--- title: The Future of Business Analytics Trends in 2026 --- # The Future of Business Analytics Trends in 2026 Last Reviewed: March 18, 2026 16 min read [3 comments](https://www.selecthub.com/business-analytics/business-analytics-trends/?noamp=mobile#comments) [ ![Ritinder Kaur](https://www.selecthub.com/wp-content/uploads/2021/06/cropped-Ritinder-Kaur-v2-1-96x96.png) ](https://www.selecthub.com/author/ritinder-kaur/) [Written by Ritinder Kaur](https://www.selecthub.com/author/ritinder-kaur/) Sr. Technical Content Writer [ ![Hunter Lowe](https://www.selecthub.com/wp-content/uploads/2023/11/cropped-Hunter-Headshot-96x96.jpg) ](https://www.selecthub.com/author/hunter-lowe/) [Edited by Hunter Lowe](https://www.selecthub.com/author/hunter-lowe/) Content Editor [ ![Sagardeep Roy](https://www.selecthub.com/wp-content/uploads/2025/01/Sagardeep-Roy-96x96.jpg) ](https://www.selecthub.com/author/sagardeep-roy/) [Technical Research by Sagardeep Roy](https://www.selecthub.com/author/sagardeep-roy/) Senior Analyst [ ![Mike Galbraith](https://www.selecthub.com/wp-content/uploads/2017/08/Mike-Galbraith-96x96.jpg) ](https://www.selecthub.com/author/mike-galbraith/) [Contributions by Mike Galbraith](https://www.selecthub.com/author/mike-galbraith/) Expert Contributor Table of Contents * [Key Takeaways](#Key%5FTakeaways "Key Takeaways") * [Top Trends for 2025](#Top%5FTrends%5Ffor%5F2025 "Top Trends for 2025") * [1\. Generative AI](#1%5FGenerative%5FAI "1. Generative AI") * [2\. Cloud Computing Innovations](#2%5FCloud%5FComputing%5FInnovations "2. Cloud Computing Innovations") * [3\. Data Sharing and Monetization](#3%5FData%5FSharing%5Fand%5FMonetization "3. Data Sharing and Monetization") * [4\. Data Mesh](#4%5FData%5FMesh "4. Data Mesh") * [5\. Data Governance](#5%5FData%5FGovernance "5. Data Governance") * [6\. Data Security](#6%5FData%5FSecurity "6. Data Security") * [7\. Automation](#7%5FAutomation "7. Automation") * [8\. Citizen Data Scientists](#8%5FCitizen%5FData%5FScientists "8. Citizen Data Scientists") * [9\. Internet of Things (IoT)](#9%5FInternet%5Fof%5FThings%5FIoT "9. Internet of Things (IoT)") * [10\. Decision Intelligence](#10%5FDecision%5FIntelligence "10. Decision Intelligence") * [Software Considerations](#Software%5FConsiderations "Software Considerations") * [Next Steps](#Next%5FSteps "Next Steps") If you’re in the market for [Business analytics software](https://www.selecthub.com/c/business-analytics-tools/), the allure of new technologies can be addictive and pull you down a rabbit hole of temptation. Tread carefully, for there’s many a slip between the cup and the lip. Being hasty in software selection can land you in hot soup if the new system fails to deliver. Learn what to expect from the latest software trends with an in-depth discussion about the future of business analytics. [Compare Top Business Analytics Software Leaders](https://pmo.selecthub.com/ba-report-pd-vers/) ![Business Analytics Trends in 2025]() ## Key Takeaways * Among business analytics trends, generative AI will drive data democratization and the need for strict usage guidelines. * Cloud computing costs will drive the quest for cost-effective querying techniques. * Data sharing will continue to break down information silos. * The [data mesh](https://aws.amazon.com/what-is/data-mesh/) will provide a viable solution to managing data at scale. * Data governance will remain a priority. * Data privacy and security concerns will be top trends in business analytics. * Citizen data scientists will take over analyst roles, but specialized skills will be in demand. * Thanks to automation, systems will deploy faster and scale flexibly. * Internet of Things (IoT) data security and costs will continue to be an improvement area for software vendors. * Decision intelligence will reduce the gaps in decision-making by combining the social sciences with AI-ML. ## Top Trends for 2025 ### 1\. Generative AI Despite data democratization, less than half of company executives can use data software independently. The calculations are too complex, building dashboards and reports is too technical, and not everyone has the required skills. Generative AI is born out of the need to address this gap. And organizations have taken to it like fish to water, as this [McKinsey Global Survey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-AIs-breakout-year) confirms. 40% of survey respondents said their organizations would increase investment in AI because of the promise generative AI shows. As part of gen(erative) AI, [LLMs (large-language models)](https://www.computerworld.com/article/3697649/what-are-large-language-models-and-how-are-they-used-in-generative-ai.html) are deep learning algorithms that perform several natural language tasks. They train by taking input and iteratively predicting the next word. #### What’s Next? Early instances of LLM usage have had mixed results, and the jury’s still out on where it fits into business processes. Naysayers were quick to dismiss AI as unreliable and out of control. Media reports about leaked company data didn’t help. But the industry isn’t ready to give up yet, and those who can are building LLM models in-house. Besides, organizations with embedded AI already have a head start. * [ThoughtSpot](https://www.selecthub.com/p/business-intelligence-tools/thoughtspot/) is among the early adopters, building Gen AI on top of its pre-existing LLM model. * [Qlik](https://www.selecthub.com/p/business-intelligence-tools/qlik-sense/) introduced OpenAI connectors to enhance AI-ML and NLP (natural language processing) capabilities. * Independent software vendors (ISVs) like [Salesforce](https://www.selecthub.com/p/crm-software/salesforce/) provide generative AI in applications by partnering with AWS. Building [LLM models for specific verticals](https://www.linkedin.com/pulse/vertically-trained-llms-unlocking-power-knowledge-david-norris) is a trend to watch out for. Bloomberg [built an LLM model](https://www.niemanlab.org/2023/04/what-if-chatgpt-was-trained-on-decades-of-financial-news-and-data-bloomberggpt-aims-to-be-a-domain-specific-ai-for-business-news/) dedicated to promoting NLP in the financial industry. There is already a noticeable shift toward reskilling across the industry. ### 2\. Cloud Computing Innovations Cloud verticals continue to be in demand, bringing the versatility of the cloud to industry-specific solutions. [Oracle Fusion Analytics](https://www.selecthub.com/p/erp-software/oracle-fusion-cloud/) provides ready-to-go [ERP](https://www.selecthub.com/c/erp-software/), [HCM](https://www.selecthub.com/hris/human-capital-management-hcm-resource-asset/), sales, finance and [supply chain management solutions](https://www.selecthub.com/c/supply-chain-management-software/). * Qlik and [Power BI](https://www.selecthub.com/p/business-intelligence-tools/power-bi/) offer app-building modules — you can design and publish apps for specific tasks, and the whole team benefits. * [Urban Outfitters](https://www.qlik.com/us/-/media/files/resource-library/global-us/direct/case-studies/cs-urban-outfitters-en.pdf?rev=fa58a06c9ee34292bad0494cefa013e9) uses over 240 Qlik apps for tracking [eCommerce](https://www.selecthub.com/category/ecommerce/) sales, store performance, operations and supply chain management. As [Marijan Nedic](https://www.linkedin.com/in/marijan-nedic/), Vice President and Head of IT Business Solutions, SAP, said: ![Marijan Nedic]() I believe what separates you from your competitors is not the majority of our operations; it’s the 5-to-10% of your operations that are unique. [Source](https://www2.deloitte.com/content/dam/Deloitte/pt/Documents/tech-trends/tech-trends-2022/DI%5FTech-trends-2022.pdf) Marijan Nedic Vice President and Head of IT Business Solutions, SAP ![Marijan Nedic Quote]() #### What’s Next? According to Allied Market Research, the [cloud services market](https://www.alliedmarketresearch.com/cloud-services-market) will reach $2.5 trillion in 2031\. But real-time analytics isn’t affordable with the high costs of live querying. Cloud subscriptions are usage-based — you pay for what you use. It includes the number of queries, allocated RAM, concurrency, data volume and server hardware. * Some vendors offer in-database analysis to avoid moving data. * TIBCO provides usage reports to identify the peak consumption times so you can scale back on low-priority queries during that time. * Restricting data access based on predetermined usage quotas or roles is another way to keep query volumes in check. We can expect a continued vendor focus on providing cost-effective querying in the future. [Compare Top Business Analytics Software Leaders](https://pmo.selecthub.com/ba-report-pd-vers/) ### 3\. Data Sharing and Monetization A data fabric is an information-sharing architecture across systems. [Data marketplaces](https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/cloud-scale-analytics/architectures/data-mesh-data-marketplace) are the first step to making it happen — allow enterprises to buy, sell and exchange data within [GDPR](https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/childrens-information/childrens-code-guidance-and-resources/age-appropriate-design-a-code-of-practice-for-online-services/9-data-sharing/). You don’t need to provision additional hardware or databases; modern processing technologies allow processing data even in encrypted form. Monetizing data in this manner benefits companies and the end user. * It gives companies much-needed customer insights to work with so they can innovate and improve their products to sell better. * Regulated industries like finance, healthcare and insurance can gain comprehensive information on patients, loan applicants and other customers. * Snowflake provides [data clean rooms](https://www.snowflake.com/trending/data-clean-room-for-business-growth/) where participants can securely combine proprietary data with 3rd party data. * John Deere sells data from sensors installed in their tractors back to farmers. * [Skywise](https://brand.airbus.com/en/guidelines/sub-identities/skywise) is an aviation-focused platform that provides access to data from over 100 airlines and spare part suppliers. It helps identify defect patterns, forecast machinery failures, and optimize parts replacement. #### What’s Next? It’s hard work. Making data securely available to other entities and processing incoming data that’s probably encrypted can seem like an additional burden to busy teams. Besides, not everyone is on board. Family-owned businesses and companies with established data practices are reluctant to expose their information externally. The demand for software that can encrypt and anonymize data for sharing will rise. Staying in silos helps no one, and the only way forward is to move ahead with guardrails and consistent security assessment. ### 4\. Data Mesh [Data management](https://www.selecthub.com/big-data-analytics/data-management/), the linchpin of BI analytics, continues to grapple with volume and complexity challenges. Operational data serves current transactional business needs, while analytical data provides historical and future insights. Manually handling the two can slow down centralized data teams. Enter the data mesh — a new concept among trends in business analytics that recognizes separate data domains. It decentralizes control from a single data team at the center to individual units that handle separate data products. According to the founder of the data mesh, Zhamak Dehghani, ![Zhamak Dehghani]() Data mesh, at core, is founded in decentralization and distribution of responsibility to people who are closest to the data in order to support continuous change and scalability. [Source](https://martinfowler.com/articles/data-mesh-principles.html) Zhamak Dehghani Founder and CEO, Nextdata ![Zhamak Dehghani Quote]() [Autodesk](https://humansofdata.atlan.com/2023/08/autodesk-data-mesh-snowflake-atlan/), a design and engineering software vendor, partners with [Atlan](https://www.selecthub.com/p/business-intelligence-tools/atlan/) to drive a modern BI platform using the data mesh concept. They have 60 domain teams with complete visibility into how end users consume their data. Each team has complete autonomy to ingest, process and publish results for consumers for that data domain. Instead of data engineers working in silos, each team has data engineers and product owners complementing each other. #### What’s Next? Assigning high-performing teams to separate data products allows serving multiple users with unique needs. However, the data mesh might not be for everyone, so please consider your requirements before going all in. Cross-functional teams working together are excellent, but it might surface skills gaps that need to be addressed, like metadata management. The data mesh might need upskilling across teams. [Compare Top Business Analytics Software Leaders](https://pmo.selecthub.com/ba-report-pd-vers/) ### 5\. Data Governance Data governance involves defining the policies, procedures and standards for managing data according to your organization’s strategic goals and compliance requirements. They include data ownership, quality standards, data lifecycle management and compliance with regulations. Governance protocols support data security, keeping information safe from malicious intent and inadvertent modifications. #### What’s Next? Despite AI-driven automation, enforcing governance at scale is challenging. How much control is too much? Who should get access and to which information? How can we share data while keeping it safe? Software that helps answer the above questions with built-in access control and meets compliance requirements will be in demand. Additionally, organizations need smooth consent workflows, efficient data mapping and AI-driven risk assessment. ### 6\. Data Security [Insider security breaches](https://www.ibm.com/topics/insider-threats) are more costly and cut deeper regarding the exposed data than external hackers, and oversight is challenging. According to the latest [IBM Cost of a Data Breach (gated) Report](https://www.ibm.com/account/reg/us-en/signup?formid=urx-52258), 82% of data breaches in the March 2022-23 period involved cloud data, and 39% of the total data breaches incurred an above-average cost of USD 4.75 million. ![Data Breach Stat]() Firewalls and zero-trust protocols are like a drop in the ocean. Securing data at internet endpoints for edge computing comes with its challenges. GDPR sets legal requirements for data security and governance. Organizations must secure personal data from breaches, unauthorized access and other security-related incidents with technical and organization-wide measures. At the same time, they need to give users access to their data on demand and open themselves up to security audits. #### What’s Next? As hackers become more innovative and users become more aware, regulations will become stricter. Big data volumes demand an engineering-first approach to customize security features for your organization. AI data governance poses fresh challenges. Can AI support cybersecurity? How can we rein in AI? It’s like giving a kid the keys to the candy store. ##### Security and AI: AI Trust, Risk and Security Management (AI-TRiSM) AI algorithms can extract information easily, which makes data leaks more probable. Pre-existing errors will likely multiply with AI’s self-learning technology. Besides, AI isn’t free from race, gender and socio-economic biases. ##### Where’s the trust? How can enterprises use AI with confidence? [AI trust, risk and security management (AI-TRiSM)](https://www.splunk.com/en%5Fus/blog/learn/ai-trism-ai-trust-risk-security-management.html) is an evolving framework pushing for AI access governance, reliability and data protection. ##### AI for Security: The Good It Can Do AI algorithms help mitigate risk as they’re pre-programmed to identify fraud and remove malware, [botnets](https://us.norton.com/blog/malware/what-is-a-botnet) and spam content. Automatic risk assessment and user authentication help deploy patches when something goes wrong. Machine learning programs have the memory to record network behavior for disaster recovery. Enterprises will seek advanced risk assessment technologies for AI in business analytics platforms. ### 7\. Automation Vendors provide automation code for individual tasks and entire infrastructures, allowing you to deploy whole systems with a few clicks. Automation does what humans can’t — it drives consistent, audited action and reduces errors. Automated tasks include system administration, monitoring, task reviews and approvals, database management, integration, systems management, and OS patching. Automation at the system level includes the management of source connections, networks, computing and storage. It drives [software-as-a-service (SaaS)](https://aws.amazon.com/what-is/saas/), providing scalable and elastic cloud computing. Oracle Autonomous Database scales automatically. #### What’s Next? Legacy systems and manually configured components can block automation efforts. A dedicated data team can lessen onboarding pangs by encouraging and enforcing adoption. [Compare Top Business Analytics Software Leaders](https://pmo.selecthub.com/ba-report-pd-vers/) ### 8\. Citizen Data Scientists According to Statista, the market size of the [BI and analytics software industry](https://www.statista.com/statistics/590054/worldwide-business-analytics-software-vendor-market/) will likely grow to over $18 billion by 2026\. It was $15.3 billion in 2021. This slow and steady march is understandable, considering we’ve been through a pandemic and an uncertain market landscape. Happily, progress might slow down but never stops. The demand for [self-service BI](https://www.selecthub.com/business-intelligence/self-service-business-intelligence/), data visualization and fast insight drives the upward trend in the analytics software market. Data democratization is a significant driver as enterprises look for ways to accelerate their go-to-market strategies. As citizens become developers, automation enables them to build and run workflows and package them into reusable apps. Will data analysts become redundant? #### What’s Next? Mike Galbraith, Vice President of Technology Strategy & Solutions at [ThoughtFocus](https://www.thoughtfocus.com/), echoed Atre’s sentiments and added his take. Data departments will become redundant as data proliferation continues, but data scientists themselves aren’t going away. ![Mike Galbraith Quote]() ![Mike Galbraith]() Yes, I do think data scientists and other dedicated roles in the business analytics area will continue to be prevalent in the coming year, but that trend for dedicated teams and roles will start to tail off as automation technologies and AI services evolve, as companies begin to get a handle on the data flowing in and around their businesses and as skills and methods within organizations mature. Mike Galbraith Vice President of Technology Strategy & Solutions, ThoughtFocus According to Galbraith, as self-service BI becomes the norm, the role of data specialists will change. ![Mike Galbraith]() The specialist titles will become more generalized as the roles and skills become more pervasive within the business function and a companies organization become more data-driven. For instance, as organizations transform to become more data-driven and their cultures change to institutionalize a more a digital way of doing business, business analysts in particular functional areas, whether in finance or supply chain or business development or manufacturing will soon have a good grasp on the technical tools and methods that make up the foundation of data analytics. Mike Galbraith Vice President of Technology Strategy & Solutions, ThoughtFocus Ryan Wilson, Vice President of Technology at [Signal Ventures LLC](https://signalv.com/), emphasized this. ![Ryan Wilson]() What we’re starting to see is very business user-friendly business intelligence platforms that can be highly automated and are starting to incorporate some data science tools that don’t require a data scientist with a Ph.D. to utilize. Ryan Wilson Vice President of Technology, Signal Ventures LLC Wilson echoed Galbraith thought on self-service BI, saying that more non-technical employees will assume analyst roles. ![Ryan Wilson]() This is going to lead to more and more companies incorporating data-driven business at every level of the business. As this happens I think we’ll start to see everyone becoming a bit of an analyst which will start to shift the role of a dedicated analyst to running, maintaining, and extending these platforms and tools in most organizations. Ryan Wilson Vice President of Technology, Signal Ventures LLC [Compare Top Business Analytics Software Leaders](https://pmo.selecthub.com/ba-report-pd-vers/) ### 9\. Internet of Things (IoT) The Internet of Things (IoT) includes connectivity to smart home devices, sensors, chatbots, digital assistants and heavy equipment. Data from these devices is part of business intelligence for vendors and end users, whether it streams live into your systems or is processed at internet endpoints like mobile devices and smartwatches. Besides, many cloud verticals pull data from off-premises, internet-connected devices that use their applications. [Manufacturing](https://www.selecthub.com/category/manufacturing/), supply chain, [transportation](https://www.selecthub.com/category/transportation-and-logistics-management/), marketing and sales management departments have staff, machinery and vehicles in the field, generating and collecting business-critical data. Predictive maintenance involves monitoring offsite equipment, anticipating wear and tear, and planning repairs and features high on the [requirements checklist](https://www.selecthub.com/business-analytics/business-analytics-tools-requirements/) of businesses with field equipment and vehicles. According to Verified Market Research, the [global predictive maintenance market](https://www.verifiedmarketresearch.com/product/predictive-maintenance-market/) will likely reach $49.54 billion by 2030. ![Global Predictive Maintenance Market]() #### What’s Next? Edge analytics is a power-saving alternative to traditional data systems that move data to local data stores before analysis. It allows the processing of data within the source device and uploading it to the central database in small packets. Many transactional applications, like mobile banking apps, process data at the edge. Capturing data from streaming and edge devices requires advanced technologies that consume power. Recording real-time data with techniques like change data capture (CDC) while the data is in transit is resource-intensive and costly. Besides, there’s the issue of data security. The internet and edge devices are all potential touch points outside your company ecosystem and prone to security breaches. Making IoT analytics affordable and securing data at the edge will be on the minds of software vendors and product owners in the coming years. ### 10\. Decision Intelligence [Gartner](https://www.gartner.com/en/information-technology/glossary/decision-intelligence) defined decision intelligence as incorporating “traditional non-deterministic techniques” into analytics. It augments decision-making with NLP, AI-ML and automation. Decision intelligence combines decision science — psychology, neuroscience and economics — with data science. It closes the gap between insights and decisions by mitigating delays in the decision cycle. How? You must define the decision pathways unique to your organization at the onset. Giving the system the decision map and linking your dataset can generate more accurate results. A lot of it has to do with AI, machine learning, and social sciences. Watch this YouTube video of [low–code decision intelligence](https://www.youtube.com/watch?v=ieG-t5%5FHi74) with [Oracle Analytics](https://www.selecthub.com/p/business-intelligence-tools/oracle-analytics-cloud/). #### What’s Next? This business analytics trend has caught the attention of leading vendors and developers. Google launched its decision engineering lab in 2018, and Alibaba recently followed suit. Very few software offers this capability yet, and the technology is in its nascent stages, but we can expect exciting developments in the coming years. ## Software Considerations You need a data visualization tool to perform quick and intuitive data exploration and present the results to non-technical audiences. A BI tool provides scalable, comprehensive reporting and analysis of historical and current data. A predictive analytics tool can help you pivot with the market and create a realistic roadmap. Which one will work best for you? Software selection can be nerve-wracking, but being mindful of your requirements can help you choose wisely. Follow our [Lean Selection Methodology](https://www.selecthub.com/miscellaneous/technology-selection/software-evaluation/) to get the best returns on your software investment. Or [talk to us](https://www.selecthub.com/managed-selection-services/) to match products to your unique business needs. [Compare Top Business Analytics Software Leaders](https://pmo.selecthub.com/ba-report-pd-vers/) ## Next Steps Data democratization puts security and data privacy at a premium, and with generative AI integrations increasing, developing AI-related guardrails will keep vendors busy. Cloud verticals, data marketplaces and automation change how enterprises set up their tech stack. Citizen developers are the new data analysts, though specialized data skills will continue to be in demand. Decision intelligence is a trend to watch out for, focusing on augmenting critical decisions with data science technologies. Are you looking for business analytics software? Analyze your preferred software with our free, customizable [comparison report](https://pmo.selecthub.com/ba-report-pd-vers/), matched to your company size. Learn about their technical and functional capabilities and vendor qualifications with a handy, downloadable scorecard. What do you think of the future of business analytics? Which trend will likely take center stage in the coming year? Let us know in the comments. ### Trending Topics #### [Business Analytics](https://www.selecthub.com/category/business-analytics/) [The 5 Best Visual Analytics Tools of 2026](https://www.selecthub.com/business-intelligence/visual-analytics-tools/) [Choosing visual analytics software can feel overwhelming. 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Contributions from Ritinder Kaur, Sagardeep Roy, Akshay Parekh, Hunter Lowe, Mike Galbraith, and Ryan Wilson. ## About the Contributors The following SelectHub team members and subject matter experts helped research, create, and review this content. [ ](https://www.selecthub.com/author/ritinder-kaur/) Written by [Ritinder Kaur](https://www.selecthub.com/author/ritinder-kaur/) Sr. Technical Content Writer Ritinder Kaur is a Senior Technical Content Writer at SelectHub and has ten years of experience writing about B2B software and quality assurance. She has a Masters degree in English language and literature and writes about Business Intelligence and Data Science. Her articles on software testing have been published on Stickyminds. [See Full Bio](https://www.selecthub.com/author/ritinder-kaur/) [ ](https://www.selecthub.com/author/sagardeep-roy/) Technical Research by [Sagardeep Roy](https://www.selecthub.com/author/sagardeep-roy/) Senior Analyst Sagardeep is a Senior Research Analyst at SelectHub, specializing in diverse technical categories. His expertise spans Business Intelligence, Analytics, Big Data, ETL, Cybersecurity, artificial intelligence and machine learning, with additional proficiency in EHR and Medical Billing. Holding a Master of Technology in Data Science from Amity University, Noida, and a Bachelor of Technology in Computer Science from West Bengal University of Technology, his experience across technology, healthcare, and market research extends back to 2016\. As a certified Data Science and Business Analytics professional, he approaches complex projects with a results-oriented mindset, prioritizing individual excellence and collaborative success. [See Full Bio](https://www.selecthub.com/author/sagardeep-roy/) [ ](https://www.selecthub.com/author/akshay-parekh/) Technical Research by [Akshay Parekh](https://www.selecthub.com/author/akshay-parekh/) Principal Analyst Akshay is a highly analytical and detail-oriented Software Research Analyst with a proven track record of generating industry-standard templates for RTs, RFIs, pricing guides, LTSRs, and more across software categories like Big Data Analytics, BI, ETL, EDI, EHR, Endpoint Security and Medical Billing. He holds a Bachelor of Technology in Computer Science Engineering and an MBA in Marketing and Analytics from IBS Hyderabad. He loves to spend time exploring spirituality, reading books, and watching sports, especially cricket, tennis, MMA, and boxing. [See Full Bio](https://www.selecthub.com/author/akshay-parekh/) [ ](https://www.selecthub.com/author/hunter-lowe/) Edited by [Hunter Lowe](https://www.selecthub.com/author/hunter-lowe/) Content Editor Hunter Lowe is a Content Editor, Writer and Market Analyst at SelectHub. His team covers categories that range from ERP and business intelligence to transportation and supply chain management. Hunter is an avid reader and Dungeons and Dragons addict who studied English and Creative Writing through college. In his free time, you'll likely find him devising new dungeons for his players to explore, checking out the latest video games, writing his next horror story or running around with his daughter. [See Full Bio](https://www.selecthub.com/author/hunter-lowe/) [ ](https://www.selecthub.com/author/mike-galbraith/) Contributions by [Mike Galbraith](https://www.selecthub.com/author/mike-galbraith/) Expert Contributor Mike Galbraith serves as the Chief Digital Innovation Officer (CDIO) at the U.S. Department of the Navy. In his career of 30 years, he has been an IT executive, CIO, digital transformation leader and delivery leader for Fortune 200 companies. He’s an expert in global IT strategy, enterprise architecture, delivery and operations, digital transformation, ERP systems, IoT, big data and analytics. His accomplishments include degrees in computer science, business administration and information management. He’s a Wharton School alum with executive leadership and corporate strategy certifications. [See Full Bio](https://www.selecthub.com/author/mike-galbraith/) [ ](https://www.selecthub.com/author/ryan-wilson/) Contributions by [Ryan Wilson](https://www.selecthub.com/author/ryan-wilson/) Expert Contributor Ryan Wilson is Vice President of Technology at Signal Ventures LLC. An experienced data analyst, Ryan built dataflows, dashboards, and cards for over 20 companies as a Domo consultant with Build Intelligence. He led a team of developers and consultants in maintaining over 3200 visualizations, 2400 datasets and 700 dataflows. [See Full Bio](https://www.selecthub.com/author/ryan-wilson/) Ritinder KaurBusiness Analytics vs. Data Analytics: What’s the Difference? * ‹ * › ### Conversation (3) ![Avatar](https://secure.gravatar.com/avatar/281d3616cf761f3582c0d76c23517846?s=32&d=mm&r=g) Write a response [Cancel reply](https://www.selecthub.com/business-analytics/business-analytics-trends/#respond) Your message Your name \* Your email \* Website Save my name, email, and website in this browser for the next time I comment. Δ * ![Avatar photo](https://secure.gravatar.com/avatar/2ecd68569a184cfb87bb64b6c37fc645d23741cf883f66cef0855cc3f10b2e36?s=96&d=mm&r=g) #### **Marina T.** \- May 23, 2022 It is so cool that you covered this topic because, from my point of view, business analytics is a really prospective and serious field which will develop to a great extent despite all its instability. Of course, technology is progressing very fast and we can say that we live in the world of technology. Because of this, I think that it is really important to adapt to such a reality and master new professions for which the future lies. It is truly wonderful that you mentioned augmented analytics because it makes a great difference in our world and it is developing to a great extent, opening new prospects for people. Augmented analytics has a great deal of advantages and I absolutely agree with you that this type of analytics will have even more information to learn from because it performs a really significant function. I think that one of the most significant advantages of augmented analytics is that it allows businesses to predict future business trends, which is so valuable. // Marina Teramond **[Reply](#comment-133107)** * ![Avatar photo](https://secure.gravatar.com/avatar/1832c775733e582af2cd692900abf9a3f2aceadb41980bcc51dc4269dd3e1945?s=96&d=mm&r=g) #### **Marnie Swindall** \- September 8, 2021 Greetings, Usually I never comment on blogs but your article is so convincing that I never stop myself to say something about it. You’re doing a great job, Keep it up. You can check out this article, might be of help 🙂 **[Reply](#comment-88410)** * ![Avatar photo](https://secure.gravatar.com/avatar/bbd04668338eb0bfe3782c1818401339194e77e22d135c9385cfd3e10d37fda8?s=96&d=mm&r=g) #### **Emanuel Roll** \- July 21, 2021 I like your blog. Its one of the great blogs online **[Reply](#comment-81457)** [ Go to mobile version ](https://www.selecthub.com/business-analytics/business-analytics-trends/?amp=1) **Tier 1:** Fully/moderately supported out-of-the-box allowing for quick and easy deployment. Fully or moderately supported out-of-the-box with industry-leading capabilities and is immediately available after installation without needing any add-ons, integrations, or custom development. **Tier 2:** Supported with workarounds or add-ons that may require additional costs. Not directly available in the software, but can be accomplished using other built-in features, workarounds, or add-ons/products from the vendor with or without any additional cost. **Tier 3:** Requires partner integrations or custom development that is often at an additional cost. Requires additional integrations, plugins, marketplace applications from a third-party vendor, or custom development using the APIs, libraries, extensions, and development framework supported by the software, with or without any additional cost. 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