The emergence of Business Intelligence
With a long history as a term that originated with IBM researcher Hans Peter Luhn in the 1950s, Business Intelligence has evolved into a global industry that incorporates multiple strategies, techniques, and methodologies.
From vendors that began providing solutions for data management and organisation, the BI market has evolved into a multi-billion-pound industry in 2021. Now, the market involves BI applications from leading organisations such as Microsoft, and the Business Intelligence community continues to thrive as engineers and architects find innovative strategies to optimise and maximise the value of data.
With new capabilities revolutionising how Business Intelligence functions, and challenges evolving constantly, the world of BI continues to grow and bring value to businesses of all sizes.
Business Intelligence vs Data Analytics
While commonly incorrectly substituted for the term Data Analytics, Business Intelligence involves a multitude of processes that allow businesses to gain a complete overview of their data – emphasising accessibility, readability, and comprehension.
Business Intelligence and Data Analytics are two terms and practices that operate very closely to each other. However, there are key differences that organisations should be aware of.
Business Intelligence creates a data landscape and environment that facilitates insights that impact present strategies, creating harmony between data sources and other platforms. This also allows for successful Data Analytics – the creation of insights into future strategies and opportunities.
Examples of Data Analytics practices include Data Visualisation and Predictive Modelling, but none of this is viable or achievable without an optimised data landscape and architecture.
Through Business Intelligence strategies, vast amounts of data are stored and collated in a manner that can be easily fed into advanced analytics tools, allowing for confidence in results, trust in insights, and perspective in approach.
Business Intelligence and complex datasets
With the ever-increasing volume of data, the proliferation of data points, as well as greater access and lower costs for accessing data, datasets can easily grow into billions of rows. This has prompted terms such as Big and Wide Data. As separate datasets continue to develop, and overall complexity increases, it becomes crucial to create an overall architecture that allows for an overarching view of all collected data.
With data consolidated in optimised solutions such as a future-proofed Data Warehouse, businesses can gain a perspective that was previously unavailable. For example, this could enable access to vital insights based on a complete view of operational data.
Common advantages of Business Intelligence
Business Intelligence brings a wealth of benefits to organisations of any size. Often encouraged as a proactive stance allowing for a scalable platform, industries such as retail, financial services, law enforcement, and many others will find universal benefits. Through successful Business Intelligence practices, organisations will gain:
- Enhanced perspective
With an overarching view of all collated data across an organisation, key decision-makers, stakeholders, and analysts can gain a wider perspective than previously possible. With a view of complete datasets, both market trends and outliers can more easily be identified.
This allows for informed strategies reinforced with confidence and trust, rather than relying on a gut-feel approach.
- Improved data quality
Business Intelligence practices such as good data management involve the creation of automated collation processes and quality control. For organisations, this means that all data stored in an overarching solution should be high-quality and secure.
For others within a business, this awareness of a secure data location limits many negative consequences, such as the formation of data silos, which can reduce the visibility of information.
- Reduction in demand for manual processes
Many aspects of contemporary data collection and storage involve tedious, and often repetitive, manual processes. An optimised Business Intelligence strategy eliminates this issue. Instead, data collation may be automated, streamlined, and rapidly integrated into an overall Data Warehouse for analysis with ease.
For analysts, this means less time spent on data collection, and more time allocated to drawing insights and strategies that enable business growth.
- Elimination of bias in strategies
Low-quality data, or incorrect/missing information, can create a shift in perspective when conducting analysis based on current datasets. With meaningful and vital data not present for analysis, outcomes are skewed, and incorrect conclusions are reached.
This bias can lead to serious consequences when devising strategies and making key decisions in competitive marketplaces. Incorrect sales or marketing campaign successes can mean that investment decisions may not be optimised – causing a potentially lasting impact on the growth of the whole organisation.
Misleading objectives due to missing data, or creating low-quality data through manual processes, can have other detrimental impacts on larger business. For optimised BI results, businesses must evaluate how they interact with, and prioritise their data. In doing so they can be sure of the results they gain – with reinforced trust in their data.
The demand for reinforcing trust in data
Trusted intelligence is a term reflecting results that are secure, confident, and reliable. This is essential for paving the way for a data-driven culture that emphasises, and prioritises, insights based on analysis and Business Intelligence.
With many contemporary data strategies that rely on manual processes, the potential for changes in results, or sudden shifts in variables, is large. This leads to key stakeholders and decision-makers prioritising their gut-feeling and judgement when making decisions over metrics and data – an unpredictable approach that threatens the success of investment and business strategies.
An optimised BI strategy enables decision-makers to justify their decisions based on necessary and valuable data that they know can be trusted and relied upon.
Common strategies incorporated in Business Intelligence
Business Intelligence can involve a wide range of practices and techniques. Integrating data into manageable solutions, as well as allowing analysis to be conducted with ease, is an intricate and strategic process that can differ from client to client.
However, there are noticeable features that Business Intelligence strategies aid in implementing. Two of these features include Data Warehouses and Data Lakes.
When implementing a BI solution, it’s often not ideal to incorporate multiple separate data sources, both for complexity and accessibility. Constructing a Data Warehouse can offer an optimum solution to this challenge, offering an all-encompassing source of the data that a business possesses.
With a Data Warehouse, data from a variety of sources is integrated into a staging table, before being loaded into a repository that end-users can access on-demand. With this repository, users can retrieve data to control and construct models such as with Predictive Analysis.
While Data Warehouses contain structured and filtered information. Data Lakes often contain pools of raw data that do not hold a specific value or destination yet. These are valuable for storing vast sets of unstructured, or semi-structured, data. However, as Data Lakes are built around raw events rather than structured tables, information stored here requires large amounts of preparation.
As each landscape and infrastructure is unique, any BI efforts must be tailored to each client.
From implementing new constructions such as Data Warehouses, to optimising pre-existing architecture, all data sources must be collated, considered, and cross-examined to reduce any chances of bias.
What are the challenges of Business Intelligence?
While Business Intelligence can boast a wide range of advantages and benefits for businesses of any size, implementing effective efforts are often accompanied by challenges that threaten to disrupt the entire process. These can include:
Many internal teams that are introduced to new BI strategies and structures may show resistance to new processes. This can not only slow down BI implementation but also threatens to undermine the possible results of Business Intelligence altogether.
To counter this resistance, it is essential to not only clearly display the advantages and benefits of BI efforts, but to also encourage self-confidence and independence with new workflows. Often when internal teams realise the full potential of Business Intelligence, they will become more receptive to adopting new techniques.
One significant example of this issue was faced when we implemented a range of BI services for Inspired Villages, a luxury property organisation that wished to move towards a smarter, more integrated BI strategy. Here, some internal teams showed some hesitancy about moving to new workflows, yet when the potential benefits of the BI strategy were fully communicated, our customer moved to create a new BI position that could focus purely on internally optimising current BI architecture.
As a result, the organisation developed a long-term, scalable BI strategy that is constantly improving and becoming more efficient and streamlined, adapting to new challenges from an experienced and knowledgeable stance.
Lack of data strategy
Lacking a clear, well-communicated data strategy may well undermine the hard work that goes into any BI project – meaning that the resultant BI project deliverables will be underutilised, un-optimised, or departments could ignore the new solution altogether. When this occurs, it may lead to businesses resorting back to their previous habits, relying on gut instinct, not data, to inform strategies.
When we are involved in implementing new solutions and services, we always take a collaborative approach with our client. This ensures that they are clearly aware of the advantages and value that best practice BI use will provide.
Once this is communicated, and organisations are made aware of the measurable value found in our efforts, they are often more willing to engage in becoming more data-centric, improving overall strategy as a result.
Speculative approaches to projects
Without a clear communication of approach, strategy, and advantages of implementation, integrating BI efforts may seem speculative and without initial visible value. That is why we prioritise clear communication throughout our involvement, ensuring that all stakeholders are aware of the key benefits that will be added to multiple operations and sectors, as well as consistently providing updates and feedback on progress, challenges, and more.
As a result of this emphasis on communication, we can successfully navigate this speculative notion, ensuring trust and confidence in our efforts.
The evolution of Business Intelligence: A Cloud-based approach
As the capabilities of Business Intelligence have evolved and developed, so too has the demand for it grown in other landscapes. While traditionally incorporated with on-premise data storage, the recent surge in demand for Cloud-hosted data has allowed many to see the benefits of migrating completely to the Cloud. Cost-effective, flexible, and unrestricted by physical space, the Cloud appears to be the future of data storage.
From on-demand accessibility to scalable, future-proofed capabilities, there are many core advantages to moving to a Cloud-based approach. Current trends predict a very large shift to Cloud-based data storage in the coming years, and Business Intelligence efforts have already evolved to integrate data to Cloud storage locations such as AWS and Azure for great effect.
Learn more about the benefits of adopting Microsoft Azure for your data.
The future of Business Intelligence
Business Intelligence has grown greatly from its first 1950’s mention. As this industry continues to evolve, many experts have attempted to predict what developments are yet to be incorporated or improved. Common trends and predictions include:
Natural Language Processing
As more and more businesses continue to realise the power of BI, ease of user experience will be emphasised.
One key feature that is beginning to develop is Natural Language Processing – the ability to utilise Artificial Intelligence to allow users to interact with their data as if asking a question to a colleague or search engine. This enables any user to conduct a thorough analysis through prioritising accessibility.
Heavy automation capabilities
While Machine Learning capabilities are already able to automate incredibly complex algorithms to model possible outcomes, an emphasis on increased reliance on automation is expected. These automation functions may increase heavily to enable a more passive approach to BI and analysis.
The advancement of Data Visualisation tools
As the capabilities of Data Visualisation tools continue to increase, an advancement in capabilities and integration can be expected. This will allow for greater data immersion, access, manipulation, and overall efficiency, to continue delivering visual insights for vaster and more complex datasets.
Read more about how we can help incorporate Machine Learning for your business here
A collaborative shift towards shared data
While contemporary Business Intelligence tools are often geared towards independent users and organisations, the shift to the Cloud may enable a more collaborative, shared approach to data management and analysis. This may be accelerated as the digital footprint of online users continues to grow and become vaster than ever.
A wealth of advantages to empower your business insights
To learn more about how Business Intelligence can streamline and your data and benefit your strategic decisions, read through our case studies.
For any further questions or business enquiries, contact us here and one of our industry experts will get in touch with you as soon as possible.