With the endless advancements in business intelligence and data analytics, it is easy to get confused by it all. Yet, as we dig deeper into the utilization of data as a tool, it is essential for managers to understand the differences between them.
Here, we unpack the differences between 'business intelligence' and 'advanced data analytics'.
- Definition of Business Intelligence
- Definition of Advanced Analytics
- Similarities between Business Intelligence and Advanced Analytics
- Differences between Business Intelligence and Advanced Analytics
- Business Intelligence Case Studies
- Advanced Analytics Case Studies
Business intelligence (BI) is the process of analyzing past and present data, and presenting this information in a digestible format in order to make insightful business decisions. Fundamentally, it is a rear-mirror view, looking at past data to explain the present situation.
Advanced data analytics on the other hand, is the examination of data using tools and techniques that are much more complex than that of BI. Deeper insights can be uncovered, allowing businesses to make predictions and even generate recommendations. The advanced statistical models that are derived from advanced analytics techniques demonstrate all possible outcomes for businesses, empowering them to be prepared and future-proof their operations accordingly.
Both BI and data analytics play significant roles in business growth. While BI focuses on reporting and querying historical data, advanced analytics is more about optimization, correlation, and prediction of the next best action or the next most likely occurrence in the future for any given business case.
One major commonality between BI and data analytics is reporting. Knowing which to use is determined by the type of information we seek to understand. Are we looking at past, present data or for future trends? The answers to these questions will determine the most suitable solution to use.
BI is often applied to increase an organization’s decision-making abilities. It analyses internal business data, engages in data mining, develops reports and improve operational capabilities. The purpose of BI is to meet organisational goals by helping users identify loopholes in transforming data into an effective decision-making tool.
A popular example of Business Insights was played out in the book and movie “Moneyball”, which is based on a true story. It documents a money-stricken baseball team’s success by using historical data of player statistics – strike rates, run rates, fouls, etc – to effectively trade and build a winning team.
Conversely, data analytics may be the better option in relation to macro-level data, such as everyday trends and market analytics; where it is applied to convert raw and unstructured data into meaningful formats. Data analytics enables firms to fulfill tedious processes such as data cleansing, modelling and transforming, resulting in clean data sets. These data sets can be leveraged on to predict future trends, empowering businesses to make the right decisions in any given scenario.
The University Advancement department of Michigan State University was responsible for obtaining donations from alumni, but their efforts lacked focus. With tens of thousands of additional alumni each year, and an existing database of 450,000 living past alumnus, it was almost impossible to know where to focus their fundraising efforts.
They turned to BI, which enabled them to implement institutional strategies leading to improved engagements and increased annual donations by an additional $34,000, while also increasing overall staff productivity.
One aerospace machine shop was facing operational issues, particularly around the effective measurement of machine availability and production output levels. With extensive capital tied up in the machinery, ensuring high levels of productive output for these resources was an important business objective.
Faced with problematic root cause analysis, the team created a graphic of the floor plan containing all the different production cells and machine resources. They then created a traffic light system which signaled any production issues. The data that stemmed from this system was then used to identify downtime and production bottlenecks, resulting in several procedural and operational improvements.
The management team was able to monitor shop floor performance from any computer within the building. They easily recognized bottlenecks in the production process. This allowed for better informed operational decisions and ultimately created effective performance gains from downtime reduction, leading to an increase in production output.
A restaurant company wanted to understand what drives their employees, and what they could do to increase business performance. They decided to undertake an analysis of staff data.
The company started by defining its goals. Using advanced analytics, they quantified employee’s behavior data to model against actual outcomes. Using the data, they built an unsupervised learning model to determine the relationship between past drivers and outcomes to predict likely future events. This resulted in:
- 100% improvement in customer satisfaction scores
- 30-second improvement in speed of service
- Substantial decrease in the attrition of new joiners and
- ~5% increase in sales
At its core, Netflix is a customer data-driven business. It collects data on user viewing habits and behaviors to understand them better. It then creates lists of recommended content tailored to individual preferences. These lists ensure that viewers are likely to continue returning to its platform and spend more time on it, thereby keeping them sticky. The more shows viewed on an account, the more data is collected. This data also feeds into a larger advanced analytics system that collate macro viewing data, which is used to influences the company’s production arm and content acquisition departments.
By harnessing the power of the data within their organisations, savvy leaders can uncover deeper insights about their business, better understand past events, as well as predict future performances.
Managers usually face the pressures of making optimal growth decisions for their companies. With many solutions available in the market, making a choice can become a tricky situation. Hence, choosing a solution that best fits an organization depends on the organization’s goals. Visionary companies that prioritize growth acceleration would immensely benefit from advanced analytics, as it offers data-backed recommendations that minimize errors. Companies like DataVLT are providing advanced analytics as a service for growth-oriented firms. Find out more about our feasibility report here.
DataVLT is an affordable, on-demand analytics platform secured by blockchain technology. It is designed to simplify the complexity of data science. Backed by artificial intelligence and machine learning capabilities, DataVLT empowers enterprises to make meaningful sense of their big data and scale cost efficiently. Essentially, it is an end-to-end data/information management platform.
Learn more at www.datavlt.com