Posts from the ‘Cloud BI’ Category

Apr 24

FinancialForce’s Spring 2021 Release Shows Why Being Customer-Centric Pays

Bottom Line: Customer revenue lifecycles are the lifeblood of any services business, making FinancialForce’s Spring 2021 release timely given the services-first revenue renaissance happening today.

The essence of an excellent services business is that it can consistently create expectations clients trust and the business regularly exceeds. Orchestrating the best people for a given project at the right time, tracking costs, revenue, and margin across all services revenue, including those associated with a client’s assets, is very challenging. Customer revenue lifecycles are in the data, yet no one can get to them because they’re hidden across multiple systems that aren’t integrated. Knowing how efficient a services business is at turning customer engagement into cash is what everyone needs to know, but no one can find. The challenge is equally as daunting for long-established services providers and those rushing into new services businesses to redefine themselves in the hope of profits that are more consistent and fewer price wars.

How Much Is Customer Engagement Is Worth?

Services businesses face the paradox of exceeding client expectations with every engagement but not knowing if extra time, resources, and staff invested are paying off with more revenue and profit. FinancialForce’s Spring 2021 release looks to solve this problem. What galvanizes the ERP, PSA, and platform announcements is a fresh intensity on customer centricity, both for the services business adopting the Spring 2021 release and the customers it’s intended to serve.

Knowing if and by how much a given customer engagement and its revenue lifecycle generate cash, and its potential is one of the core focus areas of the Spring 2021 release. It’s badly needed as many services are flying blind today, overcommitting resources for little return and too often losing control of client engagement and paying the price in lost margin and profits. FinancialForce sees that pain and wants to alleviate it with better financial visibility on all aspects of customer services revenue. FinancialForce aims to provide customer-centric financial reporting down to the revenue stream and costing measure level.

Key Takeaways From The Spring 2021 Release

Customer centricity seen through a financial lens is the cornerstone of FinancialForce’s latest release. One of the primary goals of this release is to update more applications to Salesforce Lightning to provide FinancialForce users with a more consistent user experience across all applications. Salesforce has been doubling down for years on Lightning and its user experience technologies, with FinancialForce reaping the benefits for over a decade. FinancialForce is transitioning their core Professional Services Automation (PSA), Billing, Accounting & Finance and Procurement, Order and Inventory Management to Lightning in this release in response to their customers wanting a consistent user experience across the entire FinancialForce suite of applications. The Spring 2021 release reflects how FinancialForce strives to provide a real-time understanding of customer lifetime value for their ERP and PSA customers.

Additional key takeaways include the following:

FinancialForce sees reducing days to close as one of the highest priorities they need to address today. The majority of new feature announcements center on how the days to close cycles can be streamlined, especially across multi-company and multisite locations across geographic and currency-specific regions of the world. Multi-company currency revaluation will help FinancialForce customers who operate across multiple geographies that operate in different currencies and will be especially useful for those clients creating new global channels and considering foreign acquisitions. Further showing the high priority they are putting on reducing days to close, the Spring 2021 release also includes automated eliminations, multi-company period close for software closes, which are designed to temporarily close out a financial report and revenue schedules that can provide a future view in revenues – a key factor in knowing customer revenue lifecycles.

New features and a new Lightning interface for Accounting, Billing Central, and Inventory Management simplifies complex transactions for users. FinancialForce has one of the most customer-driven product management teams in enterprise software. The depth of features they have added to inventory management, transactional and reconciliation processes for accounting, drop-ship use cases, and enhancements for adding products to billing contracts show how much FinancialForce is listening to customers.

AI-enhanced financial reporting that works with any Einstein data set. FinancialForce leads the Salesforce partner ecosystem when it comes to integrating Tableau CRM (formerly known as Einstein Analytics) into its platform. Now thirteen releases in, FinancialForce’s Spring 2021 release reflects the intuitive, adaptive intelligence that the product management team aims to achieve by integrating Einstein into their financial reporting workflows.

Professional Services Automation (PSA) Applications Including Resource Management, Project Management, and Time & Expense upgraded to Lightning. Transitioning three of the core PSA applications to Lightning will help broaden adoption and make them easier to upsell and cross-sell across the FinancialForce customer base. It will also help existing customers using these applications get new employees up to speed faster on them, given how much more streamlined Lightning is as an interface compared to previous versions.

Intelligent Staffing solves the complex challenges resource managers face when assigning the best possible associates to a given project. Designed to filter and intelligently rank potential resources based on region, practice, group skill sets, and availability, Intelligent Staffing is designed to get resource managers as close to an ideal match as possible for a given project’s requirements. This is a much-welcomed new feature by FinancialForce customers who are large-scale services providers as they’re facing the challenges of assigning the right person to the right project at the right time to ensure project success.

Integration of Salesforce AI’s Next Best Action (NBA) will raise the level of project expertise at scale across customers. Part of the customer centricity focus in Spring 2021 is focused on providing customers with new technologies and applications to share expertise and knowledge at scale. Next Best Action provides prescriptive guidance for the project manager and will see heavy use in new associate onboarding across services businesses and achieve greater corporate-wide learning at scale. This is consistent with the focus in the Spring 2021 release on bringing greater space and speed to mid-size and larger services customers.

Conclusion

FinancialForce defines customer engagement and centricity from a financial standpoint in the Spring 2021 release. Too often, services businesses commit to large-scale projects without a clear idea of the customer revenue lifecycle. With FinancialForce, they can stop and ask if the level of customer engagement they’re committing to is worth it or not – and if it isn’t, what needs to be done. FinancialForce is doubling down on user experience and accelerating time-to-close, two areas their customers want innovation to and look to them to deliver. Look for FinancialForce to scale out with more MuleSoft and Tableau integration scenarios, all aimed at capitalizing on their expertise developing on the Salesforce platform. There’s a bigger challenge to customer engagement on the horizon, and that’s providing a real-time view of financials across all customers with all available data across a business, making MuleSoft integration key to FinancialForce’s future growth.

Jul 25

10 Ways AI Is Improving New Product Development

Startups’ ambitious AI-based new product development is driving AI-related investment with $16.5B raised in 2019, driven by 695 deals according to PwC/CB Insights MoneyTree Report, Q1 2020.
AI expertise is a skill product development teams are ramping up their recruitment efforts to find, with over 7,800 open positions on Monster, over 3,400 on LinkedIn and over 4,200 on Indeed as of today.
One in ten enterprises now uses ten or more AI applications, expanding the Total Available Market for new apps and related products, including chatbots, process optimization and fraud analysis, according to MMC Ventures.

From startups to enterprises racing to get new products launched, AI and machine learning (ML) are making solid contributions to accelerating new product development. There are 15,400 job positions for DevOps and product development engineers with AI and machine learning today on Indeed, LinkedIn and Monster combined. Capgemini predicts the size of the connected products market will range between $519B to $685B this year with AI and ML-enabled services revenue models becoming commonplace.

Rapid advances in AI-based apps, products and services will also force the consolidation of the IoT platform market. The IoT platform providers concentrating on business challenges in vertical markets stand the best chance of surviving the coming IoT platform shakeout. As AI and ML get more ingrained in new product development, the IoT platforms and ecosystems supporting smarter, more connected products need to make plans now how they’re going to keep up. Relying on technology alone, like many IoT platforms are today, isn’t going to be enough to keep up with the pace of change coming. The following are 10 ways AI is improving new product development today:

14% of enterprises who are the most advanced using AI and ML for new product development earn more than 30% of their revenues from fully digital products or services and lead their peers is successfully using nine key technologies and tools. PwC found that Digital Champions are significantly ahead in generating revenue from new products and services and more than a fifth of champions (29%) earn more than 30% of revenues from new products within two years of information. Digital Champions have high expectations for gaining greater benefits from personalization as well. The following graphic from Digital Product Development 2025: Agile, Collaborative, AI-Driven and Customer Centric, PwC, 2020 (PDF, 45 pp.) compares Digital Champions’ success with AI and ML-based new product development tools versus their peers:

61% of enterprises who are the most advanced using AI and ML (Digital Champions) use fully integrated Product Lifecycle Management (PLM) systems compared to just 12% of organizations not using AI/ML today (Digital Novices). Product Development teams the most advanced in their use of AL & ML achieve greater economies of scale, efficiency and speed gains across the three core areas of development shown below. Digital Champions concentrate on gaining time-to-market and speed advantages in the areas of Digital Prototyping, PLM, co-creation of new products with customers, Product Portfolio Management and Data Analytics and AI adoption:

AI is actively being used in the planning, implementation and fine-tuning of interlocking railway equipment product lines and systems. Engineer-to-order product strategies introduce an exponential number of product, service and network options. Optimizing product configurations require an AI-based logic solver that can factor in all constraints and create a Knowledge Graph to guide deployment. Siemens’ approach to using AI to find the optimal configuration out of 10⁹⁰ possible combinations provides insights into how AI can help with new product development on a large scale. Source: Siemens, Next Level AI – Powered by Knowledge Graphs and Data Thinking, Siemens China Innovation Day, Michael May, Chengdu, May 15, 2019.

Eliminating the roadblocks to getting new products launched starts with using AI to improve demand forecast accuracy. Honeywell is using AI to reduce energy costs and negative price variance by tracking and analyzing price elasticity and price sensitivity as well. Honeywell is integrating AI and machine-learning algorithms into procurement, strategic sourcing and cost management getting solid returns across the new product development process. Source: Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.

Relying on AI-based techniques to create and fine-tune propensity models that define product line extensions and add-on products that deliver the most profitable cross-sell and up-sell opportunities by product line, customer segment and persona. It’s common to find data-driven new product development and product management teams using propensity models to define the products and services with the highest probability of being purchased. Too often, propensity models are based on imported data, built-in Microsoft Excel, making their ongoing use time-consuming. AI is streamlining creation, fine-tuning and revenue contributions of up-sell and cross-sell strategies by automating the entire progress. The screen below is an example of a propensity model created in Microsoft Power BI.

AI is enabling the next generation of frameworks that reduce time-to-market while improving product quality and flexibility in meeting unique customization requirements on every customer order. AI is making it possible to synchronize better suppliers, engineering, DevOps, product management, marketing, pricing, sales and service to ensure a higher probability of a new product succeeding in the market. Leaders in this area include BMC’s Autonomous Digital Enterprise (ADE). BMC’s ADE framework shows the potential to deliver next-generation business models for growth-minded organizations looking to run and reinvent their businesses with AI/ML capabilities and deliver value with competitive differentiation enabled by agility, customer centricity and actionable insights. The ADE framework is capable of flexing and responding more quickly to customer requirements than competitive frameworks due to the following five factors: proven ability to deliver a transcendent customer experience; automated customer interactions and operations across distributed organizations; seeing enterprise DevOps as natural evolution of software DevOps; creating the foundation for a data-driven business that operates with a data mindset and analytical capabilities to enable new revenue streams; and a platform well-suited for adaptive cybersecurity. Taken together, BMC’s ADE framework is what the future of digitally-driven business frameworks look like that can scale to support AI-driven new product development. The following graphic compares the BMC ADE framework (left) and the eight factors driving digital product development as defined by PwC (right) through their extensive research. For more information on BMC’s ADE framework, please see BMC’s Autonomous Digital Enterprise site. For additional information on PwC’s research, please see the document Digital Product Development 2025: Agile, Collaborative, AI-Driven and Customer Centric, PwC, 2020 (PDF, 45 pp.).

Using AI to analyze and provide recommendations on how product usability can be improved continuously. It’s common for DevOps, engineering and product management to run A/B tests and multivariate tests to identify the usability features, workflows and app & service responses customers prefer. Based on personal experience, one of the most challenging aspects of new product development is designing an effective, engaging and intuitive user experience that turns usability into a strength for the product. When AI techniques are part of the core new product development cycle, including usability, delivering enjoyable customer experiences, becomes possible. Instead of a new app, service, or device is a chore to use, AI can provide insights to make the experience intuitive and even fun.
Forecasting demand for new products, including the causal factors that most drive new sales is an area AI is being applied to today with strong results. From the pragmatic approaches of asking channel partners, indirect and direct sales teams, how many of a new product they will sell to using advanced statistical models, there is a wide variation in how companies forecast demand for a next-generation product. AI and ML are proving to be valuable at taking into account causal factors that influence demand yet had not been known of before.
Designing the next generation of Nissan vehicles using AI is streamlining new product development, trimming weeks off new vehicle development schedules. Nissan’s pilot program for using AI to fast-track new vehicle designs is called DriveSpark. It was launched in 2016 as an experimental program and has since proven valuable for accelerating new vehicle development while ensuring compliance and regulatory requirements are met. They’ve also used AI to extend the lifecycles of existing models as well. For more information, see the article, DriveSpark, “Nissan’s Idea: Let An Artificial Intelligence Design Our Cars,” September 2016.
Using generative design algorithms that rely on machine learning techniques to factor in design constraints and provide an optimized product design. Having constraint-optimizing logic within a CAD design environment helps GM attain the goal of rapid prototyping. Designers provide definitions of the functional requirements, materials, manufacturing methods and other constraints. In May 2018, General Motors adopted Autodesk generative design software to optimize for weight and other key product criteria essential for the parts being designed to succeed with additive manufacturing. The solution was recently tested with the prototyping of a seatbelt bracket part, which resulted in a single-piece design that is 40% lighter and 20% stronger than the original eight component design. Please see the Harvard Business School case analysis, Project Dreamcatcher: Can Generative Design Accelerate Additive Manufacturing? for additional information.

Additional reading:

2020 AI Predictions, Five ways to go from reality check to real-world payoff, PwC Consulting

Accenture, Manufacturing The Future, Artificial intelligence will fuel the next wave of growth for industrial equipment companies (PDF, 20 pp., no opt-in)

AI Priorities February 2020 5 ways to go from reality check to real-world pay off, PwC, February, 2020 (PDF, 16 pp.)

Anderson, M. (2019). Machine learning in manufacturing. Automotive Design & Production, 131(4), 30-32.

Bruno, J. (2019). How the IIoT can change business models. Manufacturing Engineering, 163(1), 12.

Digital Factories 2020: Shaping The Future Of Manufacturing, PwC DE., 2017 (PDF, 48 pp.)

Digital Product Development 2025: Agile, Collaborative, AI Driven and Customer Centric, PwC, 2020 (PDF, 45 pp.)

Enabling a digital and analytics transformation in heavy-industry manufacturing, McKinsey & Company, December 19, 2019

Global Digital Operations 2018 Survey, Strategy&, PwC, 2018

Governance and Management Economics, 7(2), 31-36.

Greenfield, D. (2019). Advice on scaling IIoT projects. ProFood World

Hayhoe, T., Podhorska, I., Siekelova, A., & Stehel, V. (2019). Sustainable manufacturing in industry 4.0: Cross-sector networks of multiple supply chains, cyber-physical production systems and AI-driven decision-making. Journal of Self-

Industry’s fast-mover advantage: Enterprise value from digital factories, McKinsey & Company, January 10, 2020

Kazuyuki, M. (2019). Digitalization of manufacturing process and open innovation: Survey results of small and medium-sized firms in japan. St. Louis: Federal Reserve Bank of St Louis.

‘Lighthouse’ manufacturers lead the way—can the rest of the world keep up? McKinsey & Company, January 7, 2019

Machine Learning in Manufacturing – Present and Future Use-Cases, Emerj Artificial Intelligence Research, last updated May 20, 2019, published by Jon Walker

Machine learning, AI are most impactful supply chain technologies. (2019). Material Handling & Logistics

MAPI Foundation, The Manufacturing Evolution: How AI Will Transform Manufacturing & the Workforce of the Future by Robert D. Atkinson, Stephen Ezell, Information Technology and Innovation Foundation (PDF, 56 pp., opt-in)

Mapping heavy industry’s digital-manufacturing opportunities, McKinsey & Company, September 24, 2018

McKinsey, AI in production: A game changer for manufacturers with heavy assets, by Eleftherios Charalambous, Robert Feldmann, Gérard Richter and Christoph Schmitz

McKinsey, Digital Manufacturing – escaping pilot purgatory (PDF, 24 pp., no opt-in)

McKinsey, Driving Impact and Scale from Automation and AI, February 2019 (PDF, 100 pp., no opt-in).

McKinsey, ‘Lighthouse’ manufacturers, lead the way—can the rest of the world keep up?,by Enno de Boer, Helena Leurent and Adrian Widmer; January, 2019.

McKinsey, Manufacturing: Analytics unleashes productivity and profitability, by Valerio Dilda, Lapo Mori, Olivier Noterdaeme and Christoph Schmitz, March, 2019

McKinsey/Harvard Business Review, Most of AI’s business uses will be in two areas,

Morey, B. (2019). Manufacturing and AI: Promises and pitfalls. Manufacturing Engineering, 163(1), 10.

Preparing for the next normal via digital manufacturing’s scaling potential, McKinsey & Company, April 10, 2020

Reducing the barriers to entry in advanced analytics. (2019). Manufacturing.Net,

Scaling AI in Manufacturing Operations: A Practitioners Perspective, Capgemini, January, 2020

Seven ways real-time monitoring is driving smart manufacturing. (2019). Manufacturing.Net,

Siemens, Next Level AI – Powered by Knowledge Graphs and Data Thinking, Siemens China Innovation Day, Michael May, Chengdu, May 15, 2019

Smart Factories: Issues of Information Governance Manufacturing Policy Initiative School of Public and Environmental Affairs Indiana University, March 2019 (PDF, 68 pp., no opt-in)

Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.

Team predicts the useful life of batteries with data and AI. (2019, March 28). R & D.

The AI-powered enterprise: Unlocking the potential of AI at scale, Capgemini Research, July 2020

The Future of AI and Manufacturing, Microsoft, Greg Shaw (PDF, 73 pp., PDF, no opt-in).

The Rise of the AI-Powered Company in the Postcrisis World, Boston Consulting Group, April 2, 2020

Top 8 Data Science Use Cases in Manufacturing, ActiveWizards: A Machine Learning Company Igor Bobriakov, March 12, 2019

Walker, M. E. (2019). Armed with analytics: Manufacturing as a martial art. Industry Week

Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144–156.

Zulick, J. (2019). How machine learning is transforming industrial production. Machine Design

Apr 15

The State Of Cloud Business Intelligence, 2019

An all-time high 48% of organizations say cloud BI is either “critical” or “very important” to their operations in 2019.
Marketing & Sales place the greatest importance on cloud BI in 2019.
Small organizations of 100 employees or less are the most enthusiastic, perennial adopters and supporters of cloud BI.
The most preferred cloud BI providers are Amazon Web Services and Microsoft Azure.

These and other insights are from Dresner Advisory Services’ 2019 Cloud Computing and Business Intelligence Market Study. The 8^th annual report focuses on end-user deployment trends and attitudes toward cloud computing and business intelligence (BI), defined as the technologies, tools, and solutions that rely on one or more cloud deployment models. What makes the study noteworthy is the depth of focus around the perceived benefits and barriers for cloud BI, the importance of cloud BI, and current and planned usage.

“We began tracking and analyzing the cloud BI market dynamic in 2012 when adoption was nascent. Since that time, deployments of public cloud BI applications are increasing, with organizations citing substantial benefits versus traditional on-premises implementations,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. Please see page 10 of the study for specifics on the methodology.

Key insights gained from the report include the following:

An all-time high 48% of organizations say cloud BI is either “critical” or “very important” to their operations in 2019. Organizations have more confidence in cloud BI than ever before, according to the study’s results. 2019 is seeing a sharp upturn in cloud BI’s importance, driven by the trust and credibility organizations have for accessing, analyzing and storing sensitive company data on cloud platforms running BI applications.

Marketing & Sales place the greatest importance on cloud BI in 2019. Business Intelligence Competency Centers (BICC) and IT departments have an above-average interest in cloud BI as well, with their combined critical and very important scores being over 50%. Dresner’s research team found that Operations had the greatest duality of scores, with critical and not important being reported at comparable levels for this functional area. Dresner’s analysis indicates Operations departments often rely on cloud BI to benchmark and improve existing processes while re-engineering legacy process areas.

Small organizations of 100 employees or less are the most enthusiastic, perennial adopters and supporters of cloud BI. As has been the case in previous years’ studies, small organizations are leading all others in adopting cloud BI systems and platforms. Perceived importance declines only slightly in mid-sized organizations (101-1,000 employees) and some large organizations (1,001-5,000 employees), where minimum scores of important offset declines in critical.

The retail/wholesale industry considers cloud BI the most important, followed by technology and advertising industries. Organizations competing in the retail/wholesale industry see the greatest value in adopting cloud BI to gain insights into improving their customer experiences and streamlining supply chains. Technology and advertising industries are industries that also see cloud BI as very important to their operations. Just over 30% of respondents in the education industry see cloud BI as very important.

R&D departments are the most prolific users of cloud BI systems today, followed by Marketing & Sales. The study highlights that R&D leading all other departments in existing cloud BI use reflects broader potential use cases being evaluated in 2019. Marketing & Sales is the next most prolific department using cloud BI systems.

Finance leads all others in their adoption of private cloud BI platforms, rivaling IT in their lack of adoption for public clouds. R&D departments are the next most likely to be relying on private clouds currently. Marketing and Sales are the most likely to take a balanced approach to private and public cloud adoption, equally adopting private and public cloud BI.

Advanced visualization, support for ad-hoc queries, personalized dashboards, and data integration/data quality tools/ETL tools are the four most popular cloud BI requirements in 2019. Dresner’s research team found the lowest-ranked cloud BI feature priorities in 2019 are social media analysis, complex event processing, big data, text analytics, and natural language analytics. This years’ analysis of most and least popular cloud BI requirements closely mirror traditional BI feature requirements.

Marketing and Sales have the greatest interest in several of the most-required features including personalized dashboards, data discovery, data catalog, collaborative support, and natural language analytics. Marketing & Sales also have the highest level of interest in the ability to write to transactional applications. R&D leads interest in ad-hoc query, big data, text analytics, and social media analytics.

The Retail/Wholesale industry leads interest in several features including ad-hoc query, dashboards, data integration, data discovery, production reporting, search interface, data catalog, and ability to write to transactional systems. Technology organizations give the highest score to advanced visualization and end-user self-service. Healthcare respondents prioritize data mining, end-user data blending, and location analytics, the latter likely for asset tracking purposes. In-memory support scores highest with Financial Services respondent organizations.

Marketing & Sales rely on a broader base of third party data connectors to get greater value from their cloud BI systems than their peers. The greater the scale, scope and depth of third-party connectors and integrations, the more valuable marketing and sales data becomes. Relying on connectors for greater insights into sales productivity & performance, social media, online marketing, online data storage, and simple productivity improvements are common in Marketing & Sales. Finance requiring integration to Salesforce reflects the CRM applications’ success transcending customer relationships into advanced accounting and financial reporting.

Subscription models are now the most preferred licensing strategy for cloud BI and have progressed over the last several years due to lower risk, lower entry costs, and lower carrying costs. Dresner’s research team found that subscription license and free trial (including trial and buy, which may also lead to subscription) are the two most preferred licensing strategies by cloud BI customers in 2019. Dresner Advisory Services predicts new engagements will be earned using subscription models, which is now seen as, at a minimum, important to approximately 90% of the base of respondents.

60% of organizations adopting cloud BI rank Amazon Web Services first, and 85% rank AWS first or second. 43% choose Microsoft Azure first and 69% pick Azure first or second. Google Cloud closely trails Azure as the first choice among users but trails more widely after that. IBM Bluemix is the first choice of 12% of organizations responding in 2019.

Jan 5

Which Analytics And BI Technologies Will Be The Highest Priority In 2019?

82% of enterprises are prioritizing analytics and BI as part of their budgets for new technologies and cloud-based services.
54% say AI, Machine Learning and Natural Language Processing (NLP) are also a high investment priority.
50% of enterprises say their stronger focus on metrics and Key Performance Indicators (KPIs) company-wide are a major driver of new investment in analytics and BI.
43% plan to both build and buy AI and machine learning applications and platforms.
42% are seeking to improve user experiences by automating discovery of data insights and 26% are using AI to provide user recommendations.

These and many other fascinating insights are from the recent TDWI Best Practices Report, BI and Analytics in the Age of AI and Big Data. An executive summary of the study is available online here. The entire study is available for download here (39 PP., PDF, free, opt-in). The study found that enterprises are placing a high priority on augmenting existing systems and replacing older technologies and data platforms with new cloud-based BI and predictive analytics ones. Transforming Data with Intelligence (TDWI) is a global community of AI, analytics, data science and machine learning professionals interested in staying current in these and more technology areas as part of their professional development. Please see page 3 of the study for specifics regarding the methodology.

Key takeaways from the study include the following:

82% of enterprises are prioritizing analytics and BI applications and platforms as part of their budgets for new technologies and cloud-based services. 78% of enterprises are prioritizing advanced analytics, and 76% data preparation. 54% say AI, machine learning and Natural Language Processing (NLP) are also a high investment priority. The following graphic ranks enterprises’ investment priorities for acquiring or subscribing to new technologies and cloud-based services by analytics and BI initiatives or strategies. Please click on the graphic to expand for easier reading.

Data warehouse or mart in the cloud (41%), data lake in the cloud (39%) and BI platform in the cloud (38%) are the top three types of technologies enterprises are planning to use. Based on this finding and others in the study, cloud platforms are the new normal in enterprises’ analytics and Bi strategies going into 2019. Cloud data storage (object, file, or block) and data virtualization or federation (both 32%) are the next-most planned for technologies by enterprises when it comes to investing in the analytics and BI initiatives. Please click on the graphic to expand for easier reading.

The three most important factors in delivering a positive user experience include good query performance (61%), creating and editing visualizations (60%), and personalizing dashboards and reports (also 60%). The three activities that lead to the least amount of satisfaction are using predictive analytics and forecasting tools (27% dissatisfied), “What if” analysis and deriving new data (25%) and searching across data and reports (24%). Please click on the graphic to expand for easier reading.

82% of enterprises are looking to broaden the base of analytics and BI platforms they rely on for insights and intelligence, not just stay with the solutions they have in place today. Just 18% of enterprises plan to add more instances of existing platforms and systems. Cloud-native platforms (38%), a new analytics platform (35%) and cloud-based data lakes (31%) are the top three system areas enterprises are planning to augment or replace existing BI, analytics, and data warehousing systems in. Please click on the graphic to expand for easier reading.

The majority of enterprises plan to both build and buy Artificial Intelligence (AI) and machine learning (ML) solutions so that they can customize them to their specific needs. 43% of enterprises surveyed plan to both build and buy AI and ML applications and platforms, a figure higher than any other recent survey on this aspect of enterprise AI adoption. 13% of responding enterprises say they will exclusively build their own AI and ML applications.

Capitalizing on machine learning’s innate strengths of applying algorithms to large volumes of data to find actionable new insights (54%) is what’s most important to the majority of enterprises. 47% of enterprises look to AI and machine learning to improve the accuracy and quality of information. And 42% are configuring AI and machine learning applications and platforms to augment user decision making by giving recommendations. Please click on the graphic to expand for easier reading.

Apr 22

The State Of Cloud Business Intelligence, 2018

Cloud BI adoption is soaring in 2018, nearly doubling 2016 adoption levels.
Over 90% of Sales & Marketing teams say that Cloud BI is essential for getting their work done in 2018, leading all categories in the survey.
66% of organizations that consider themselves completely successful with Business Intelligence (BI) initiatives currently use the cloud.
Financial Services (62%), Technology (54%), and Education (54%) have the highest Cloud BI adoption rates in 2018.
86% of Cloud BI adopters name Amazon AWS as their first choice, 82% name Microsoft Azure, 66% name Google Cloud, and 36% identify IBM Bluemix as their preferred provider of cloud BI services.

These and other many other fascinating insights are from Dresner Advisory Services 2018 Cloud Computing and Business Intelligence Market Study (client access reqd.) of the Wisdom of Crowds® series of research. The goal of the 7^th annual edition of the study seeks to quantify end-user deployment trends and attitudes toward cloud computing and business intelligence (BI), defined as the technologies, tools, and solutions that employ one or more cloud deployment models. Dresner Advisory Services defines the scope of Business Intelligence (BI) tools and technologies to include query and reporting, OLAP (online analytical processing), data mining and advanced analytics, end-user tools for ad hoc query and analysis, and dashboards for performance monitoring. Please see page 10 of the study for the methodology. The study found the primary barriers to greater cloud BI adoption are enterprises’ concerns regarding data privacy and security.

Key takeaways from the study include the following:

Cloud BI’s importance continues to accelerate in 2018, with the majority of respondents considering it an important element of their broader analytics strategies. The study found that mean level of sentiment rose from 2.68 to 3.22 (above the level of “important”) between 2017 and 2018, indicating the increased importance of Cloud BI over the last year. By region, Asia-Pacific respondents continue to be the strongest proponents of cloud computing regarding both adjusted mean (4.2 or “very important”) and levels of criticality. The following graphic illustrates Cloud BI’s growing importance between 2012 and 2018.

Over 90% of Sales & Marketing teams say Cloud BI apps are important to getting their work done in 2018, leading all respondent categories in the survey. The study found that Cloud BI importance in 2018 is highest among Sales/Marketing and Executive Management respondents. One of the key factors driving this is the fact that both Sales & Marketing and Executive Management are increasingly relying on cloud-based front office applications and services that are integrated with and generate cloud-based data to track progress towards goals.

Cloud BI is most critical to Financial Services & Insurance, Technology, and Retail & Wholesale Trade industries. The study recorded its highest-ever levels of Cloud Bi importance in 2018. Financial Services has the highest weighted mean interest in cloud BI (3.8, which approaches “very important” status shown in the figure below). Technology organizations, where half of the respondents say cloud BI is “critical” or “very important,” are the next most interested. Close to 90% of Retail/Wholesale respondents say SaaS/cloud BI is at least “important” to them. As it has been over time, Healthcare remains the industry least open to managed services for data and business intelligence.

Cloud BI adoption is soaring in 2018, nearly doubling 2016 adoption levels. The study finds that the percentage of respondents using Cloud BI in 2018 nearly doubled from 25% of enterprise users in 2016. Year over year, current use rose from 31% to 49%. In the same time frame, the percentage of respondents with no plans to use cloud BI dropped by half, from 38% to 19%. This study has been completed for the last seven years, showing a steady progression of Cloud BI awareness and adoption, with 2018 being the first one showing the most significant rise in adoption levels ever.

Sales & Marketing leads all departments in current use and planning for Cloud BI applications. Business Intelligence Competency Centers (BICC) are a close second, each with over 60% adoption rates for Cloud BI today. Operations including manufacturing and supply chains and services are the next most likely to use Cloud BI currently. Marketing and BICC lead current adoption and are contributing catalysts of Cloud BI’s soaring growth between 2016 and 2018. Both of these departments often have time-constrained and revenue-driven goals where quantifying contributions to company growth and achievement ad critical.

Financial Services (62%), Technology (54%), and Education (54%) industries have the highest Cloud BI adoption rates in 2018. The retail/wholesale industry has the fourth-highest level of Cloud BI adoption and the greatest number of companies who are currently evaluating Cloud BI today. The least likely current or future users are found in manufacturing and security-sensitive healthcare organizations, where 45% respondents report no plans for cloud-based BI/analytics.

Dashboards, advanced visualization, ad-hoc query, data integration, and self-service are the most-required Cloud BI features in 2018. Sales & Marketing need real-time feedback on key initiatives, programs, strategies, and progress towards goals. Dashboards and advanced visualization features’ dominance of feature requirements reflect this department’s ongoing need for real-time feedback on the progress of their teams towards goals. Reporting, data discovery, and end-user data blending (data preparation) make up the next tier of importance.

Manufacturers have the greatest interest in dashboards, ad-hoc query, production reporting, search interface, location intelligence, and ability to write to transactional applications. Education respondents report the greatest interest in advanced visualization along with data integration, data mining, end-user data blending, data catalog, and collaborative support for group-based analysis. Financial Services respondents are highly interested in advanced visualization and lead all industries in self-service. Healthcare industry respondents lead interest only in in-memory support. Retail/Wholesale and Healthcare industry respondents are the least feature interested overall.

Interest in cloud application connections to Salesforce, NetSuite, and other cloud-based platforms has increased 12% this year. Getting end-to-end visibility across supply chains, manufacturing centers, and distribution channels requires Cloud BI apps be integrated with cloud-based platforms and on-premises applications and data. Expect to see this accelerate in 2019 as Cloud BI apps become more pervasive across Marketing & Sales and Executive Management, in addition to Operations including supply chain management and manufacturing where real-time shop floor monitoring is growing rapidly.

Retail/Wholesale, Business Services, Education and Financial Services & Insurance industries are most interested in Google Analytics connectors to obtain data for their Cloud BI apps. Respondents from Technology industries prioritize Salesforce integration and connectors above all others. Education respondents are most interested in MySQL and Google Drive integration and connectors. Manufacturers are most interested in connectors to Google AdWords, SurveyMonkey, and The Healthcare industry respondents prioritize SAP Cloud BI services and also interested in ServiceNow connectors.