IDG Contributor Network: Data governance 2.0

At Bristol-Myers Squibb, I have the privilege of working for a company singularly focused on our mission to discover, develop, and deliver innovative medicines that help patients prevail over serious diseases. Accurate, high quality, and trustworthy data is central to our work in R&D, manufacturing, sales and marketing, and corporate functions. In IT, we strive to make sure the right data is available to the right audience at the right time with the right quality and controls to advance our company’s mission. With the digital data and analytic transformation that is pervasive across the health care industry, as an IT and a data professional, there has never been a more exciting time than now to transform how we manage, protect, and consume data to help patients prevail over serious diseases.      

The digital data and analytic transformation is not unique to health care. Everywhere you turn, in industry after industry, the focus is on digital and analytic transformation with companies in a race to become the digital enterprise powered by machine learning and AI. This transformation thirsts for trusted good quality data. Yet the one common theme, in my conversations with IT, analytics, and business leaders across industries, is the persistent dissatisfaction on the state of data in the modern enterprise. There is no disagreement on the aspirations of treating data as an asset and a fuel for the modern enterprise. Yet almost all enterprises suffer from the weight of legacy data infrastructure, dysfunctional data stewardship and poor rate of return on organizational investments in data management. 

So what is my solution?

I believe it is data governance 2.0, a pragmatic, relentless, self-sustaining data governance aided by machine-assisted data stewardship. I define data governance 2.0 as the combination of people, process, and technology that precisely articulates the data domains and assets that are critical to the enterprise (high risk and/or high value), defines the baseline of where the enterprise is today in managing the data (data ownership, data quality, data readiness), defines the target state of where the organization needs to be, orchestrates pragmatic ownership and asset management processes that efficiently fits in the organizational structure and culture, relentlessly monitors utilization and value, and course corrects without dogma when needed. This data governance 2.0 should use algorithmic automations, machine learning, and AI to reduce the organizational burden and bureaucracy so that human involvement in data governance shifts from mundane data stewardship tasks to qualitative action directed by the “machine.”

I recognize data governance is not new and perhaps it is the most overused phrase in the annals of data management. But there is no getting around the fact that unless the modern enterprise establishes a bedrock of good data governance, the edifices of digital and analytics transformation will erode and dissipate like the statue of Ozymandias looming over the rubbles in the desert. The time has come to leverage the analytic and AI advancements of today to reboot data governance and elevate it to the same level of importance as good financial governance.

So what are the key considerations for establishing this data governance 2.0 foundation? I plan to explore this through conversations with CDOs, industry leaders, peers, and practitioners. 

As the first in that series I had a chance to discuss the topic of data governance with Jason Fishbain, the chief data officer (CDO) at the University of Wisconsin at Madison. Fishbain is a passionate proponent of pragmatic data governance striving to build a strong data foundation at the University of Wisconsin at Madison. Synthesized below are the key takeaways from that discussion.

Data governance tied to strategic business objectives

Successful data governance efforts must be linked to business objectives. In his efforts at University of Wisconsin-Madison, Fishbain tied the need for good data governance closely to the educational analytic needs required to achieve the university’s strategic business objectives ranging from student recruitment to graduation goals. This enabled him to create rapid buy-in among the academic leaders on the need to own and manage the data effectively at the source. According to Fishbain, a chief data officer must be a strategic leader and a data evangelist deftly navigating the organizational structure to consistently and relentlessly advance the data governance goals. He focuses on informal and formal outreach to academic leaders to understand their priorities and see how he can enable them with the right quality data.

It is all about the outcome, be flexible on the governance model

Organizations often get bogged down in debates on data ownership and data governance models. In our discussion on how to define the right data governance models, Fishbain pointed out that in his experience, effective organizations eschew fidelity to organizational models in favor of a pragmatic selection of a model or models that best enable the outcome. Effective CDOs show the willingness to lead or to serve, be a COE or be a data stewardship unit all in the cause of enterprise data outcomes. If a certain department has the necessary skills, resources and the willingness to manage the data, then he believes the CDO should support them with standards, best practices and tools. If other areas lack this expertise, then the CDO should offer data management as a service.   

Data on the state of data is key

 According to Fishbain, a CDO must define and publish a few but relevant metrics on the “state of the data” to the organizational leaders tying them as much as possible to the attainment of strategic business objectives. As Fishbain puts it, if the CDO does not have data on the state of the data, then how can you shine a spotlight and mobilize organizational action? As in any good business metrics and KPI, these “state of the data” metrics must be limited, focused, and action-oriented.

Business process changes and technology shifts are opportunities for a data strategy refresh

Fishbain astutely observed that new capability deployments—be at a new CRM or ERP implementation or a redesign of a business process, are perfect opportunities for a CDO to advocate for a relook at the associated data strategy. Is the data ownership clear? What are the data quality objectives? What are the data consumption aspirations? These are all questions to ask at the launch of a new business process or technology initiative to call attention to the fact that in today’s enterprise, a lack of focus on data strategy is a surefire way to undercut the expected ROI of most capability investments.

In conclusion, there are pragmatic approaches to data governance which will allow the practitioners of its art and science—the CDOs and data leaders to steer the enterprise towards a future where effective data governance is the default than the exception.

This article is published as part of the IDG Contributor Network. Want to Join?

Source: InfoWorld Big Data

Technology of the Year 2018: The best hardware, software, and cloud services

Was 2017 the year that every product under the sun was marketed as being cognitive, having machine learning, or being artificially intelligent? Well, yes. But don’t hate all of them. In many cases, machine learning actually did improve the functionality of products, sometimes in surprising ways.

Our reviewers didn’t give any prizes for incorporating AI, but did pick out the most prominent tools for building and training models. These include the deep learning frameworks Tensor­Flow and PyTorch, the automated model-building package H2O.ai Driverless AI, and the solid machine learning toolbox Scikit-learn.

The MLlib portion of Apache Spark fits into this group as well, as does the 25-year-old(!) R programming language, of which our reviewer says, “No matter what the machine learning problem, there is likely a solution in CPAN, the comprehensive repository for R code, and in all likelihood it was written by an expert in the domain.”

2017 was also the year when you could pick a database without making huge compromises. Do you need SQL, geographic distribution, horizontal scalability, and strong consistency? Both Google Cloud Spanner and CockroachDB have all of that. Do you need a distributed NoSQL database with a choice of APIs and consistency models? That would be Microsoft’s Azure Cosmos DB.

Are you serving data from multiple endpoints? You’ll probably want to use GraphQL to query them, and you might use Apollo Server as a driver if your client is a Node.js application. Taking a more graph-oriented view of data, a GraphQL query looks something like a JSON structure with the data left out.

As for graph database servers, consider Neo4j, which offers highly available clusters, ACID transactions, and causal consistency. Are you looking for an in-memory GPU-based SQL database that can update geospatial displays of billions of locations in milliseconds? MapD is what you need.

Two up-and-coming programming languages made the cut, for completely different domains. Kotlin looks like a streamlined version of object-oriented Java, but it is also a full-blown functional programming language, and most importantly eliminates the danger of null pointer references and eases the handling of null values. Rust, on the other hand, offers memory safety in an alternative to C and C++ that is designed for bare-metal and systems-level programming.

Speaking of safety, we also salute two security products—one for making it easier for developers to build secure applications, the other for extending security defenses to modern application environments. GitHub security alerts notify you when GitHub detects a vulnerability in one of your GitHub project dependencies, and suggest known fixes from the GitHub community. Signal Sciences protects against threats to your web applications and APIs. 

If you’ve started deploying Docker containers, sooner or later you’re going to want to orchestrate and manage clusters of them. For that, you’ll most likely want Kubernetes, either by itself, or as a service in the AWS, Azure, or Google clouds. Honeycomb goes beyond monitoring and logging to give your distributed systems observability.

Recently, the heavyweight Angular and React frameworks have dominated the discussion of JavaScript web applications. There’s a simpler framework that is gaining mindshare, however: Vue.js. Vue.js still builds a virtual DOM, but it doesn’t make you learn non-standard syntax or install a specialized tool chain just to deploy a site.

Microsoft’s relationship with Linux has been troubled over the years, to say the least. For example, in 2001 Steve Ballmer called Linux a “cancer.” The need for Linux in the Azure cloud changed all that, and the Windows Subsystem for Linux allows you to run a for-real Ubuntu or Suse Bash shell in Windows 10, allowing you to install and run legitimate Linux binary apps from the standard repositories, including the Azure Bash command line.

Read about all of these winning products, and many more, in our tour of 2018 Technology of the Year Award winners.

Source: InfoWorld Big Data

InfoWorld’s 2018 Technology of the Year Award winners

The Open Compute Project’s open hardware standards have done much to push forward the development of cloud-scale hardware. By sharing designs for connectors, racks, servers, switches, and storage hardware, the OCP has defined a new generation of data center technologies and made them widely available – and able to be manufactured at the scale the big public clouds need.

Project Olympus is one of Microsoft’s open hardware designs, shared with OCP members and driven as an open design project with multiple forks of Microsoft’s initial spec. Built around the OCP’s Universal Motherboard specification, Project Olympus is a flexible compute server, with support for Intel, AMD, and ARM64 processors as well as FPGA, GPGPU, and other specialized silicon to add features as needed.

The initial Project Olympus hardware has been joined by a second, deeper chassis design, the Project Olympus Hyperscale GPU Accelerator. The “HGX-1” hosts eight Pascal-class Nvidia GPUs for machine learning workloads. Four HGX-1 servers can be linked together via Nvidia’s NVLink to give you up to 32 GPUS, ideal for complex workloads.

Cloud data centers need lots of hardware, but workloads are moving away from the one-size-fits-all x86 server. The flexible Project Olympus design allows the same chassis to support different motherboards and thus handle the varied workloads running on modern cloud infrastructures. And as it’s open hardware, it can be produced by any manufacturer, ensuring wide availability and low prices.

— Simon Bisson

Source: InfoWorld Big Data

IDG Contributor Network: Harmonizing big data with an enterprise knowledge graph

One of the most significant results of the big data era is the broadening diversity of data types required to solidify data as an enterprise asset. The maturation of technologies addressing scale and speed has done little to decrease the difficulties associated with complexity, schema transformation and integration of data necessary for informed action.

The influence of cloud computing, mobile technologies, and distributed computing environments contribute to today’s variegated IT landscape for big data. Conventional approaches to master data management and data lakes lack critical requirements to unite data—regardless of location—across the enterprise for singular control over multiple sources.

The enterprise knowledge graph concept directly addresses these limitations, heralding an evolutionary leap forward in big data management. It provides singular access for data across the enterprise in any form, harmonizes those data in a standardized format, and assists with the facilitation of action required to repeatedly leverage them for use cases spanning organizations and verticals.

Enterprise-spanning connections and data representation

An enterprise data fabric delivers these benefits by successfully extending the notion of master data management and data lakes. The former is predominantly a means of describing the underlying data, typically via unified schema. In their original inception data lakes grant universal access to data in their native formats, yet lack the necessary metadata and semantic consistency for long term sustainability.  

Enterprise knowledge graphs, however, include the metadata and semantic benefits of MDM hubs but link all data together in adherence to semantic standards. The combination of enterprise-wide ontologies, taxonomies, and terminology delivers data in a representation (in terms of meaning and schema) immediately identifiable to the user. These linked data approaches connect all data uniformly.

Health care providers, for example, can connect the voluminous types of data relevant to their industry by creating an exhaustive list of events such as diagnostics, patient outcomes, operations, billing codes, and others, describing them with standardized models and fortifying them with uniform terminology across the data spectrum.

Regardless of where data is—whether in the cloud, a cache, or awaiting computation—users can link them in the same consistent format that has meaning to their business purposes. The standardized ontologies, which are malleable to incorporate new events, and unified terminology align all data to the knowledge graph’s schema regardless of their origination or other points of distinctions.

Active automation

The true value of an enterprise knowledge graph’s access layer is in the automated action it facilitates. With data stemming from any number of disparate source systems, the automatic generation of code for analytics or transformation is invaluable. Such automation is one of the crucial advantages of an enterprise knowledge graph that reduces the difficulty in not only accessing data, but also applying them to necessary action.

The use cases provisioned by this combination are innumerable. A health care organization attempting to predict the event of respiratory failure for patients in multiple locations could use knowledge graph applications to monitor the blood pressure of all hospital incumbents. The graph would enable the organization to create an abstract description of the blood pressure data related to this event (respiratory failure), then automatically compile that description into code which obtains the prediction data.

The overarching value proposition of this approach is that the user simply issues a query for the data he or she needs regardless of where data originated. The automation capabilities of an enterprise knowledge graph create the action whereby the data that pertains to the query is attained. The key difference is the user need not necessarily know the source system or the particularities of its schema to get the data. Access is much more expedient since all of the data engineering work of cleansing and transforming data is done upfront prior to issuing queries.

In addition, the data relevant to the query can stem from multiple source systems, but is still accessed from a single place (the knowledge graph). The user is not responsible for personally accessing those systems; instead, the query mechanism is able to cull the appropriate data from the varying systems accessible from the centralized knowledge graph.

Unification

An enterprise knowledge graph effectively unifies several aspects of the considerable variations intrinsic to big data. It unifies the means of accessing, representing, automating and even moving data from an array of source systems and architectural complexities. In addition to streamlining how users retrieve diverse data via automation capabilities, the knowledge graph standardizes those data according to relevant business terms and models. The result is a homogenized data set wrought from any number of data types and sources.

This article is published as part of the IDG Contributor Network. Want to Join?

Source: InfoWorld Big Data

IDG Contributor Network: AI disruptions and the power of intelligent data

Machine learning and Artificial Intelligence brings in transformations through practically every domain and industry in an unprecedented way. Fueled by ever increasing computing resources, evolution of faster algorithms, developments in machine learning backed by vast amounts of data—AI is bringing rapid changes the existing business processes.

It is important that an AI system is engineered to interpret and demonstrate a general intelligence as humans, demonstrate a level of intelligence that is not specific to one category of tasks or at least be able to generalize those specifics, and relate those understandings in the context of real world tasks, issues and cases.

Ability to balance this interpretation in the right manner enables an AI system to deal with new situations which are very different that the ones system has encountered earlier.

The “intelligence” in the data

Companies are striving to bring transformations, areas such as operations optimization, fraud detection and prevention, and financial performance improvements are becoming more and more focused, and one of the key factors that’s drives these initiatives to success is the capability to drive them with intelligent data from trusted source of repositories. As for any other digital strategies organizations build, data is pivotal to success of artificial intelligence strategy. Said differently, AI systems can only be as intelligent as the data they deal with.

TensorFlow, an open-source library and part of Google brain project for machine learning, performs language modeling for sentiment analysis and predicting the next words in sentences given the history of previous words. Language modeling is one of the core tasks of natural language processing and is widely used in the areas of image captioning, machine translation, and speech recognition. TensorFlow creates predictive models by training machine learning algorithms with large data sets, and the algorithm iteratively makes predictions based on the training data, but desired result is not to create a model predicting the test data sets but to create a model with a good generalization. TensorFlow is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural networks, large datasets generated from YouTube videos, and photos repositories, to name a few.

Why is enterprise data so critical for AI?

Data is the primary source that builds knowledge base with timely and meaningful insights. Data velocity and volume, both play a crucial role in defining an accurate and information based decision. The logic applies to machine learning. AI systems modeled with data from wide variety of sources are expected to produce results with better accuracy, in a timely manner. This is important for any organization in any industry that wants to build AI capabilities.

Machine learning algorithms do the job to fit a specific AI model to data. First a model is built and then it gets trained by presenting large volumes of data sets to it. The model adjusts itself to tune to various datasets, however, affinity to one data set or training data may bring more challenges when a new scenario is encountered.

Generalization and specialization

People learn many things from examples. Teachers teach many things to children by citing examples. They learn to differentiate between two similar objects by knowing more smaller attributes about those objects. Barring the exception of some superkids, it is difficult for most two-year-old kids to differentiate cars models. Human intelligence at that age forms a model with the knowledge based on inductive learning. This technique is referred as generalization. Inductive learning requires the model to grow by exposing more cars to it, and build a belief based on the developing cognitive abilities and from observing more cars. Based on the initial belief, it can be assumed that a car has four wheels.

The specialization technique is exactly opposite of generalization. Human intelligence grows with age, and human cognition recognizes more attributes in people’s model of understanding to differentiate things based on those attributes. Likewise, AI agents need specialization when inductive learning on a very large data make the model overgeneralized. For example, after observing restaurants and their menu, we may build a general belief that all restaurants serve food. But not all restaurants serve kosher food. Here, we must use specialization to restrict the generalization of the belief to find special restaurants that serve kosher food. In simple words, specialization can be achieved by adding extra conditions to the predefined model.

AI strategies to teach machines to think like people do

AI based systems utilize generalization and specialization to build their core knowledge base and train their model. The idea is to maintain a single model based on the training data and to adjust it as new scenarios arrive to maintain consistency. This requires them to process data or information about a subject area in different manner based on the need. To create better predictive models that are capable of generalizing, systems also need to know when to stop training the model so that it doesn’t overfit.

Overfitting can happen when the model becomes very large and the AI agent starts making decisions based on training data. If the training data sets are too narrow and do not contain many scenarios, it may cause underfitting problem as well. In the opposite strategy, AI system needs to evaluate many scenarios and build the model in a way that it can represent some sort of specialization.

Role of quality enterprise data

Not all the data sets are equally useful though, consider the AI agent in a self-driving car trained only on straight highways will have a hard time navigating in a desert unless it was trained by using similar models. Good AI agents need simple data sets to build a generalized model first and then also need diverse data sets to address tasks where specialization is needed. This requires a constant inflow and wide variety of metadata and data to train the machine learning system, and provide a trusted and effective artificial intelligence.

Enterprises can build great AI systems by using data across repositories and by building rich training data sets and catalogues and further train AI agents using those training data sets. Meta data can also be integrated from range of systems like mainframes, HR, CRM, and social media and can be gathered in to the training data sets and catalogs.

This article is published as part of the IDG Contributor Network. Want to Join?

Source: InfoWorld Big Data

TensorFlow review: The best deep learning library gets better

If you looked at TensorFlow as a deep learning framework last year and decided that it was too hard or too immature to use, it might be time to give it another look.

Since I reviewed TensorFlow r0.10 in October 2016, Google’s open source framework for deep learning has become more mature, implemented more algorithms and deployment options, and become easier to program. TensorFlow is now up to version r1.4.1 (stable version and web documentation), r1.5 (release candidate), and pre-release r1.6 (master branch and daily builds).

The TensorFlow project has been quite active. As a crude measure, the TensorFlow repository on GitHub currently has about 27 thousand commits, 85 thousand stars, and 42 thousand forks. These are impressive numbers reflecting high activity and interest, exceeding even the activity on the Node.js repo. A comparable framework, MXNet, which is strongly supported by Amazon, has considerably lower activity metrics: less than 7 thousand commits, about 13 thousand stars, and less than 5 thousand forks. Another statistic of note, from the TensorFlow r1.0 release in February 2017, is that people were using TensorFlow in more than 6,000 open source repositories online.

Much of the information in my TensorFlow r0.10 review and my November 2016 TensorFlow tutorial is still relevant. In this review I will concentrate on the current state of TensorFlow as of January 2018, and bring out the important features added in the last year or so.

Source: InfoWorld Big Data

Comcast Selects AWS As Its Preferred Public Cloud Provider

Amazon Web Services, Inc. (AWS) has announced that Comcast Cable has selected AWS as its preferred public cloud infrastructure provider. Comcast Cable will expand its use of AWS by migrating material workloads and building new applications on AWS.

AWS provides important infrastructure and services to Comcast as it focuses on building cloud-native products and services on AWS that adapt and evolve to meet the needs of customers. That focus is reflected in the class-defining X1 Platform, award-winning voice-control technology, and Xfinity xFi, Comcast’s personalized Wi-Fi experience that gives customers pinpoint control over their home networks. Comcast’s primary businesses, Comcast Cable and NBCUniversal, are currently running workloads on AWS which has enabled these businesses to become more nimble and launch new, revenue-generating initiatives in the competitive entertainment industry.

“We have deepened our strategic relationship with AWS, making the industry’s leading cloud our preferred public cloud provider,” said Jan Hofmeyr, chief network and operations officer and senior vice president at Comcast Cable. “Over the years, we have leveraged the breadth and depth of AWS’s services, including compute, storage, and analytics. In that process, we’ve found AWS to be extremely customer focused.”

“For industry leaders like Comcast Cable, the quest to anticipate and exceed consumers’ media and entertainment needs is never ending,” said Mike Clayville, vice president, worldwide commercial sales at AWS. “Comcast Cable’s goal has always been to stay a step ahead of the competition. In order to do that, they wanted solutions that were agile, flexible and ready for what’s next. Together, AWS and Comcast Cable collaborated to enable them to confidently move core business workloads, build new applications with ease, and gain the agility they required by using AWS.”

Source: CloudStrategyMag

Cloud Standards Customer Council Updates White Paper

The Cloud Standards Customer Council™ (CSCC™) has published version 2.0 of its white paper, Interoperability and Portability for Cloud Computing: A Guide. Authors of the paper will present a complimentary webinar on January 24, 2018, from 11:00am – 12:00pm ET to introduce the paper.

Today, organizations are employing a wide range of cloud services and transitioning data and applications to cloud computing environments. The topics of “interoperability” and “portability” are significant considerations in relation to the use of cloud services, but there is also confusion and misunderstanding of exactly what this entails. The aim of this guide is to provide a clear definition of interoperability and of portability and how these relate to various aspects of cloud computing and to cloud services.

Version 2.0 has been updated to reflect the new ISO/IEC 19941 Cloud Computing Interoperability and Portability standard and its facet models of interoperability, data portability, and application portability. The model of an application and the process of porting an application have been updated to reflect new thinking contained in ISO/IEC 19941. Additionally, containers and the role of automation have been addressed, as these have become dominant trends for cloud users.

The paper provides guidance for avoiding vendor-lock in, which allows customers to make the best use of multiple diverse cloud services that can cooperate and interoperate with each other, which is critical to future cloud service adoption and the realization of the benefits of computing as a utility.

Source: CloudStrategyMag

IDG Contributor Network: Dawn of intelligent applications

Data remains a foundational element of computing. Recently, Hadoop and big data have been a central part of data progression, allowing you to capture data at scale. But companies now look to the expanding use of cloud computing and machine learning to create more intelligent applications.

This new generation of applications use all the data they can, including incoming real-time data, to respond in the moment to changing circumstances and formulate advantageous outcomes. This includes delivering on the digital transformation promise sought by companies to deliver rich customer experiences.

Intelligent applications can converge database and data warehouse workloads, allowing companies to respond and react to changing conditions in real time.

This builds on a theme covered by nearly every large industry analyst firm regarding the merging of transactional and analytical functions. Gartner refers to this convergence as hybrid transaction analytical processing, or HTAP; 451 Research refers to it as hybrid operational analytical processing, or HOAP; and Forrester refers to it as translytical data platforms.

According to Forrester:

Analytics at the speed of transactions has become an important agenda item for organizations. Translytical data platforms, an emerging technology, deliver faster access to business data to support various workloads and use cases. Enterprise architecture pros can use them to drive new business initiatives.

451 Research also calls out the idea of seizing the moment:

Organizations are zeroing in on the so-called “transaction window” and realizing that it presents a significant opportunity—and once it’s gone, it’s gone for good.

Intelligent applications in finance, media, and energy sectors

The largest industry sectors are using these converged technologies for their intelligent applications. These applications collect and process data from a variety of sources, provide experiences in real time, and make use of the latest techniques in machine learning and artificial intelligence to push their usefulness forward.

Consider the following examples.

Finance

A popular intelligent application in finance is the new frontier of digital wealth management, including real-time portfolio analytics for clients across any platform. As one example, JP Morgan Chase highlighted its investment in digital wealth management in an investor presentation last year.

Behind the scenes, many of these digital wealth services are powered by digital startups such as InvestCloud, which states that its wealth management products “allow wealth managers to get a whole view of their clients—instantaneously and at scale.” Other companies in this space include SigFig, which showcases “experience integrating our platform with TD Ameritrade, Charles Schwab, Vanguard, E*Trade, among others.”

Energy

In the energy sector, intelligent applications include IoT data pipelines using sensor data. Real-time capture and analysis of this data, with machine learning model scoring, provides helpful downtime mitigation and savings for global companies. Shell describes its advanced analytics for sensor collection on its website:

Digital sensors installed in our operations around the world—from production fields to manufacturing complexes—produce a constant flow of data which we analyze to improve processes and take better business decisions.

The technology can optimize the performance of plants by predicting when maintenance will be needed, to avoid unplanned downtime and lost productivity.

More than 5,000 machines globally connect to the system, which is thought to have saved more than 3.5 million barrels in lost production since its introduction.

Media

Perhaps no transformation is more visible in media than the shift from broadcast and cable television to real-time streaming. This change drives media companies to seek end user analytics and advertising opportunities tied to streaming, and key intelligent applications to drive revenue. This technology race led Disney to acquire a majority stake in BAMTech. Disney said the following in a news release:

The media landscape is increasingly defined by direct relationships between content creators and consumers, and our control of BAMTech’s full array of innovative technology will give us the power to forge those connections, along with the flexibility to quickly adapt to shifts in the market.

4 steps towards intelligent applications

Nearly every company is charting its own digital transformation blueprint. Adding intelligent applications to the mix can jumpstart digital results. Here are four quick steps to get started:

1. Identify corporate growth areas

Projects aligned with company objectives often get faster funding and resources.

2. Capture new and existing data sources

Showcase both to enhance existing value and demonstrate new data.

3. Design an architecture that supports a real-time feedback loop

Merge transactions and analytics where possible to incorporate real-time intelligence, including machine learning model scoring.

4. Build intelligent applications using the latest best practices across industries

Track primary applications in high growth areas of finance, media, and energy to see how companies are putting technology to use.

New intelligent applications have the power to transform businesses, drive new revenue, deepen customer engagement, and optimize operations. With a simple action plan and examples from industry leaders you can set your own company on a path to success.

This article is published as part of the IDG Contributor Network. Want to Join?

Source: InfoWorld Big Data

CloudHealth Technologies And ParkMyCloud Partner

CloudHealth Technologies and ParkMyCloud have announced they are partnering to marry the hybrid cloud governance of CloudHealth with the automated cost control of ParkMyCloud.

Customers leveraging the integrated solution will experience greater return on their cloud investments. They will be able to automate cloud cost control, simplify management, and consequently free up teams to focus on driving more strategic projects within their organizations.

Public cloud provides agility, efficiency, and flexibility; however, as organizations ramp up in the public cloud, they often find consumption growing rapidly, leading to overspending and inefficient resource utilization. For many, this rising cost is unaccounted for. According to Gartner, “With increasing business unit IT spending on cloud services, IT leaders must prevent new risks, sprawl, cost overruns and missed SLAs. Dynamic optimization technology can help balance the benefits of agility with required governance controls for cloud services and virtualized infrastructure.”1

With CloudHealth’s expertise in cloud governance and management, and ParkMyCloud’s specialization in cloud cost control, they can together provide complete visibility and control over multi-cloud environments, enabling customers to drive better business value. Joint customers will experience a seamless, integrated solution, including: 

Improved Cloud ROI: Users will realize immediate cost savings with non-disruptive, policy-driven automation that helps to eliminate tedious tasks and enable teams to roll out new offerings and updates faster to market.

Ability to Drive More Strategic Projects: Rather than focusing on “keeping the lights on,” technical experts can shift their efforts to continuously innovate and thereby maintain a competitive advantage—not just business as usual.

Simplified Hybrid Cloud Governance: Broken down by environment, department, application, resource and more, this integrated offering empowers teams to implement better resource management through simple and easy-to-use, customizable dashboards for different personas such as CFO and CTO, among others.

Better Analytics: Users gain unparalleled insight and can use this visibility to make smarter business decisions.

“There are huge business gains to be reaped in the public cloud, but business transformation also brings complexity,” said Tom Axbey, CEO and president, CloudHealth Technologies. “In partnering with ParkMyCloud, we’re eliminating cloud management inefficiency and bridging the divide between cost and utilization. We’re uniting disparate cloud environments, business ops and DevOps teams by equipping them with the tools they need to be agile and productive.”

“Our goal at ParkMyCloud has always been to help our customers do more with their cloud by saving time and money,” said Jay Chapel, CEO and founder, ParkMyCloud. “That’s why we provide them with automated cost control, which we do by finding and turning off idle resources in AWS, Azure, and Google Cloud. By collaborating with CloudHealth Technologies, we are providing customers with end-to-end visibility and control over their environments for optimized cloud usage and spend.”

“With ParkMyCloud, anyone in your organization can be responsible for their own cloud costs,” said Reed Savory, Connotate. “That’s immensely valuable and brings real cost savings. CloudHealth offers unparalleled rightsizing capabilities, among other things, so we know we aren’t leaving money on the table. Integrating these solutions will only further the simplicity and efficiency with which our team operates – even as we manage a rapidly scaling cloud environment.”

1. Gartner, Innovation Insight for Dynamic Optimization Technology for Infrastructure Resources and Cloud Services, Donna Scott and Milind Govekar, Refreshed: 6 February 2017 | Published: 29 February 2016.

Source: CloudStrategyMag