Is Your Data Ready for AI and Analytics?
A recent Deloitte survey found that 51% of CEOs consider data challenges the primary obstacle to generating business value with analytics and Artificial Intelligence (AI).
Clean, accurate and reliable data is essential to turning AI and analytics into powerful assets that let executives, managers and others use data to derive actionable insights. In many businesses, though, data is scattered across multiple systems, departments, and formats. This fragmentation creates silos that prevent a unified view of the business and make it difficult to draw meaningful insights. Siloed data is often riddled with quality issues, too, involving inaccuracies, inconsistencies, and missing values that can lead to erroneous insights and decisions.
In addition to these challenges, enabling more people across the business to easily access and analyze core company data on their own is key to effective AI and analytics initiatives. Such data democratization also allows data analysts to shift more of their efforts away from time-consuming data wrangling and preparation tasks to assessing critical insights from the business side instead.
How A Data Hub Can Help …
A United View of Data Across the Business
A data hub eliminates the cost and performance drain of connecting directly to individual data sources to support AI and analytics. Instead, it streamlines access to data and systematically transforms and manages the data in a single place for secure access to and analysis by users across the business.
This is particularly crucial for casual users that need quick access to a carefully selected set of “operational-level” data to support insight-driven decision making. For companies in manufacturing and distribution, for example, this curated data set will usually include sales, inventory, purchasing, production, fulfillment and other similar info – all which needs to be cleansed and made analytics-ready using proper business logic.
While information from ERP and other business systems can be accessed with today’s reporting toolsets, the data structures of these systems are quite complex. This leads to users not being able to easily attain the data they need or comprehend it once they have it. Plus, every piece of data that analysts need does not reside solely in transactional systems. This includes plans, budgets, forecasts and other information that employees, suppliers, customers and other third parties may provide, like point-of-sale data.
A data hub-based platform for AI and analytics plays a crucial role in translating raw data of virtually any type into meaningful business insights. It integrates and harmonizes data from multiple sources to yield a unified view of the business, and it connects business terms to the underlying data to ensure consistent definitions across the organization.
A data hub safeguards the consistent reporting and interpretation of data by modeling an organization’s information with clearly defined values and dimensions so that any report or dashboard referencing “total revenue by month” (for example) will use the same definition. This capability also enables higher-level concepts like KPIs to be defined and calculated consistently and accurately, too.
Beyond that, a data hub can enrich data for improved AI and analytics – like augmenting customer and product data with additional info. not included in your ERP or other core business systems. This focused approach yields greater consistency and trust of data and is vital for teams in sales, finance, operations, and other departments that often store information in numerous spreadsheets, business applications and other sources.
Democratized Access to Valuable Data
A data hub facilitates democratized access to valuable data and speeds up time to insight for a wide range of business users from the C-suite to the plant floor. Data analysts additionally benefit from the tedious task of locating and extracting data for AI and analytics so they can focus more on analyzing and deriving key insights from the data.
As organizations increasingly rely on data-driven insights and metrics, the importance of the data hub in AI, analytics and decision-making will continue to grow. It will become a cornerstone of analytical tools (like the data hub-driven analytics platform offered by Silvon), simplifying and accelerating tasks like these use cases below.
Enhanced Data Analysis
A data hub together with AI and analytics can uncover patterns, trends, and correlations that may be difficult for humans to identify manually. This leads to more accurate and comprehensive data analysis, providing deeper insights and enabling data-driven decision-making.
On-the-Fly Dashboards & Reports
With a data hub platform, people with no prior experience in the field of data visualization will be able to select the data they’d like to include in a dashboard (or use a prompt to pick the best view from their analytics tool to include in a chart or graph). Plus, users will be able to generate and interact with reports based on data they wish to view simply by leveraging an AI prompt or assistant.
Easily Explained Insights
Using AI prompts, both data analysts and business users alike will be able to answer questions about their data hub-driven reports, like why a specific data point on a chart is behaving a certain way. AI also provides the capability to share which business views of information generated by the data hub platform are used most and why. This can help facilitate additional collaboration among teams via the sharing of key reports, while helping an organization streamline the number of reports it maintains by stratifying those less used.
Actionable Information
Another key function of a data hub-driven AI and analytics platform is the ability to make data actionable. With it, users can drill down from their dashboards to view more granular levels of detail in the data hub’s data to drive greater insights and actionable information. Plus, the platform can support event-driven elements like alerts and notifications that immediately notify and share information with users about business situations that may require their immediate attention.
Cross-Functional Planning & Budgeting Support
Since a data hub manages data from multiple sources, the potential scope of budgeting and planning processes can take into account far more external variables like promotions and competition for more effective planning results. Data-driven collaboration across several functional areas using a data hub-driven analytics and AI platform is also possible since multi-source budget and sales data can reside in a single plan.
A Solid Playing Field for Predictive Analytics
With a unified data hub, predictive models can have access to a comprehensive dataset that represents the full spectrum of business activities and enables more accurate and insightful predictions. For instance, a model predicting product demand can factor in not just past sales data but also marketing campaigns, customer service interactions, and external factors like market trends or economic indicators—all integrated via the data hub.
Streamlined Data Management
A data hub-based AI and analytics platform also offers the ability to auto validate and correct errors before data is extracted, aligned, integrated and managed within the data hub. And with built-in AI prompts, developers and analysts can rely on the platform to easily map new data sources to the hub as they wish to bring in and analyze additional data.
By amassing vast amounts of business information from numerous sources, a data hub-based AI and analytics platform bridges the gap between data and business teams. And it simplifies data access across the business and unlocking data from silos to make it both quicker and easier to understand and act upon.
To learn more about Silvon’s data hub-driven analytics solution, visit www.silvon.com