The Future of Business Intelligence: Overcoming Challenges With Latest Technologies

Introduction

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Most business leaders agree that data is critical for their success, and still, effective use of data remains a challenge for many companies. Missing data points, outdated static dashboards, slow response times from data teams are among the common obstacles in business intelligence (BI). In this blog post, we will address these challenges and explore strategies on how to overcome them with the latest technology trends. Last but not least, we’ll also paint a brighter picture for the future of BI. 

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Current BI Challenges

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Despite claiming to be data-driven, organisations still rely heavily on intuition or gut instinct to make important decisions. In the past year we had the opportunity to interview hundreds of relevant business and data leaders and concluded that there are 4 key root causes for the lack of data usage that we summarised below.

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1. Challenges in data discovery

One of the biggest challenges in BI is the ability to find relevant data according to business needs. For organisations with large data sets, finding the right information becomes a time-consuming and often frustrating task - for instance, it’s not straightforward to know in which table the relevant information can be found or decide which data point is the source of truth among the many similarly named ones. These issues usually stem from lack of adequate data governance that can manifest in poorly designed data schemas, non-standardised naming conventions or having multiple data sources instead of a centralised data warehouse. As a result, decision-makers may miss important insights or waste valuable resources searching for data.

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2. Lack of trust in data

Organisations that struggle with data quality issues often experience lack of trust in their data and therefore uninformed decision-making. Probably the most common issue is the lack of standardised definitions across metrics that will result in different results in various queries, destroying the trust in decision makers. Another regular issue is when a data pipeline breaks and certain important data points are missing from the report based on which business stakeholders make their conclusions. When decision-makers lack confidence in data, they are more likely to rely on intuition or experience, which undermines the data-driven decision culture.

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3. Static (outdated) data points

A critical BI challenge is handling static or outdated data points as fast-paced business environments require timely, up-to-date data for informed decision-making. Unfortunately, many organisations grapple with outdated data that may provide a false sense of comprehensive understanding while offering little actionable insight, especially if datasets aren't refreshed regularly. Outdated infrastructure exacerbates the issue, as legacy systems may not support real-time updates or automated data pipelines.

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4. Bottlenecks in accessing data

Inefficient reporting processes characterised by repetitive requests and manual data preparation consume valuable time and resources and this is the fourth main root cause that leads to underutilised data in companies.  Business users may rely on teams of IT or data analysts to create reports or charts, leading to bottlenecks and delays in gaining key insights, which negatively impacts the competitive advantage. Manual reporting will undoubtedly create space for human errors, leading to the second key root (data is not trusted), increased labour costs, and data privacy risks. 

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Technologies Shaping the Future of BI

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Luckily there are several technologies and innovations that are trending now and can resolve the pain points mentioned above. These technologies shaping the future are revolutionising the way companies analyse, and utilise data to drive insights and decision-making. We have collected the ones with the largest expected impact. 

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1. Cloud-based data warehouses and data lakes

Cloud-based data warehouses and data lakes (such as Google Bigquery, Amazon Redshift or Snowflake) are essential in BI due to their scalability, cost efficiency, real-time data processing, integration capabilities, security, and the ability to leverage advanced analytics. They empower organisations to extract maximum value from their data, gain deeper insights, and drive data-driven decision-making across the entire enterprise. Furthermore, they help companies break down silos by centralising data from various sources into one. 

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2. Data observability tools & metric stores

Tools that support data cleanliness – such as data observability systems (e.g., Monte Carlo Data) and metric stores (e.g.: MetricFlow) –  will most probably be key players in BI because they ensure data quality, enhance trust in insights, provide visibility into data lineage, facilitate proactive issue identification and create a single source of truth for metric definitions. By leveraging these tools, organisations can maintain high-quality data, drive reliable decision-making, and maximise the value derived from their BI initiatives.

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3. Increased compute performance and storage capacity

Advances in computing power and storage capacity enable organisations to efficiently manage complex analytics tasks. High-performance computing environments enable faster processing and analysis of large data sets, providing real-time insights and reducing latency in decision-making. This exponential growth in computing power and decrease in computing costs makes data refresh updates more frequent (or real-time) as well as the ability to build advanced analytics models more economically viable. 

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4. Generative AI as a new interface

Last but not least, generative AI (or large language models) based on machine learning and natural language processing will provide a new interface for BI. By removing the requirement to know SQL or Python to work with data, it will democratise data access, and accelerate insights generation. GenAI will also provide intelligent recommendations, understand contextual queries, and continuously learn and improve advanced analytics models. By implementing generative AI interfaces, organisations can enhance the accessibility, usability, and value of their BI systems, empowering users to make data-driven decisions more efficiently and effectively.

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Future of BI

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If we understand these technologies, we can also infer what the future of BI will look like. Predictably, it will be characterised by the following 4 ideas, with a particular focus on enhancing customer experience, scalability and flexibility and collaboration and democratisation of data.

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1. Quick & easy to set up

In the future, BI solutions will become easier to implement, reducing the time and resources needed to set up and maintain these systems. Cloud-based solutions and pre-built connectors will simplify integration with existing data sources, making the initial setup quick and hassle-free. Automated data processing and self-learning algorithms will ease the burden of data governance and cleanup, ensuring a smoother transition towards becoming a truly data-driven organisation.

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2. Self-service for business users:

Today BI processes are most often run by special teams and data professionals, yet the future of BI will emphasise self-service capabilities, to allow and entice business users to explore data and generate insights independently. Intuitive user interfaces and no-code functionalities will empower users to create personalised monitors, dashboards, and reports tailored to their specific business needs. The self-service approach reduces dependence on IT and data teams and alters the workforce's vision of data utilisation.

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3. Real-time insights:

With advancements in computing power and storage capacity, the ability to process and analyse data in real-time will become a standard feature of BI systems. With advancements in technologies such as streaming data processing and event-driven architectures, organisations will have access to up-to-the-minute data, enabling them to make proactive and agile decisions. Real-time dashboards, alerts, and notifications will keep decision-makers informed about critical changes and emerging trends, ensuring timely actions.

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4. Actionability with automation:

The future of BI is not just about observing and understanding data, it's about driving action. Automated workflows triggered by data insights will become increasingly prevalent, integrating BI with operations. For instance, a sudden surge in product demand can automatically trigger increased production. By making insights directly actionable, organisations can enhance their efficiency and responsiveness, freeing up time for employees to focus on strategic tasks rather than routine operations. This future BI landscape signifies a move from passive data consumption to active data utilisation, optimising decision-making and operational processes.

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In conclusion, the future of BI holds immense promise for organisations. By embracing cloud-based infrastructure, data cleanliness tools, increased computing power, and generative AI interfaces, companies can overcome existing challenges and unlock the full potential of their data. The future of BI is a landscape where data-driven decision-making is the norm, enabling organisations to stay ahead of the competition and drive sustainable growth.

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Flawless

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Flawless embodies the future of business intelligence by leveraging the key ideas shaping the BI landscape. Built on the principles of quick and easy setup, self-service capabilities, real-time insights, and actionable automation, Flawless revolutionises the way organisations analyse and utilise data. Flawless is already utilised by forward-looking companies in various industries, such as ecommerce, logistics or healthcare. If you’re also interested in giving it a try, request a free trial via our website.