BUSINESS TECHNOLOGY MEDIA COMPANY
Companies
Sectors

The future of business intelligence: Six key trends

By Keyrus sales director Stephen Coull

Johannesburg, 18 Nov 2021
Read time 7min 50sec

Business intelligence (BI) has undergone significant transformation in recent years and certainly is not being superseded by artificial intelligence. In fact, it has become more efficient and user-friendly since moving to the cloud, utilising AI and machine learning, and its embedded implementation. 

Today's business leaders know that data is a valuable asset that needs to be used effectively to ensure efficient company processes. They require powerful data analysis tools to help them in the decision-making process. Businesses are embracing sophisticated analytics and data science to gain insights and make more informed decisions. The result – turning your BI department into a profit centre.

BI is a valuable tool for both small and large businesses, it is evolving at a rapid pace and will determine how companies will work with data in the future. It therefore needs to be mobile, flexible and user-friendly. Here are six key trends that will shape the future of BI:

Cloud-based BI

The future of business is in the cloud, and this is also the way big data is now moving. Nearly all BI elements, including data sources and models, analytics, data storage and computing power, have already moved to the cloud. Businesses need a connected cloud strategy to ensure reduced risk and flexibility.

Cloud services are by far the biggest trend in BI, especially with the wide adoption of remote working. Cloud-based BI makes it possible for apps and data to be accessed anywhere and anytime. Software as a service (SaaS) apps are becoming more popular and can be accessed via any web browser, allowing data, insights and answers to be accessed anytime, anywhere and on any device.

Improve data quality

Extracting new value and insights from business data is critical, especially to become a fully data-driven organisation, delivering actionable intelligence to the entire organisation.

However, data quality is still one of the biggest challenges for data analysts. Good data quality is critical when trying to obtain the accurate insights from available data to make the right business decisions. Businesses are realising the huge cost implications of basing decisions on poor data quality and, as a result, they are implementing a data quality management (DQM) policy to ensure efficient data analysis.

According to Gartner, poor data quality costs organisations an average $12.9 million every year. Apart from the immediate impact on revenue, over the long term, poor quality data increases the complexity of data ecosystems and leads to poor decision-making, as stated in the Gartner 2021 report on Data and Analytics Governance.

Gartner predicts that by 2022, 70% of organisations will rigorously track data quality levels via metrics, improving it by 60% to significantly reduce operational risks and costs.

Automation is key

Automation and AI have the power to get any company ahead of the competition. This can only be achieved through enhancing productivity and precision across business processes, and to focus on higher value work.

Gartner expects that by 2023, organisations will be able to run 25% more tasks autonomously. While much of this will be achieved through the use of robotic process automation (RPA) in front-end offices, critical operations, infrastructure and data processes will need to be automated with more robust orchestration and automation tools that provide programmatic integrations and deeper functionality.

Data collection, cleansing and data repair can be time-consuming, but automation can significantly reduce the time and effort analysts spend on preparing data. The leaders in modern analytics solutions offer visual analytics with powerful AI, data management and collaboration.

Whether it’s for making sales projections or assessing customer value, predictive analytics and reporting are some of the most prominent advances in BI. Based on existing data patterns, businesses can now predict future business trends.

RPA automates everyday processes that once required human action, it offers a fast, reliable method of extracting data from various systems. It then performs initial quality checks and compiles data into a single file or report, ready for analysis. RPA promises to boost productivity and efficiency for most organisations.

Self-service

Traditional BI systems were built around a central data warehouse and data storage, an infrastructure that is inadequate for modern businesses that require data access anywhere and anytime. As a result, the self-service BI model emerged, proving enterprise wide data access.

Transforming raw data into actionable insights can be a challenge. Self-service BI enables access to meaningful data for more people within the organisation, so they can be more productive and effective. Through self-service and big data, users now have access to easy-to-use, cloud-based remote analysis and reporting.

In short, self-service BI encompasses the processes, tools and software to empower users to analyse large amounts of company data and then independently build custom dashboards. They no longer need to rely on IT or dedicated data analysts to prepare custom reports.

Collaborative BI

Collaborative BI or social BI is the merging of traditional business intelligence with collaboration tools such as social media and web technologies. It allows for easy sharing of reports as well as increased engagement between stakeholders and subject matter experts to make better business decisions.

Geared towards improved issue-solving, collaborative BI allows for the exchange of corporate ideas or problem solutions using Web 2.0 platforms like Wiki and blogging. It allows access to data from outside, providing greater visibility and accessibility for everyone involved in the decision-making process.

Collaborative BI is an essential tool to be agile, it gives everyone the ability to see real-time data and make decisions faster.

Augmented analytics

Augmented analytics is a key component of the future of data; it is a disruptive trend that leverages machine automation and AI. It transforms how you prepare data, generate and share insights, and create and operationalise data science and machine learning (ML) models.

By 2025, data stories will be the most widespread way of consuming analytics. Augmented analytics techniques will automatically generate 75% of those stories. According to Gartner, augmented analytics solutions allow even non-technical people to create sophisticated data analytics models and quickly draw deeper insights from them.

Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyse data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning and AI model development, management and deployment.

According to the Augmented Analytics Market report by MarketsAndMarkets, the global augmented analytics market size is expected to grow from USD 4.8 billion in 2018 to USD 18.4 billion by 2023.

Solutions and successes

At Keyrus, we use our technical expertise to empower our clients with strategic, actionable, data-driven insights. Beyond simply understanding data, we use it as a driving force for progress and innovation – a means to a better future.

All our projects are both present and future-oriented. We implement solutions that solve current challenges and add immediate value while also looking ahead at future opportunities for innovation. This enables our clients to proactively reinvent their business models and offerings.

We are tool agnostic, opting for the solution that will solve our clients' business challenge. Our team of experts are certified and experienced with the Gartner Quadrant leading solutions such as Tableau, Microsoft’s Power BI and Qlik, to name a few.

Our expertise as South Africa’s leading technology consultancy is truly demonstrated through the implementations that we have delivered for our clients, which include:

Grindrod, a world-class freight logistics company, has improved productivity with an embedded BI application. Tableau was implemented within their terminal operations to aggregate data from various mechanical and human interface sources across the company.

National supply chain specialist Vector Logistics is reaping significant benefits from Alteryx, an automated data analytics solution implemented by Keyrus. These include substantial time savings, reduced key person dependency and improved efficiency.

The Mediclinic group is one of the modern private healthcare providers that understands the need to implement and effectively utilise analytics and BI solutions, including Gartner’s BI Magic Quadrant leading business intelligence (BI) tool Tableau. Mediclinic’s leadership recognises how vital it is for their staff to have the capability to gather, analyse and interpret data in order to recommend effective treatments or make important business decisions.

Conclusion

The BI landscape is continually evolving, and rapidly too. The transition from IT-driven BI delivery to self-service analytics continues at pace and is now enhanced by the latest major trend of embedded analytics. The BI environment is no longer something separate from line of business systems. BI and advanced analytics are now being embedded within core operational systems with tangible benefits.

See also