What is Business Intelligence
Business intelligence refers to an umbrella term that covers all features for gathering, sorting, integrating, analyzing, and presenting business information. The goal of a business intelligence tool is to convert information into actionable insights and help businesses make data-driven business decisions.
Figure below shows example of a business intelligence tool with key insights and influencers:
Business Intelligence Vs. Business Analytics
Utilizing effective tools and technologies is the key to leverage this immense amount of data and convert it to meaningful information. This is where business intelligence and business analytics come into picture! Both provide methods and tools crucial for interpreting business data and deriving valuable insights to create effective actionable plans.
This article tries its best to enlighten the readers with some of the most obvious and detailed differences between business intelligence and business analytics.
As its name suggests, business intelligence relies on effective utilization of data to take intelligence business decisions. Focus is given on collecting, monitoring, and interpreting data to decipher patterns and trends in the past and utilize these findings to improvise current business scenario.
Figure below shows an example of how a business intelligence dashboard looks like with visualizations showing projected sales in the past 4 quarters.
Defining business analytics requires a foremost understanding of business intelligence since the former brings the best out of business intelligence through its predictive analytics approach to analyzing current data. The core of business analytics lies in implementing predictive modeling and advanced statistics to evaluate potential happenings in the business.
Figure below shows example of a business analytics dashboard with predictive analysis approach:
What do Business Intelligence and Business Analytics Involve?
Business Intelligence can be considered as an umbrella term covering all the processes and methods for collecting, storing, modelling, and analyzing data.
Info graphic given below shows different processes in modern business intelligence:
On the other hand, business analytics can be considered a subset of business intelligence. It incorporates sophisticated data, quantitative analysis, and mathematical approach to facilitate solutions for data driven issues. A typical business analytics methodology involves the following processes:
Who is a Business Analyst and a Business Intelligence Expert?
A business analyst generally monitors productivity of one or more internal departments within a corporation, measuring the work produced under current administrative system
A good business analyst needs to have any or all the below given skills:
- Core Skills like good communication skills (for effective communication with relevant stakeholders and absorb relevant information), problem solving skills and critical thinking skills.
- Business Analytics Skills like understanding key levels of analysis, basic office tools like word, excel, visual tools like MS Visio, requirement management tools like DOORS, etc.
- Key Soft skills such as relationship building skills, self-managing skills, etc.
- Although knowledge of Technical Skills like SQL, .NET, PEARL, and VBScript is essential, a business analyst need not have knowledge of coding or running database queries.
- Methodology Skills like agile business analysis, six sigma, rational unified process, etc. would be an added advantage for a business analyst.
Given below are few interesting facts about career in business analytics:
- According to the Bureau of Labor Statistics, there would be an estimated increase of 14 percent in the need for business analysts by 2024.
- Job projection for IT business analysts will increase up to 9 percent by 2026, as per Bureau of Labor Statistics.
- Average salary for a business salary is about 74000 USD per annum as of May 6, 2020.
- 66 percent of analytics processes will provide solutions to business issues instead of mere reporting, by 2021 and business analysts will play a pivotal role in suggesting the solutions or review insights.
On the other hand, a business intelligence analyst leverages a company’s gathered data to increase company’s efficiency and maximize profits. He or She needs to have the following skills:
- Data Analysis skills to be able to parse through huge volume of data and make relevant accurate inferences over the same.
- Specific Industry Knowledge skills to have the ability to utilize data in business problem solving.
- Problem Solving skills to create business strategies and provide solutions to real world business issues through the data.
- Communication skills to have the ability to effectively communicate results to other professionals within an organization.
- Technical skills like database design and data architecture, data mining and analytics, database queries like SQL, proficiency in ETL are needed along with knowledge of Hadoop, R, SAS, etc.
Given below are few interesting facts about a business intelligence expert:
- According to US Bureau of Labor Statistics, demand for qualified business intelligence analysts would increase by 14 percent by 2026.
- A junior level business intelligence expert earns about 109K EUR per annum in Switzerland, while a senior level expert earns about 150K EUR per annum.
- Global business intelligence market would grow to about 29.48 billion USD by 2022, with a CAGR of 11.1%.
Despite the subtle differences between business intelligence and business analytics, it is quite hard to count a certain software solution as business intelligence or business analytics solution. However, it is quite important to have precise knowledge of technologies, tools, and approaches to reach a certain business goal. Therefore, knowledge about difference between business intelligence and business analytics is essential.
Why is Business Intelligence Important?
Implementing an effective business intelligence solution is always crucial to improve your organization’s decision-making skills at every management levels. Business intelligence has now become a paramount asset for companies looking forward to boosting their strategic and tactic management processes.
Given below are top 5 reasons why every enterprise should consider a robust business intelligence strategy and system to invest in:
In-depth analysis of business operations and processes helps unveil the hidden issues, faults, or errors, and clear any possible bottlenecks. This is turn would help increase productivity, and eventually increase organizational efficiency.
Improve Customer Satisfaction
One of the obvious benefits with business intelligence is the ability to gain information about customer trends.
Companies can leverage customer spending patterns, social media activities, and other related insights to detect supply and demand trends amid the potential audience. This in turn would vouch for improved customer satisfaction through planned strategy, production, manufacturing, and expenditure.
With BI systems in place, companies can now get relief from the tedious process of skimming through thousands of excel records to process business data. An effective BI system will improve visibility of business processes and identify possible areas of improvement, thus allowing you to be proactive and not just reactive.
Access to Real Time Data
Conventional reporting processes have often been protracted and error prone with much time being taken for compilation at each stage, by various departments. By the time reports are submitted for review, they are already outdated. This issue is resolved by BI systems which provide access to real time data, allowing hundreds of reports to be collected, interpreted, and shared among imperative stakeholders.
Gain Competitive Edge
Apart from all the other benefits, the most important one is the ability to gain insights into your competitor’s business processes including their strategies and current market trends. This would help you to make beneficial business decisions, plan future endeavors, and stay ahead in the competition.
Given below are some key statistics on business intelligence adaption by various companies around the globe:
- Business services, insurance, and technology industries have the highest adoption rate of business intelligence in today’s era.
- Small businesses with 100 employers were the most common users of business intelligence in 2018.
- 37% of manufacturers in Asia Pacific use business intelligence for improving product quality management.
- More than 46% businesses are implementing business intelligence as their core business strategy.
- About 90% of sales and marketing teams considered cloud BI to be important for achieving desired goals in 2018.
- North America is the largest business intelligence software market in terms of adoption and expenditure rates.
- Average ROI for companies using business intelligence for their business processes in about 1300%.
- Operations, executive management, and sales teams are the main drivers of business intelligence in companies.
- Big Data, analytics, and business intelligence are the topmost influencer factors in achieving success for many enterprises.
Figure below shows usage of cloud business intelligence versus on-premise business intelligence by companies:
Business intelligence is vital for data driven businesses in data integration, analyzing the data, extracting valuable insights, and sharing this information to important stakeholders in the business.
Key Challenges of Business Intelligence
Business intelligence has undoubtedly enabled enterprises to reach zeniths of success through the intelligent combination of intuition and analytics. Modern day BI tools are a testament to an organization’s success in achieving desired goals. However, despite the growing demand for an effective business intelligence to remain agile, innovative, and competitive, companies are still lagging in capitalizing on full potential of business intelligence.
Research says your company can gain an average ROI of 11 USD on a dollar investment in business data analytics, but still it is doubtful unless you use your BI solution efficaciously.
Gartner says: “Up to 70 percent of BI implementations end up failing to meet all the business goals.”
This statement is enough to alarm us about the growing challenges of business intelligence in today’s data driven world, with some of them listed below:
Growing Volume of Poor-Quality Data
In this information-oriented age, organizations are literary swimming in pools of data and sifting through it to gain valuable insights is quite difficult to achieve. One of the main prevalent issues in data related to BI is data latency submerging important information beneath tons of data in systems, applications, and platforms. Another issue is poor, inaccurate, and inefficient performance of BI tools delivering poor, complicated insights of little or no use.
Lack of well-defined BI strategy
With a plethora of technologies around the corner, access to wealth of data is now quite easier for enterprises. However, there is still a dearth of well enunciated strategy to unleash the potential of big data. Not defining the problem before you implement the solution actually results in utter confusion ultimately leading to technology failure.
Lack of proper execution and training
Despite having well-articulated BI strategy, required tools setup, and all other necessities, companies still lag behind in having well trained personnel with strong technical skills in designing, building, maintaining, and supporting the BI applications.
This lack of training often results in poor execution of the business intelligence project by unskilled or partially skilled business users and analysts. This in turn is actually a challenge for the data democratization goal of many organizations.
Increasing Volume of Unstructured Data in Business Intelligence
Abundance of data sources along with emergence of IOT has increased the volume of data to be processed in BI systems with mostly being occupied by unstructured data. Cleaning, organizing, and structuring the data is quite time taking, resulting in delayed completion of important assignments. Problem occurs in integrating all the data sources, analyzing the data, unearthing new relations, and recognizing patterns in the unstructured data.
Lack of unified approach to business intelligence
A successful analytics project is possible only through full-fledged involvement of every stakeholder at each stage. Often discrepancies in matters related to reports, metrics, key performance indicators are prevalent in organizations which ultimately curtails the benefits of analytics. In fact, isolated business intelligence practices and lack of universal adoption is a key business intelligence challenge.
Apart from the above-mentioned challenges, other issues like lack of approach to tackle disparate data sources, difficulty in deploying mobile business intelligence, inefficiency in providing true self service analytics, and many others are the major factors preventing organizations from achieving desired success from the BI system. These challenges might sound demotivating and dissuade companies from investing in business intelligence. An overall solution is to treat BI as a business process and not as an IT-centric initiative.
Business Intelligence Best Practices
Modern day business intelligence is having a profound impact on enterprise functionalities with the fabric of existing business decisions relying heavily on acuity of data science more than before. Scope of business intelligence includes everything from assembling, integrating, and presenting business information.
Given below are some best practices outlined for enterprises to leverage business intelligence in producing actionable insights which can influence key business decisions:
Get a green signal from every stakeholder
Before implementing any BI project, or in fact any project, it is always advisable to take each and every stakeholder’s opinion on key matters. It is imperative to make sure all the concerned parties are on the same page in terms of priorities and objectives. Pertinent involvement of right departments such as IT and data engineering will not only ensure proper data governance and security but will also promote transparency and integrity in running a successful BI solution.
Always articulate a blueprint before moving ahead
A well cohesive bi strategy should always be formulated keeping in mind the technological, reporting, scalability, and integrity needs, along with a concise, quantifiable, and unique objective. This should lay the groundwork for later stages of development by clearly defining the ownership and tasks of every individual or department.
A high-quality data governance framework is a must
While businesses are overwhelmed with tons of data, it is always necessary to implement a data governance strategy to ensure policies, rights, accountability, and enforcement during all information related operations. A finely orchestrated data governance framework would be effective in maintaining compliance with regulations, improve decision making, streamline data management, and most importantly maintain data security.
Start your BI execution in small steps
When you are planning to involve every concerned employee in your business intelligence project, having a lot of business questions and requirements at individual level is quite obvious. Additionally, every staff member would clamor for data, and this paves our next best practice – to segregate tasks in terms of their priority and time consumption.
Look out for answers to the most crucial questions, review substantial deliverables, and then send timely alerts to stakeholders regarding any changes in implementation or impactful findings. There is a high chance the answers will lead to more questions to be added to your road map.
Promote data literacy in your organization
Data is the crux of any business intelligence project and it is important for every employee to be well versed with all its aspects. Providing proper training on company nomenclature and metrics will ensure smooth comprehension and high-quality performance in achieving business objectives.
Above given suggestions are some of the best practices organizations need to follow in order to maximize their BI investments and remain competitive among their peers. Make sure to deploy a business intelligence tool which aims to provide end user flexibility and helps unveil actionable insights.
Difference between Traditional BI and Modern BI
History of business intelligence dates back to the pre-computer era when companies were still in their nascent stages of capitalizing on information to develop a business strategy. The term business intelligence was first coined by Mr. Richard Miller Devens, then emphasized for enterprise revolution by IBM researcher Hans Peter Luhn.
Reliance on data for business processes has resulted in gradual growth of data storage technologies, thus paving way to the evolution of traditional business intelligence 1.0 to self-service business intelligence in the 21st century.
While traditional business intelligence has always been about having technical skills to reach, understand, and analyse data to visualize reports, modern day business intelligence allows even the most naive non-technical user to get relevant insights from the most complex unstructured data. In fact, this reliance on technical experts often lead to extensive time lag between report generation and decision making, thus effecting the overall business operations.
Given below are 3 major differences between traditional BI and modern BI
Firstly, Features of traditional business intelligence are accessible only to the IT or technical expertise, and as a result missed out on leveraging true potential of every business user in solving business problems.
On the other hand, modern day business intelligence is accessible to every stakeholder involved in the project, thus supporting more personalization, user adoption, and data democratization.
Secondly, traditional BI tools rely on historical data and results to conclude about present events or ‘what is happening’. Moreover, the data needs to be structured before being utilized.
On the contrary, modern day business intelligence tools are equipped to extract and compile data from multiple sources in real time, extract relevant insights, and help users uncover new opportunities in future, thus providing both predictive and prescriptive reporting along with historical reporting.
Thirdly, mere involvement of technical staff and IT support ensures clean, authentic, and secured data through out the entire process in a traditional business intelligence setup.
However, modern business intelligence systems require a robust data governance framework to secured data accessibility and utilization in compliance with standard regulations.
Traditional business intelligence might be the old school way of deploying intelligence into business processes, but it is definitely not the outdated process. However, modern day business intelligence tools like BIRD are always preferred for their ease of customization, data accessibility to users, and most importantly, self-service business analytics at a fast-paced rate.
Benefits of Visual Analytics and Data Visualization
Visual analytics is an integral part of business intelligence providing an interactive, cognitive, and perceptual approach to big data analytics. The focus is mainly on analytical reasoning technique to enable users in gaining insights into complex business problems.
Data visualization is a segment of visual analytics communicating imperative information to relevant stakeholders in a clear, concise, and graphical manner.
Given below are 5 important benefits of visual analytics and data visualization:
- It helps user visualize important information in a comprehensible and coherent way such that it is always easy to make subtle business decisions at an accelerated rate.
- Through visual techniques, it is always easy to identify any prevalent anomalies and pattern otherwise hidden beneath the heaps of data.
- Visual analytics provides you with a sophisticated way of presenting your story to essential stakeholders in the business.
- Modern day tools like BIRD leverage visual analytics to improve data exploration capabilities through features like drill down.
- Advanced visual analytics techniques even allow business users to get in-depth insights into customer requirements through sentiment analysis techniques.
Visual analytics comprising of 3-D data visualization techniques are the crux of a business intelligence process providing a visualized outlook to important information through graphs, charts, maps, etc.
Functions of Business Intelligence
Business intelligence is an umbrella term encompassing of different processes leveraging data to ultimately make data driven business decisions. These different processes or functions are as given below:
- Reporting provides answers to the question of what has happened in the business process. While traditional reporting has been static, advanced techniques have paved way to a more real time dynamic reporting with advanced exploratory features.
- Analysis focuses on finding reasons behind the occurrences or events. Techniques like visualizations and online analytical processing allow users to decipher correlations between data points along with key trends and patterns.
- Monitoring can be seen as an advanced form of reporting providing us real time information about ongoing events or trends. It involves use of dashboards and scorecards.
- Collaboration not only removes data silos in departmental or organizational level, but also allows every involved stakeholder to be on the same page during the entire business process.
- Predictive Analysis is an advanced form of analysis using artificial intelligence and machine learning techniques to provide insights into prospective opportunities for businesses.
Examples of Business Intelligence
Implementing a sophisticated business intelligence system undoubtedly adds value to any busines operation, promoting comprehensibility and data democratization among teams to enrich their data driven decision making processes.
While there are numerous applications or examples of business intelligence in real world, given below are 3 such excerpts from the vast list:
Automatic Claim Processing on Vehicle Warranty by a Fortune 100 Company:
A leading manufacturer of light weight vehicles recently leveraged our full stack modern business intelligence tool (read BIRD) to process vehicle warranty claims through predictive analysis techniques.
The company’s database was accessed to carry out extensive analysis and in-depth understanding of the core situation. Later, advanced predictive models were developed using NLP techniques to improvise inventory management.
Real Time River Water Quality Monitoring Funded by World Bank:
The task was to assess river water quality through implementation of large number of sensors and IOT devices. Modern day business intelligence solution was deployed to ingest humongous volume of data from IOT devices at an accelerated rate, along with feasibility to monitor individual sensor station through dynamic visual analytics features.
Scalable Analytics Solution to Accommodate a Large User Base:
A leading stock exchange from a developing country was in an urgent requirement to deploy business intelligence solution which would be easily used by non-tech save business users for data analysis. The company utilized BIRD’s full stack management business intelligence platform with scalable architecture to provide self service analytics functionality to its large concurrent users, providing a fast, dynamic reporting tool and simultaneously reducing the burden on IT team.
Business Intelligence Vs. Reporting
As the competitive landscape within a business universe becomes more and more complex, the need to have updated, succinct and accurate information is growing at a faster pace. Compiling the available data, analysing it, and converting it into valuable information is necessary for every business to assess their actual position in the market. This lays the foundation for the need of a reporting tool as default solution to get enterprise information and process results.
However, a simple reporting tool would only provide a one-dimensional solution to the enterprise requirement of current and accurate data with actionable insights. The requirement is to have a scalable, secured, and affordable business intelligence environment which can transform the given information to an intuitive analytical environment.
Reporting and Business Intelligence are often confused to be the same side of a coin, each provided by tools with fancy visualizations. But there is much wider difference between them, even beyond presenting data through charts or correlating data.
Reporting tells you what happened while Business Intelligence focuses on the Whys and How.
An important aspect of reporting is that it provides you with details of past or current happenings (or event) within an organization. Reports can provide you with information about a particular dataset such as monthly sales, daily customer orders, etc.
On the other hand, business intelligence would provide you with answers to why or how that happened. In the previous example, business intelligence would consider various factors like destination proximity, weather, promotional activities, etc. before allowing you to finalize an event location and time.
Reporting formats are static while Business Intelligence is flexible
Reports often tend to have a fixed template providing information about a specific aspect of your business. The reporting format is quite static in nature and cannot be changed. You need to use the same format for different timeframes.
However, from a strategic point of view, when the requirement is to thrive in a competitive and dynamic environment, reporting needs to change. This is a tedious and expensive task as you either need to approach an IT specialist to design a new report format or purchase a whole new reporting tool.
A better option would be to use an agile and flexible business intelligence tool. Except few cases, majority of the business intelligence tools are designed for non-technical users to get required answers to their business and operational questions, without having to use a standard format.
Reporting provides with data while business intelligence allows to interact with data.
A fundamental difference between reporting and business intelligence is the latter’s functionality to allow its users in interacting with data. By focusing on the reasons behind the data, Business Intelligence provides you with the next version of your data. Unlike a standard reporting tool which just provides you with a glance of the data, a business intelligence tools allows you to drill down deep into the data. You can perform multi-dimensional analysis to derive meaningful insights which can be converted to profitable actions.
Business Intelligence supports performance management unlike Reporting.
By synchronizing with live databases, many reporting tools provide updated and real time information within the enterprise applications. Since these reports are time based; daily, weekly, monthly, etc., they can be helpful in managing operational processes. However, a report doesn’t provide you with any means to improve your performance.
On the other hand, a business intelligence application is equipped with tools like scorecard, dashboards, alert reports, etc. for a better performance management functionality and improve existing business process.
When the requirement is to have a more sophisticated and immediate response to ongoing market changes and customer concerns, business intelligence is always preferred over conventional reporting. Reporting is a good option when you just need to view crucial information in a visualized format. However, business intelligence tools are a necessity when analyzing objective metrics and key performance metrics and take confident business decisions.
Future of Business Intelligence
As data-driven strategies continue to take a front seat in today’s business processes, adoption of business intelligence is at a growing trajectory. In the coming years, business intelligence software is going to be more collaborative, proactive, insightful, and efficient in handling big data. Big data has evolved a long way from traditional reporting to augmented self-service business intelligence and is continuing to do so.
Business intelligence market is expected to grow at a CAGR of 11%, to about 29.48 Billion USD in 2022. As business intelligence tools have surpassed usage of modern spreadsheets and moved towards advanced visualizations, the biggest question is what lies ahead.
Well, given below are some of the important BI predictions for coming years:
BI systems will integrate into existing business applications:
Emergence of advanced Application Programming Interfaces (APIs) are facilitating integration abilities in BI software to merge with existing user systems. By embedding third party application into your business intelligence tool, you can work on your data without juggling between the two systems.
Machine Learning will fuel business growth:
The advent of machine learning and artificial intelligence have taken analytics to a new level by allowing businesses to extract meaningful insights from past data. Real-time analytics and alerts have replaced static and passive reports. Machine learning will now prove beneficial to business users in making accurate predictions and take proper measures to achieve the desired business goal.
Data pro-activity will increase, bringing relevant data to your doorstep:
Jason Kolb, a Quora user, wrote this on the platform in 2011: “Relevant data will find you, and not vice versa”. Through advanced technologies like chatbots, artificial intelligence, and augmented analytics, rich data will be delivered to business users and customers irrespective of them being engaged with the system or not.
Network infrastructure will expand to ingest larger volume of data and allow seamless flow of information:
While enterprises are continuously outsourcing business intelligence and analytics processes to third party vendors and cloud deployments are taking over a larger part of the business spheres, there is a higher demand for a robust network architecture to accommodate dynamic infrastructure scalability, meet data storage requirements, and enable effective handling of overarching business processes in a single platform.
Business intelligence world will grow to be more collaborative:
Even though existing tools and applications are isolated and work independently, unconnected to any broader network, the day is not far away when these siloed tools and platforms will be part of a broader spectrum and connect with each other. The next generation of business intelligence is expected to accommodate larger set of users and facilitate better system inter-connectivity.
Chat-bots will boost cognitive intelligence in business intelligence:
Upcoming advancement in customer relationship management lies in automating customer support and chatbots are the solution. This technology will be crucial in providing real time information to consumers and business users, making it easier to track and resolve issues more proactively.
Business Intelligence will be on every mobile:
Mobile BI is the new trend with about 92% of senior business executives using a smartphone, thus proving its credibility in today’s business world. With business intelligence at their handsets, it is possible to access real time data at any time, at any place. Also, this would facilitate faster reaction times, timely alerts, and improved communications.
Apart from the above given important trends and future insights, figure below layouts the top 10 prevailing business intelligence trends in 2020 in terms of their popularity and ratings:
The success of business intelligence and analytics can be gauged by its widespread popularity among enterprises in different domains. Business intelligence is being optimized and refined frequently to not only improve business processes and facilitate day to day operations but also handle data in compliance with legal and regulatory guidelines.
Click here to leverage BIRD’s full-stack self-service business intelligence tool.