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.
Key Highlights
- BI tools use interactive filters, intuitive dashboards, custom charts, and graphs, thus noticeably simplifying the interpretation of big data.
- BI pulls data from every source you connect to and presents the insights in one dashboard, eliminating the need to jump between platforms.
- Modern BI platforms can spot data duplicates, errors, and inconsistencies, and even fix them before presenting them through the dashboard.
- By updating dashboards in real time, business intelligence solutions help enterprises identify crucial issues early on and make better 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. However, for sectors like healthcare, insurance, or retail that deal with a huge amount of data daily, it’s crucial to manage them effectively to achieve robust data analytics.
Here is where BI comes in handy. It stands for the tools, processes, and technologies that collect, analyze, and present relevant insights understandably.
That is to say, the intersection of big data and business intelligence serves as a powerful asset for businesses to make sense of their variables.
Being a large-scale business, you may not question the importance of these technologies to streamline analytics, yet you may still struggle to leverage them effectively. So, let’s now zoom in on the core implementation steps, discuss what could go wrong, and explore how you can handle challenges.
1. Data Integration and Centralization
Challenge
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 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.
Solution
In a world where data is spread across multiple sources, such as databases, spreadsheets, and cloud services, BI tools assist in bringing them together. They collect data from every source you connect to and present the insights through a single dashboard. This eliminates the need to switch between multiple platforms.
For example, customer-related data typically lives across several systems, such as a CRM like Salesforce or Creatio, Google Analytics 4 (GA4), payment gateways, and other similar tools. BI brings all of it together, letting you see customer preferences, web behavior, purchase history, and campaign results side by side.
Discover how Velvetech Helped the Client Integrate Computer Telephony Directly with Salesforce
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
Challenge
As an enterprise, you probably deal with plenty of data sources such as CRMs, SaaS products, email marketing tools, ERP systems, analytics platforms, and the like. Diverse data sources always carry the same risk: a high chance of data inconsistency and inaccuracy. Let’s see how BI can assist in tackling these challenges.
Solution
Many modern BI platforms like Power BI, Tableau, and Looker assist with data cleansing. Specifically, they may spot duplicates, errors, and inconsistencies and even fix them. This provides peace of mind that your dashboards represent cleaner and more accurate data.
Discover how we assisted our client in Automating Data Cleansing
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, allowing you to prevent potential analytical inaccuracies.
Watch IBM’s video to discover more about data quality and the ways to get expected outcomes.
3. Interactive Dashboards and Reports
Challenge
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 poor analytics.
Let’s see how you can navigate such kinds of issues.
Solution
Generally, BI tools come with powerful customization options like intuitive dashboards, thus noticeably simplifying the interpretation of big data. Let’s say recently, you’ve crafted a specialized program for patients with heart-related conditions. You’ve already collected the relevant data required for personalized offerings. However, your marketers are struggling to deal with them as is.
By investing in reports and dashboards, which typically offer BI tools, you can help your team unlock the full potential of data analytics to craft a targeted marketing strategy.
But the benefits of dashboards and reports go beyond merely supporting your marketers. For example, BI tools can play a crucial role in fostering a culture of data literacy across your company.
As a result, your employees will be able to create personalized boards and share them across the whole staff. And having access to the same boards will allow your team to have a single source of truth. Additionally, it will bring better collaboration with data.
4. Real-Time Data Monitoring
Challenge
Even the best data analytics insights may fall short if they don’t arrive on time. And that is exactly one of the core pain points of many enterprises that employ data analytics strategies, yet overlook the importance of real-time insights.
Solution
Many modern BI tools are designed with this consideration in mind. They support real-time big data analytics, enabling businesses to capitalize on opportunities and navigate the dynamic market with confidence.
Check key Data Analysis Types for Better Decision-Making
One of the tools that you can leverage is Microsoft’s Power BI. It integrates with various data sources, allowing you to build interactive visualizations and reports that reflect changes in data in real time.
5. Automated Insights
Challenge
You will probably agree that analyzing complex big data manually just isn’t worth it. The process is highly prone to errors. Let alone the fact that it is incredibly time-consuming.
Solution
Many modern BI platforms incorporate automation and machine learning tools powered by artificial intelligence to enhance the efficiency of data analysis. As such, you can streamline the accuracy of analytics and simply identify hidden patterns and trends within large datasets.
Discover more on how AI Can Bring Innovation to Your Business
ML models also learn from historical data, can detect anomalies in your environment, and can promptly notify your team. For example, automated security systems can continuously monitor network traffic. In case of any unusual or potentially malicious activities, your information security department is immediately alerted.
6. Cost-Effectiveness
Challenge
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’s not going to be a cheap pleasure.
Solution
Typically, BI solutions come with pre-built functionalities and tools like data integration, visualization, and reporting. It’s way more cost-effective to utilize a BI platform rather than employing all those data tools and processes separately or building analytical solutions from scratch. On top of that, these tools are quite user-friendly and easy to leverage.
7. Resource Optimization
Challenge
Let’s say you’re a large-scale logistics provider aiming to cut unnecessary operational costs. Ideally, you need to calculate routes correctly to avoid long distances and rough roads that may cause breakdowns in your vehicles. You should also spot any inefficient fuel consumption and cut down on it. It’s also worth monitoring maintenance schedules to avoid costly repairs down the line.
And now you probably think that these kinds of variables could only be processed by a big team of business analysts. Not necessarily. And here’s why.
Solution
Business intelligence can become a valuable asset for data analytics in this regard. So, once you collect the needed variables, process them through BI tools, and let them do the rest.
Specifically, BI solutions integrated with machine learning and predictive models serve best to forecast potential issues based on historical data. BI customization tools that we’ve already mentioned (real-time tracking and intuitive dashboards) become a perfect addition to make data more understandable and quickly accessible.
Even better, you can create dashboards for each metric you want to track. This will give you a big picture of how your resources are being used, thus empowering you to optimize them.
8. Risk Management and Fraud Detection
Challenge
Dealing with big data is always risky. And once you start processing these variables through analytics tools, the risk only grows. Not all analytics tools are highly protected, yet you’re going to trust them with your business and customer-sensitive data. Any fraudulent activities can compromise your business’s trustworthiness and lead to heavy fines.
Solution
BI tools can identify unusual patterns and anomalies that might signify fraudulent behavior.
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 potentially 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.
Discover how Predictive Analytics Can Be Beneficial for Businesses
9. Data Governance and Compliance
Challenge
Data governance and compliance are critical components of big data and business intelligence, which ensure data accuracy, security, and regulatory adherence. It involves setting up rules, procedures, roles, and responsibilities that define how information should be managed. The goal is to verify that everyone understands how to properly handle data.
It is also critical to collect customer data aligned with industry-specific requirements. Otherwise, non-compliance can lead to severe legal and financial consequences. As a result, your organization can jump into legal and reputational risks.
Solution
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.
Explore Key Components of Effective Data Management
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.
10. Cloud Connectivity
Challenge
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, again, it is critical to ensure data safety and security during transfer.
Keep reading to discover How to Optimize Cloud Costs
Solution
BI solutions not only seamlessly integrate with cloud technologies, such as Amazon Web Services, Google Cloud, or Microsoft Azure, but also bring their own set of built-in security measures, such as access controls and encryption. They complement the cloud’s security infrastructure.
With that being said, by combining BI solutions with cloud technology, you can address security measures and effectively reduce potential issues in a timely manner.
BI for Business
Key Capabilities Your BI Tool Needs to Effectively Process Big Data
Given the immense role business intelligence tools play in making huge datasets digestible, you might not question their importance. However, without certain capabilities, they can hardly be useful. Here is what to consider when implementing a BI solution:
- Scalability: When it comes to processing big data, a scalable solution has no alternative. That is to say, your BI tool should be capable of handling large volumes of data without compromising your systems’ speed and performance.
- Real-Time Data Presenting: We’ve already touched on the importance of getting real-time insights to make accurate business decisions. Consequently, your BI tool should have this feature.
- Integration: To ensure smooth data flow, business intelligence solutions should seamlessly integrate with other systems like cloud services, machine learning tools, and databases. Precisely these integrations make it possible to smoothly handle data from different sources and process it effectively.
- Visualization: This feature is like the heart of every BI tool. Intuitive dashboards empower people, even those with no analytical skills at all, to gain a solid understanding of the data.
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. Additionally, we offer dedicated Power BI consulting services to accelerate your analytics initiatives.
Reach out to us today and unleash the full potential of big data analytics.
About the author
