connectors to data sources. That way, business users can quickly get access to new information themselves and make informed decisions in real-time.
Augmented analytics capabilities
The incorporation of AI and ML capabilities helps automate many of the data management and analytics activities, from data quality management to data visualization, performed by IT specialists and data analytics teams in traditional BI environments. Although it may seem too futuristic for companies at the very start of their BI journey, augmented analytics capabilities are already implemented into most self-service BI platforms and have become the key differentiator for most companies.
Intuitive user interface
Graphical drag-and-drop user interface with support for natural language querying and generation, intuitive navigation, etc. is an obligatory element of a self-service BI solution. Its value lies in empowering business users with a comprehensive feature set to curate data sets, run analysis, create dashboards, etc.
Rich reporting capabilities
While some business users cannot wait to get down to exploring corporate information and building their own charts and graphs, others may find self-service BI software a nuisance. That is why a self-service BI solution should offer customizable report and dashboard templates as well. In this case, a user may simply pick a suitable template, configure it according to their role, select the refresh interval for automatic data update, and set up automatic report or dashboard send-out time and mode (via emails, secure viewer areas, URLs, embedding, mobile reporting, etc.).
Semantic data catalogs
Semantic data catalogs help BI consumers autonomously find relevant information with familiar business terms (customer, prospect, sales, etc.). Having constantly updated and well-governed semantic data catalogs helps ensure all business users across the company get a consistent view of corporate information and can collaborate efficiently.
Self-service BI software should not only enable its users to access and handle data, but also share the discovered insights with colleagues. Self-service BI software supports this capability with content sharing and embedding (scheduled and ad-hoc), role-based content editing, alerts, and notifications on report updates/changes/etc.
Benefits of self-service BI
In addition to reaping the benefits inherent to traditional BI systems, self-service BI software adopters can expect to see:
Traditional BI solutions are good as long as there are enough human resources to carry out the related activities and IT specialists can closely cooperate with business users. If this is not the case, the backlog of tasks and poor analytics and reporting is inevitable.
With self-service BI software, business users are empowered to get insights whenever they need them and timely act on them with no third parties involved.
Decreased reliance on IT teams
Self-service BI is a win-win solution for both business users and IT teams. The former can be more self-sufficient in their data-related activities, while the latter can stop running monotonous analytics and reporting and concentrate on other tasks – curating data, governing data management processes, data modeling, etc.
Increased data literacy
Traditional BI systems are run by data analysts and technicians, preventing business users from building their ability to read, write, and communicate data in context. Self-service BI, on the contrary, motivates business users to be active participants in the data analytics workflows, thereby increasing data literacy, which, according to Gartner’s Annual Chief Data Officer Survey, will become essential in driving business value by 2023.
Top self-service BI platforms on the market
- 100+ data source connectors
- Self-service data preparation
- Augmented analytics capabilities, including smart narratives, anomaly detection, text, sentiment and image analytics, etc.
- Natural language processing capability
- Data security, including data encryption, row-level and workspace-level security, etc.
- Pre-build and custom visuals
- Embedded analytics and reporting
- Native mobile BI apps for Windows, iOS, and Android
- Free version available
- Per-user and per-capacity licensing
- Available as a SaaS option in the Azure cloud and as an on-premises option in Power BI Report Server
- 90+ data source connectors
- Automated data management and augmented analytics (AI-powered predictions, what-if scenario planning, etc.)
- Visual analytics and data storytelling capabilities
- Intuitive dashboard creation (drag-and-drop, drill-down, NLP, etc.)
- Comprehensive authentication and authorization mechanisms, row-level security, etc.
- Embedded analytics and reporting
- Provides a mobile version for Android and iOS
- Free trial available
- Role-based licensing
- Cloud and on-premises deployment options
- Hundreds of data source connectors
- Search and conversational analytics
- AI-assisted data management, analytics and visualization
- Various collaboration options (personal spaces, shared spaces with user control, managed spaces, content co-development)
- Web client and native apps for iOS and Android.
- Embedded analytics and reporting
- Row-level and column-level data security
- Deployment flexibility (on-premises, any major cloud provider, multi cloud, or hybrid)
- Free trial available
- Per-user and per capacity licensing
Partner up with Itransition to build an agile self-service BI solution
Why does self-service BI fail?
No self-service BI governance strategy
Self-service BI can make a difference only if data value, quality and security are not compromised. To grant data access to business users and still maintain high data quality and overall control over the data management and analytics workflows, companies need a full revision of their data governance strategy. Otherwise, they face the following risks:
- Data inconsistency, or having multiple answers to one question because of different data management and analytics approaches.
- Duplicated efforts, with teams from disparate business departments performing data management activities (collection, transformation, cleansing, etc.) with the same data sets.
- Poor data quality (incomplete data, unidentified data errors, etc.), leading to faulty decision-making.
- Data security breaches resulting from the lack of granular data access controls, data misuse, sensitive data exposure, etc.
- BI decentralization, with departments functioning independently from each other while adhering to separate policies and standards, using different software, etc.
Resistance from business users
Self-service BI works if business teams invest their time in it. However, self-service capabilities are not always much awaited, with business users being too accustomed to pixel-perfect reports prepared by dedicated teams or feeling insecure because of their insufficient skills. To mitigate this risk, we recommend adhering to the following practices:
- Involving all stakeholders and users in the self-service BI implementation project from its planning stage.
- Dedicate time and resources for user training and onboarding, because even though the software offers an intuitive user interface, end-users still need some time to get acquainted with it.
- Find a strong business sponsor to head up self-service BI implementation that business users can look up to or turn to when issues arise.
In conclusion: building an agile self-service BI solution
While exploring the concept of self-service BI and its promises, one may think that the solution has all the answers and is a panacea for all BI challenges. However, it’s clear from the above that adopters can face many hurdles on the way to actual benefits. In conclusion, we will share with you a set of best practices for ensuring self-service BI success:
- Empower – provide your business users with a handpicked set of tools to access and manipulate data.
- Educate – create a collaborative environment and make your employees the advocates of self-service BI by bridging the data literacy gap with a training program and solid support for new users.
- Govern – set up and maintain control over the data management and analytics workflows in a way that encourages people to embrace BI capabilities.
- Make self-service BI an evolution, not revolution – you cannot force people to use self-service BI, but you can carefully guide them towards self-service BI appreciation and gradual adoption.