In our technologically driven world, there is no shortage of non-stop information stemming from a host of intelligent systems which provide a wide range of functions from harvesting user information and behaviour to curating digital footprints. But the true advantage of these systems is not in the rigorous collection of data, but in the ability to better understand the relationships between data sets – and from these relationships, start to unlock data’s full potential to solve current problems and capture future opportunities.
This is where Business Intelligence steps up to take full advantage of the continuous flow of information to prevent the loss of opportunities it potentially presents while redirecting attention to where it is needed the most. The massive collection of business data from systems such as ERP could likewise be fully utilised by Business Intelligence to drive strategic and modern real time business decisions, turning data into actionable insights and forecasts.
Business Intelligence vs Business Analytics
There is often an overlap of distinction between business intelligence and business analytics, however there is a difference – the former focuses on describing past and current events or information, while the latter places emphasis on the descriptive and analytical aspect of the information and situation.
These could include why something happened or is likely to happen and the actions for the best possible outcome – these can be described as diagnostic, predictive, and prescriptive analytics accordingly. Regardless of the titles, both systems are vital in providing the big picture insights decision makers need, while solving the solving the problems at hand and reaching their goals.
What is Business Intelligence (and why Context is King)
Business intelligence – in its contemporary context – is a business framework that is powered by technology and ultimately, it can help companies make better decisions by showing present and historical data within their business context.
These decisions can be used to make an organization run smoother and more efficiently, or to better leverage on market trends to increase sales or revenue. It can also be used in compliance, employee engagement or talent development. The key is in not just being able to identify relevant data, but for a system to be able to understand and provide the context to the data in a way that is actionable by a business. In this way, any process that generates data, or is influenced by data, has the potential to be optimized by BI. Some of this includes:
- Identifying ways to increase profit
- Analysing customer behaviour to accurately identify pain points and opportunities to scale up on areas that resonate with customers
- Comparing data with competitors to identify strengths, weaknesses, threats, and opportunities
- Tracking performance against projected KPI
- Optimising operations for reduced costs, higher efficiency, and effectiveness
- Predicting success with viable and sustainable projections
- Spotting easily overlooked market trends due to biases
- Discovering issues or problems before they become major weaknesses and threats
A successful BI program shines light on ways to increase profits and performance, discover issues, optimize operations, and much more.
Practical Business Intelligence examples in action
It is increasingly clear that a clear and concise BI strategy could make or break an organisation, in line with the ever-increasing trend to capitalise on gains, minimise losses and covering gaps in coverage within the organisation – here are some trends which are currently deployed by organisations seeking to fully utilise the array of tools in their arsenal.
Trends in BI
- The proliferation of natural language interface which make it more intuitive for users
- Low-code and no-code development which increases usage of the system with lower dependence on IT assistance should roadblock occur
- Increased use of the cloud which characteristically enables users to access data any time any where
- Efforts to improve data literacy and self-service which are in line with the democratisation of BI systems for greater user penetration across services
Furthermore, today’s BI tools make it easier for functions across an organization to access, analyse, and act on current and historical data. Here are a few examples of BI use cases in different business areas:
- BI for marketing: Marketers can use business intelligence to track campaign results, such as e-mail open rates, click-through rates, and landing page conversions – and then tailor future promotions to make them more effective.
- Business intelligence for finance: Finance departments can consolidate financial data and monitor cash flow, margins, expenses, revenue streams, and more in real time. They can keep a sharp eye on profitability and make decisions that improve the bottom line.
- BI for HR: HR teams can use BI to monitor metrics such as time and attendance, productivity rates, employee turnover, and engagement. They can use BI to make better hiring decisions, identify training needs, optimize staff schedules, and more.
In addition, BI can of course be used for more than just business. For example, BI could be deployed toward humanitarian and disaster response. One of the biggest challenges for these organisations is in allocating resources effectively – and with the tools provided by BI, these resources can be more effectively deployed. Even weather data could be factored into planning decisions – with sharp drops in weather or rainfall helping providing predictive models on where volunteers and resources could be more likely needed, which would help speed up critical responses.
The general five-step process of BI
We highlight the process and flow to how a successful BI system works:
- Data consolidation into repositories from source systems
- Data organization into analytical data models for analysis
- Analytical employment by user-driven query
- Translation into data visualization of reports, online portals, and dashboards from query results
- Decision makers leverage data for strategic planning
BI has come a long way – 30 years, from its traditional methods of being driven by IT. Critical business questions then were submitted to an unintegrated IT team were provided with answers in the form of a frustratingly static report. As every business decision expands, follow up questions naturally followed, and this was sent through the mill again. It was incredibly resource intensive and time consuming. Thankfully, with the rapid development of the space, it was replaced by modern BI – which is far more intuitive, auto-mated and increasingly “self-service”
Modern, self-service BI tools let business users query data themselves, set up dashboards, generate reports, and share their findings from any Web browser or mobile device – all on the go with minimal IT involvement. Recent efforts into the Internet of Things, artificial intelligence (AI) and machine learning technologies have expedited this process through even simpler – and faster – automation of BI processes, including data discovery and the creation of reports and visualizations.
Increasingly, companies are choosing cloud-based BI tools that connect to more data sources and are available 24/7 from anywhere. Solutions that offer embedded BI have exploded in popularity – by embedding directly into workflows and processes, well informed users can make better strategic decisions in the moment and in context.
Modern BI platforms today combine business intelligence, advanced and predictive analytics, and planning tools in a single but powerful analytics cloud solution. They are further augmented by AI and machine learning technologies, embedment can occur in any process, cementing the democratisation of BI and analytics by making them easy and widely available for use – not just IT departments or professional analysts.
What are the Business Intelligence tools?
The Nuts and Bolts of BI
The overarching principle of BI is easy to grasp, but the execution is complex and technical. To get a better grasp, it’s important to understand the process, which first includes a glossary of terms:
- Data mining: Using databases, statistics, and machine learning to uncover trends in large datasets.
- Reporting: Sharing data analysis to stakeholders so they can draw conclusions and make decisions.
- Performance metrics and benchmarking: Comparing current performance data to historical data to track performance against goals, typically using customized dashboards.
- Descriptive analytics: Using preliminary data analysis to find out what happened.
- Querying: Asking the data specific questions, BI pulling the answers from the datasets.
- Statistical analysis: Taking the results from descriptive analytics and further exploring the data using statistics such as how this trend happened and why.
- Data visualization: Turning data analysis into visual representations such as charts, graphs, and histograms to more easily consume data.
- Visual analysis: Exploring data through visual storytelling to communicate insights on the fly and stay in the flow of analysis.
- Data preparation: Compiling multiple data sources, identifying the dimensions and measurements, preparing it for data analysis.
There are many different tools used in a business intelligence system. Here are some of the most common:
- BI reporting: presenting data and insights to end users in a way that is easy to understand and act on – is fundamental to every business. Reports use summaries and visual elements like charts and graphs to show users’ trends over time, relationships between variables, and much more. They are also interactive, so users can slice and dice tables or drill deeper into data as needed. Reports can be automated and sent out on a regular, predetermined schedule – or ad hoc and generated on the fly.
Querying tools allow users to ask business questions and get answers through intuitive interfaces. With modern querying tools, submitting a query can be as simple as asking Google (or even Siri) a question – like “Where are shipping delays happening?”, “Did quarterly sales meet their targets?”, or “How many widgets were sold yesterday?”
Dashboards are one of the most popular BI tools. They use continually updated charts, graphs, tables, and other types of data visualization to track pre-defined KPIs and other business metrics – and provide an at-a-glance overview of performance in near-real time. Managers and employees can use interactive features to customize which information they want to view, drill into data for further analysis, and share results with other stakeholders.
The ability to visualize data and see it in context is one area where BI really shines. Charts, graphs, maps, and other visual formats bring data to life in a way that can be quickly and easily understood. Trends and outliers are more apparent. Colors and patterns paint a picture of the story behind data in a way that columns and rows in a spreadsheet never could. Data visualization is used throughout a BI system – in reports, as answers to queries, and in dashboards.
Online analytical processing (OLAP) is a technology that powers the data discovery capabilities in many business intelligence systems. OLAP allows for fast, multidimensional analysis across huge volumes of information stored in a data warehouse or other central data store.
Data preparation involves compiling multiple data sources and generally preparing it for data analysis. Using a process called extract, transform, and load (ETL), raw data is cleansed, categorized, and then loaded into a data warehouse. Good BI systems automate many of these processes and allow for setting dimensions and measures.
A data warehouse holds aggregated data from multiple sources that’s been cleansed and formatted so that it can be accessed by BI and other analytics tools.
In considering the future of any organisation, BI has proven to be a fool proof method in adjusting resources to prevent losses, capitalise on gains and opportunities, and increase coverage for vulnerable and threatened areas within the organisation. By effectively harnessing and channelling Business Intelligence, an organisation steps up its competitive advantage to extract the full benefit of the continuous flow of information to prevent the loss of opportunities it potentially presents while redirecting attention to where it is needed the most.
Paired with the massive collection of business data from systems such as ERP, organisations could likewise fully utilise Business Intelligence to drive strategic and modern real time business decisions, turning data into actionable insights and forecasts for a truly digitally intelligent impact.