Perficient Blogs https://blogs.perficient.com Expert Insights Thu, 26 Apr 2018 12:32:51 +0000 en-US hourly 1 https://wordpress.org/?v=4.9.5 30508587 Copyright © Perficient Blogs 2011 gserafini@gmail.com (Perficient Blogs) gserafini@gmail.com (Perficient Blogs) 1440 https://blogs.perficient.com/wp-content/plugins/podpress/images/powered_by_podpress.jpg Perficient Blogs https://blogs.perficient.com 144 144 Blogs at Perficient Perficient Blogs Perficient Blogs gserafini@gmail.com no no [Guide] 2018 State of the Car Rental Industry https://blogs.perficient.com/2018/04/26/guide-2018-state-of-the-car-rental-industry/ https://blogs.perficient.com/2018/04/26/guide-2018-state-of-the-car-rental-industry/#respond Thu, 26 Apr 2018 12:22:39 +0000 https://blogs.perficient.com/?p=186834 Everyone in business knows: if you’re too late to the game, your competitors can overstep your position and take away market share. But it can be incredibly challenging to determine the right time to pivot and focus on the trends that may completely transform an industry.

Enterprise Holdings, Hertz Global Holdings, Avis Budget Group and other leaders in the car rental industry are embracing people’s changing habits and the new operating environment. They are responding with intelligent strategies, innovative ideas, and technology investments that can help drive operational efficiency, growth, and profitability.

In a new guide, we discuss the strategic initiatives on which industry executives must focus to remain relevant. Download it today.

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Requirements Gathering For FDIC Part 370 https://blogs.perficient.com/2018/04/26/requirements-gathering-for-fdic-part-370/ https://blogs.perficient.com/2018/04/26/requirements-gathering-for-fdic-part-370/#respond Thu, 26 Apr 2018 12:09:02 +0000 https://blogs.perficient.com/?p=173762 For each of the technical work streams involved in a company’s Part 370 response, requirements will need to be defined and vetted with key stakeholders across the organization, as well as potentially with regulators. We have extensive experience creating business, functional, and technical requirements across a number of different companies in the financial services space. We can help a company gather requirements for Part 370 by:

  • Validating existing Part 370 requirements from the FDIC, as well as interpreting additional guidance as becomes available
  • Working with stakeholders across business, technology, and operations to create business requirements documents and drive signoff
  • Translating business requirements into both functional and technical requirements to enable a seamless transition to development activities
  • Creating traceability matrices between various phases of requirements definition
  • Managing versioning and storage to avoid a disorganized document lifecycle

Companies will likely have in-house resources who are tasked with creating some or all of these requirements in a normal project. However, having dedicated resources who can write and manage requirements for an entire program will help ensure continuity between different functions and phases. As requirements grow and change, trying to address this with possibly dozens of different business analysts across groups could bring this program to a standstill. Companies will benefit greatly from having individuals who are tasked with writing requirements at a program level.

If you are interested in learning more about FDIC Part 370 and how we can help you comply with the rule, please download our comprehensive guide or complete the contact form at the bottom of this page.

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DataStax Advanced Security : Eat your vegetables first https://blogs.perficient.com/2018/04/25/datastax-advanced-security-eat-your-vegetables-first/ https://blogs.perficient.com/2018/04/25/datastax-advanced-security-eat-your-vegetables-first/#respond Thu, 26 Apr 2018 01:54:34 +0000 https://blogs.perficient.com/?p=204576 I see companies start down their Big Data/NoSQL journey with a Proof of Concept mindset and they almost always end up funding a science project by confusing early wins on established products with progress. Cassandra is ten years old and DataStax has 500 customers in 50 countries. This stuff works; what you need is a Proof of Compliance. Can you go to production at your specific company? Most of the projects I see fall down on security compliance, not performance. In their latest upgrade, DataStax Enterprise has improved their advanced security offering making it easier to develop a small-scope, time-boxed PoC that can actually demonstrate how a highly available, highly scalable, always on persistence layer can also demonstrate rigorous enterprise compliance characteristics of your current databases. There’s a lot of fun to be had with DSE, but first you need to eat your vegetables.

Implementing sufficient global data security measures to ensure compliance around PII and NPI is a real challenge for open-source NoSQL databases. Sarbanes Oxley, Basel II, the Health Insurance Portability and Accountability Act (HIPAA), the General Data Protection Regulation (GDPR), and the Payment Card Industry Data Security Standard (PCI DSS) expose regulated industries to substantial reputational and financial risk. Let’s take a look at what the open source product offers.

Apache Cassandra’s security model provides for role-based authentication and authorization at the internal, or database, level.  An internal-only authentication model precludes integrating external authentication models such as LDAP, Kerberos and Active Directory. This is almost certainly a deal-breaker from a corporate security perspective. Cassandra’s TLS/SSL encryption is available between both the client and the database cluster as well as intra-node to provide for encryption for data in-flight. However, there are many use cases where encryption for data-at-rest, often for a relatively small subset of data, is mandatory. If you were to start along this open-source path, you would be able to get a feel for how to use a columnar store database, but this actually give very little insight into how to offer a columnar store database solution in a regulated industry. I hear the argument that it’s best to start with small steps. Honestly, DataStax Academy makes getting a small, realistic use case up and running very easy. It’s better to focus on how you are going to get to production from day one.

Do the hard things first. — Michael Bloomberg

I propose that instead of starting with a small “Hello, World” use case that is pretty much guaranteed to work, you start with a small “Hello, My Company” project. In this project, we are going to do the simplest version of a production-grade customer journey. The assumption is that DSE is fast enough, resilient enough and scalable enough to meet your data requirements. We need to know if the following are sufficient for your corporate security and governance requirements:

  • authentication
  • authorization
  • encryption
  • auditing

DataStax Enterprise supports SSL encryption for client-to-node encryption and node-to-node encryption. All communication occurs over TCP sockets and can be secured by using the standard Java Security SSL/TLS implementation in the JVM. Since DSE support BYO root certificate authority, you can just use a self-signed certificate. Use the OpsCenter LifecycleManager to automate the process of preparing the certificates and distributing certificates. Use all for inter-node encryption.

Use DSE Unified Authentication to manage external authentication and authorization with LDAP or kerberos. (I’m going to assume LDAP.) Unified Authentication is composed of DSE Authenticator, DSE Role Manager and DSE Authorizer. Authenticator supports validating user identity with either LDAP or kerberos and is a prerequisite for enabling authorization and role management. Role Manager matches the user LDAP group names to DSE roles. Authorizer analyzes a request against a resource’s role permissions before allowing execution.

By enabling SSL with LCM and locking down ports and integrating our database access with the corporate LDAP, we have a persistence layer that isn’t in directly violation of most corporate security policies. One step below compliance is negligence, so this is not really that impressive yet. Let’s get better.

We have already set up encryption for data over the wire. Next we need to setup transparent data encryption for tables, hint files, commit logs, and configuration properties. Using local encryption at this stage and then consider using a KMIP encryption key for remote storage and management. You do have to do the key distribution manually across the cluster with the local encryption because this isn’t handled by LCM.

The final step in setting up a NoSQL persistence layer with production-grade enterprise characteristics is an auditing mechanism. You can capture data to a log file or a table. I recommend a log file because there may already be mechanisms in place in your organization that you can leverage out-of-the-box. In the logback.xml file, configure logging levels and other options the same way you do in a java application but also mask sensitive data. From the beginning, use a regex for keyspace filtering for targeting keyspaces and limit the number of event categories (maybe just data manipulation) and specify roles to filter but choose to monitor the whole cluster. To mask sensitive data change the umask setting on the audit files to 600 for OS-level security breaches  and use the encoder.pattern element of the logback.xml to redact sensitive information.

 %replace(%msg){"password='.*'", "password='xxxxx'"}

Now imagine if you create two tables: customer_sales and product.  Create some basic prepared statements to do CRUD operations that minimize SQL Injection exposure. Create a setup of users and groups that have different levels of access and use Authorizer to enable row-level access control (RLAC). Now come up with a process for making sure that legal operations are permitted and illegal operations are are captured by a rapid identification and response mechanism. Maybe quantify your processes and practices using a guide.

Now its time to put in some quality Patrick McFadin time and build out a ridiculously fast, highly-scalable data powerhouse on top of your production-grade Proof of Compliance.

 

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Unleashing Innovation with Application Modernization at #G2I2018 https://blogs.perficient.com/2018/04/25/unleashing-innovation-at-2018-gateway-to-innovation-st-louis/ https://blogs.perficient.com/2018/04/25/unleashing-innovation-at-2018-gateway-to-innovation-st-louis/#respond Wed, 25 Apr 2018 19:36:07 +0000 https://blogs.perficient.com/?p=196989 Perficient is partnering with Pivotal at the 2018 Gateway to Innovation Conference in St. Louis on:

Wednesday, May 16 | 8:00 AM – 5:00 PM | America’s Center Convention Complex

It’s time for your business to meet the digital demands of today’s world. Innovation can’t happen without modernization. But adapting your existing IT infrastructure and applications is challenging and costly. How can you successfully and practically modernize applications to realize new possibilities for your business?

Join us for an interactive session “Unleash Innovation with Application Modernization” led by experts from the National Geospatial-Intelligence Agency (NGA), TD Ameritrade and First National Bank of Omaha (FNBO). Find out how leading enterprise organizations are shifting their traditional business practices by leveraging cloud-based platforms to extend the value of legacy applications, accelerate speed to market, increase profits, and unleash innovation.

Unleash Innovation with Application Modernization | 3:15pm – 4:00pm | Room 241 – Second Floor

 

Representatives from Perficient and Pivotal will be together at a table throughout the Gateway to Innovation Conference on the second floor of America’s Center to discuss technology needs and explain how their companies can digitally transform their businesses to achieve value-driven results.

Register to Attend – Gateway to Innovation Conference

#g2i2018

Learn more about Perficient’s Pivotal practice

 

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MarTech: 5 Disruptions Happening to Marketing https://blogs.perficient.com/2018/04/25/martech-5-disruptions-happening-to-marketing/ https://blogs.perficient.com/2018/04/25/martech-5-disruptions-happening-to-marketing/#respond Wed, 25 Apr 2018 15:55:32 +0000 https://blogs.perficient.com/?p=200271 What trends do you think are disrupting marketing right now?  GDPR? Advertising overload? Privacy concerns with Facebook?   While those are certainly disrupting, at the MarTech West conference Scott Brinker spoke about the five disruptions he sees. He started by talking about all the hype going on with marketing technology.  AI, AR, ChatBots, Bots, and many more technologies are going through inflated expectations right now.  See Gartner’s Hype Cycle research for an explanation of the Hype Cycle.   But overall, Scott thinks that marketing technology is achieving a lot and many good technologies have emerged that haven proven valuable.

Scott’s 5 Disruptions:

1.     Digital Transformation has been driving a lot of change as companies try to become more customer focused.  He sites this statistic as a key piece of evidence in this disruption:  60% of marketing leaders have restructured to better leverage marketing tech.  Marketing has a mandate (67% of companies surveyed) for cross-organization growth, customer experience, and champion a customer centric corporate culture and mindset. For marketing,  transformation is about thinking like a CEO, not a CMO. tranformation picture

2.     Microservices & API – The explosion of cloud has been very disrupting in a good way for most.  Scott cited that the average number of cloud apps is 1,000 per company.  Marketing services accounts for 91 of the 1,000.  HR has 90.  IT has 25.  So non-IT departments are driving the cloud disruption and hence microservices & APIs.  iPaaS systems have been growing tremendously.  iPaaS systems provide orchestration of services as well as actual services and APIs.  Salesforce just purchased MuleSoft for 6.5B which shows the importance of services to their business. Microservices is enabling quicker deployment of new business capabilities.  They can also enable ‘citizen developers’ to create applications.

3.     Vertical Competition – battles for exclusive touch points with customers.  There is not a one-to-one touchpoint from marketer to customer.  There are many intermediaries, like agencies, consultants, internet services, client interfaces, devices.  Vertical CompetitionThese different channels/providers/etc. try to interject into the customer experience.  They also set the rules in some cases.  For example, Amazon Echo / Alexa is a tightly controlled touchpoint between a marketer and consumer.  The marketer may want to get a particular message out, but Alexa may have different ideas.

4.     Digital Everything – Digital is no longer just one or two channels.  We have an explosion of channels – Wearables, Chat, POS, IoT, Voice, Social, Mobile, Email, Web, etc. Standards are essential because they enable the orchestration of complexity at scale.  Think of a ballet – the movements come down to 5 foot positions, 5 arm positions, etc.  They are very simple standards that can be combined in an almost unlimited way to produce an experience.

5.     Artificial Intelligence – automation / complexity explosion.  Scott ran out of time to discuss this disruption.  However, we are seeing many major vendors trying to incorporate AI into  their products.  Adobe Sensei, IBM Watson, Salesforce Einstein are all branded versions of AI embedded into marketing platforms.

What do you think of these disruptive forces?  Do you see any disruptions that Scott missed?

 

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[Guide] 2018 State of the Airline Industry https://blogs.perficient.com/2018/04/25/guide-2018-state-of-the-airline-industry/ https://blogs.perficient.com/2018/04/25/guide-2018-state-of-the-airline-industry/#respond Wed, 25 Apr 2018 13:49:44 +0000 https://blogs.perficient.com/?p=186855 The airline industry is scrutinized by passengers and analysts alike, mostly for two reasons. One, many people take one or more flights a year and have personal experiences that leave lasting impressions. Two, the industry’s economic impact is unprecedented.

According to the Federal Aviation Administration (FAA), “Aviation accounts for more than 5% of our [United States] Gross Domestic Product, contributes $1.6 trillion in total economic activity and supports nearly 11 million jobs.” In a closely related industry, the manufacturing of planes is the country’s “top net export.” The industry is also a major contributor to national productivity growth.

From a global perspective, the impact on the world economy is even more substantial. A study conducted by Oxford Economics on behalf of the Air Transport Action Group (ATAG), an independent coalition of organizations and companies in the air transport industry, indicates that the estimated global economic impact of air transport is equivalent to 8% of the world’s gross domestic product.

Digest these numbers for a minute. In 2015, the world’s airlines carried more than three billion passengers, and today aviation and related tourism supports more than 60 million jobs worldwide. Almost 10 million people work directly in the aviation and air transport industries. More impressive is the fact that, according to research, 25% of all companies’ sales depend on air transport.

While many types of businesses (e.g., manufacturers of commercial jetliners) can be considered part of the aviation sector, our new guide focuses on the airline industry – the companies that operate air transport networks. It looks at the current and future state of the industry. Specifically, it discusses some of the initiatives on which airlines are focusing in the hopes of driving growth and value for their businesses, as well as their customers, employees, partners, and shareholders.

Download the guide.

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[Guide] 2018 State of the Insurance Industry https://blogs.perficient.com/2018/04/25/guide-2018-state-of-the-insurance-industry/ https://blogs.perficient.com/2018/04/25/guide-2018-state-of-the-insurance-industry/#respond Wed, 25 Apr 2018 11:45:07 +0000 https://blogs.perficient.com/?p=181946 Creating value for customers, employees, partners, and shareholders has always been the core objective of insurance companies. Today, the only difference is that stakeholder expectations are evolving faster because of technical innovation. While expectations are increasing, it is becoming more challenging to establish and retain relationships, and grow at more profitable levels.

Our new guide highlights the:

  • State of the insurance industry based on our own insights, as well as those from leaders in the space
  • Most significant challenges diversified insurance, life insurance, and property and casualty insurance companies face and how they are solving them
  • Initiatives insurance companies are focusing on to drive innovation, efficiency, and profitability

Click here to download.

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Perficient’s Award-Winning Partnership with Salesforce https://blogs.perficient.com/2018/04/24/perficients-award-winning-partnership-with-salesforce/ https://blogs.perficient.com/2018/04/24/perficients-award-winning-partnership-with-salesforce/#respond Tue, 24 Apr 2018 21:18:40 +0000 https://blogs.perficient.com/?p=196995 Take a closer look at Perficient’s award-winning partnership with Salesforce.

We have implemented more than 3,000 Salesforce solutions that empower businesses to become more responsive, efficient, and relevant. Whether you’re looking to drive efficiency, insight, reliability, simplicity, scale, or collaboration, we’ll unlock the right Salesforce solution for you.

2016 Salesforce Partner Innovation Award

The 2016 Salesforce Partner Innovation Award in Healthcare and Life Sciences spotlights our work with NextGen Healthcare. The Partner Innovation Awards are given annually during Dreamforce to honor Salesforce’s consulting partner community and recognize the contributions they make in helping clients innovate and connect with customers in a whole new way.

Deliver a Connected Service Experience

In a highly competitive marketplace where consumers are in control, brands are no longer competing on just product or price – they’re competing on the customer experience itself as a way to differentiate and turn customer satisfaction into ROI. As a result, companies are viewing customer service as the new marketing. Our end-to-end customer service solutions and services are designed to help deliver a more connected service experience for customers – from the call center to the field.

Deliver a Connected Service Experience

In a highly competitive marketplace where consumers are in control, brands are no longer competing on just product or price – they’re competing on the customer experience itself as a way to differentiate and turn customer satisfaction into ROI. As a result, companies are viewing customer service as the new marketing.

Turn Insights into Action to Build Customer Loyalty

Successful marketers integrate social and marketing automation into a strategy that’s driven by what customers, partners, prospects, and employees are saying. The Salesforce Marketing Cloud revolutionizes how you listen, engage, gain insight, publish, advertise, and measure social marketing programs.

Set Your IT Free

It’s a mobile- and app-driven world. Whether it’s a new concept or enhancing an existing application, we can accelerate your time-to-market with Salesforce. Free your IT from building and maintaining infrastructure and release developers from reinventing the wheel. We’ll show you a better way to innovate.

Unleash CRM to Increase Productivity and Revenue

Companies that can connect with customers anytime, anywhere are able to close bigger deals faster. Sales Cloud unlocks CRM capabilities on the go and gives you a 360-degree view of your customer, efficiently manages your pipeline and forecasts, addresses complex commission models, facilitates order management, and much more.

Stay Connected

Stay on top of Salesforce technologies by following Perficient here:

 

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Artificial Neural Networks: Solving Challenges in Health Sciences https://blogs.perficient.com/2018/04/24/artificial-neural-networks-solving-challenges-health-sciences/ https://blogs.perficient.com/2018/04/24/artificial-neural-networks-solving-challenges-health-sciences/#respond Tue, 24 Apr 2018 17:56:57 +0000 https://blogs.perficient.com/healthcare/?p=11926 There is a lot of buzz in healthcare and life sciences right now around Artificial Intelligence, and the potential uses for Artificial Neural Networks (ANN) and Deep Learning to solve for all manner of messy and complex problems. Deep-Learning software attempts to mimic the activity in layers of neurons in the neocortex[1], this includes cognitive processes such as pattern recognition and classification, concept association, learning, sensorial perception, and optimization.

ANN’s are being implemented today to address a myriad of applications. For example in healthcare:

  • Informing clinical diagnosis
  • Predicting future disease
  • Analyzing images

And in life sciences:

  • Signal interpretation
  • Real World Evidence/Drug Development
  • Market Research and Customer Service

What is an Artificial Neural Network and how does it work?

An Artificial Neural Network is a computational approach also referred to as a Connectionist System used in machine learning. ANN’s are loosely modeled after the biological neural network in an attempt to replicate the way in which we learn as humans. Think of it as a computing system, structured as a series of layers, with each layer composed of one or more neurons. The layers comprise input, output and hidden layers as follows:

  • Input neurons receive various forms of external information that the network will learn about, recognize, and process
  • Output units sit on the opposite side of the network and signal how it responds to the information it’s learned
  • Hidden units sit in between input and output units serving as the mechanism to transform signals for interpretation between input and output

Figure 1: Depiction of a Neural Network, where each circle is a neuron and the arrows indicate the connections between neurons in consecutive layers.[1]

One technique that led to the broader development of ANN’s is the Backpropogation algorithm, which allows the neural network to be trained, and provides detailed insights into how changing the weights and biases across all the connections, changes the overall behavior of the network to allow the ANN to arrive at the right answer. Backpropogation uses the comparison between the outputs a network actually produces with the output it was supposed to produce, applying the difference between the two to modify the weights of the connections between the units, so the network can get it right.
Figure 1: Depiction of a Neural Network, where each circle is a neuron and the arrows indicate the connections between neurons in consecutive layers.[2]

Deep learning neural networks, use different layers within a multilayer network to extract different features until it can recognize what it is looking for. A number or weight represents the connection between one unit and another, which can be positive or negative. The higher or lower the weight, the more or less influence one unit has on another. In an ANN the data is fed forward through the network layer-by-layer, until it reaches the final layer, and it is only when the data reaches the final layer’s activations that the network’s predictions are made.

How is an Artificial Neural Network or Deep Learning System Trained?

ANN’s or Deep Learning Systems can be trained in one of three ways[3]:

  1. From Scratch

This is by far the most time consuming method and involves gathering a very large data set containing metadata and the design of a network architecture that will learn the features and model. This approach is used for new applications, or applications with a large number of output categories. Because of the amount of data and learning involved, this method of training the network can take days or weeks to fully train the model.

  1. Transfer Learning

This involves tuning a pre-trained model and is the more common approach and much faster than #1. Most deep learning applications use the transfer learning approach, a process that involves fine-tuning a pre-trained model. You start with an existing network, and feed in new data containing previously unknown classes. After making some tweaks to the network, you can now perform a new task.

  1. Feature extraction

While less common than the other two more specialized approach to deep learning is to use the network as a feature extractor. Since all the layers are tasked with learning certain features from the inputs, we can pull these features out of the network at any time during the training process and these features can then be used as input to a machine learning model.

There are two modes of learning in an ANN: 1) Supervised learning where the network is trained using a set of input-output pairs. The goal is to’ teach’ the network to identify the given input with the desired output; and 2) Unsupervised learning whereby the network is trained using input signals only and the network organizes internally to produce outputs that are consistent with a particular stimulus or group of stimuli.

Where are Artificial Neural Networks and Deep Learning Systems Being Used Today?

There is a host of applications in production, as well as in active research as proof of concepts, or being contemplated as future possibilities across healthcare and life sciences, and we have listed several below to provide perspective on the opportunity for ANN’s in this space, however the list is by no means exhaustive:

Disease Identification and Diagnosis[4]

  • In radiology for disease identification and diagnosis, deep learning systems are trained to detect the presence or absence of disease in medical images and from unstructured text in radiology reports, helping doctors come up with better interpretations

Personalized Medicine

  • In personalized medicine to treat cancer patients, establishing standards of care and cancer treatment recommendations based upon the latest medical research literature, evidence based medicine, in combination with the patient diagnosis and medical history
  • Matching patients based upon their diagnosis, medical history and other factors to the optimal clinical trials available locally and nationwide

Drug Discovery and Manufacturing

  • Remote monitoring and real-time data access for increased safety; such as monitoring biological and other signals for any sign of harm or death to participants
  • Early stage drug discovery e.g. from initial screening of drug compounds to predicted success rate based on biological factors

Predicting and Managing Epidemic Outbreaks

  • Monitoring and predicting epidemic outbreaks based on data collected from satellites, historical information on the web, real-time social media updates, and other sources

 

In summary, Artificial Neural Networks and Deep learning are key technologies to achieving results never before seen, allowing us to realize a host of use cases that span research, drug development, diagnosis, treatment, prevention, patient safety and beyond. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers enabling speech based and other natural language inputs and structure data interactions.

Training neural networks requires a great deal of data, and that is just to allow them to recognize known features. Beware that not all applications are well-suited to ANN’s or deep learning, so it is important to understand when to leverage ANN’s and Deep Learning, verses other type of Machine Learning techniques to achieve the desired results. Also don’t underestimate the effort, particularly when training an Artificial Neural Network from scratch.

The list of opportunities is for ANN’s and Deep Learning is endless, and innovators across healthcare (payer and provider) and life sciences (pharma, biotech and medical device manufacturing) are beginning to invest in this area to achieve state-of-the-art accuracy, with many hoping to exceed human-level performance.

Sources:

  1. Robert D. Hof, MIT Technology Review 2013. Deep Learning: https://www.technologyreview.com/s/513696/deep-learning/
  2. Rohan & Lenny #1: Neural Networks & The Backpropagation Algorithm, Explained, March 3rd, 2016: https://ayearofai.com/rohan-lenny-1-neural-networks-the-backpropagation-algorithm-explained-abf4609d4f9d
  3. https://www.mathworks.com/discovery/deep-learning.html
  4. Deep Learning Applications in Medical Imaging, September 14, 2017 by Abder-Rahman Ali: https://www.techemergence.com/deep-learning-applications-in-medical-imaging/

 

 

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The 2018 MarTech Landscape is Here https://blogs.perficient.com/2018/04/24/the-2018-martech-landscape-is-here/ https://blogs.perficient.com/2018/04/24/the-2018-martech-landscape-is-here/#respond Tue, 24 Apr 2018 17:22:53 +0000 https://blogs.perficient.com/?p=196978 Today at the MarTech West conference in San Jose, Scott Brinker and team unveiled the 2018 MarTech Landscape chart.  Scott has produced The Marketing Technology Landscape chart since 2011.  The chart shows all the companies in the MarTech landscape grouped over many categories, such as Advertising & Promotion, Content & Experience, etc.   While the chart is a massive display of logos, it serves to show which vendors play in each category of the market.

MarTech Landscape 2018

For 2018, here are some numbers:

  • Number of solutions has grown to almost 6,700 companies
  • Growth of solutions was 27% growth from 2017.
  • Only 4.5% of solutions from 2017 were removed from the landscape.
  • Enterprise companies – 6.3% of companies
  • Small companies make up 40%
  • Investor funding in this space grown 4x since 2012

The largest category of solutions is Sales.  This goes to show how well much Sales should be tied Marketing.

Predictive Analytics was removed from the landscape in 2018.  Why?  When they looked at the analaytics companies, they found the companies incorporated analytics in many other categories, so it no longer stands alone.  Artificial Intelligence is being incorporated in so many other products, that it won’t have a standalone category either.

Conversational Interfaces is a new area.  This is where bots come into play.  While many pundits have declared ChatBots, dead, see my recent article on why I don’t think they are dead.

Compliance and Privacy is a new area of focus (can you say Facebook?).  There are now many solutions in this space. The keyword is Trust and building trust with customers is the focus of compliance and security.

In addition to this landscape, Scott showed how 54 companies created “Stackies”, which are graphics similar to the landscape, but show the MarTech landscape within a particular company.  Its interested to see these individual landscapes because it shows where there is overlap in technologies within one category.  It also shows which categories a company focuses on and if there are any missing categories.  I think creating the landscape for you company is a useful exercise.

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