Business Intelligence (BI) transformation plays a critical role here, by allowing businesses to drive agility in their business operations.
In this article, we will dig deeper into the role of BI transformation in driving business growth.
What is BI?
Business Intelligence is a strategic approach comprising plans
and tools that enterprises use to ensure effective data analysis and management. On the other hand, BI transformation is when you go beyond traditional BI practices – moving from retrospective reporting to a process driven by proactive, predictive, and prescriptive analytics.
It involves using standardised BI tools equipped with advanced technologies that fosters consistency and interoperability across the organisation.
At the core of BI transformation is advanced data analytics, representation and visualisation tools that enable effective analysis of a sheer volume of data (both structured and unstructured) and extracting actionable insight from it.
Thus, businesses can keep the guesswork away from their business processes and make data-based decisions to augment business productivity.
The steps in the BI process:
Preparing data by categorising and modelling data sets for analysis
Performing analytics query of the data prepared for analysis
Providing business personnels with the information gathered on performance KPIs
This information is then used by business users to make decisions.
Debottlenecking Technology with Technology: How BI transformation Enters the Scene
At the advent of invention, BI was more about knowledge discovery. At that time, researchers focused on how they can use general principles and methodologies to extract insight from data. Modern BI came into play when they tried to develop and implement fact-based strategies and tools to augment decision-making.
Since then, BI is evolving faster than ever – with advancement in technology piloting this transformation. Today, BI is more than a range of software and systems built around an organisation.
With digital transformation and regulatory compliance taking the centre stage, the demand for effective harnessing of data is surging. However, driving this much-needed agility and digitisation in business processes is impossible with legacy BI systems that remain in silos.
Legacy BI tools that were in vogue even five-six years ago have now become obsolete – they are not scalable enough, rigid and thus cannot adapt to your evolving business requirements.
The solution: implementation of tech-driven BI strategies, with standardised BI tools equipped with advanced technologies. This transformation is brought about by equipping traditional BI tools with advanced AI, ML algorithms, Big Data analytics, predictive analytics and IoT technologies.
For example, predictive and prescriptive analytics models are widely used by businesses to identify potential trends in today’s dynamic market landscape.
They are also getting suggestions for improving their services for better revenue. BI transformation also helps adhere to data governance frameworks and encourages the implementation of self-service BI software. It thus shifts the paradigm to a much more data-driven business decision.
In short, advanced BI tools help business leaders pull data from multiple sources into a single centralised system, and implement algorithms for efficient data analysis. To one-up your competitors by making more informed decisions, transforming your BI tools stands as a strategic imperative.
BI transformation is fast gaining momentum, by reinventing business processes with advanced technologies.
BI Transformation Framework
Achieving BI transformation is a holistic approach that requires you to follow a slew of steps described below:
Tool Selection and Standerdisation
Bring all stakeholders and business leaders on one table to evaluate the current BI requirements as well as how it will evolve in the future.
An effort in coordination is pivotal to select the most suitable technology stack for your business. Plus, it would help consolidate BI into a single, cohesive and top-tier platform tailored to your needs. This standardisation process is reported to have immediate and direct impact on TCO (total cost of ownership) and trustworthiness.
While trustworthiness is increased by 20 to 25% enterprise semantic models , TCO is slashed by 30% when standerdised BI tools are deployed across an organisation. Standardising BI tools also ensures compliance with governance standards while fostering uniformity in analysis and reporting across all BI tools a company is built around.
Report Rationalisation
Organisations are often inundated with an influx of reports generated from a sheer of BI tools they use. Without keeping a tab on these reports, having them in your system is of no use.
Organisations must cash in on an automated and structured process to rationalise these reports. It can help slash around 25% cost from your operational expenses while also streamlining your report generation and maintenance system.
Platform Transformation
BI transformation is all about reinventing your BI tech stack by equipping them with advanced technologies. However, transforming a platform is a complex task that involves both change management and technology migration.
To expedite time-to-market while limiting human intervention, leveraging a migration tool is critical. Such a system can enable a seamless platform transition by efficiently extracting and mapping metadata from the existing system and feeding it to the target one.
Maintain regular communication with business users about the best practices, system configuration, etc, to ensure they can effectively use the new advanced BI system.
Define Data Governance Policies
A BI transformation plan that doesn’t include a clearly defined roadmap to governance policy based on your deployment model is of little use.
Case in point: abiding by the governance framework in business operations is reported to be 20% more beneficial to the company. Again, make sure this plan is on par with organisational goals, user demands and existing technologies.
Benefits of BI Transformation
Embracing BI transformation offers a range of benefits:
Augmented Decision-Making: Advanced BI tools equipped with AI, predictive analytics and ML algorithms help businesses foresee the potential impact of their actions, while also providing actionable insights into data that flows into it. The result is augmented decision-making that keeps revenue rolling in.
Enhanced Operational Efficiency: Automating manual tasks and data processing through BI transformation enhances operational efficacy of an organisation.
Competitive Advantage: Leveraging BI transformation enables organisations to efficiently and timely identify market trends, and user demands, thereby successfully seizing opportunities in dynamic business landscape.
Customer Insights: One of the key elements of advanced BI tools is predictive analytics capabilities that allows businesses to dig deeper into their customer behaviour, requirements and preferences.
By categorising users based on your set criteria, BI tools enable you to roll out hyper–targeted marketing campaigns.
In addition, with the advanced visualisation capabilities with these tools, businesses can efficiently and in real-time track the progress of their marketing campaigns.
Steps to Implement Business Intelligence Transformation
Implementing BI transformation is a complex task that requires an effort in coordination and a range of steps to follow:
1. Assessing Current BI Capabilities
Get the ball rolling with by taking a stock of where you are with your data sources, current BI capabilities, reporting functionalities, etc. Evaluating your current BI capabilities would help track down performance loopholes within your business system, thus allowing you to understand which business areas need a BI transformation boost.
2. Setting Clear Objectives and Goals
The next step is to define the goals you want to achieve through transforming your BI processes. Set realistic key performance indicators (KPI) that align with your goals. With advanced BI tools, you can track these KPIs to measure the success rate of your business initiatives while also gauging how actionale and attainable your established goals are.
3. Choosing the Right BI Tools and Technologies
As we already stated, the key elements of a successful BI transformation are advanced BI systems. Cash in on a tool that is scalable, easy-to-navigate, and compatible with your existing systems while also offering extensive integration capabilities.
4. Building a Skilled BI Team
Your team can make or break your BI transformation efforts. To ensure you get the most out of the process, build a team with high-level expertise in BI, data analytics, visualisation and reporting. Assemble a skilled BI team with expertise in data analytics, data visualisation, and business intelligence.
5. Data Integration and Management
For a successful BI transformation you need to ensure the system you have deployed can sync with a range of software, extract data from them and consolide it in a single unified system.
Make sure you implement data governance practices to uphold data quality, and data integrity throughout the BI transformation journey.
Key Technologies in BI Transformation
ETL (Extract, Transform, Load) Processes: ETL is one of the key technologies in BI transformation process that consolidates data from multiple sources (structured and unstructured) a business exists into data warehouses.
ELT applies advanced business rules to clean, prepare and categorise this raw data to ensure only accurate data is being processed and analysed when predictive analytics and ML algorithms are being applied.
Data Warehousing: Data warehousing – a specialised data management technology – is a key component in BI transformation that pulls out and stores business data from multiple sources into a single centralised repository (also called data warehouse).
Data warehouse stores data into parts to enable and support BI activities, more specifically, data analytics. Businesses can perform analytics tasks right from the data warehouse through their BI tools.
Data Visualisation: In BI, the outcome of data analytics is represented graphically using advanced data visualisation techniques. With visual formats like maps, plot, graphs and charts, it becomes effortless for business users to understand trends and patterns and any changes in KPIs.
Data visualisation tools help decipher data analytics results by enabling effective contextualisation of data.
AI and ML: The scope of BI with AI and ML is now intertwining – Advanced AI and ML algorithms are making BI tools even more sophisticated and capable of doing what cannot be done with traditional BI tools. AI-driven BI tools are an essential part of BI transformation that enables businesses to perform analysis of sheer volume of data at once.
This data may include customer preferences, trendsm, historic sales data and data on various factors that may impact future sales. AI facilitates BI transformation by enabling sentiment analysis. Thus, businesses can use a massive trove of textual data and sentiment signals to turn unstructured data into actionable insights.
ML algorithms are used in BI tools to help identify anomalies, inconsistencies and errors in data/. Thus proper measures can be taken before the issues escalate into hefty operational costs.
Challenges with BI transformation and The Solutions
Transforming BI is critical to driving sustainable business growth; however, it’s a challenging task that often daunts organisations. But here’s the catch: businesses that don’t drive BI transformation may lose around 20% of their annual ROI due to poor data literacy and data inconsistency.
Now you know why it’s so significant. However, your BI transformation is not going to be a smooth journey. Let’s go through what challenges a business can face while transforming their business operations:
Data Integration Complexity
Organisations often scuffle with their BI transformation as it needs to integrate and sync data from a range of sources, channels and departments they use. The integration complexity is further doubled down by data of various formats residing in disparate systems, often plagued by inaccuracy and inconsistency.
Cashing in on advanced BI tools that can connect to and integrate static data from both internal and external data sources in real-time can help. These sources can be either spreadsheet, date warehouses, emails, CRM or data lakes.
Data Quality Issues
In business processes where data is considered a currency, inaccurate and inconsistent data is the last thing an organisation can tolerate. This is because inaccurate data can add errors in your data analytics results and reporting that can affect your marketing and sales efforts.
To address this issue, implement rigorous data governance policies, data cleansing and validation processes with your BI processes. Make sure you clean and audit your business data periodically.
Lack of Business Alignment
Organisations that cannot align their BI transformation initiative with their business goals and existing technology stack are doomed to failure.
A seamless connection between strategic objectives and implemented advanced technology is paramount to successful BI transformation. Organisations are suggested to strengthen cross-departmental collaboration, more specifically,between the technical and business departments. Any BI initiative should be equipped enough to deliver value by helping solve critical business issues.
Lack of Data Governance
Organisations that put data governance on the back corner in their BI transformation initiative often bog down for the long haul. Data governance strategies, such as buying a governance solution, modifying internal business processes, etc., can make data more accessible, and reliable, thus maximising its value.
However, data governance is itself a challenging task and the challenges spiralles by complex data ecosystems, lack of data literacy, organisational silos and fragmentation, etc.
Setting thorough and realistic data governance policies that clearly define data stewardship, data ownership and the process of data lifecycle management can help. Make sure your data processing techniques adhere to data regulatory standards.
The BI landscape is fast evolving with organisations striving to enable seamless synergy between BI and emerging technologies. Incorporation of technologies such as NLP, Big Data, self-service BI, advanced predictive and prospective analytics, ML/AI algorithm, etc., are expected to further transform BI.
Data Science and Business Intelligence Strategist at Symbolic Data
George Wilson is the Lead Editor at Symbolic Data, where he spearheads the editorial direction and content strategy. With over a decade of experience in business intelligence and data management, George has established himself as a thought leader in the field. His expertise lies in translating complex data concepts into actionable insights for business executives and CEOs.
