Ever wondered how some businesses always seem to be one step ahead of the competition?
At the core of every industry-leading company is an advanced understanding of data, using information points to guide their road map and support operational efficiencies while minimising the risk of poor decision-making. This data-centric approach to unlocking business-critical information is often referred to as business intelligence.
17 October 2023
At a high level, Business Intelligence (BI) covers a wide range of internal and external functions and is defined as “the process by which enterprises use strategies and technologies for analysing current and historical data” to support decision-makers and business strategy. Using this definition, business intelligence can be broken down into three general stages:
Data Collation
This phase covers the collection and storage of relevant data that will then inform the basis of any analysis used to extract insights. Organised and consistent data collation procedures are essential to keep the results of analysis verifiable. This also helps prevent unaccounted variables from skewing the quality of your data outputs.
Data Analysis
This is where the heavy lifting of business intelligence takes place when data collected from relevant sources is run through some type of analytical process to extract insights. The “process” chosen for your diagnostic analysis will vary significantly depending on your project goal. For a software provider, the objective of conducting business intelligence may be to find more efficient use of existing datasets, while a consulting firm will gather external data to provide a strategic, qualitative report to inform a C-level meeting.
Data Outputs
Data collection and complex analysis is only as good as its delivery to the right stakeholders. Critical to effective use of business intelligence is the ability to package the analytical results in a manner that is easily digestible and actionable. There is no room for “fluff” data that is irrelevant to business operations. Identifying what to include, and more importantly, what not to include in business intelligence reports dictates the pay off all the hard work in the previous stages.
With this understanding, the rest of this article will place the importance of BI in the context of different business use cases, demonstrating how data-driven intel can form a competitive advantage in the right hands.
Use of BI for Competitive Research
Competitor research is a common practice across most business sectors, providing your team with a better understanding of the corporate landscape and comparative analysis between your solutions and those offered by companies in the same space.
BI tools and analysis comprise an essential component of effective competitor research, with several content feeds – including news data, social media data, company press releases and market activity – often used to better understand what other companies are prioritising. Armed with these business insights, enables your organisation to position new products and other actions to maximise results.
Media Monitoring for Real-time Insights
Media monitoring, similar to competitor research, both leverages BI and is a sub-section of large-scale BI reporting. BI analytics are particularly useful for media monitoring tasks dealing with brand monitoring, reputational risk, and campaign tracking. PR and communications professionals depend on access to the most comprehensive data pools to support their day-to-day tasks. A process that is made exponentially easier with accurate and tailored BI capabilities.
Business Intelligence in action for MMOs
Brand Monitoring is one of the leading business use cases for media monitoring firms, providing their end clients with invaluable insight to help drive business activity and inform a range of functions.
An example of this in action is how Nike acquired BI companies Zodiac and Celect in 2018 and 2019 respectively. Seeing the advancement of AI capabilities and the opportunity of predictive analytics, Nike saw these purchases as a way to power consumer insights and improve sales in underperforming product areas/jurisdictions. In tandem, forward-looking BI capabilities enable brands like Nike to blunt emerging reputational concerns, and proactively respond to situations at the earliest possible stage.
Conclusion
There is no one-size-fits-all solution to guiding company decisions and corporate policy. Effective data analysis is an increasingly integral guide in how to respond to situations that impact business operations. However, accessing more data does not directly translate to better decisions. Therein lies the value of effective BI capabilities, taking a growing volume of data from an array of sources and sifting through millions of content points to identify key drivers worth your attention.
At Opoint, our global news data toolkit is designed to be an invaluable data stream to help power your BI insights, providing the richest, most comprehensive global news content on any given company, entity or individual.
Your questions, answered.
The quality of what goes in determines what comes out. For BI reliability, this is no different, with the first step of maintaining reliable BI for competitive research down to the data inputs you are using to conduct your analysis. The use of poor or incomplete datasets will inherently raise the risk of errors and discrepancies, undermining the quality of the results and the acionability of the BI insights.
BI tools are an excellent way to increase confidence and monitor critical business performance during a crisis event. Whether this is monitoring negative media and reputational risk, or tracking economic data and predictive analytics to forecast financial outcomes, BI and data analysis more generally are crucial to navigating out of high-risk scenarios.
Similar to Question 1, there is no singular “right” way to get started with BI, and it comes down to what you plan to use BI tools for that will determine how you approach your business intelligence workflow. For instance, media monitoring firms may require unique datasets such as social media content and editorial news, using both streams to unlock audience insights, whereas an asset management company could examine trading signals and news coverage, looking for correlations that may inform market activity.
