Durch Self-Service-Analytics können User ohne die Hilfe von IT-Experten Einblicke gewinnen und sich Berichte und Visualisierungen anzeigen lassen.
Why is Self-Service Analytics Important?
Benutzer in verschiedenen Geschäftsbereichen müssen nicht mehr das IT-Team bitten, Berichte zu erstellen. Die Anbieter haben neue Funktionen entwickelt, die es den Benutzern erleichtern, leistungsstarke Business Intelligence (BI)-Dashboards für ihre spezifischen Geschäftsfunktionen zu erstellen.
The main reason to embrace this new paradigm is to allow users to operate autonomously without relying on central IT functions. Starter packs, video guides, in-context help, and cloud-based subscriptions will enable line of business users to explore their own operational data and make data-based decisions with greater agility.
Key Capabilities of Self-Service Analytics
The following features help to make traditional BI self-service:
Ease of Use
The BI user interface must be intuitive with simple drag-and-drop charting for business users to succeed with self-service analytics.
AI Assistance
Artificial intelligence (AI) enables self-service by providing a natural language interface for writing queries rather than writing more complex SQL queries. The natural language approach can take a conversational chat-based approach, prompting the user for the required information. The SQL query the user is building can be displayed as the prompts are completed to teach the user how to write queries more directly. The second area where AI can help is by making it easier to select appropriate algorithms for analyzing and interpreting business data and developing forecasts.
Cloud-Based Service Delivery
Departmental users usually have operational expense accounts, which they can use to fund self-service analytics subscriptions without involving slow and cumbersome procurement cycles.
Built-In Training
In-context demos and video training make the initial learning curve for the BI application less steep.
Pre-Built Templates
Many analytics providers host marketplaces containing templates that are designed to offer a head start. Pre-built templates can be focused on horizontal and vertical lines of business use cases. Examples of dashboards for horizontal use cases might include Customer 360, marketing, sales, and finance. These dashboards use relevant feeds from applications such as ERP, social media, and CRM systems.
Benefits of Self-Service Analytics
Below are some of the many benefits of self-service analytics:
- Analytics expertise is developed across the business by delegating analytics to individuals in lines of business so they can reduce their reliance on centralized BI and IT experts.
- More corporate data assets are used for decision-making because more people can access self-service tools.
- Increased business responsiveness to changing customer behavior and market conditions as users are more in sync with such changes.
- Provide greater analytics expertise for departments and empower them to share insights with shared dashboards.
- Democratizing analytics by bringing it into the reach of more business users, thanks to increased ease of use and convenience.
- Improved decision-making translates into greater efficiency and profitability.
Examples of Self-Service Analytics
Customer Support
Self-Service-Analysen können der Kundensupport-Organisation helfen, proaktiver zu sein. Grundlegende Analysen des Kundensupports verfolgen die Anzahl der aktiven Anrufe in verschiedenen Schweregraden und die Zeit, die zum Schließen von Trouble Tickets benötigt wird. Anspruchsvollere Analysen beinhalten die Korrelation von Account-Management-, Vertriebs- und Support-Aktivitäten, um sicherzustellen, dass Support-Probleme keine Auswirkungen auf Lizenzverlängerungen haben. Durch die Untersuchung der Personen, die die meisten Supportfälle öffnen, können Schulungsmöglichkeiten aufgedeckt werden, oder es können höhere Supportstufen, einschließlich On-Premises-Audits, angeboten werden, um schlechte Konfigurationen weniger fehleranfällig zu machen.
Sales
Self-service analytics can help sales teams be more responsive. If sales have direct access to data feeds from marketing systems that track the buyer’s journey, the inside sales team could turn Marketo contacts into active contacts by calling prospects minutes after downloading a gated asset instead of waiting for a notification from Salesforce.
Customer retention teams can proactively track contacts that visit competitors’ websites or search for specific keywords so they can reach out with their best offer and ensure a renewal.
Marketing
Most marketing teams have difficulty getting a cohesive view of prospects at each stage of their buying journey down the funnel. Self-service dashboards that show paid and organic search activity, landing page visits, downloads, trials, and outbound sales prospecting activity provides the big-picture view they need. Marketing must justify spending on campaigns, etc., so dashboards that demonstrate the return on different campaigns and tactics help make the case for repeat activities.
Advanced Analytics With Actian
Actian Data Intelligence Platform is purpose-built to help organizations unify, manage, and understand their data across hybrid environments. It brings together metadata management, governance, lineage, quality monitoring, and automation in a single platform. This enables teams to see where data comes from, how it’s used, and whether it meets internal and external requirements.
Through its centralized interface, Actian supports real-time insight into data structures and flows, making it easier to apply policies, resolve issues, and collaborate across departments. The platform also helps connect data to business context, enabling teams to use data more effectively and responsibly. Actian’s platform is designed to scale with evolving data ecosystems, supporting consistent, intelligent, and secure data use across the enterprise. Request your personalized demo.
