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Valuing the Artificial Intelligence Market, Graphs and Predictions

In order to put together an executive brief for market size and projected growth of AI, I’ve molded this article around (a) AI-related industry market research forecasts, and (b) a limited number of reputable research sources for further insight into AI valuation and forecasting, in addition to select and relevant quotes.

The goal of this article is do provide a short consensus on well-researched projections of AI’s growth and market value in the coming decade, and to (as always) provide amble references for further exploration for those of you who aim to go deeper. We’ve aimed to stick to sources whose reputation rests on their objectivity, rather than on excited statements of companies whose incentive is to see the future their way (such as IBM’s CEO claiming a potential $2 trillion dollar market for “cognitive computing”).

(The following profiles and forecasts are updated on a quarterly basis) Select quotes: In 2017, these technologies will increase businesses’ access to data, broaden the types of data that can be analyzed, and raise the level of sophistication of the resulting insight.

The vast majority of firms believe that having an organizational model that supports analytics is critical to breaking down the silos of customer knowledge that exist throughout the enterprise…Enterprises are starting to show signs of elevating the priority of, and investment in, initiatives to get rid of existing silos.

3) Leading CI practices will be the poster child for business transformation Select quotes: Tractica forecasts that the revenue generated from the direct and indirect application of AI software is estimated to grow from $643.7 million in 2016 to $36.8 billion by 2025.

The chart below shows Tractica’s top 10 AI use cases in terms of revenue in 2025: Tractica has assumed a somewhat conservative adoption of AI in the hedge fund and investment community, with an assumption that roughly 50% of the hedge fund assets traded by 2025 will be AI-driven.

Spiderbook’s current data visualization of companies investing in AI: Study context: Accenture, in association with Frontier Economics, modeled the potential impact of AI for 12 developed economies that together generate more than 50 percent of the world’s economic output.

and To fulfill the promise of AI as a new factor of production that can reignite economic growth, relevant stakeholders must be thoroughly prepared—intellectually, technologically, politically, ethically, socially—to address the challenges that arise as artificial intelligence becomes more integrated in our lives.

“The average business expects to spend $8 million on big data-related initiatives this year,” according to the Kearney report, which also says each IT job created in the process of upgrading will create three additional jobs outside IT.

Forrester says that in 2016, machine learning will begin to replace manual data wrangling and data governance dirty work, and vendors will market these solutions as a way to make data ingestion, preparation, and discovery quicker.

Autonomous Agents and Things Machine learning gives rise to a spectrum of smart machine implementations — including robots, autonomous vehicles, virtual personal assistants (VPAs) and smart advisors — that act in an autonomous (or at least semiautonomous) manner.

Select quotes: 1)    By 2018, 20 percent of business content will be authored by machines. Technologies with the ability to proactively assemble and deliver information through automated composition engines are fostering a movement from human- to machine-generated business content…

2)    By 2018, six billion connected things will be requesting support. In the era of digital business, when physical and digital lines are increasingly blurred, enterprises will need to begin viewing things as customers of services — and to treat them accordingly…

6)    By 2018, 45 percent of the fastest-growing companies will have fewer employees than instances of smart machines. Gartner believes the initial group of companies that will leverage smart machine technologies most rapidly and effectively will be startups and other newer companies…

There are likely to be leaps and bounds in the next decade in gleaning insights from “unstructured data”, while applying predictive analytics and building business models is a more oft-used approach to implementing machine learning and data mining technologies at present.

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students: 30 June 2019 Early-Bird discounts, amounting to €1.000, will be recognized to candidates who will send their applications by April 29th, 2019 (valid only for registrations confirmed within 2 weeks from receipt of the admission documents).

Additionally, non-EU applicants must take into consideration that getting a student visa for Belgium implies the obligation to prove their sufficient and regular means of subsistence (631EUR net/month in 2016-2017) or their sponsor’s. If they can’t, Solvay can support them if the applicant transfers the requested amount covering 12 months on a school deposit account (which will be redistributed on a monthly basis to the student as soon as a Belgian bank account has been created).

Business Intelligence and Analytics Trends 2019 - DATAVERSITY

In 2019, Advanced Analytics capabilities with minimal manual efforts will remain the hallmark of all competitive Business Intelligence solutions.

slideshow from Information Management, 10 Predictions on Advanced Analytics and BI Trends, confirms that Gartner analysts Douglas Laney et al., (authors of a market report titled 100 Data and Analytics Predictions Through 2022) think that “the power of analysis” is a game changer in today’s business environment.

Quick Round-up of Major BI Trends of 2018 Business Intelligence and Analytics Trends in 2018 lists the major milestones achieved in the BI world this year, and suggests many of these trends will continue in 2019: Finally, the sudden rise of Advanced Business Analytics across businesses of all shapes and sizes can be directly attributed to cheap storage, easy availability of massive volumes of data, and IoT devices.

All businesses waiting eagerly for fresh functionality on their favorite BI platforms can expect the following enhancements in the coming year: Top Business Intelligence Trends 2019 seems to back up the above claims about upcoming BI trends.

discussion about the future of BI trends is hardly complete without mentioning “personalized interactions.” At least two types of personalized interactions are clearly visible in the current context: Image used under license from

Top 10 Analytics And Business Intelligence Trends for 2019

The development of business intelligence to analyze and extract value from the countless sources of data that we gather at a high scale, brought alongside a bunch of errors and low-quality reports: the disparity of data sources and data types added some more complexity to the data integration process.

It is not only important to gather as much information possible, but the quality and the context in which data is being used and interpreted serves as the main focus for the future of business intelligence.

We detailed the benefits and costs of good or bad quality data in our previous article on data quality management, where you can read the five important pillars to follow.

By implementing company-wide data quality processes, organizations improve their ability to leverage business intelligence and gain thus a competitive advantage that allows them to maximize their returns on BI investment.

Using data visualization tools to perform those actions is becoming an invaluable resource to produce relevant insights and create a sustainable decision-making process.

That being said, business users require software that is: Since humans process visual data better, the data discovery trend will find increment as one of the most important BI trends in 2019.

We have also developed a new feature called Insights, also AI-based, that fully analyzes your dataset automatically without needing an effort on your end. You simply choose the data source you want to analyze and the column/variable (for instance, Revenue) that our decision support system software should focus on.

That is an incredible time gain as what is usually handled by a data scientist will be performed by a tool, providing business users with access to high-quality insights and a better understanding of their information, even without a strong IT background.

The demand for real-time data analysis tools is increasing and the arrival of the IoT (Internet of Things) is also bringing an uncountable amount of data, which will promote the statistical analysis and management at the top of the priorities list.

Accordingly, predictive and prescriptive analytics are by far the most discussed analytics trends among the BI professionals, especially since big data is becoming the main focus of analytics processes that is being leveraged not just by big enterprises, but small and medium-sized businesses alike.

Applied to business, predictive analytics is used to analyze current data and historical facts in order to better understand customers, products, and partners and to identify potential risks and opportunities for a company.

Marketers determine customer responses or purchases and set up cross-sell opportunities, whereas bankers use it to generate a credit score – the number generated by a predictive model that incorporates all of the data relevant to a person’s creditworthiness.

This procedure allows for capturing associations or discovering regularities within a set of patterns with the considerable volume, number of variables or diversity of the data. ARIMA is a model used for time series analysis that applies data from the past to model the existing data and make predictions about the future.

The analysis includes inspection of the autocorrelations – comparing how the current data values depend on past values – especially choosing how many steps into the past should be taken into consideration when making predictions.

Any deviations in these points can bring insight into the data series behavior, predicting new anomalies or helping to discover underlying patterns not visible by bare eye.

Moreover, entrepreneurs will learn how to embrace the power of cloud analytics, where most of the elements – data sources, data models, processing applications, computing power, analytic models and data storage – are located in the cloud.  There are more and more organizations moving their data and all of their applications to the cloud.

Opting for a multi-cloud strategy is then an option as it reduces risk and provides more flexibility – but such flexibility comes with certain costs, as you need several providers as well as training your teams to learn multiple platforms.

That being said, using multi clouds is becoming an increasing part of the BI strategy of a company, and giants like Alibaba, Amazon, Google, and Microsoft all turned attention in developing these growing business intelligence market trends.

More collaborative processes will be created to help both IT teams and end-users agree and implement modern data governance models, maximizing the business value of analytics while not jeopardizing security.

In order to improve business outcomes, organizations have started to view data governance as a necessity, but the lack of experience creates challenges into implementing and combining data quality, risk, ethics, privacy, and security to drive reliable business values, according to Gartner.

In this context, a usually hot debate is the decision between on-premises or cloud BI tools: whether the software is installed locally in the company’s own servers, or if the solution is hosted in the cloud.

After Facebook has witnessed the biggest data breach in which 50 million accounts were compromised, rumors spread that Google tracked Mastercard’s users’ buying habits – these controversies made consumers more aware of their personal information and online habits.

As Gartner stated: “The conversation should move from ‘Are we compliant?’ toward ‘Are we doing the right thing?’” We can safely affirm that today, data and analytics are getting to the core of every business.

But today, the data and analytics volume and role are getting so big that a new position emerged: the CDO, or Chief Data Officer, and CAO, or Chief Analytics Officer. Today, their role has increased and they have one of the toughest seats in the executive table.

While it complements the role of the CDO and CIO (Chief Information Officer), it is also becoming a dominant skill set sought-after recruiting companies – “If the CDO is about data enablement, then the CAO role is about how you drive insights off that data.

How you make the data actionable.” Gartner has implemented the CAO as the hot topic of their 2019 Summit in Orlando, hence the business intelligence market trends will incorporate this role as one that brings value and increased significance in business.

“By 2021, 15% of all customer service interactions will be completely handled by AI, an increase of 400% from 2017.” – Gartner Customer analytics and continuous experience will be one of the focus areas of business intelligence trends in 2019.

Customer journey analytics, emotion detection, speech analytics, customer engagement center (CEC) interaction analytics, analytics for customer intelligence – a lot of analytics awaits service leaders to visualize and connect the consumer journey across multiple devices and channels.

We’ve summed up in this article what the close future of business intelligence looks like for us. Here are the top 10 analytics and business intelligence trends we will talk about in 2019: Being data-driven is no longer an ideal;

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