Data connectivity: Activate your data ecosystem for better insights and analytics

Businesses have access to more data than ever before. But just having data doesn’t mean you’re getting value from it. Are you collecting, organizing, and displaying genuinely personalized and valuable insights? Or is that data siloed and disconnected, waiting on the sidelines to be put into play?

As a steward of your organization’s data, you have the opportunity to put a wealth of intel at users’ fingertips—and data connectivity is the key to tapping into your data’s power. Connecting all your data fuels your ability to tell personalized, relevant stories to your internal stakeholders and your product’s end-users alike. This is especially true for embedded analytics experiences, which require data connectivity to tell a meaningful story.

What is data connectivity?

Data connectivity is the process of integrating data across multiple sources and destinations, including different devices, systems, and applications. It creates a comprehensive source of information, which is the foundation of effective analytics.

Every tool you add to your tech stack complicates your data ecosystem. Eventually, making your data available and actionable is challenging because it’s spread across multiple systems. Data connectivity builds scaffolding across all your tools using databases, APIs, and cloud services, linking everything together. This process of exchanging, combining, and unifying disparate data turns it into actionable business intelligence.

Modern businesses have data flowing in from multiple sources, and need to deliver insights in a variety of destinations. Once you’ve built solid data connectivity scaffolding, you can effectively generate analytics that tap into the full power of your data ecosystem.

Timely, data-driven decision-making

Before connected data, we could only perform batch analysis for multi-source reporting—pulling data and extracting insights from lagging indicators. While still relevant in some cases, today’s business needs increasingly demand real-time (or near real-time) analysis. That can only be achieved with efficient, connected data sources. Data connectivity powers the collection, synchronization, processing, and distribution of large data sets so insights can be delivered quickly and automatically.

Real-time analysis is vital for various internal and end-user scenarios, especially when up-to-the-minute intel is required to drive meaningful action.

• For internal teams: Real-time data empowers teams to react in the moment to pressing user needs and business challenges that require rapid response.
• For end-users: Real-time analytics ensure they can trust in-platform analytics to accurately reflect their most recent activity.

Operational efficiency and cost reduction

Connected data improves operational efficiency by giving organizations a holistic view of their information. The ability to see the big picture and how all the data fits together allows teams to streamline operations, reduce errors, and lower operating costs.

• For internal teams: Data connectivity helps teams avoid data silos that slow down processes, create redundant work, and cause avoidable errors.
• For end-users: Connected data ensures dashboards are accessible and accurate for every end-user, driving insights they can put to work immediately.

Enhanced innovation

Great innovation stems from great information. Your data points offer all the pieces you need, while data connectivity unifies them to unveil the overarching insights—the patterns and opportunities you can act on.

By enabling teams to swiftly access, organize, and analyze substantial amounts of data, connected data reveals hidden insights and facilitates digital transformation. Fully connected data can help optimize the products and processes you already use, or unveil new opportunities you’ve never noticed. 

• For internal teams: Connected data enables teams to recognize user trends, leverage insights from diverse sources, and improve decision-making for faster innovation.
• For end-users: Embedded analytics—fueled by connected data—in your tools, app, or platform, give your end-users that same bird’s-eye view, so they can enhance innovation too.

Security, compliance, and trust

Secure data connectivity ensures compliance with regulations like GDPR, strengthens overall security, and builds stakeholder trust. By standardizing data connectivity and how it’s handled, you reduce risk and improve your team’s compliance and data protection.

• For internal teams: Fully connected data systems are easier for teams to maintain and update as compliance regulations change and new security risks arise. A robust data connectivity solution ensures no data lives in silos that can be overlooked and ensures data is protected at every stage.
• For end-users: End-users rely on your platform for rigorous security and compliance; the fallout if things go awry harms their confidence and your reputation. Connected data, designed to minimize data breaches, secures end-user trust and keeps their information safe.

Customer engagement and retention

The more connected your data, the better you understand your users. Unified customer profiles—created with data pulled from your CRM, website, app, and other sources—allow you to hyper-personalize your customers’ journeys with targeted communications. Plus, those insights add value to your embedded analytics, allowing end-users to understand how they use and benefit from your app. 

• For internal teams: Your internal teams can use connected data to glean more profound insights into user preferences and behavior. That’s the basis for more strategic marketing, product development, and customer support.
• For end-users: Your end-users benefit from the results of your internal team’s learnings as well as the personalized insights you can embed in their app or platform experience.

Key features of a data connectivity solution

When selecting your data connectivity solution, consider several factors to prevent data silos, ensure efficient integrations, and support uninterrupted data flow.

Real-time synchronization and analytics

Data latency can have a significant impact on your reporting and dashboard capabilities. Real-time analysis requires real-time (or near real-time) data transfer and processing. Connections must be able to handle large amounts of data—often big data—with high availability and low response times. 

Sisense live connections runs queries directly against data sources, keeping your data nearly as current as the sources themselves. This works by creating a live model that includes data sources’ credentials and connection details. 

Ease of use

Data connectivity tools are designed to take complex data, connect it, and simplify it. This process makes the data usable for both end-users and internal teams, regardless of their technical expertise. While this can be a time-consuming and technical task, many tools now leverage AI or incorporate no-code builders to make data connectivity more accessible.

Sisense makes building your data connectivity scaffolding easy, even for non-technical users. Whether your data sits in databases, spreadsheets, cloud services, or web applications, you can seamlessly pull it into the platform, model it, and visualize it. 

Scalability

As your business expands, you’ll acquire more data sources that need to be integrated into your system efficiently. That’s why scalability is so important in data connectivity: you need a solution that will seamlessly adapt to your growing needs. It must be capable of handling increases in volume, the number of devices, traffic, and the variety of data sources—all while maintaining data integrity.

In the context of analytics, scalability should be built into every point in the process, from data connectivity to the end-point analytics integrations. The Sisense platform is purpose-built to grow as you grow, with flexible models for building a scalable data infrastructure. 

Flexibility and interoperability

Your embedded analytics can’t deliver robust insights unless your data connections support a variety of formats, platforms, databases, and applications. If your data connections can ensure interoperability between systems, you can ensure an uninterrupted data flow for both internal teams and end-users.

Sisense makes it easy to connect data from every source. With over 400 data connectors and a flexible data engine, you can ingest, unify, and activate massive data sets with lightning speed—and deliver insights to any destination. For developers, Sisense provides SDKs and APIs that make it easy to integrate analytics into any tech stack—reducing time spent on data prep and letting teams focus on building differentiated experiences.

Security and governance

When you’re dealing with data, you’re dealing with security. In fact, security concerns are often one of the biggest blockers to data connectivity, as organizations juggle the need to protect data with the mandate to make it accessible for appropriate use. Data on the move is especially vulnerable. That’s why data governance is essential to the success of any data connectivity solution. 

At Sisense, we categorize security into three main areas: users, data, and the Sisense platform itself. By establishing clear data governance and security management at all levels, we ensure that you can effectively and safely navigate evolving vulnerabilities and compliance regulations. This end-to-end security ensures you can deliver analytics to your team and end-users while keeping every bit of data safe. 

Rapid data modeling and exploration

Fully connected data provides a launchpad for rapid insights. When your data connectivity solution offers real-time synchronization and comprehensive connections, you can rapidly model and explore data for internal analytics and embedded reporting. Scalability is critical here too: your data models should keep pace with your development, avoiding unnecessary remodeling and limitations. 

The Sisense platform transforms raw data into actionable insights faster with dynamic data modeling and AI-driven tools to simplify data preparation. The result: a rapid path to intuitive exploration, accelerated insights, and informed decision-making at every level.

Data connectivity best practices: Strategies for success

Data connectivity involves more than just linking a few data sources; it requires establishing a reliable, efficient, and innovative framework for your data ecosystem. These best practices and actionable strategies will help you optimize connectivity to harness your data’s full potential.

Prioritize data security

It bears repeating: data security is paramount when implementing data connectivity. A data security strategy safeguards your business against financial, reputational, and legal risks. That means defending against vulnerabilities throughout your entire data process, especially at integration points, where your data moves between systems.

Smart security strategies take a two-pronged approach: both passive and proactive measures. Passive measures include encryption techniques, multi-factor authentication, audit logs, and user access controls. Proactively, conduct regular vulnerability assessments and penetration tests to ensure ongoing security. Build security directly into your workflow by including security testing in your regular sprints.

Break down your data silos

When data lives in silos, you can only catch glimpses of the full story. Even if you’ve connected most of your data, lingering silos can leave you trying to put together a puzzle without all the pieces. Silos get in the way of collaboration, reduce efficiency, and block data-driven decision-making.

Connected data, on the other hand, breaks down these silos and gives you the power to put the pieces together. A data connectivity solution can bring everything together—but only if you actually hook it all up.

As you implement a data connectivity solution, do some legwork to ensure you don’t leave valuable data languishing in a silo:

• Audit all the data sources and destinations across your organization.
• Map the overall flow of data through the entire user journey (including data from user touchpoints in marketing, sales, support, and product experiences).
• Ensure seamless integrations to your analytics platform are available for all your data sources.
• Select a platform that’s flexible and accessible for internal teams and end-users, including non-technical users.
• Ensure your data connectivity solution offers the scalability you need to bring in more data sources as you evolve.

Build with data integrity in mind

Low-integrity data leads to untrustworthy analytics. A big part of data connectivity is designing an infrastructure that keeps your data clean and consistent across systems. When you build software on faulty data systems, you lose hours of future productivity to patching and gluing, holding things together until the next crack appears.

Take these steps to ensure integrity in your data connectivity systems:

• Address data duplication, inconsistency, and formatting as you build out data connectivity scaffolding.
• Set up automatic monitoring systems to check for unexpected changes or errors. Regularly use these systems to audit performance.
• Create copies of your data at scheduled intervals to use as a changelog and as backup. That way, you can restore data quickly should something go wrong.

Minimize latency to maximize performance

Connecting, modeling, and querying data for real-time and embedded analytics requires minimum latency. As your data systems evolve and expand, you’ll want to monitor and test latency to maintain timely data access and processing. This is especially important for software creators: your end-users expect real-time insights, and lagging performance leads to a sub-par experience.

• Optimize data pipelines: Optimize data at the source to improve performance, quality, and consistency across endpoints. This minimizes latency and cost by reducing the overall data load. When integrating Sisense, you could build an integration layer or leverage our analytics database to merge and query data from multiple sources without the need to pre-aggregate data.
• Leverage real-time data processing: Cloud-based data warehouses can process data in nearly real time, eliminating delays. With Sisense, real-time synchronization allows you to analyze data from your cloud data warehouse as it’s ingested to deliver high-performance analytics.

Activate data for rapid insights

Data alone is inert. It can’t tell you the whole story. Data connectivity gives you the power to activate your data—all of it—so you can put it to work for faster, more effective decision-making. Your analytics platform is where the action happens.

Once you’ve eliminated your silos and connected all your data, you can hand off the complex preparation and modeling to the Sisense platform, where connected data can flow into the dashboards and visualizations that bring it to life. Layer on AI-assisted data exploration, and you can make your analytics more efficient and impactful than ever before.

Data connectivity: Harness the full power of your data

You’re already collecting data—now it’s time to connect it. Data connectivity drives better analytics and insights to improve business processes and end-user experiences. When no data is left behind, you’re able to generate and embed real insights.

And while data connectivity is about connecting data to data, there’s another angle to keep in mind: connecting data to people. Modern analytics is all about democratizing access to insights so that everyone—both within your organization and across your end-users—can make data-driven decisions quickly. The Sisense analytics platform was created for that very purpose. As an Analytics Platform as a Service (AnPaaS), Sisense empowers both technical and non-technical teams to connect data and create contextual analytics within workflows, enabling real-time, insight-driven decisions.

Book a demo to see how Sisense can bridge the gap between insights and action for your organization.