Businesses are now generating and collecting all sorts of data at scale. These information signals come from any number of sources, such as applications, customer interactions, and daily operations.
While this may sound like good news because diverse data can yield richer insights, the practical reality is different. The data analytics workflows of most organizations have multiple limitations that prevent teams from extracting those deeper insights.
For instance, most business intelligence (BI) processes are too technical. In many organizations, line-of-business decision makers need to collaborate with a data analytics professional who can query vast datasets and build specialized predictive models effectively.
Another challenge is that analytics processes are siloed across applications. Each tool in your tech stack comes with its own reporting capabilities, dashboards and ways of measuring metrics, keeping you from unifying data.
To top it off, enterprise-grade tools often lack flexibility. Consequently, your team may find it difficult to build BI workflows around their insight requirements. Siloed data analytics infrastructure also makes your systems vulnerable to cyber threats, because there are too many access points, increasing the attack surface.
In this article, let’s look at a few effective ways you can future-proof your business’ data analytics strategy.
Integrate AI-Powered Analytics
AI democratizes BI by layering large language models (LLMs) on top of querying tools. This allows users to ask data-driven questions in natural language such as, Which region reported the highest sales in the last quarter?, and receive comprehensive responses.
The generative AI models analyze these inputs and convert them into machine-readable queries that fetch the relevant data points from various sources. Then, the generative AI model uses those data points to provide an answer in a conversational format, such as, The Northeastern US reported the highest sales in the last quarter by 24%.
Decision-makers can ask follow-up questions to drill down further and find the “why” behind the trends, streamlining the overall data analytics process.
CEO and co-founder of Pyramid Analytics, Omri Kohl, explains, “This is where generative BI (GenBI) comes in, and I believe it’s about to change everything. Harnessing the power of generative AI, GenBI can finally bridge that gap and put data-driven insights into the hands of decision makers at every level of tech savviness.” This shift means teams will no longer need to rely on specialists and engineers to run queries for BI. It will allow you to promote a company-wide data-driven culture where every team member can explore data independently.
And no, this doesn’t mean that AI deserves a seat in your boardroom. “AI could never replace people who are curious, excited, and engaged,” adds Kohl. “These people will be asking the non-trivial questions and bringing their own perspectives into the mix, while AI will be there to provide the information they need and walk them through the journey.”
Such an architecture and workflow can easily accommodate your future needs. If you hire new team members, they can quickly get the hang of your GenBI workflows to reference actionable data insights.
Embrace Embedded Analytics
Embedded analytics bring analytics and visualizations right where you work. Let’s say you are managing a new business unit rollout. With embedded analytics, you can see the task completion rates and per-person late delivery rates on to-do lists, right from the dashboard of your project management software.
Similarly, embedded analytics empowers sales managers to view per-product close rates, geo-based trends and enriched lead data while replying to emails from the CRM.
This boosts productivity, as you and your colleagues won’t have to go to a different application to monitor the relevant metrics. Additionally, it also makes data analytics a default component of every workflow within your business, future-proofing your BI processes.
Yulia Kosarenko, author of Business Analyst: a Profession and a Mindset, emphasizes, “Instead of having business users look for reports, navigate charts or wait for a daily progress report email, we must think about embedding analytics in the workflows.”
Moreover, she recognizes the significance of identifying an organization’s analytics requirements before integrating graphs, charts, or dashboards into different software. Kosarenko suggests, “This is where business analysts need to have a deep understanding of their company’s analytics solutions and capabilities to become trusted advisors to businesses and help them formulate their requirements.”
A simple way to do that is to list all the crucial revenue metrics (MRR, CLTV, etc.) and business performance metrics (overhead costs, time it takes to complete a task, etc.). Then, map them to specific strategic initiatives and operational efforts. Finally, track how these initiatives and efforts can be enhanced by making data analytics more accessible to the team members.
Leverage Modular, API-First Architecture
APIs connect two software systems. For example, Google Maps lends location and route functionalities to platforms like Uber and Lyft through APIs. In an API-first data analytics architecture, every system in your BI tech stack is developed around APIs. This will allow you to bring any kind of functionality or data stream anywhere else – without needing to redesign core systems.
Spotify leverages this API-first architecture to deliver one of the best music and podcast streaming services across the world. Their core systems, such as the music player, recommendation algorithm, and user profiles, are managed through APIs.
The relevant data from these core systems is called via APIs across platforms, such as Android and iOS apps, and interfaces, such as web embeds and widgets.
Kyrylo Osadchuk, CTO of OSKI Solutions, a software development company, highlights, “API First Design guarantees consistency, flexibility, and interoperability, and it is the cornerstone of modern software development.”
A similar approach will enable teams to build or swap tools, integrate third-party services, or expose analytics via internal dashboards to future-proof business analytics. The decoupled APIs can seamlessly integrate any two core systems to bring insights to any team member in any workflow.
Go With Zero-Trust
Zero-Trust is a security framework that works on the principle: never trust, always verify, no matter the user or the device. It authenticates and validates every access request, even if they emerge from the internal network, to protect your data assets.
This is crucial for modern BI processes, as they involve multiple tools that collect data from various sources, which are used by several stakeholders with different access levels. Consequently, the number of access points increases, broadening the attack surface.
As Ryan Terry, a senior product marketing manager at CrowdStrike, explains, “The framework is designed to secure modern digital infrastructures that may include a mix of local networks, cloud-based environments, and hybrid models.” It gives organizations peace of mind knowing they can adopt any kind of system or technology and maintain the same standards of security in the future.
He further elaborates on how zero-trust can help enterprises of all types: “This flexibility makes it suitable for organizations with remote workers, organizations with diverse cloud environments, or organizations facing sophisticated threats like ransomware.”
Additionally, zero-trust minimizes breach impact through least-privilege access and micro-segmentation. This contains the threat in the affected device or part of the network, enabling efficient troubleshooting and reducing downtime.
Wrapping Up
Business data analytics workflows need to be future-proofed to handle diverse and larger datasets that provide insights to more types of stakeholders. Companies must invest in secure, agile, and accessible BI foundations that streamline real-time, data-backed decision-making.
First, integrating AI into analytical workflows to empower teams to query datasets in natural language is pivotal. Then, embedded analytics can bring insights to daily-use applications, boosting productivity.
When building out systems like these, it’s crucial to adopt an API-first architecture. This makes it easy to develop new interconnected microservices and workflows without redesigning the core components. As a result, you can create custom analytical applications or dashboards seamlessly.
Finally, the “analytics everywhere” approach makes it essential to leverage the zero-trust security framework, to protect your data and prevent unauthorized access in your evolving BI workflows.
