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Imagine you have a women’s online clothing store. After a year of steady growth, you want to scale up and launch your first physical store. How will you do so? You won’t and shouldn’t just dive headfirst without testing the waters. You may want to use business intelligence and data analytics to aid you through the process
You collect and compile data from your online store, for example, the number of clothing items you produced, the number sold, who bought the most clothing items and when. You gather this data for a certain period and analyze it by noticing trends and customer preferences: what month has the highest sales, what kind of products are being sold the most, which designs are the most popular, and so on. You do this for several products, observe patterns, and perhaps use an excel sheet to gain some insights from this raw data.
This activity qualifies as data analytics. But it is also business intelligence (BI).
You basically need data analytics for BI, as the latter relies heavily on the former. Think of it as a car's rearview mirror – it provides a clear view of what's already happened on the road. Data analytics, on the other hand, is akin to the windshield - it gives you a broader perspective and helps you anticipate what's ahead.
Businesses often use BI and data analytics together. BI provides the foundational data for analysis, while data analytics helps extract deeper insights to support strategic decision-making. While BI is now one of the major ways in which data analytics is used, it is applicable in many other fields, too.
Let’s go back to the online clothing store for now. How will you use BI to scale up? If you know through data analytics that demand for sleeveless clothing items goes up in summers, the decision to capitalize on that and increase supply accordingly is the business intelligence.
Now that you are familiar with both ideas, let’s take a deep dive.
Data analytics is a vast field having several branches to it, with each one having its own characteristics and purpose. Businesses may employ any one or a combination of more than one to contribute towards their business goals.
This includes sales reports, customer satisfaction scores, website traffic analysis and similar descriptive metrics. As the name suggests, this branch of analytics deals with analyzing historical data to gain insights into what has happened in the past. It employs key performance indicators (KPIs) to dissect business performance.
This answers the why of the situation. Data is analyzed to understand why something happened in the past. By analyzing patterns and relationships within the data, the root cause of an issue is identified. Diagnostic analytics examples include product defect analysis and customer churn analysis.
Helping businesses identify potential risks and opportunities before they occur is predictive analytics. This includes using statistical models and machine learning algorithms to predict future outcomes based on historical data. Examples of predictive analytics include demand forecasting, fraud detection, and customer lifetime value prediction.
What actions should a business take to meet their targets? Prescriptive analytics uses data and analytics to answer such questions. It helps businesses make calculated decisions by providing actionable insights. Examples of prescriptive analytics include supply chain optimization, pricing optimization, and marketing campaign optimization.
(Source: Fivetran)
Pro tip: Shift into high gear! Connect with us today and discover how our business intelligence and data analytics services can transform your decision-making and propel your business forward.
The global BI market is predicted to grow to $33.3 billion by 2025. Currently, more than 46% of businesses are already using a BI tool as a core part of their business strategy.
(Source: Medium)
Think of business intelligence as any action taken to improve the health and revenue of your business, with its usage ranging from marketing to sales to operations and finance. For example, in marketing, business intelligence tools can provide real-time campaign tracking, measure each effort’s performance, plan for future campaigns and give the marketing team more visibility into overall performance. In sales, operation managers use business intelligence dashboards and KPIs to quickly access complex information like discount analysis, customer profitability and customer lifetime value. Using this, they can monitor revenue targets, sales reps’ performance and status of the sales pipeline.
The main purpose of business intelligence is to help organizations make data-driven decisions, identify areas for improvement, and gain an advantage over competitors. Using BI, companies can gain insights into customer behaviour, market trends, and overall performance, which can then help them strategize. Unlike data analytics, there are not any specific types of business intelligence but there are numerous business intelligence tools that can be chosen.
The most effective and prominent business intelligence tools are:
By now you must have a good idea of what BI and data analytics mean for businesses. But what are the major differences and how can you tell one from the other?
While data analytics is focused on discovering patterns and trends in large, complex data sets, business intelligence is typically focused on using that data to monitor business performance and make informed decisions. Data Analytics is also a broader field: it aims to explore and uncover hidden patterns using a variety of statistical techniques. Business intelligence operates in a more focused manner by giving specific insights to decision makers.
Business strategists also use both business intelligence and data analytics for different time periods. The idea behind using data analytics is to focus on a long-term strategy for the business. Data analysts make predictions for future scenarios and how to navigate them efficiently. Business intelligence operates in real time and looks at the short-term vision by supporting operational decisions and monitor performance on a day-to-day basis.
Business intelligence tools are more user friendly. They include easy to understand interfaces and intuitive visualizations to make it easier for business users to understand and interpret data. This is done so they can be understood and accessed by a wide range of people that may include anyone from front line employees to managers to executives. Data analytics tools, as the name suggests, do not have this requirement. They are purely for the analysts and scientists, who are already well versed with the complexities of data. These professionals are responsible for conducting in-depth analysis, generating insights from complex data sets and transforming data visualizations into simple dashboards for the rest of the team.
Despite the differences, the most successful companies use a combination of business intelligence and data analytics to drive growth and innovation.
We have three interesting cases for you on the subject.
The first one is Spotify.
Spotify is one such company that uses data analytics very smartly. Instead of just using data analytics for backend purposes, they also use data smartly for users to see. They realized that the very thing they used for app development and optimization was also something people loved to see: data which is personalized. Nothing excites people more than looking at data showing their interests and how they change over time. For instance, finding out how much money you spent and saved over the past year. It’s relatable.
Enter Spotify Wrapped.
Spotify Wrapped is evidence that data isn’t just for analysts and engineers. With this out-of-the-box and clever marketing strategy, Spotify shows users what songs and artists they listened to the most in the previous year. Not only this, but Spotify also employs statistics to showcase if a user was among the top 1% listeners of a specific artist or how many times they listened to their favourite song. It looks something like this:
This is an example of data analytics being used to market a product and increase business. Business intelligence has also played a key role in helping businesses reach new heights.
Second case is Coca-Cola which everyone is familiar with. Such large multinational corporations have a massive social media following, and they know how to use it to their advantage. Coca-Cola used AI-powered recognition technology to collect photographs of their drinks being posted online. They paired this data with business intelligence to gain valuable insights into where their drinks were being consumed and by whom. When they got these demographics down, they were able to target their advertising efforts more effectively.
Third case is of another well-known service, Netflix, which uses its 148 million subscribers to give itself a massive business intelligence edge. The company optimizes its algorithm by formulating programming ideas based on previously viewed programs. Netflix’s technique of getting people to engage with their content using business intelligence has proved to be so successful that their recommendation system drives over 80% of streamed content!
(Source: Oracle)
Data analytics is a way to achieve business intelligence.
With all this knowledge, start thinking about how you can leverage these tools to outsmart your competitor. Start thinking about where your business is lacking, and which tool can it benefit from. Is it in need of consolidating data to obtain key insights? Or do you need to enhance your team’s efficiency by incentivising them through various tactics?
Whatever your business needs, start thinking.
And if you plan to scale your women’s online clothing store, you’ll need a reliable partner to back you up from a technical perspective. Don’t hesitate to contact experts from Data Pilot. Our team will help you maximize growth exponentially.
By Mohaimin Rana
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