2017: THE YEAR OF INTELLIGENCE (Chapter 2.3 of “All AWS Data Analytics Services”)

2017: The Year of Intelligence (image courtesy of vanrijmenam)

2017: The Year of Intelligence
(image courtesy of vanrijmenam)

2.3  2017: THE YEAR OF INTELLIGENCE

(I apologize in advance: I love this topic so much I got a bit carried away – for those that just want the facts, please skip over my section on The Augmented World Expo 😉 , in italics, contained between horizontal lines)

Now that the buzzword “Big Data” has finally waned, companies can finally make use of it! 2017 promises to be “The Year of Intelligence.”

2017 will see a rise in Big-Data-as-a-Self-Service solutions.

“Big-Data-as-a-Service”: Catapult Big Data Analytic Adoption
(image courtesy of vanrijmenam)

Self-service big data analytics will enable organizations to monetize their data and to use the insights to improve their business. These solutions do not require months of planning and preparation or the development of an IT infrastructure. Instead, you can simply connect your data sources and get to work. These platforms will enable agility, short time-to-implementation and offer increased productivity for small-to-medium-sized enterprises. Knowing that there are approximately 125 million SMEs in the world, it’s a massive market up for grabs. Big-Data-as-a-Self-Service Solutions, enabling organizations to prepare data irrespective of the type of data, whether structured, semi-structured or unstructured, could, therefore, be the killer app for big data adoption in 2017. Solutions found in AWS Marketplace are plentiful to help your Big-Data-as-a-Service initiatives.

The key applications companies are exploring in 2017 apart from self-service big data analytics are the Internet of Things (IoT), Machine Learning (ML), and Artificial Intelligence (AI), in combination with Augmented Reality (AR) and Virtual Reality (VR).

The tremendous success of Pokemon GO AR application has proven to businesses that AR  (and VR) are realistic revenue generators – suddenly thrust into the mainstream. This opens up the gates for workplace gamification for improved employee engagement, retention, and customer experience.


(Shameless plug: my very great friend, a creator of what’s now known as Google Earth, co-founded the first and now largest AR/VR/Emerging Tech Conference & Expo, The Augmented World Expo (AWE). I encourage all readers to go, or at least follow online. It’s a “B2B” conference where all the organizations, companies, and universities that are 7-10 years in the future of technology all gather in one place once a year. The first year I went, my mind was blown & I’ve been an advocate ever since!

Below are some photos and video I could find of AWE – and if I can find the “really cool” photos, I might update this in the future:

The above video is of an augmented reality children’s book. It’s like going into the world of J.K. Rowlings! And this was from 3 years ago!

DAQRI's 4D Human Stewardess and Me Through an iPad!

DAQRI’s 4D Human Stewardess and Me Through an iPad!

AR App That Helps Tourism See Full Ruins as If They Were Whole

AR App That Helps Tourism See Full Ruins as If They Were Whole

“Metaio Junaio”, Bought by Apple, AR Web App

DAQRI's "Skeleton to Outer Space" App with Me Sitting Beside It in First Image

DAQRI’s “Skeleton to Outer Space” App with Me Sitting Beside It in First Image

INTEL's Reaching Inside Monitor to Steal Dragon Egg & Get Attacked by Dragon Mother

INTEL’s Reaching Inside Monitor to Steal Dragon Egg & Get Attacked by Dragon Mother

architecture

How AR Helps Architects with Their Drawings: Visualize the Renderings in 3D with AR

AR Aiding the Setup or Repair of Anything from Sound Systems to Automobile Motors

AR Aiding the Setup or Repair of Anything from Sound Systems to Automobile Motors

INTEL's 3D AR T-Shirt with Dinosaur Popping Out

INTEL’s 3D AR T-Shirt with Dinosaur Popping Out

The Person Who Did All the AR for "IronMan" Gave a Demo

The Person Who Did All the AR for “IronMan” Gave a Demo

Part of What Went Into Creating the AR for the Ironman Movie

Part of What Went Into Creating the AR for the Ironman Movie

Ok, enough fun on AR 😉 )


“Mixed Reality” (MR), sometimes referred to as “hybrid reality”, is the merging of real & virtual worlds to produce new environments and visualizations where physical & digital objects co-exist and interact in real-time. Existing devices that enable this are Microsoft Hololens & the technologies created by Magic Leap. Mixed Reality offers tremendous opportunities for organizations to perform tasks better and to better understand the data generated by the organization. I’ve seen many of these types of devices at the conference mentioned above. Mixed reality is currently being used in manufacturing to enable better repairs, faster product development, or improved inventory management, for example. Mixed Reality will also help decision-makers understand very complex data sets, leading to better decision-making. Below is an image of how Mixed Reality will dramatically improve data visualizations and decision-making:

Mixed Reality Data Visualization (image by vanrijmenam)

Mixed Reality Data Visualization
(image courtesy of vanrijmenam)

IoT offers immeasurable insight into a customer’s mind. It’s also changing how daily life operates by helping create more efficient cities and leaner enterprises. With an estimated 50 billion IoT sensors by 2020 and more “things” on the internet by 2030, it’s undeniable that IoT will not only be transformative, but disruptive to business models.

“Conversational AI”: Intelligent Apps will Revolutionize Interactions
(image courtesy of vanrijmenam)

Many Fortune 500 brands are already using “chatbots”, and many more are developing them. Since chatbots are only as valuable as the relationships they build & support, their level of sophistication will make or break them. Investing in AI is one piece of that puzzle, and 2017 will be the year that companies need to expand their AI initiatives while also doubling on investing to improve them with new data streams & channel integrations.

Connected devices will become truly smart in 2017. Robots, autonomous vehicles or boats, drones and any other IoT product will become increasingly intelligent. These devices will become a lot better at understanding the user and adapting the product or service to the needs of the user. Software updates will be done over-the-air, reducing the need to constantly buy a new product.

When these smart devices are connected to intelligent applications such as Siri, Amazon Alexa, Viv, Microsoft Cortana or Google Home, the possibilities become endless. Conversational AI will enable high-level conversations with these intelligent applications. At the moment, these applications are primarily used to control your phone, play music or order a pizza but in 2017 that is about to change drastically.

Already, Amazon Alexa owners can control their car from inside their home and turn on the engine, but soon you will be able to control almost any device using your voice. Especially the development of Viv, which is coined as the next generation of Siri, will be able to do anything that you ask. As such, these bots, as said by Microsoft CEO Satya Nadella, will be the next apps. 2017 will see the convergence of these intelligent applications with many IoT devices and with Amazon announcing a new startup accelerator focused on conversational AI, it will change how your organization will have to deal with customers.

The algorithmic business has the potential to change society and 2016 saw a significant increase in the development of algorithms. Algorithms won the game of Go, it can translate languages it does not know and even detect a criminal simply by looking at an image of a face. AI will not stop there and in the coming years we will move more and more towards a form of artificial general intelligence; Siri that can also drive your car!

Artificial general intelligence is becoming possible because of deep learning. Deep learning is a subfield of machine learning and is inspired by the neural networks in our brain. The objective is to create artificial neural networks that can find patterns in vast amounts of data. Deep learning is becoming widely available now, because of the increased computing power and large data sets that are available to scientists around the globe. Therefore, in 2017 we will see many new deep learning applications that could significantly impact our lives.

Deep Learning Becomes Smarter, Bringing Us Closer to Artificial General Intelligence (image courtesy of vanrijmenam)

Deep Learning Becomes Smarter, Bringing Us Closer to Artificial General Intelligence
(image courtesy of vanrijmenam)

Deep learning algorithms are not trained by humans. Rather, they are exposed to massive data sets, millions of videos / images / articles, etc. and the algorithms must figure out for itself how to recognize different objects, sentences, images, etc. As a result, it can come up with solutions no humans could have thought of. An example is a set of algorithms that just developed an encryption algorithm humans could not decipher using patterns humans would never use. Thus, if in 2017 you have the feeling that your computer talks in a secret code to you, that could very well be true :-0

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The next post begins to focus more on AWS Data Analytic Services. This background should make you aware of why AWS Data Analytics is so important to your business!

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THE NEW BUZZWORD: “DIGITAL TRANSFORMATION” (Chapter 2.2 of “All AWS Data Analytics Services”)

“Digital Transformation”

2.2  THE “NEW” BUZZWORD: “DIGITAL TRANSFORMATION”

Wikipedia Definition of “Digital Transformation”:Digital transformation may be thought of as the third stage of embracing digital technologies: digital competence –> digital usage –> digital transformation, with usage and transformative ability informing “digital literacy”. The transformation stage means that digital usages inherently enables new types of innovation and creativity in a particular domain, rather than simply enhancing and supporting the traditional methods.

Digital Transformation really means “Digital Fungibility.” What??? While “transformation” is the act of making substantive change, “fungibility” is the intrinsic ability to be substantially changed.

A tactical approach to digital transformation centers on using new tools and related processes to get better results. Those new tools are based on new or reworked ideas, so they’re not a direct substitution for the tools you already have.

What’s key is that they make you think differently, as the Internet, cloud computing, and mobile computing have done.

Thinking differently is perhaps the most important ingredient in digital transformation, in fact. If you keep thinking the same, all that new technology will be used to do more of the same. That’s the opposite of transformation.

As our relationships to technology continues to evolve, machines are able to learn and adapt to their environments. Artificial Intelligence (AI) is able to work collaboratively with human professionals to solve intensely complex problems. AI stands to become one of the most disruptive forces in the IT world.

In the age of Digital Transformation, practically everything can be measured. Every important decision can and should be supported by the application of data and analytics. AI, Machine Learning (ML), Deep Learning technologies like Apache MXNet that runs on AWS, and Neuro-Linguistic Programming (NLP = encompasses the 3 most influential components involved in producing human experience: neurology, language, and programming) have become mainstream in the past year. The democratization of data, the self-service movement in AI/ML tools and data’s continued simplicity means more people will be leveraging it in more applications. Technology is sharpening the workforce & putting the power of data into the hands of business users. When we effectively scale ML, we can greatly increase the action-taking bandwidth of an enterprise.

Shifting data science from an ivory tower function and giving everyone in the organization access to advanced, interactive AI will help each employee become smarter and more productive. When data can inform each and every decision a business user is making, businesses will see a real competitive advantage and business outcomes.

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Posted in Amazon Artificial Intelligence, Amazon Machine Learning, Amazon Web Services, Amazon Web Services Analytic Services, AWS Analytic Services, AWS Analytics, Digital Fungibility, Digital Transformation, MXNet on AWS, New Buzzword: Digital Transformation | Leave a comment

THE CURRENT STATE OF DATA & ANALYTICS (Chapter 2.1 of “All AWS Data Analytics Services”)

“The Age of Algorithms”

2.1  THE “OLD” BUZZWORD: “BIG DATA”

The term “big data” was first described in 1944 as “information explosion.” In 2001 an article published by the Meta Group, “3D Data Management: Controlling Data Volume, Velocity, and Variety“, first described what has been generally accepted as the 3 defining definitions of big data (the “3 V’s”). For you history buffs, you can read an interesting story published in Forbes entitled “A Very Short History of Big Data” here.

The sheer volume of data generated by applications and infrastructure is increasing beyond comprehension: however for the first time, teams will be embracing an algorithmic approach – known as “Algorithmic IT Operations” (AIOps) – to see what’s happening in the network in real time, diagnose the issue and then automate a fix.

For decades, companies have been making business decisions based on traditional relational enterprise data, such as transactions. Then, “big data” came into the picture. Along with “big data” came massive volumes of both structured and unstructured data that’s so large it’s difficult to process using traditional database and software techniques. In fact, there’s more unstructured data in the world today than structured. The volume is too big, it comes from many different sources in many different formats, it moves too fast, and it normally exceeds processing capabilities available on-premises. But this data, when captured, formatted, manipulated and stored pulls powerful insights – some never before imagined – through analytics.

The focus has now shifted from “advanced analytics” to “advancing analytics”, which will be brought into self-service tools. With more users advancing their analytics, AI will play a bigger role in organizations.

In 2017, “big data” will be subsumed into the topic of Artificial Intelligence (AI). Big data is an enabler of AI and not an end in itself.

The shift is an increased valuation of critical thinking in the workplace as people realize there’s not a deficit of data in the enterprise, but a deficit of insight. The question for big data is “what can I learn from it?, or “where can I make meaningful insights?” AI and machine learning (ML) will be the big players, and companies will need to ask questions that their data can answer through these 2 transformative technologies.

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Posted in Algorithmic IT Operations, Amazon Web Services, Amazon Web Services Analytic Services, AWS Analytic Services, AWS Analytics, AWS BI, Old Buzzword: Big Data | Tagged , , | Leave a comment

DATA MIXOLOGY AND THE END OF DATA SILOS (Chapter 1.4 of “All AWS Data Analytic Services”)

Data Mixology & the End of Data Silos

Data Mixology & the End of Data Silos

1.4  DATA MIXOLOGY AND THE END OF DATA SILOS

The role of the CIO has changed dramatically over the past decade. With rise to new roles like the Chief Digital Officer and the Chief Customer Officer, we are seeing a rise in the importance of digital transformation happening NOW, and the importance of it happening not just in the technology of a company, but across the entire organization. Traditional solutions are more multidimensional and technology CANNOT be used as a crutch. A focus on breaking down silos, will give innovation more room to flourish and collaboration becomes easier.

AWS removes limits to the types of database and storage technologies you can use by providing managed database services that offer enterprise performance at open source cost. This results in applications running on many different data technologies, using the right technology for each workload.

Sample 1 of the Different Data Involved in One Solution

Sample 1 of the Different Data Involved in One Solution

Sample 2 of the Different Data Involved in One Solution

Sample 2 of the Different Data Involved in One Solution

Sample 3 of the Different Data Involved in One Solution

Sample 3 of the Different Data Involved in One Solution

Sample 4 of the Different Data Involved in One Solution

Sample 4 of the Different Data Involved in One Solution

Sample 5 of the Different Data Involved in One Solution

Sample 5 of the Different Data Involved in One Solution

These new trends and technologies will be at the core of digital transformation efforts in 2017 and many will continue far beyond the next year. There is no question that digital transformation is no longer an option as the need to build an organization that can change both its technology and its culture rapidly will be core to not only surviving in the time of business disruption, but building a business model that is agile, adaptable and designed to thrive long into the future where change is the only constant.

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Posted in Amazon Web Services, Amazon Web Services Analytic Services, AWS Analytics, AWS BI, AWS Marketplace, Data Mixology, End of Data Silos | Leave a comment

FASTER TIME TO ACTIONABLE INSIGHT (Chapter 1.3 in “All-AWS-Data-Analytic-Services”)

Faster Time to Actionable Insights & ROI

Faster Time to Actionable Insights & ROI

1.3  FASTER TIME TO ACTIONABLE INSIGHTS & ROI

AWS resources can be instantiated in seconds, you can treat these as “disposable” resources – not hardware or software you’ve spent months deciding which to choose and spending a significant up-front expenditure without knowing if it will solve your problems. The “Services not Servers” mantra of AWS provides many ways to increase developer productivity, operational efficiency and the ability to “try on” various solutions available on AWS Marketplace – the largest ecosystem of popular software vendors and integrators of any provider —  to find the perfect fit for your business needs without commitment to long-term contracts. Spin up a pre-configured analytical software solution in minutes, not months.

AWS provides an extensive set of managed services that help you build, secure, and scale big data analytics applications quickly and easily. Whether your applications require real-time streaming, a data warehouse solution, or batch data processing, AWS provides the infrastructure and tools to perform virtually any type of big data project.

When you combine the managed AWS services with software solutions available from popular software vendors on AWS Marketplace, you can get the precise business intelligence and big data analytical solutions you want that augment and enhances your project beyond what the services themselves provide. You get to data-driven results faster by decreasing the time it takes to plan, forecast, and make software provisioning decisions. This greatly improves the way you build business analytics solutions and run your business, giving your organization the agility to experiment and innovate with the click of a button.

Ongoing developments in AWS cloud computing are rapidly moving the promise of deriving business value from big data in real-time into a reality. With billions of devices globally already streaming data, forward-thinking companies have begun to leverage AWS to reap huge benefits from this data storm.

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Posted in Amazon Web Services, Amazon Web Services Analytic Services, AWS Analytic Services, AWS Analytics, AWS BI, AWS Marketplace, Faster Time to Data-Driven Results, Faster Time to ROI, Rapid Analytical Insights, Rapid Innovation | Leave a comment

KEY ENABLERS TO BIG DATA ANALYTICS FOR EVERYONE (Chapter 1.2 in “All-AWS-Data-Analytics-Services”)

Big Data Analytics for Everyone

Big Data Analytics for Everyone

1.2 KEY ENABLERS TO BIG DATA ANALYTICS FOR EVERYONE

Digital transformation reshapes every aspect of a business. As digital technology continues to evolve, successful digital transformation will require careful collaboration, thoughtful planning, and the inclusion of every department.

Digital transformation has morphed from a trend to a central component of modern business strategy. IT has become the data hero. It’s finally IT’s time to break the cycle and evolve from producer to enabler. IT is at the helm of this digital transformation of self-service analytics at scale. IT is providing the flexibility and agility a business needs to innovate while balancing governance, data security, and compliance. Organizations are empowered to make data-driven decisions at the speed of business, shaping the future of how successful companies are run.

A Partial List of Key Enablers

  • Cloud Computing: The adoption of cloud computing has made expensive analytics affordable. Analyzing extensive data sets require noteworthy compute capacity that can fluctuate in size based on the data inputs and type of analytics. This characteristic of scaling workloads is perfectly suited AWS’s pay-as-you-go cloud model, where applications can scale up and down based on demand. Being able to analyze data quickly to derive valuable insights can be done within minutes rather than months, and you only pay for what you use.
  • Adaptability: Change happens fast in business. Modern enterprises succeed when they adapt to industry and marketplace shifts and incorporate new technology into company culture and regular operations. However, digital transformation isn’t only about technology, it’s about bringing together the power of technology with a culture that embraces the change that it can lead for the organization.
  • The Importance of User Experience: The customer experience is the ultimate goal of any digital transformation. Customers are more cautious than ever; they’ll turn away from brands that don’t align with their values and needs. A top-notch user experience is paramount to keeping customers involved and engaged with your brand. This is a comprehensive process. Anywhere and everywhere customers can interact with your business, and the experience must be consistent and positive. For example, entrepreneurs can use analytics to discover where customers are coming from (whether it’s a social media platform, a blog, or somewhere else) and streamline the interaction in those high-traffic areas. Every touch point matters, and those leading the transformation should constantly be asking how are we removing friction and enhancing the experience for every customer regardless of where they are in the journey.
  • Rapid Innovation: Proactive innovation is one of the best ways to stay competitive in an evolving marketplace. New technology needs to be assessed, tested, analyzed, and judged more quickly than ever before. Businesses can no longer afford to waste time and resources implementing new tools that offer no real value. This means a “Fail fast, to succeed faster,” mentality. Some projects will work straight away, others will have significant learning curves. The faster your organization can go from idea to implementation the more it can embrace opportunities to transform and even disrupt markets and internal business models. Using the AWS Marketplace, explained throughout this blog series, will help you understand an inexpensive and convenient way to do this.
  • Leverage Remote Workers: Young professionals prefer flexibility over compensation. Mobile technology and bandwidth proliferation allow businesses to connect with and retain top talent anywhere in the world. Digital transformations encourage telecommute capabilities, giving the ability to hire the most talented employees.
  • Application Programming Interfaces (APIs): New tools and technologies need more than one avenue for viability; otherwise, their value quickly drops. APIs are a powerful way to embrace true digital transformation. eBay and PayPal are two companies that relied significantly on these technologies, enabling them to manage an incredible high volume of transactions. Companies need to tie together more best of breed technologies via APIs. Rather than marrying a platform, the API will open doors for multiple platforms together in a fast, flexible ecosystem. Sourcing from multiple vendors creates the best user experience. This will be further confirmed many times in this blog series when I talk about leveraging AWS Marketplace with AWS Services.

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Posted in Amazon Web Services, Analytical User Experience, Analytics-Driven Organization, AWS Analytic Services, AWS Analytics, AWS Marketplace, Business Adaptability, Cloud Computing, Digital Transformation, Key Enablers Analytics for Everyone, Leverage APIs, Rapid Innovation | Leave a comment

HOW ANALYTICS WILL CHANGE YOUR BUSINESS, FOR THE BETTER, FOREVER (Chapter 1.1 in “All-AWS-Data-Analytics-Services”)

We've Surpassed This Already...

We’ve Surpassed This Already…

  1. WE’RE LIVING IN A METRICS-DRIVEN WORLD

We live in a data-driven world. For Fortune 500 companies, the value of data is clear and compelling. They invest millions of dollars annually in information systems that improve their performance and outcomes. Independent businesses have the same need to be data-driven; however there’s a persistent entrepreneurial resistance to becoming truly metrics-driven. Founders are often tempted to postpone building necessary metrics in favor of spending time and resources on building products.

While that might work in the short-term, it will very quickly come back to haunt them. Very few companies have successfully achieved exponential growth, raised capital, or negotiated strong exits without first having a solid analytics model that has been iterated upon for many months or years.

“The Analytics-Driven Organization”

As companies become analytics-driven, they aren’t just enjoying incremental improvements. The benefits enabled by analytical data processing becomes the heart of the business – enabling new applications and business processes, using a variety of data sources and analytical solutions – giving insight into their data never dreamed of and giving them a great competitive advantage.

Below you’ll find a description of some of the types of data analytic insight types, and common use cases for each.

Descriptive: Descriptive Analytics uses business intelligence and data mining to ask “What has happened?” Descriptive Analytics mines data to provide trending information on past or current events that can give businesses the context they need for future actions. Descriptive Analytics are characterized by the use of KPIs. It drills down into data to uncover details such as the frequency of events, the cost of operations and the root cause of failures. Most traditional business intelligence reporting falls into this realm, but complex and sophisticated analytic techniques also fall into this realm when their purpose is to describe or characterize past events and states. Summary statistics, clustering techniques, and association rules used in market basket analysis are all examples of Descriptive Analytics.

Diagnostic: Diagnostic Analytics examines data or content to answer the question “Why did it happen?” It’s characterized by techniques such as drill-down, data discovery, data mining and correlations. You can think of it as the casual inference and the comparative effect of different variables on a particular outcome. While Descriptive Analytics might be concerned with describing how large or significant a particular outcome is, it’s more focused on determining what factors and events contributed to the outcomes. As more and more cases are included in a particular analysis and more factors or dimensions are included, it may be impossible to determine precise, limited statements regarding sequences and outcomes. Contradictory cases, data sparseness, missing factors (“unknown unknowns”), and data sampling and preparation techniques all contribute to uncertainty and the need to qualify conclusions in Diagnostic Analytics as occurring in a “probability space”. Training algorithms for classification and regression techniques can be seen as falling into this space since they combine the analysis of past events and states with probability distributions. Other examples of Diagnostic Analytics include attribute importance, principle component analysis, sensitivity analysis and conjoint analysis.

Discovery: Discovery Analytics doesn’t begin with a pre-definition but rather with a goal. It approaches the data in an iterative process of “explore, discover, verify and operationalize.” This method uncovers new insights and then builds and operationalizes new analytic models that provide value back to the business. The key to delivering the most value through Discovery Analytics is to enable as many users as possible across the organization to participate in it to harness the collective intelligence. Discovery Analytics searches for patterns or specific items in a data set. It uses applications such as geographical maps, pivot tables and heat maps to make the process of finding patterns or specific items rapid and intuitive. Examples of Discovery Analytics include using advanced analytical geospatial mapping to find location intelligence or frequency analysis to find concentrations of insurance claims to detect fraud.

Predictive: Predictive Analytics asks “What could happen?” It’s used to make predictions about unknown future events. It uses many techniques from data mining, machine learning and artificial intelligence. This type of analytics is all about understanding predictions based on quantitative analysis on data sets. It’s in the realm of “predictive modeling” and statistical evaluation of those models. Examples of Predictive Analytics includes classification models, regression models, Monte Carlo analysis, random forest models and Bayesian analysis. It helps businesses anticipate likely scenarios so they can plan ahead, rather than reacting to what already happened.

Prescriptive: Prescriptive Analytics uses optimization and simulation to ask “What should we do?” It explores a set of possible actions and suggests actions based on Descriptive and Predictive Analyses of complex data. It’s all about automating future actions or decisions which are defined programmatically through an analytical process. The emphasis is on defined future responses or actions and rules that specify what actions to take. While simple threshold based “if then” statements are included in Prescriptive Analytics, highly sophisticated algorithms such as neural nets are also typically in the realm of Prescriptive Analytics because they’re focused on making a specific prediction. Examples include recommendation engines, next best offer analysis, cueing analysis with automated assignment systems and most operations research optimization analyses.

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