Top 10 Business Intelligence Trends You Should Watch Out for in 2022

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Top 10 Business Intelligence Trends You Should Watch Out for in 2022

The Key to Business Survival in 2022

Over the past decade, there has been tremendous transformation in how data is processed, used and presented in businesses. Spreadsheets took a backseat, while data visualisations and business dashboards became irreplaceable companions of business owners. The evolution of the artificial intelligence industry also made the once impenetrable and seemingly unpredictable data that only make sense to data analysts accessible to people of all proficiency. All these signify the revolution of the business intelligence industry. 

Business intelligence encompasses data mining, data management, data analysis, data visualisation, querying, data reporting, descriptive analytics, performance metrics and benchmarking, statistical analysis, as well as data tools and infrastructures that enable all these processes. To put it simply, business intelligence refers to the collective processes and methods of collecting, parsing, storing and analysing data produced by business operations, used to inform business decisions.

Many businesses have learnt their lesson the hard way. They learnt the need to refer to data and business intelligence in business decision-making, ever since they observed the consequences of not doing so during the pandemic. In particular, without forward-looking data and insights, businesses have to make decisions based on hindsight, which are bound to be risky in this turbulent economy.  As we march towards 2022, a post-lockdown world still surrounded by the uncertainties of COVID-19 pandemic, these top 10 trends in business intelligence will help you to navigate business uncertainties with smarter and more resilient choices. 

#1 Augmented Analytics 

Augmented analytics is predicted to be among the most popular trends in business intelligence in 2022. By 2023, the global market for augmented analytics is anticipated to be worth US$13 billion. 

Augmented analytics is the use of artificial intelligence (AI) and machine learning (ML) to enhance data analytics. Augmented analytics can not only analyse massive quantities of data quickly and deliver recommendations based on the data, it can also spot any abnormalities or unexpected trends using sophisticated neural networks. This process is often referred to as “data discovery” – a combination of data preparation, data visualization, statistical analysis and data reporting. Augmented analytics can enhance business owners’ capabilities massively, allowing business owners to focus on the more important business decisions rather than the technical aspects of business data processing.  

Examples of augmented analytics can be found widely in the e-commerce sector. Augmented analytics allows online retailers to better understand the behaviours of customers and advertise and sell their products using multichannel marketing.  For instance, HANA (high-performance analytic appliance) is a cloud platform used by Walmart, one of the largest retail corporations in the United States, to provide business insights and spot irregularities in business transactions and data. Another example is Apptus,  an AI technology which specializes in connecting a customer’s intent to buy and the realization of revenue by a company. 

The future is already here. Whether a business survives the upcoming challenges in 2022 depends on how willing a business is in adapting towards a world dominated by business intelligence technologies. 

#2 Predictive Analytics

Predictive analytics is another top trend in business intelligence in 2022. It is a subset of augmented analytics, where forecasts and projections are made possible through AI and ML. Predictive analytics  is characterized by techniques such as neural networks, machine learning, graph analysis, simulation, complex event processing, recommendation engines, and heuristic learning. Unlike its predecessor data mining which refers only to past data patterns and anomalies, predictive analytics includes estimated future data, alternative scenarios and risk assessment. 

Given that uncertainties will continue to be the undercurrents of 2022’s economy, forward-looking data garnered by predictive analytics is the way to go for businesses. With some inkling of the possible scenarios in the future, businesses can optimize scheduling, production, inventory, and supply chain design to not only deliver what their customers want, but also to prevent unnecessary losses. Due to its usefulness, predictive analytics is widely utilised across different industries. For instance, airlines and hotels use predictive analytics to forecast consumer demand and calculate the best pricing to maximize occupancy and increase revenue. Banks use predictive analytics to generate credit scores, whereas marketers use it to project customer responses or purchases and set up cross-sell opportunities.

#3 Natural Language Processing (NLP)

Linked closely with the previous 2 trends is natural language processing (NLP). NLP is a subset of machine learning characterized by the ability of a computer to understand, analyse and manipulate human language. From Google Duplex (an AI that helps completes tasks like making reservations and appointments over phone) and Generative Pre-trained Transformer 3 (an ML model able to create articles from scratch) to natural language processing models that are able to detect fake news and cyberbullying, NLP has undergone massive revolution over recent years. When applied to business intelligence, NLP brings the historical divide between data developers or analysts and non-technical people from the C-suite to the sales, customer service and marketing teams, making big data insights accessible to users of all proficiency.

This is because traditionally, in order to extract information from a relational data stream management / business intelligence system, it is necessary for users to possess SQL (Structured Query Language) programming knowledge. But with NLP, by simply asking the right questions about the data in one’s native language, one with zero technical know-how can get the results they want quickly and easily. For instance, one could query a database system with a question like, “What was the inventory turnover rate difference between this and last fiscal year?”, and the system would convert the phrase into programming language, search for the requested data, and return the result in a natural language format. 

One of the most applicable recent use cases of NLP is in COVID-19 research, where NLP aid researchers in sifting through and reading a huge amount of scholarly articles in the database and return relevant responses to the queries of the researchers. This has tremendously fasten up the pace of virus understanding and vaccine development. In the near future, we can expect NLP applied in data analytics to also adopt voice search function, and it won’t be long before these NLP features are released in mobile-based tools, making business insights accessible anywhere, anytime. 

#4 Data Quality Management (DQM) and Data Governance 

Next, data quality management (DQM) would prove to be extremely crucial in the year of 2022 when many businesses cannot afford more profit losses as they have just recently emerged from the economic downfall. Data quality consists of data correctness, completeness, validity, consistency, timeliness and accuracy. Having these qualities in business data is crucial because the cost of bad and incorrect data is huge. To illustrate the negative impact of bad data, a  research study published in MIT Sloan Management Review showed that companies lose around 15% to 25% of their revenues due to poor data quality. Not only that, poor data management can result in the damaging of customer relationships, loss of customers, and in worse cases, irreparable damages to generations of people, for instance, when governments implement policy decisions based on incorrect data.

Fortunately, ML algorithms in business intelligence can prevent the negative costs of bad data. One of the biggest strengths of ML is that it does the job of data cleaning quickly and reliably. Data cleaning is the process of “fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset”. With quality and cleaned data, a business can avoid unnecessarily losses and costly mistakes in the current times.

#5 Data Governance

Tightly linked to data quality management is data governance. According to a survey by the Business Application Research Center, data quality management along with data governance rank in the top three most important business intelligence trends for 2022. 

Data governance is a collection of processes, policies, guidelines and metrics to ensure the quality of data in an organization. Data governance can be seen as the core component of all data management in businesses, tying together nine other disciplines, including data quality, reference and master data management, data security, database operations, metadata management, and data warehousing

An example of data governance in action can be found in the healthcare industry. For instance, data governance allows retention requirements (e.g., history of who changed what information and when) to be well-defined and ensures a company stays compliant with relevant government requirements, such as the  General Data Protection Regulations (GDPR). It is not difficult to see why a well-planned data governance framework is fundamental to the success of every business, for without clearly defined roles, responsibilities and rules, it is nearly impossible for an organization to deal with high volume, diversified and well as uncertain data in the times of the pandemic efficiently.

#6 Mobile / Self-service Business Intelligence 

Traditional business intelligence systems that are built around a centralized data warehouse and data storage is insufficient for today’s business operations. Especially with the rise in remote working, data access anytime, anywhere and by anyone is the key.  More importantly, the COVID-19 pandemic has taught us the importance of crisis management – the need to be ready to respond to unpredictable scenarios anytime. 

Hence, mobile business intelligence which is self-service in essence has emerged as an important market trend ever since, and it is only going to continue rising and improvising through the years to come. As such, mobile business intelligence has been evaluated at USD 6.18 BN in 2018, and it is predicted to grow with a Compound Annual Growth Rate (CAGR) of 22.43% by reaching 2024, according to Mordor Intelligence

A few powerful features and benefits that mobile business intelligence bring include live dashboards as well as business forecasts on-the-go. These allow businesses to access real-time data updates anytime, anywhere so they can react first-hand to any events that happen. The COVID-19 crisis has taught business owners the importance of adaptability and responsiveness. By placing the power of data and insights directly in the hands of business owners and decision makers of a company, faster decision-making, shorter workflows and more effective internal communication can be achieved. Having access to the right tools and technologies can be the determining factor to why some companies can survive, and others not in times of uncertainties like these. With the benefits it brings, mobile business intelligence is undoubtedly going to stay as one of the top business trends adopted in 2022 despite some negligible limitations that hinder some to ride on these trends, like screen size and interface usability issues. 

#7 Collaborative Business Intelligence 

Collaborative business intelligence is a shorthand term for the collection of business intelligence tools and other collaborative tools in businesses, including social media and other 2.0 technologies. Business intelligence tools can also be integrated with existing infrastructures including accounting software, HR information systems, point of sales apps, billing systems and marketing analytics like Xero and Hubspot. These business intelligence tools make remote working and collaboration seamless and effortless. Imagine being able to set up business intelligence alerts, share public or embedded business dashboards to specific people at specific times, or even track the progress of meetings, calls and emails exchanges. All these possibilities are made available through innovations and the transition to a more mobile-centric and collaborative business intelligence climate.  

#8 Embedded Business Intelligence 

Embedded business intelligence is another trend that became popular in recent years, and is anticipated to soar in 2022 and 2023. In fact, according to Allied Market research, the embedded analytics market is projected to reach $60.28 BN by 2023, with a CAGR of 13.6% by reaching 2023. 

As the name suggests, embedded analytics allows businesses to embed business intelligence solutions such as business reporting, KPI dashboards or data visualizations into native applications. Embedded analytics found on public web pages including on national health department pages, used to tabulate and display up-to-date infection rates and infection hotspots. Here is also an example of Sigma dashboard embedded into Salesforce, allowing users to manipulate the data without exiting the SaaS app. 

With embedded analytics, data can be presented, processed and analysed much quicker without having to be transferred to another business intelligence tool or software. Less is more. Just as many companies are racing to create their own super apps where all daily operations can be performed on a single app, the world is transitioning to a state where simplicity is oftentimes preferred over complexity. The convenience that super apps offer is exactly the convenience business owners are looking for when it comes to professional business tasks management without the need to go through multiple doorways. As a result, we will see more businesses recognizing the potential of embedding business intelligence solutions into their own existing application and adopting embedded analytics in 2022 and the years to come.

#9 SaaS Business Intelligence

Moving on, software-as-a-service business intelligence (SaaS BI) is a blessing in the times of COVID-19. SaaS BI is a “a business intelligence delivery model in which applications are implemented outside a company and usually employed at a hosted location accessed by an end user via protected Internet access”. In other words, organizations can access business intelligence tools on the cloud without on-site maintenance with SaaS BI providers. SaaS BI is also typically a pay-as-you-go or subscription model. Apart from its flexibility of data movement and access from multiple locations, SaaS business also allows organizations to save time and cost and concentrate on important business decisions, by removing any on-site installation or maintenance efforts and costs. With that said, there is no reason to think that they will go away anytime soon.  

SaaS BI can be seen as the perfect complement of mobile business intelligence. This is because many software-as-a-service business intelligence solutions can be delivered through mobile apps. For instance, Birst provides a SaaS application with features such as data warehouse automation, enterprise reporting, ad-hoc querying, dashboarding and data visualization, and it can be delivered through the cloud or native mobile business intelligence app. 

#10 Data Culture / Data Literacy

Finally, most if not all organizations that survived the COVID-19 crisis realize the importance of treating data analytics and decision-making as one interconnected process. Organized and integrated data enabled by business intelligence allow business owners to make data-informed decisions that are less susceptible to the volatile and uncertain business climate like this. 

In addition, it is also crucial for business owners to acknowledge the importance of cultivating data literacy or a data culture in their organizations to maximize the effectiveness of business intelligence tools and solutions. Data literacy is “the ability to read, write and communicate data in context”. To put it simply, it is the ability to understand and communicate data anywhere, anytime. With more and more organizations adapting to the business intelligence trends and adopting business intelligence solutions, we see the world heading towards data democratization – that is, a world without data literacy gap between data analysts and non-technical users. A data literate organization, with its employers and employees equipped with the knowledge and skills to leverage on business data and insights, would be the winners of 2022 and beyond. To confirm this, a study from the Data Literacy Index showed that improving data literacy in businesses resulted in $320 to $534 million in higher enterprise value over businesses with lower data literacy rates.

What’s Next? 

2022 will be a year when we see data becoming on-the-go, more interconnected and democratized than ever. With finite time and money, every organization should make their business decisions wisely and with care, and definitely not without business intelligence. As Matt Trifiro, CMO at Vapor IO put it, “Time and money are your scarcest resources. You want to make sure you’re allocating them in the highest-impact areas. Data reveals impact, and with data, you can bring more science to your decisions.” While the COVID-19 pandemic has taught us that past data cannot be the sure answer to the future, with the trends and innovations in business intelligence we have observed so far, it is safe to say that it can be, at the very least, a dependable guide. 

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