The COVID-19 pandemic has spurred technological developments and adoption worldwide and it’s no different in the realm of business intelligence (BI). With more people and businesses shopping online, working online, learning online, and generally moving a huge part of their daily lives online, BI has never been more swamped with data that quantifying the humongous volume of information on the internet in gazillions seems inadequate.
In this article, we take a look at business intelligence software trends, including market growth, future developments in AI, the convergence of business intelligence and big data, and the next wave in data infrastructure. Hopefully, these trends will help prepare your company to meet a more hyperscale, connected, and data-driven global market in the next few years.
The enterprise business intelligence software market stands at $22.80 billion in 2021, an 8.5% increase from the previous year. But behind the steady increase in market revenue, BI software vendors will see a gradual decrease in their year-on-year revenue growth rate from 2022, wherefrom a peak of 8.5%, the rate starts to slide to 7.8% in 2023 and up to 6.8% by 2026 (Statista, 2021).
However, this doesn’t mean fewer companies will be using BI tools in the coming years. In fact, the opposite is true. BI is being well-integrated into organizations’ day-to-day processes to a point where we are seeing a steady increase in the average spend per employee over the years: from $5.06 in 2016 to $9.23 in 2026 (Statista, 2021).
The industry’s revenue growth slowdown will possibly be caused by BI’s integration with other systems like ERP and CRM. We’ve seen the same happen with collaboration software, which started off as its own market before evolving into a regular feature of other software categories, such as task management and communication apps. Thus, BI may “lose revenues” to ERP and CRM products down the line.
However, don’t expect the BI software industry to ride off into the sunset. Rather, we are likely to see a vertical enterprise BI market creating its own moat separate from a general BI software market.
Panorama Consulting Group noted that an ERP likely has a BI feature but it may not be robust enough for enterprise users. Hence, enterprise BI, one that can handle vast amounts of data and can be tailor-fit to an organization’s unique needs, is here to stay.
In the meantime, the general BI market is going the way of embedded BI that plugs into other business applications. We get a sense of this vertical-embedded BI ecosphere by looking at the top business intelligence providers spearheaded by SAP SE ($30.16 M in 2020 revenues). Several of them provide embedded analytics on top of enterprise analytics (Xignite, 2021).
Source: Apps Run the World; Statista
We are seeing a slight movement in the share of offensive and defensive developments driving artificial intelligence in general. Offensive AI points to developments that lead to transformation and innovation or anything that gives an organization a competitive advantage. On the other hand, defensive AI developments aspire to cost-savings or compliance, among others.
Back in 2019, AI drivers were flagrantly offensive at 91.7% versus 8.3% defensive (NewVantage Partners, 2021). We were excited at what AI can create (and still do) and less about what it could protect. Two years hence, and a slew of privacy and security concerns brought about by big data and a hyper-connected world, defensive AI developments have increased to 17.3% share, cutting offensive AI outcomes down to 82.7%.
In the BI ecosystem, we will see more defensive AI developments in the form of security. The fact that 2021 saw a record-breaking number of breaches—Identity Theft Resource Center puts it at 17% more breaches than in 2020—we can only see an uptick in this part of AI space in the coming years.
Already, we see the continuous development in proactive analytics lending to BI advanced neural networks that detect system anomalies before any issues happen. The movie Minority Report comes to mind but at a very infant level where system administrators are alerted to a potential user breach based on historical patterns, present context, and a forecast of outcomes.
Coinciding with beefing up system security, AI, or to be specific, ethical AI, is now a thing. Pundits point to several aspects that underline ethical AI, but it’s not hard to imagine privacy being a major driver given the exorbitant regulatory penalties being and to be imposed, by the EU and in the US. Other ethical AI guidelines include “justice and fairness,” “transparency,” “freedom and autonomy” and “sustainability.”
Of course, the bulk of AI developments in BI will still be offensive in nature. Thus, expert AI developments in hyper-automation, system engineering, big data analytics, and virtual assistants will be shaping the top BI systems all in the name of efficiency, productivity, and profitability.
Source: Allianz, 2021
To this day, mobile BI acts as an endpoint extension of BI applications. But if we are to go by the ubiquity of mobile phones and key smartphone statistics—90% of smartphone usage is on apps—BI vendors will start going the way of communication apps, which have since pursued a mobile-first strategy to keep up with user preference.
However, the BI infrastructure will still be intact, a mobile or web app built on a proprietary system. What you’ll see is a more interactive mobile BI app far from what we have today, apps that push static data at best.
We may see some developers ditching native apps for HTML 5 clients. They are leveraging Rich Internet Application content to ensure device compatibility and, of course, user satisfaction.
That’s because users are demanding simpler but purpose-built mobile BI apps. We have left the sphere of static BI data push by highly trained analysts and are now in the realm of the casual BI user: John from sales or Pam from logistics, whose main endpoint device for the BI system is the smartphone. They want deeper analytics yet with dashboards and visuals that are easily navigable.
Still, there are forks ahead in the road for BI developers if building simpler but sophisticated mobile apps proves cumbersome. In 2021, 32% of users needed online help for business intelligence tools, the fourth-highest among business applications (CSA Research, 2021). An alternate future, one that seemingly contradicts the democratization of BI, is turning the industry on its head spurred by big data: the centralization of business intelligence.
More and more we are seeing the boundaries between big data analytics and business intelligence blur.
Big data analytics started as a field of its own, where external unstructured data is processed to generate insights. On the other hand, BI, in its traditional sense, refers to the processing of internal business information, for example, sales data, financial reports, and CRM data.
But with business data spilling over to social media and the Internet of Things, BI is practically processing big data now for business insights.
Big data presents to BI huge volumes of diverse datasets from multiple sources and types that can be used for strategy and operations. Consider for a moment the volume of data that’ll be created, captured, copied, and consumed worldwide in 2022: 120 zettabytes (that’s 120 trillion gigabytes) (IDC/Seagate, 2020). That will nearly double by 2025.
Using various tools like automation, machine learning, and predictive and prescriptive analytics, forward-looking BI solutions consolidate and analyze big data from multiple sources and derive and communicate insights to users for data-driven, real-time decisions.
BI allows users to run queries and create dashboards and reports based on their current needs leading to fewer errors or miscalculations. For example, businesses can personalize music, products, or content for each of the billions of consumers in the world with precision based on social or IoT data, or manage inventory and shipping around market movements down by the hour.
But with organizations relying more on big data to make the right decisions, so too are they finding it expensive to parse zettabytes of data. For most companies, managing big data is simply out of their budget.
Enter Analytics-as-a-Service or AaaS.
AaaS provides businesses with end-to-end big data analytics, from data collection to cleaning, organizing, and processing huge and disparate datasets through the internet and tailored-fit to a business specification.
It’s no surprise that Google, IBM, and AWS lead the pack in the AaaS industry, as they have extensive data warehousing and cloud infrastructure to take on big data. In terms of solution vs. service, AaaS still lags behind enterprise vendors in market share. But AaaS will exhibit the highest CAGR at 40.3% within the 2019-2026 period, enough to cut, and even surpass, the lead of analytics software vendors in the coming years (Allied Market Research, 2019).
By industry vertical, AaaS vendors will see the highest CAGR in the BFSI (banking, financial services, and insurance) sector through 2026, at 32.8%. Other industries exhibiting a CAGR spike in the same period include government & public, healthcare, manufacturing, and retail.
Meanwhile, more companies are expected to rely on the AaaS business model if its predecessor, Software-as-a-Service, is of any indication, where users pay only when they use the service. From $8.38 billion in 2018, AaaS market growth is forecasted to increase more than ten times, to $101.29 in 2026 (Statista, 2021). Already, we are seeing first adopters in the BFSI sector. Trailing but predicted to catch up in the next few years are the sectors of retail and wholesale, telecom, I.T., and government. (Verified Market Research, 2020).
Source: IDC, Statista
The analytics market is fast shaping up around the type of analytics businesses demand. We can slice it into four segments by purpose and in order of complexity:
Allied Market Research sees a demand surge in predictive analytics, having the highest CAGR as a service at 44.3% through 2026, followed by descriptive analytics and prescriptive analytics. Fewer companies will find the need to get help for diagnostic analytics, as the ability to interpret data fits into the role of any company’s leader.
We stand more confident over the last few years that robots replacing humans will not be as we’ve imagined it, causing massive unemployment. Manufacturing in the US is still robust at 12.5 million employees in 2021, the third-highest employment by industry (retail is at no. 1 followed by government) (Statista, 2021).
Instead of bumping us off the factory floor, next-generation robots are shaping up to be excellent colleagues (or underlings) to humans. Robots will continue to focus on repetitive tasks but will do things smarter. The evolving technology framework built on the Internet of Things, AI and analytics allows companies to process business intelligence with greater clarity, depth, and precision. Thus, manufacturing is being pushed towards collaborative production, real-time decision-making, predictive and remote maintenance, and simulation and optimization (Statista, 2021).
In the meantime, humans will find themselves doing higher-level tasks such as strategy, management, and design.
Data-driven decisions are only as good as data integrity, which makes data quality management a critical focus of business intelligence software trends.
We are seeing data warehouse modernization scale exponentially in the coming years to meet the unstoppable onslaught of big data. Pundits put the total date center IP traffic at 20.6 ZB in 2021 (ResearchandMarkets.com, 2020).
To meet the challenge head-on, BI vendors along with the broader analytics industry will continue to advance their warehousing tools and systems, as businesses pay close attention to the right platform for their needs and data infrastructure.
We shall see further integration of machine learning algorithms in BI processes. AI-powered features will be put to task to manage exhausting warehousing processes, mainly for descriptive analytics and predictive analytics. Tasks such as parsing historical data, data benchmarking, forecast modeling, and simulation of numerous scenarios will be handed over entirely to machine learning. Meanwhile, humans will focus on tasks that require context, creativity, and collaboration, mainly the processes in diagnostic analytics and prescriptive analytics.
The future of data centers sits solid-strong in the realm of hyperscale operators, Google, Amazon, Microsoft, and IBM. Outside of governments, only the tech giants have the financial strength to build industry-scale cloud infrastructure.
Between 2020 and 2021, Google, Amazon, and Microsoft have not let up in big-ticket cloud developments. Google has invested $10 billion in the US in 2020. This was dwarfed by AWS’ $35 billion investment in Northern Virginia data centers a year hence.
Likewise, the tech giants are aggressive in expanding not just their data management capabilities, but an entire country’s, too. For instance, Microsoft has announced the multi-year investment plan Digital AmBEtion last November 2021 to scale Belgium’s digitization. We will see similar programs by the tech giants around the globe as they aim to bump up other country’s infrastructure for faster cloud access, advanced data security, and cloud solutions to drive regional economies.
These business intelligence software trends 2025 and beyond will propel your business forward with more efficient operations, deeper market insights, and, eventually, more profits. If you haven’t yet, you will benefit from harmonized data with a uniform understanding of data, smooth data distribution between business units, enhanced data privacy and security, and, overall, business intelligence tailored-fit to your specific needs.
But only those who are prepared for a data-driven future will reap the fruits of the next wave of BI trends. This means, your business intelligence is placed on employees with defined roles and responsibilities and data literacy among employees is imposed to make them aware of the devastating effects of improper data handling. Additionally, a quality assurance workflow is ready to ensure stringent data governance and, in general, the business assumes a holistic view of the entire data pipeline.
FinancesOnline is available for free for all business professionals interested in an efficient way to find top-notch SaaS solutions. We are able to keep our service free of charge thanks to cooperation with some of the vendors, who are willing to pay us for traffic and sales opportunities provided by our website. Please note, that FinancesOnline lists all vendors, we’re not limited only to the ones that pay us, and all software providers have an equal opportunity to get featured in our rankings and comparisons, win awards, gather user reviews, all in our effort to give you reliable advice that will enable you to make well-informed purchase decisions.