The past decade has witnessed a remarkable data-driven makeover in the world of business. And if there’s one lesson that has stood out, it’s that even the big guys aren’t safe, and adapting to change is the only way to grow. To move forward, data-driven insights are necessary and that’s where business intelligence (BI) comes in.
In this article, I'll answer exactly what business intelligence is, cover the different stages it entails, and explain how you can automate the process using artificial intelligence (AI).
What Is Business Intelligence (BI)?
Business intelligence, or BI for short, is all about analyzing business information. The goal of BI is to provide historical, present, and predictive views of business operations through data.
The technologies that power some of the best business intelligence software seek to break down and interpret big data, which can help develop new business opportunities. BI provides key information to executives and business users across functions and levels, enabling them to work smarter. This results in several benefits such as:
- A more accurate understanding of business performance
- Better communication among decision-makers
- Early warning of business, financial, and operational issues
- Accurate benchmarking and competitive analysis
- Better prediction of cash flow, market, and demand trends
- Improved decision-making process
The 5 Basic Stages Of Business Intelligence
Most BI platforms and BI systems follow these five basic stages of managing and making sense of the data.
This involves gathering relevant data from various internal and external sources, like spreadsheets, files, data warehouses, or even cloud storage platforms.
Let's consider the example of an online bakery and assume you're running an email marketing campaign for said bakery.
To evaluate the success of your campaign and improve future campaigns, you'll need performance data from the email marketing tool, look at changes in the website traffic, and maybe even install conversion tracking for your website. A step up would be to actively engage with your customer base through surveys and feedback forms to learn more about their preferences and buying behavior.
All this collected data will give you the information needed to formulate a business objective such as building brand loyalty or spurring revenue growth and identify ways to achieve it.
Once you’ve collected the data, the next step is to prepare it for analysis. This involves ruling out inconsistencies, duplicates, and errors to ensure the final dataset is clean and accurate. Organizing and transforming are the two key steps when dealing with unstructured data before you analyze it.
Building on the previous example of an online bakery, your initial data collection could include performance metrics along with a large inventory of different products you advertised such as cakes, cupcakes, pastries, scones, etc. that will need to be properly categorized for analysis. This is important to ensure that your insights are accurate.
Data analysis is the technical process through which you obtain actionable insights from raw data. It takes a narrower focus, using techniques like data mining, algorithm development, modeling, and simulations to solve specific business problems, analyze key performance indicators, predict future trends, and reveal patterns and insights.
With respect to the online bakery, data analysis can give you insights into the flavors your customers prefer, what's popular, what's not, if a specific season or month impacts your sales and more. These insights will help you refine your marketing strategy to improve customer experience and your bottom line.
The whole process has a bit of a Sherlock vibe to it. You're almost like a detective looking for anomalies, trends, and patterns to weave a story and get into the psyche of your customer base.
Data visualization takes all the information and turns it into an array of visual formats such as charts, graphs, maps, and plots to understand the data and identify key insights. It allows executives to understand patterns and relationships and identify correlations that you may miss in rows and columns of raw data.
Data visualization software is a powerful tool for conveying complex information and telling a story. In business and sales intelligence, it can track a company's KPIs across key indicators and compare it to competitors. It can also assess the effectiveness of a marketing campaign based on relevant metrics and determine whether it is on track to reach its goal.
The purpose of BI is to provide actionable insights to managers and executives, allowing them to improve processes and make better decisions. These decisions may range from basic operations, such as determining the amount of production, to broader strategic considerations, such as identifying which products to sell and which markets to target.
Business intelligence is a crucial asset for companies that use it to gain value from all their data and make data-driven decisions regarding the allocation of their resources.
5 Ways To Automate BI With Artificial Intelligence
According to the Data and Analytics Leadership Annual Executive Survey, 10.6% of organizations ranked data quality as their highest investment priority, with 41.5% indicating that they would be increasing investment in data quality in 2023.
By using AI in BI, your company can get useful insights from large databases in a digestible format within minutes. AI will help you break down the problem and suggest ways to explore the data for more insights. It can break down a problem and solve it by turning insights into actionable results.
Here are five ways you can automate BI with Artificial Intelligence.
Automate Data Mining
Data mining is a process of converting raw data into structured data for analysis. By studying patterns and relationships hidden in large datasets, you can create models to make business projections, and operational predictions, suggest new procedures, and more.
Artificial Intelligence simplifies the data mining process by eliminating repetitive manual work. It identifies relevant datasets that may contain valuable information and extracts and analyzes information to highlight any patterns or trends. This analysis is difficult to perform manually but can be done in minutes with AI.
For example, airlines can improve their services and offerings by accessing flight data at scale from trip searches and bookings with web, social media, and call center interactions. They can use these insights to understand customer behavior and tailor personalized travel experiences.
In marketing, data mining can be used in market research to improve audience segmentation. AI can analyze areas such as customers' demographics, and preferences, and how they correlate to predict behavior and maximize results by personalizing campaigns.
Automate Data Processing
Data processing is the conversion of raw data into useful information for various purposes. This process involves collecting, organizing, analyzing, and presenting data using different methods and tools.
AI can improve data processing by automating complex and repetitive tasks, extracting insights from large data sources, and producing new data or predictions based on existing data.
Additionally, AI can use natural language processing (NLP) to analyze text data, computer vision to recognize images and videos, and machine learning algorithms to find patterns and trends. By automating manual, time-consuming tasks such as data cleaning, normalization, feature extraction, and transformation, AI can significantly improve the efficiency of data processing.
Machine learning algorithms can also learn patterns and relationships in data to preprocess it. For instance, they can detect anomalies or inconsistencies in data, predict missing values, recognize and categorize outliers, and perform data normalization and transformation, thereby reducing the need for manual intervention.
Advance Data Analysis
Advanced analytics refers to a set of techniques and methodologies used to analyze large data sets, including statistical, computational, and machine-learning techniques. By uncovering patterns, identifying opportunities or pain points, making predictions, and generating insights, these techniques can help drive better business processes.
AI helps identify correlations or complex patterns within data that may be missed by human analysts. This reduces the risk of human errors while analyzing data, leading to more accurate insights and predictions. Additionally, AI algorithms are continuously trained to learn and adapt to new data, improving accuracy over time.
Automating advanced data analysis with AI has the added benefit of predictive modeling, which can offer real-time, actionable insights and predictions about future outcomes.
Using these insights, you can identify potential risks and mitigate them in advance, minimizing potential losses. Predictive modeling can also provide personalized recommendations for a specific group or behavior, leading to stronger and more accurate predictions.
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Improve Data Visualization
According to a report by Aberdeen Group, 74% of companies use data visualization tools to customize workflows to meet their team's needs. When companies use the right data visualization tools, they are also five times more likely to make important business decisions that give them a competitive advantage.
The total amount of data in the world is expected to reach 175 zettabytes (or 175 trillion gigabytes) by 2025. Projects involving data science have become more popular due to this tremendous growth in data, emphasizing the importance of visualization.
While pie charts, histograms, and graphs are useful, more complex visualizations such as heat maps and fever charts provide better context on business data. AI and ML tools play a crucial role in making granular data visualization possible.
For example, AI can use algorithms to transform data from one format to another, such as converting area sales figures into geophysical visualizations. Or when dealing with time series data, the system may suggest line charts. AI can also personalize dashboards for individual users based on their roles, past interactions, and preferences.
Utilize Predictive Analytics
Predictive analytics is the process of using data, statistical methods, and machine learning to forecast future outcomes and trends. Machine learning models can learn from past data to forecast future trends, sales, customer behaviors, and more. These predictions can help your team address potential issues or seize opportunities.
For example, AI can predict customer behavior, such as churn, retention, lifetime value, and satisfaction, based on their interactions, preferences, and feedback. This business analytics can help improve marketing, sales, and service strategies.
In another example, AI can predict demand, supply, and pricing based on historical data, market trends, and external factors. This can optimize inventory, production, and distribution while increasing profitability and efficiency. Besides, AI can forecast risks, fraud, and anomalies based on patterns, rules, and signals. This can prevent losses, protect assets, and ensure compliance with regulations.
AI can predict outcomes, scenarios, and actions based on simulations, data models, and optimization. This can help test hypotheses and explore options for expansion.
Tips & Best Practices For Automating BI
Integrating AI with business intelligence systems can help you get the right data to determine your business growth. Be mindful of these tips and best practices when starting the process.
- To integrate AI and BI, first design a plan that outlines your goals and how you will measure success.
- When choosing AI and BI tools and platforms, check their compatibility, scalability, and flexibility to avoid data silos, duplication, and inconsistency.
- For a successful AI and BI integration, provide ample training and support. Educate your team on the solutions' benefits, features, functions, and ethical use.
- Make your business intelligent by identifying your report's target audience. Know what your stakeholders want to know and select data visualization styles, dashboard types, and report formats that address those questions.
- Ensure organization-wide buy-in to maximize the chances of success.
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