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The global business landscape is something that rarely stands still. On the contrary, it’s always changing and evolving rapidly, which is why businesses of all scales and practically across every sector are leveraging business intelligence (BI) and big data to outsmart, outpace, and outmanoeuvre the competition.
Big data and the components of business intelligence are very important to understand in the modern business landscape. The analysis of big data and business intelligence can potentially provide some very valuable insights into, say, market trends, operational efficiency, or customer behaviour – all of which can help you make decisions faster, smarter, and with significantly less risk-taking.
When businesses understand how to leverage the right big data analytics and business intelligence technology, they gain access to vast amounts of information (not available through regular channels) which they can use to fine-tune nearly every aspect of their operations and to improve their ROI.
However, the ability to understand how you can use BI and big data in the most effective way possible, is an art in itself, and an essential proponent of success in the largely digitally-driven world.
Sit back and refresh your coffee supply, as we delve deep into how big data and business intelligence intersect, why it’s important for businesses to understand this intersection, the difference between business intelligence and big data, and more.
Big data refers to the huge volumes of structured and unstructured data that businesses generate each day. This data may originate in the form of website activity, social media interactions, customer transactions, and a lot more. It’s important to understand that the sheer volume of this data is something that the average person will likely not be able to fathom. However, with the right big data tools and components of business intelligence in place, businesses can find the proverbial needle in a haystack to unearth incredibly valuable insights.
This covers a very basic explanation of what big data is.
Business intelligence (BI) is the collection, analysis, and interpretation of data to help businesses drive better decision-making. Where big data and business intelligence intersect is that the former provides the raw material necessary for analysis, while the latter helps organisations analyse and interpret it to identify important trends, patterns, opportunities, and correlations. All these ‘mini goldmines’ of information would have remained hidden if businesses had not used big data analysis to begin with.
The above rather basic explanation, however, isn’t enough to understand how big data impacts business intelligence because it does so in a major way – arming businesses with real-time insights to pave the way for highly calculated and well-informed decisions, where significantly less risk is involved.
Let’s take this example: through big data analytics and business intelligence, retail businesses can track customer behaviour across all their channels, thus, gaining a deeper understanding of their unique buying patterns and shopping preferences. In turn, they can use this information to improve product offerings, come up with strategies to improve customer loyalty, or create better, more personalised marketing campaigns.
In the wine industry, for example – an industry where very large amounts of data on sales, production, consumer preferences, and more, is generated each day – big data and business intelligence may be used to acquire insights into the latest industry trends like the types of wine that are most popular, and which parts of the world they are being sold.
Wineries may use this information to make better-informed decisions around the kinds of wines they should produce and how they might market them across different regions. They may also use the BI and big data to better understand customer preferences, buying patterns in the global market or, for example, to come up with the most effective strategies for targeting an audience in a specific location or region.
Another major way big data and business intelligence intersect is by enabling predictive analysis. When organisations analyse historical data, they can essentially uncover patterns and forecast future trends. This can help them to make much better decisions on a variety of aspects – from inventory management and complaints handling to product development and future research.
Therefore, it’s important to understand this correlation between business intelligence and big data because big data in itself is revolutionising the way companies approach business intelligence. By gaining access to massive volumes of data, organisations can gain insights which were either impossible to find previously or not even on their radar as something they could pursue.
Again, to make it all work, businesses must invest in the right tools and strategies for big data analytics and business intelligence, as it can not only be a strong driver for growth but also give them a serious competitive edge.
The key difference between big data and business intelligence is equally important to understand. Some people use both terms interchangeably, even though they do not point to the same thing.
Big data and big data analytics are a broad term which includes business intelligence and other activities such as, text analysis, predictive modelling, and data mining.
Business intelligence is a subset of big data analytics – a collection of different technologies and processes which help in the gathering, storing, analysing, and reporting of data to help drive better business decision-making.
If we look at it from a benefits standpoint, then big data helps business with:
Whereas, BI helps businesses with:
Let’s also explore a unique example in each case as we try to answer “what is the difference between big data and business intelligence”:
Big data – A major social media platform like Facebook plays host to millions of people who post and share things, as well as interact with other Facebook users. The platform generates massive amounts of data in the form of images, text, videos, and more. This data is so large and complex that it cannot be analysed through traditional methods. But with big data analytics and technologies, this data can be processed and interpreted quite efficiently and quickly.
Business intelligence – Now, let’s consider a retail business which collects various data on sales, inventory, and customer buyer patterns each day. It uses this data to create reports and dashboards which provide insight into the business’s performance. For example, the retail business may want to analyse specific sales data to understand which products are hot sellers and which aren’t. They can then use this information to optimise their stock levels and make more informed decisions around marketing, advertising, and promotions.
Therefore, we can conclude that the difference between business intelligence and big data is the type of data analysed vs. the tools and technologies used for analysis.
The increasing integration of BI and big data has certainly brought a number of challenges, along with the various opportunities businesses have been presented with to improve operations and revenue streams.
For instance, a common challenge is the collection and management of huge volumes of data from several sources. As one might imagine, this requires a major investment in technology and professional skills, in order to ensure that that the structured and unstructured data is captured in the most accurate and efficient way possible. Once that data has been collected, it needs to be stored and managed with certain best practices in mind, so that it is always secure, accessible, and usable. Again, this requires highly specialised technologies as well as infrastructure which have been specifically designed and built to handle massive amounts of data.
Another prominent challenge that comes to mind is quality assurance. The underlying quality of your big data and business intelligence is very important because you must rely on it for day-to-day decision making – however, for this to work as intended, the data has to be fully accurate and reliable. This can be a challenge in itself, particularly when we must deal with large amounts of data from multiple sources.
As organisations collect and store such massive volumes of data on any given day, protecting it also becomes challenging. Businesses need to ensure that the data always remains secure and private, as well as complies with the latest data privacy and protection regulations (such as GDPR). If these regulations are not met, it could lead to some very serious financial and legal repercussions.
A major challenge that businesses may face is the complexity involved in analysing big data. It requires a highly specialised set of skills and tools, and this is something many businesses are finding challenging, often because they lack the in-house resources and expertise to properly analyse and interpret that data once it is collected.
Lastly, the implementation of big data systems and processes is usually a costly process, more so for SMEs. Unfortunately, this high cost may pose as a barrier to entry, preventing startups and SMEs from fully understanding and capitalising on the benefits of big data and business intelligence combined.
With the above challenges in mind, most companies still recognise BI and big data’s potential, investing proactively in the technologies and expertise necessary to take advantage of it. And, by leveraging BI/big data in an effective way, they can gain highly valuable and even game-changing insights into optimising operational efficiencies, driving innovation, and improving customer behaviour as well as employee satisfaction.
Big data and business intelligence are transforming how businesses operate, as we speak. Opportunities to edge ahead of the competition is just one major advantage that’s immediately perceivable on the surface.
If we look a little deeper into big data and the components of business intelligence, we can understand (and appreciate) that both can help organisations improve their customer experience significantly across the board. This can achieved by gaining meaningful insights into their customers’ needs, preferences, values, motivations, etc., allowing them to create an even more engaging and personalised experience.
It’s also worth acknowledging the fact that big data analytics and business intelligence can act as a gateway to help increase operational efficiency – namely, by identifying existing inefficiencies or shortcomings in processes, thereby cutting costs and boosting overall business performance.
When businesses are able to access predictive big data analytics and business intelligence in real-time, they can effectively make strategic decisions which align with their bottom line, as well as capitalise on opportunities before their competitors can, and even anticipate future trends quickly.
Additionally, BI and big data insights may also be used to come up with more innovative products and/or services – particularly in markets which shift rapidly, thus, enabling businesses to gain a larger market share and improve revenue numbers.
As the trend of big data analytics and business intelligence continues to gain more widespread popularity, new opportunities will always come with it. Organisations of all scales will feel empowered to make better decisions around their operations, while being able to analyse larger, more diverse, and complex data sets. This will certainly help them to identify trends in, say, market conditions or customer behaviour, which may otherwise be very challenging to uncover.
Through this data-driven form of decision-making, organisations will be in a more strategic position to anticipate shifting customer needs, which means they will almost always be able to beat their competitors to the punch, in terms of the efficiency and speed with which they deliver solutions.
There are a number of ways organisations can maximise the value of their big data and the components of business intelligence involved.
One of the key BI (big data) strategies you must have is to identify specific KPIs (key performance indicators). And, by determining which metrics to track, you can understand how your big data and business intelligence can be better leveraged to improve performance across multiple areas – typically, customer acquisition and retention, revenue growth and tracking, risk mitigation, etc.
Investing in a robust cloud computing solution is very important to help you unlock the true value of BI/big data. This means investing in the right hardware and software, as well as the necessary networking resources which are already optimised fully to capture, store, and analyse data.
You must also develop the right data governance policies, so that your data is always secure, with specific policies in place to ensure its quality control and lineage. This is an important practice to incorporate because it guarantees the reliability and accuracy of all your data assets. Organisations that are proactive about fostering a culture of decision-making through data-driven insights, are always in a better position to leverage big data in the most effective way – than those which don’t.
By providing the necessary training and access to your employees around data visualisation tools and data analysis, you can promote a culture of data-driven decision-making at practically all levels of the organisation.
In our observation, big data initiatives are always most effective where collaborative efforts are made across multiple departments within the business. So, this means getting cross-functional teams to collaborate, so that they can collectively identify areas of opportunity and specific overlaps, in order to leverage data to develop more innovative solutions which benefit the business and its customers.
By incorporating the above strategies (for starters), you can begin maximising the value of your big data and business intelligence assets, driving better business growth through data-focused decision-making.
Up until now, we’ve discussed the difference between business intelligence and big data and how big data analytics and business intelligence intersect. We’ve also discussed some of the potential applications of this intersection and how businesses can come up with strategies to leverage their big data assets to the fullest.
Therefore, it’s also worth discussing some of the implications for businesses in more detail when we talk about the integration of big data analytics and business intelligence.
With real-time access to big data analytics either on company systems or mobile devices, business owners can make more informed decisions while on the move. Gone are the days of waiting for subordinates to produce a report or being confined to your desk to view critical business data for decision-making. This level of agility offers a serious competitive edge in today’s ultra-competitive business landscape.
As we briefly touched on earlier in the article, this integration or intersection helps to foster an entire culture around data-driven decision-making. As more and more team members have access to big data and business intelligence tools on their devices, data-driven decision-making becomes an integral part of the business’s culture.
This can lay the groundwork for better business performance as decisions are no longer made on assumptions, forecasts, or ‘gut’ feelings, but rather facts and insights.
The intersection of business intelligence and big data can also drive innovation, and often in areas that you either missed or those where you believed it was not possible to innovate. The ability to access and analyse vast amounts of structured and unstructured data on practically any device imaginable can lead to the creation of new ideas or solutions. By identifying new trends and patterns (which were previously not identifiable), businesses can discover innovation in their products, services, and even operational model.
Big data, when leveraged correctly, can provide organisations with unmatched business intelligence. Once businesses understand the correlation between big data and business intelligence, they can use it to:
With the advancements in technologies like AI, ML, and NLP, for instance, as well as the increased availability of both structured and unstructured data, business intelligence has greatly evolved, becoming more complex and sophisticated than ever. For example, the advancements in ML and AI algorithms alone have enabled businesses to better leverage big data analytics and business intelligence, uncovering hidden insights, patterns, and trends that were simply not available otherwise. This has opened up way more opportunities for businesses to better understand performance bottlenecks, find ways to optimise daily operations, and drive innovation across multiple business functions.
With that said, the integration of business intelligence and big data has presented some unique challenges as well – for instance, data privacy and data security concerns, the need for advanced data processing and management systems, and, the acute need to hire skilled personnel who can properly analyse and interpret the data.
Businesses must understand the benefits to be had as well as the unique challenges that come with it, to harness the true potential of this intersection between big data and business intelligence we’re witnessing.
With Fast Data Science as your partner, you’ll be able to uncover insights to achieve everything from outsmarting your competitors to improving the customer experience to increasing business efficiency, and more.
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