Behind the scenes, an intricate web of data is being woven, holding the secrets to an unparalleled competitive edge. It’s a realm where big data reveals the nuances of customer behavior, unveils the hidden gems of market trends, and helps businesses improve their performance.
However, with data volumes surging to unprecedented levels, extracting meaningful insights can be akin to finding a needle in a haystack. This is where business intelligence (BI) comes to assist with the intricacies of big data. These two technologies help businesses identify inefficiencies, increase productivity, and unlock a new level of strategic clarity and operational efficiency.
In today’s article, we are going to delve into the essence of big data and business intelligence collaboration. Particularly, we will explore how they are revolutionizing the way organizations derive insights and make informed decisions.
Transforming Big Data Analytics with Business Intelligence
You’ve probably come across numerous discussions on the synergy between business intelligence and big data. Yet, we’ll give a quick reminder of what they are so we’re all on the same page.
Big data means enormously big sets of information that come in all sorts of types, arrive really quickly, and need to be checked for accuracy. Typically, traditional data management tools can’t handle these large and complex datasets.
As for BI, it stands for the tools, processes, and technologies that collect, analyze, and present relevant insight in an understandable manner.
Without diving deeper into the nature of these technologies, let’s have a look at how companies can leverage them to reap benefits and solve possible issues along the way.
1. Data Integration and Centralization
To harness the full potential of data assets, businesses need effective integration and centralization of diverse data sources. However, because of varying formats and quality issues, ensuring coherent integration and analysis becomes more complex.
Imagine the situation, when you don’t have a single source of information. You may probably deal with incomplete and siloed data. In such a scenario, you can’t get appropriate reports and insights and thus can’t make critical business decisions.
In a world where data is spread across multiple sources, such as databases, spreadsheets, and cloud services, BI tools assist in bringing them together. These tools act like bridges between different data streams, allowing organizations to find correlations and insights that might otherwise remain hidden.
Let’s take a look at an example of how business intelligence collaborates with other tools to integrate and centralize data. For instance, BI can closely interact with CRM systems to gather and manage customer information, interactions, and transactions in one cohesive platform. By utilizing a CRM of your choice, like Salesforce or Creatio, you can track customer preferences, purchase history, and support inquiries.
Google Analytics 4 (GA4) can be another helpful tool for data integration. GA4 focuses on website and app analytics, offering an advanced understanding of user behavior and engagement.
Together with GA4 and CRMs, BI streamlines data collection and analysis, empowering businesses to make informed decisions based on an in-depth understanding of their audience.
2. Data Quality and Consistency
“Contact data ages like fish, not wine … it gets worse as it gets older, not better.”
— Gregg Thaler, Senior Director, Adobe at Medallia
When dealing with diverse data sources, there is a high risk of encountering inconsistency and inaccuracy. Hence, ensuring data quality and reliability becomes a significant challenge. Otherwise, you will get irrelevant analysis and make inaccurate conclusions. And frankly, how can you make business-driven decisions relying on incorrect data?
Business intelligence tools streamline the process of preparing and cleansing data, which is especially vital in big data analytics. BI platforms offer functionalities to identify and rectify inconsistencies, errors, and redundancies within datasets. For example, they remove duplicate entries, correct inaccurate values, etc.
Furthermore, BI technologies provide mechanisms for monitoring data quality over time. This involves setting up rules and thresholds for data accuracy, completeness, and consistency. If data breaks from the established standards, the platform may send notifications. So you can take rapid action to prevent potential issues.
Watch IBM’s video to discover more about data quality and the ways to get expected outcomes.
3. Interactive Dashboards and Reports
Understanding big data can be tough, especially for non-technical users. To make data meaningful and digestible for them, you need well-structured reports and dashboards. Otherwise, the information can be misinterpreted, leading to incorrect conclusions.
Let’s see how you can navigate such kinds of issues.
By investing in reports and dashboards, organizations can unlock the full potential of their data analytics and drive better business outcomes. Basically, these BI tools provide a visual representation of data, which empowers users to grasp meaningful insights quickly.
You can even take a step further and foster a culture of data literacy in your company. As a result, your employees will be able to create personalized dashboards and share them across the whole staff. And having access to the same dashboards will allow your team to have a single source of truth. Additionally, it will bring better collaboration with data.
4. Real-Time Data Monitoring
In this ever-changing world, there is no need to speak about the importance of timely decisions. Of course, it is crucial to get accurate insights, but if they weren’t given on time, you risk being kicked off from the competition.
Fortunately, today BI provides tools for real-time big data analytics. Thus, enabling businesses to capitalize on opportunities and navigate the dynamic market with confidence.
One of the tools that you can leverage is Microsoft’s Power BI. It integrates with various data sources, including real-time streaming data. As a result, users can build interactive visualizations and reports that reflect changes in data in real time.
5. Automated Insights
Big data often involves massive and complex datasets that can be time-consuming and resource-intensive to analyze manually. Additionally, there is a high risk of mistakes and inconsistencies, leading to inaccurate insights.
Many modern BI platforms incorporate automation and machine learning tools powered by artificial intelligence to enhance the efficiency of data analysis. Typically, automation has numerous benefits to offer — from reducing human errors to identifying patterns and trends within large datasets.
Automation goes beyond reducing the risk of manual errors and discovering insights that might have otherwise been missed. It can also detect some anomalies and send a notification to the staff. For example, automated security systems can continuously monitor network traffic. In case of any unusual or potentially malicious activities, they can notify the information security department.
It’s not a secret that data analysis requires investment. Especially, if you choose to build a custom analytics platform to handle big data needs. This process involves extensive coding, strong infrastructure, and experienced specialists like data engineers. Obviously, it comes with high costs.
Cost-effectiveness is one of the reasons why BI solutions are becoming more and more popular in big data analytics. Typically, it comes with pre-built functionalities and tools like data integration, visualization, and reporting. On top of that, these tools are quite user-friendly and easy to leverage, so you can analyze information quite fast.
Thanks to this significant benefit of business intelligence, there is no need to build analytical solutions from scratch.
7. Risk Management and Fraud Detection
Needless to say, appropriate risk mitigation and fraud detection are vital for data analytics. However, managing risks and detecting fraud in the realm of big data pose significant challenges.
If you do not take timely actions to prevent the above issues, you can undermine the reliability and trustworthiness of data. It can impact both the business bottom line and threaten the credibility of your organization.
You may probably think that BI tools can identify unusual patterns and anomalies that might signify fraudulent behavior. And you are quite right.
By integrating business intelligence with big data, organizations can collect historical information from diverse sources like mobile apps, web platforms, and IoT devices. Monitoring these data enables the detection of potential suspicious activity.
Using ML and predictive modeling can assist in building better risk management in big data analytics. They help uncover patterns, correlations, and trends within databases. As a result, businesses can forecast possible threats and harness them effectively.
8. Data Governance and Compliance
Data governance and compliance are critical components of big data and business intelligence, which ensure data accuracy, security, and regulatory adherence. To be more precise, it involves setting up rules, procedures, roles, and responsibilities that define how information should be managed. Its goal is to verify that everyone understands how to properly handle data.
It is also important to make sure that data is collected in accordance with applicable regulations and industry standards. Otherwise, non-compliance can lead to severe legal and financial consequences. As a result, your organization can jump into legal and reputational risks.
Many BI tools, like Tableau and Power BI, provide features for data governance and compliance. This ensures your data management is in line with regulations and industry standards. Consequently, it prevents costly breaches and penalties for non-compliance.
Data governance and compliance can also foster a culture of transparency and accountability within your company. On top of that, providing reliable insights will positively affect your organization’s reputation.
9. Cloud Connectivity
With effective cloud connectivity, you can harness the full potential of cloud resources without sacrificing data accessibility. Though cloud technologies bring scalability, they come with some challenges. Here it is vital to ensure data safety and security during transfer, effectively manage costs, address latency issues, etc.
BI solutions can significantly improve data security by seamlessly integrating with cloud technologies, such as Amazon Web Services, Google Cloud, or Microsoft Azure. Business intelligence systems bring their own set of built-in security measures, such as access controls and encryption. They complement the cloud’s security infrastructure.
As a result, businesses can easily define and harness issues like potential threats or unauthorized access. Simply put, by combining BI solutions with cloud technology you can address security measures and effectively reduce potential issues in a timely manner.
Leverage Your Big Data with BI Capabilities
As we see, BI and big data complement each other, providing valuable insights and advantages for businesses. Now you know how business intelligence can serve as a helping hand for big data analytics to support you in mitigating issues and improving your business performance.
Each business requirement can vary, so you need to find appropriate tools aligned with your unique needs. If you are running short of skilled professionals, Velvetech can help you build robust BI solutions and integrate big data best practices.
Reach out to us today and unleash the full potential of big data analytics.