6 Data Analytics and Business Intelligence Trends to Fire Up Your Business

In today’s data-driven world, harnessing the power of data analytics and business intelligence has become essential for organisations seeking a competitive edge and sustainable growth. The ability to extract meaningful insights from vast volumes of data can revolutionise decision-making processes, optimise operations, and drive innovation across industries. As we venture into the heart of 2023, the landscape of data analytics and business intelligence continues to evolve rapidly, presenting new opportunities and challenges for businesses aiming to stay ahead of the curve.

In this article, we explore six cutting-edge trends that are igniting the realm of data analytics and business intelligence, empowering businesses to unlock the true potential of their data. From the transformative impact of artificial intelligence and machine learning to the rise of augmented analytics and data storytelling, these trends hold the key to unleashing the full power of data-driven strategies. Join us as we delve into the latest advancements and industry shifts that are firing up businesses in 2023, and discover how your organisation can leverage these trends to navigate the ever-expanding data landscape with confidence and foresight. Let’s embark on a journey to fuel your business’s growth through data-driven insights and intelligence.

Some of the trends that can be seen in Data Analytics and BI are as follows:

Analytics Of Things

Analytics of things is the next buzzword after the popularity of Internet of things(IoT). The Internet of things (IoT) generates a massive amount of data which Analytics of things (AoT) analyses to make a decision relevant to business. Analytics are decisive to make connected devices smart and to perform intelligent actions. Analytics of IoT devices makes them more efficient. AoT analyses the huge data generated by IoT and only by analysing the data becomes meaningful and not by collecting them.

However, IoT itself is evolving and AoT is at an incipient stage. One of the major challenges that AoT faces is Data Storage issues of real-time data that IoT generates. The data generated by each sensor is sizable and managing such huge data is a difficult task. Two major challenges faced by businesses are avoiding junk data and ensuring data privacy. It is vital to protect the data generated from devices, especially at confidential places.

Consumer Experience

“Excellent customer service is the number one job in any company! It is the personality of the company and the reason customers come back. Without customers, there is no company!”- Connie Elder

Gartner has recently developed a Hype Cycle for supporting the customer process by analysing them with critical technologies. It is because many business leaders are relying now on technology to deliver the desired customer experience. Future trends in customer experience will depend on collecting quality data rather than massive data.

According to Bill Gates, “your most unhappy customers are your greatest source of learning” makes the customer journey easy with many analytics. Only by trial and error, you can give the perfect customer experience through multiple devices and channels in this digitalized world. 

Some of the customer journey analytics include:

• Emotion detection
• Speech analytics
• CEC or the customer engagement center
• Interaction analytics
• Customer intelligence analytics

“By 2021, 15 % of all customer service interactions will be completely handled by AI, an increase of 400% from 2017”.

The above prediction of Gartner in the 2019 Strategic Technology Trends report is a significant indicator of AI influence in customer experience. It promulgates AI into future customer experience enhancements for better customer satisfaction. AI and its subfield Machine learning are revolutionising businesses and data management.

The benefits of AI for better customer experience:

• Live dashboards help businesses not only to monitor every second happening but also to get alerts when something is not right
• Algorithms based on advanced neural networks to provide high accuracy in anomaly detection
• Automated analysis of datasets to provide high-quality insights and a better understanding of information even without an IT background.
• Online verification processes like CAPTCHA technologies are enabled with GANs or Generative Adversarial Networks to determine if the image is artificial or not.

Data-Driven Future

“Not everything that can be counted counts and not everything that counts can be counted.” –Albert Einstein

In the era of data, Einstein’s words ring true, emphasising the significance of choosing the right data. Not all data holds practical value, making it essential to prioritise the analysis of relevant information aligned with business needs. This approach becomes the key to ensuring business sustainability and long-term success.

Reports indicate a growing emphasis on two critical aspects: data quality management and data discovery. Businesses are recognizing the importance of maintaining high data quality standards to comply with stringent regulations and meet evolving demands. Moreover, leveraging data to predict customer behavior has become indispensable in today’s digital world, enabling companies to make informed decisions and stay ahead in the competitive landscape.

Data Management:

Data management is another crucial trend in the data-driven future. Organisations are investing heavily in advanced solutions to ensure the security and accuracy of their data. The core objective that companies pursue is to create a unified view of customer data that enables them to gain deeper insights into their customers’ needs and behaviours. With more industries becoming increasingly data-centric, this trend is likely to continue its growth in the coming years.

Data Discovery:

Data discovery is another popular trend, allowing businesses to uncover hidden correlations and patterns from data. With the help of advanced analytics tools, companies can easily identify meaningful trends in the data that may have been previously overlooked. Data discovery allows for faster decision-making without compromising on accuracy or quality. Furthermore, it enables organisations to forecast potential future outcomes more accurately by leveraging predictive analytics.

With recent undesirable experiences of Google and Facebook of a data breach, the consumers’ awareness is high now. They are now more concerned about their personal information and online habits.

GDPR of Europe and CCPA of California sets the precedent of data protection strategies. It has become essential for app developers to know about GDPR Compliance.

Evolution Of Self-Service BI

Self-service business intelligence (BI) tools have proven to be highly successful for numerous companies, as they empower business professionals to explore and analyse new data sets with minimal reliance on IT support. The increasing popularity of visual data discovery tools has made them synonymous with self-service BI. By leveraging visual data discovery, businesses can uncover unexpected insights, leading to more informed and error-reduced decision-making processes. Additionally, BI as a service provides users with a comprehensive solution, encompassing data extraction and organisation from large databases, high-performance data warehousing, and user-friendly interfaces for accessing and processing data efficiently.

The user interface of self-service BI software plays a crucial role, particularly for non-tech-savvy users. A simple and intuitive dashboard and navigation are imperative to facilitate easy utilisation of BI tools. With self-service BI, business professionals gain autonomy in creating and accessing various BI reports, queries, and analytics without interruption from IT teams. This democratisation of BI applications broadens their reach, addressing a wide range of business challenges and requirements.

In today’s data-driven landscape, self-service BI not only empowers business professionals but also drives organisational agility and responsiveness. By enabling users to independently analyse data and generate insights, businesses can make data-driven decisions faster, adapt to changing market conditions, and gain a competitive advantage. Moreover, the seamless integration of self-service BI tools with user-friendly interfaces and purpose-built apps fosters a culture of data exploration and empowers employees across departments to become active contributors to the data-driven decision-making process. As a result, self-service BI becomes a transformative force, propelling businesses towards growth and success in an increasingly data-centric world.

Collaborative Business Intelligence

According to the Gartner report, the latest focus of new business intelligence (BI) tools revolves around enhancing collaboration, information sharing, and decision-making processes. However, it’s crucial to go beyond mere document sharing and updates. Instead, BI tools should continuously track and integrate all aspects of business progress, including meetings, calls, email exchanges, and the gathering of ideas.

As the competitive business landscape demands increased interactions between managers and workers, collaborative BI is rapidly gaining traction. This approach seamlessly combines collaboration tools such as social media and 2.0 technologies with online BI tools, revolutionising the way analysis is conducted in today’s fast-paced business world. Collaborative BI has emerged as a successful approach to facilitate efficient data-driven decision-making and foster a culture of teamwork and idea exchange within organisations.

Collaborative BI makes sharing easier in generating automated reports as per schedule time and people. It also enables business with many functions accessible to all types of devices. The features include:

• Intelligence alerts
• Share public dashboards
• Share embedded dashboards with a flexible level of interactivity

Rise Of Open Source

R, Hadoop, and Python are increasingly becoming essential components of enterprise-scale data science, making significant strides into the mainstream. Initially met with scepticism and considered risky, open-source tools have now earned appreciation for delivering tangible value. The coupling of open-source technology and data science has emerged as a prevailing trend, empowering marketers to gain deeper insights into their target market’s behaviour. Database and analytical software vendors are anticipated to embrace open-source functionality by integrating it into their products. This is evident in the growing support for R programming in various commercial databases and statistical computing platforms. By leveraging open-source tools, new opportunities for collaboration and innovation are unlocked, promising exciting possibilities for the future.

In today’s data-driven landscape, the integration of open-source tools like R, Hadoop, and Python has become instrumental in enterprise-level data science endeavours. Overcoming initial doubts about their reliability, these open-source solutions have proven their value, driving increased adoption across industries. Marketers are reaping the benefits of this trend, as it allows them to gain valuable insights into customer behaviour and market dynamics. The fusion of open-source technology with data science is driving innovation and collaboration, as evident in the support from database and analytical software vendors for integrating R programming. As this trend continues to evolve, enterprises are set to explore new horizons of data-driven decision-making and problem-solving, facilitated by the flexibility and potential of open-source tools.

Supercharge Your Data Analytics and Business Intelligence with 9NEXUS as Your Outstaffing Partner

In the rapidly evolving landscape of data analytics and business intelligence, staying ahead of the curve requires access to top-notch talent and specialised expertise. Partnering with 9NEXUS as your outstaffing service provider can be the key to unlocking the full potential of your data-driven strategies. With a pool of skilled professionals experienced in cutting-edge technologies like R, Hadoop, Python, and more, 9NEXUS empowers your organisation to harness the power of data effectively. Whether you need a team to implement collaborative BI solutions, develop advanced data models, or integrate open-source tools seamlessly, 9NEXUS offers tailor-made solutions to propel your business to new heights of success.

9NEXUS goes beyond conventional outstaffing services by providing you with a dedicated team that seamlessly integrates with your existing workflows and understands your business goals. With 9NEXUS as your reliable partner, you can streamline your data analytics and business intelligence efforts, enabling you to make faster, well-informed decisions and respond to market changes proactively. Leveraging the expertise and experience of 9NEXUS professionals, you can overcome data challenges, navigate complex trends, and optimise your data-driven strategies for sustainable growth. Embrace the future of data analytics and business intelligence with 9NEXUS as your trusted outstaffing service provider.

Conclusion

The widespread popularity of self-service BI, collaborative BI, and open-source tools is transforming the way data science is conducted within organisations. Business intelligence professionals are now able to leverage these technologies to make data-driven decisions faster, more accurately, and with greater confidence. Self-service BI enables users to independently access various reports, queries, and analytics without involving IT teams. Collaborative BI combines collaboration tools with online BI tools, revolutionising the way data is being analysed. Open-source tools like R, Hadoop, and Python are becoming increasingly important for enterprise-level data science initiatives. By leveraging these technologies, organisations can explore new opportunities to drive innovation and gain insights into customer behaviour and market dynamics.

These trends are likely to continue to shape the future of data science, presenting exciting possibilities for businesses and professionals alike. As organisations gain an edge in today’s competitive landscape by leveraging these technologies, the importance of data-driven decision-making will continue to grow. The integration of self-service BI, collaborative BI, and open-source tools into enterprise architecture will become increasingly important in unlocking the potential of data science and delivering real-world results.