Let’s turn the focus to one industry as an interesting example. For restaurants and fast food chains, consistency in taste is universal to their success. There is an unspoken rule that any restaurant dish should not deviate more than 95% any given day. Customer retention drives the majority of fast food revenue, and customers come back because they liked the food and expect the same taste every time.
Hence, for fast-food restaurants, quality control broadened from hygiene to well-controlled ingredients quality, cooking process, and preparation. Almost all restaurants and cafes religiously follow this practice.
Fun fact- Back in the early 1900’s, Guinness brewery, the most famous beer brand then, wanted to follow this same exercise for their breweries to maintain their position as the top beer company in Ireland. This meant they had to brew at a carefully controlled industrial scale which was challenging especially for products that require fermentation. Can you imagine having to exercise control a hundred years back with limited industrial set-up?
Luckily, Guinness found a solution thanks to a chemist they hired from Oxford. Not any student but a rank holder in mathematics and chemistry who was tasked with assessing the quality of their brew in a cost effective manner. His name was William Gosset and he is credited to have developed the student t-test. He went on to be the head brewer of Guinness and we thank him for not just great hand-crafted beers but subsequent research breakthroughs in statistical research as well. Data allowed Guinness to hold their market dominance for long.
That was in the 1900’s. Times may have changed, old ways may have died but the process has only been upgraded. Now, businesses, regardless of the industry, are leveraging data and Business Intelligence to stay ahead of the competition.
Fast food companies like Domino’s, McDonald’s and KFC collect massive amounts of data which includes customer data and other key business metrics for their own operations. That is how they ensure their taste is consistent throughout. Also, it is using customer data that they experiment and roll out new products every month.
In brief, business intelligence is about how well you leverage, manage and analyze business data. When data is stored in silos and the back-end systems are not able to process the massive amounts of data seamlessly, critical information may be lost.
We get critical business insights based on how well we leverage our business data. Such insights can be excellent fuel to marketing strategy, analytics, and campaign optimization. All of which can be used to increase profitability, gain better ROIs, and be better adapted to changing economic landscape and consumer behavior.
The more effectively a company uses data, the better it performs. Cutting down latency or delay is now one of the most crucial elements of business intelligence strategy in present times. As a data analytics company, we have been observing a trend among certain large enterprises who are looking for real-time data streaming for analytics. When information is at your fingertips, the possibilities are endless. As a result, many corporations have been allocating more budget for upgrading their BI system.
To stay relevant in the market and to increase brand awareness, organizations use big data analytics and business intelligence to navigate their way after getting a full understanding of their ideal customers and their behavior before and during the buying journey. So much so that they can predict certain aspects about their customers with high accuracy.
For business intelligence to work out for your business–
Define your data strategy roadmap
Your data strategy and roadmap will eventually lead you to a BI strategy. So, make sure you have a data strategy in place. How exactly can BI help your business achieve your business goals is a question to define in order to reap the benefits of BI.
The easiest way to tap into data is integrating all your data to get a detailed understanding of your operations and your customers.
Data is usually accessed from multiple data sources which adversely restricts the information that you can have access to.
For example, analyzing social media ad performance analytics, organic reach and payment methods separately will not allow for customer journey mapping. Whereas, integrating data sources can provide you with a picture of where your customer is coming from, how long they spend on your website, what can be improved in the entire buying process among others. Integrating data allows you to perform cross-database queries, which like portals provide you with endless possibilities. Integrating data through data warehouses and data lakes is one of the standard industry best practices for optimizing business intelligence.
Organizations accumulate terabytes of information everyday, hence, manually studying patterns and relationships becomes increasingly complex. Data mining is a technique used for refining data by removing any anomalies to identify and understand relationships between variables. In simpler terms, analyzing large amounts of data for outcomes and quality insights. Data mining allows refining and analyzing of the data on a near-real time basis. Visual Analytics employs data mining to identify patterns and trends which would have been incredibly difficult to find without it.
Visual Analytics and Data Visualization
Both play a significant role in Business Intelligence but both have their stark differences.
Visual analytics is way more complex in terms of what it does and what it can do for you. Data visualization is used to depict stories graphically using dashboards. Stories that is typically hard to picture with large data sets. Visual Analytics empower businesses with a better understanding of the situation. It is the easiest way to consume and share information, and hence, can be used to make multiple info-graphics to provide good insights like trends and patterns. While visual analytics serves almost the same purpose but with a different and more sophisticated approach.
Visual Analytics produces output with the help of automated analytical processes such as data mining and Machine Learning algorithms. The automated process involves continuously analyzing, identifying, and revealing valuable insights from increasingly complex data sets. Despite being more complex, the output is delivered in record time. Data stored in data lakes and data warehouses can be converted to visualization for business reporting. Companies use strategic dashboards with drill-down capabilities to delve deeper and carefully analyze information from the root. Data integration and visual analytics go hand-in-hand and together provide a wealth of information for your business.
All of these components together create a comprehensive and modern business intelligence solution. There are a few other components which have not been covered here like ad hoc reporting (which allows companies to quickly build reports based on priority like addressing customer concerns) and predictive analytics which are best practices that are increasingly being adopted across all major sectors.
Do let us know if you found the blog useful in the comments. If you have questions about getting started with BI or optimizing your current BI system, BizAcuity has helped many businesses in the past decade by transforming their traditional reporting systems. Get in touch with us and get all your questions answered.