Thoughts on HPE’s Acquisition of MapR

In June I posted a blog commenting on the cloud management company MapRclosing its headquarters and laying off 122 workers. At that time Cloudera, another cloud big data management company, announced reduced earnings and reduced outlook which drove its stock down over 38% to around $5. It was only in January of this year when Cloudera and Hortonworks, two of the biggest players in the Hadoop big data space, announced an all-stock merger, which was expected to give new life to these companies in the big data analytics market.

 

Therefore it was not surprising to hear today’s news of the acquisition of MapR by HPE. With this acquisition HPE said the deal includes MapR’s technology, intellectual property and expertise in artificial intelligence and data management. There will also be a number of employees joining with this acquisition. The MapR technology will be folded into HPE’s Intelligent Data Platform, a set of technologies for data lifecycle management.

 

 

Just a few years ago, cloud data management companies like MapR, Cloudera, and Hortonworks were Unicorns, the darlings of Wall Street. Analysts attribute this decline to a natural consolidation of the “surplus of enterprise Hadoop companies” after the hype and frenzy of the VC community reached its peak. The cost of sales and services are very high and the pay per use model puts a squeeze on cash flow. The difficulty of monetizing a business based upon open-source software is challenging. Analysts are also predicting that the Big Data Analytics ecosystem will converge around AWS, Azure, and Google Cloud and many of these smaller companies will be acquired or displaced by the large public cloud vendors.

 

MapR’s technology will be particularly valuable to HPE in helping customers stitch together data pipelines across multiple on-premises and cloud environments as well as to run multiple workloads in the same environment. According to Patrick Osborne, vice president of big data and secondary storage at HPE is quoted as saying, “ MapR’s distributed file system provides the capability of a data fabric that allows people to manage their analytics on the edge as well as in the core. We didn’t have a technology that would allow customers to do that.”

 

Although there are many advantages for HPE in this acquisition, they still have to compete with the hyper-cloud vendors in the big data analytics cloud ecosystem, AWS, Azure, and Google cloud who have a clear head start. The recent acquisition of Tableau by SalesForce and Looker by Google, are indicative of a trend by public cloud providers moving to provide end to end big data analytics solutions across multiple clouds. HPE will have to play catchup while integrating the technology, personnel, partners, and customers of MapR. They will also have to solve the revenue problems that plagued MapR. 

 

While Hybrid cloud provides an opportunity to augment public cloud offerings, HPE and MapR will have upfront development and support costs which will impact cash flow.  Customers want to move to the cloud faster than MapR can allow them. These customers do not have the luxury of waiting for and trialing CDP while there are other options that are available today.

 

Hitachi Vantara customers have several options for accelerating their movement to the cloud and big data analytics for structured and unstructured data. One approach is to develop a data lake with Pentaho and other best of breed data ingestion and data orchestration tools for big data analytics that can span multiple cloud delivery platforms with a common meta data catalog and schema. Pentaho’s low code approach can simplify and accelerate the implementation of big data analytics. Hitachi Vantara has taken this approach internally for our enterprise data that need to reside within our private cloud

 

Another option from Hitachi Vantara is to use our REAN Cloud ,a global Cloud Systems Integrator (CSI), Managed Service Provider (MSP) and Premier Consulting Partner in the Amazon Web Services (AWS) Partner Network (APN) and Microsoft's Azure Silver Partner membership. REAN Cloud offers consulting & professional services, including cloud strategy, assessment, cloud migration, and implementation to realize our customers’ vision. REAN Cloud provides a REAN Cloud Accelerated Migration Program (RAMP) which can accelerate the migration to public cloud from a matter of weeks to days with their automated services and migration consulting expertise. Migration to the hyper cloud vendors enables the use of their menu of analytics tools. REAN Cloud incudes 47Lining an AWS Advanced Consulting Partner with Big Data Competency designation. 47Lining develops big data solutions and delivers big data managed services built from underlying AWS building blocks like Amazon Redshift, Kinesis, S3, DynamoDB, Machine Learning and Elastic MapReduce.

 

A full transition to the cloud has proved more challenging than anticipated and many companies are looking to hybrid cloud solutions to transition to the cloud at their own pace and at a lower risk and cost. Companies are looking for DataOps tools and platforms, and systems integrators that can help them create data lakes and deliver big data analytics in a timely manner. They want proven vendors who will be with them for the long term and who already have the platforms and services for hybrid cloud and big data analytics that can work within the ecosystem of public and private clouds.

 

All comments are my own and should not be considered to reflect the opinions of Hitachi Vantara.

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Research in Regenerative Medicine

This week I am at the Hitachi Central research Lab in Kokubunji, Japan, to attend Hitachi’s annual Kenpatsu. This is an event where the Central research lab provides an update to the global Hitachi organizations on the many projects that they are working on. Since Hitachi is a technology company with extensive R&D capabilities, you can imagine how exciting this was for me to attend especially with the explosion of technology that is going on today. The main focus for Hitachi’s research is on Social Innovation, making the world a smarter, healthier, and safer world.

 

 

 

One project that was especially interesting for me (considering my advanced age) was the work that was being done in regenerative medicine. This work is being done in our Kobe Lab, in research partnership with Kyoto University and Sumitomo Dainippon Pharma Co., Ltd. Kyoto University is where Dr. Shinyu Yamanaka was awarded the Nobel Prize for the discovery that mature cells could be converted to stem cells for use in regenerative medicine.

 

Regenerative medicine is a branch of research in tissue engineering and molecular biology which deals with the "process of replacing, engineering or regenerating human cells, tissues or organs to restore or establish normal function”. 

 

The Hitachi Kobe lab is developing equipment to automate the production of Induced pluripotent stem cells or iPS cells which were first generated by Dr. Yamanaka. In 2006, Dr. Yamanaka established that by introducing a small number of genes into ordinary human somatic (differentiated) cells, these pluripotent cells can differentiate into any type of cell in the body and proliferate indefinitely in culture. The process of changing a cell from a differentiated to a pluripotent state is called reprogramming. The method developed by the Yamanaka has been shown to be highly reproducible, relatively simple, and is considered a major scientific breakthrough. Currently, cell cultures are produced by hand, but only experts are capable of producing medical-grade quality cells. If medical-grade cells are only able to be cultured by certain skilled people, regenerative medicine will not become generally available. Hitachi would like to change that by developing automated culture equipment capable of the stable mass production of iPS cells.

 

 

 

The prior alternative was to use human embryonic stem (ES) cells which were produced by removing cells from a 6-7 day old embryo and growing them in culture. While embryonic stem cells are natural, induced pluripotent stem cells can be generated using cells from an adult body, such as skin, which are plentiful and harmless to remove. As this does not require the destruction of an embryo, it avoids many of the ethical issues that surround human ES cells. Furthermore, unlike human ES cells, it is possible to derive patient-specific iPS cells and induce them into differentiated cells of various types, which can then be transplanted back into the patient without risk of immune rejection.

 

Hitachi began the research at their Center for Exploratory research in Saitama. Once they established the feasibility of building a machine to automate the development of iPS cells, Hitachi established the Hitachi Kobe Laboratory ("Kobe Lab") within the Kobe Biomedical Innovation Cluster ("KBIC"), where many people are carrying out cutting-edge advanced research in medical treatment,  and moved the research team outside of the company in preparation for the social implementation phase. Hitachi felt that if they were to just stay within Hitachi, it would be difficult to develop a truly useful automated cell culture equipment. Hitachi believes in co-creation, that conducting R&D in the KBIC is the best environment to create truly useful automated cell culture equipment for the field of regenerative medicine. Hitachi Kobe Laboratory joined the Kobe Biomedical Innovation Center in the Kobe Biomedical Innovation Cluster.

 

 

Hitachi is developing automated culturing technology and process based on this research and clinical work that is capable of cultivating large quantities of high-quality medical grade cells for widespread use in regenerative medicine in the future. Hitachi has just overcome the first hurdle. This was to process cells with automated equipment that were of the same quality as those processed by expert human technicians. The next step is to be able to develop automated culture equipment capable of the stable mass production of cells of a quality exceeding that of the experts. The quality of the cultured cells is extremely important in regenerative medicine, and the "fight against bacteria" is a major issue. There are microorganisms in the air, and if just one of these bacteria enters the culture fluid, their presence will increase exponentially and destroy the human cell culture in an instant. In this event, those cells cannot be introduced to the body. This is a very difficult process and maintaining the sterility of the equipment is key when culturing cells.

 

Clinical research to confirm safety in humans commenced in 2013. For safety and other reasons, there is no fixed date, but researchers aim to make medical applications available as soon as possible. According to current research findings reported from Japan and overseas, iPS cells are capable of differentiation into the constituent cells of a wide range of tissues and organs, including nerves, cardiac muscle, and blood. (Think of the possibility of replacing brain cells that were damaged by Alzheimer)  However, organs are more complex because of their three-dimensional (3D) structure. Small livers have been reported but there are as yet no reports of large 3D, functional organs of human size. This is an area that requires a combination of iPS cell technologies with 3D printers, biomaterials, and other technologies. This could be the next challenge for the Kobe Lab. 

 

This was just one of many projects that I was able to hear about at our Hitachi Research Lab. If you are interested in hearing and seeing more of what Hitachi is researching for Social Innovation, you don't have to go to Kobe or Kokubunji. You can see them by visiting our NEXT 2019 event in Las Vegas, October 8-10 at the MGM Grand. You can click here to register.

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Interrogare And demandi Realize DataOps Advantage with Hitachi Vantara

Interrogare, a 20 year old market research company in Germany, conducts more than two million interviews each year across a range of industry sectors and focus areas, including product pricing, employee engagement, brand awareness and customer loyalty. Legacy tools and labor intensive manual processes were making it increasingly difficult to not only gather, store and analyze survey data but also to share findings with clients. According to their executives there were a lot of inefficient and expensive manual stepsCreating a centralized view of different data sources could take several weeks.

 

In 2015Interrogare decided to found demanti as a company to build the technical foundation for a fully automated market research solution to streamline the process and incorporate new data sources as part of its customer insights activities. The customer insights managers at demandihelp organizations to maximize the value of business information by creating 360-degree customer views. These views combine insights from enterprise resource management (ERP) platforms, customer relationship management (CRM) solutions and satisfaction surveys.

 

Jens Adams, the CEO of demandi described the challenges: “We needed to be able to ingest data from web analytics and cloud-based platforms, such as Salesforce. We also wanted to improve our visualization capabilities, so we could present research findings in different formats according to client preferences,”

 

demandi began by implementing the open-source version of Pentaho but found it difficult as their only source of knowledge was from internet articles and e-books. But everything changed when they met the Hitachi Vantara team at a conference. Shortly afterwards, in 2017, demandi deployed the enterprise version of Pentaho Data Integration (PDI). “Hitachi Vantara was so enthusiastic about how Pentaho could help transform our business. We felt that the team understood our challenges and we knew it was the right solution for us. With Hitachi Vantara, we can tap into powerful data extraction, transformation and loading capabilities,” says Adam.

 

Hitachi Vantara’s extensive data management expertise helped to accelerate and optimize the deployment and configuration of the new solution. “The best practices shared by the Hitachi Vantara team were invaluable. They helped us establish a stable operational environment and maximize the ‘drag and drop’ data management features of Pentaho.” Hosted on Amazon Web Services (AWS), demandi’s infrastructure made good use of Pentaho’s rich library of prebuilt connectors during implementation. “The Amazon Redshift and S3 connectors gave us a head start on integrating the new solution with our cloud environment,” says Adam. “Pentaho is a very open system, which is extremely important for us.”

 

This was a classic example of a DataOps implementation effort. DataOps, at a high level, is the process of delivering the right data to the right place at the right time. There are many tools available to deliver DataOps processes. In fact, there are often too many options, which can easily add to confusion and unnecessary delays and restarts. The DataOps advantage that Hitachi Vantara delivered was the data management expertise that our personnel were able to deliver and the tried and tested library of prebuilt connectors in the enterprise version of Pentaho.

 

Another DataOps advantage that was cited by Mr. Adams was Pentaho’s unique Metadata injection capability. Metadata injection streamlines the initial implementation and accelerates the onboarding of new clients and the loading of new data. For example, you might have a simple transformation to load transaction data values from a supplier, filter specific values, and output them to a file. If you have more than one supplier, you would need to run this simple transformation for each supplier. Yet, with metadata injection, you can expand this simple repetitive transformation by inserting metadata from another transformation that contains the ETL Metadata Injection step. This step coordinates the data values from the various inputs through the metadata you define. This process reduces the need for you to adjust and run the repetitive transformation for each specific input.

 

 

The Hitachi Vantara DataOps advantage has helped demandi differentiate its services in an industry where many companies are only just starting to think about new technologies. For example, it can now use intelligence captured in Pentaho to help clients track trends and real-time key performance indicators (KPIs). With this capability, the company can gain insights into key business processes and functions, such as product returns, purchase volumes and customer satisfaction.

 

 “We’ve transformed how we manage data and how we take our services to market,” says Adam. “With smarter data integration and ingestion, demandi and Interrogare can position themselves as a disruptor in the market research industry.”

 

Please click on this demandi link to see their DataOps story.

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Data Challenges are Killing AI Projects

Today’s May 28, 2019, Wall Street Journal reports that data challenges are halting AI projects. They quoted IBM executive Arvind Krishna as saying, “ Data-related challenges are a top reason IBM clients have halted or canceled artificial-intelligence projects”.” About 80% of the work with an AI project is collecting and preparing data. Some companies aren’t prepared for the cost and work associated with that going in”, he added.

 

This is not a criticism of IBM’s AI tools. Our AI tools would have the same problems if the data was not collected and curated properly. This is supported by a report this month by Forrester Research Inc. which found that data quality is among the biggest AI project challenges. This report said that companies pursuing such projects generally lack an expert understanding of what data is needed for machine-learning models and struggle with preparing data in a way that’s beneficial to those systems.

 

At Hitachi Vantara, we appreciate the importance of preparing data for analytics, and we include that in our DataOps initiatives. DataOps is a framework of tools and collaborative techniques that enable data engineering organizations to deliver rapid, comprehensive and curated data to their users. It is the intersection of data engineering, data integration, data governance and data security that attempts to unify all the roles and responsibilities in the data engineering domain by applying collaborative techniques to a team that includes the data scientists and the business analysts. We have a number of tools like Pentaho Data Integration, PDI, and Hitachi Content Platform, HCP, but we also include other best of breed tools to fit different analytic and reporting requirements.

 

Its time to press your DataOps Advantage

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Losing Revenue in a Growing Market

The latest International Data Corporation (IDC) Worldwide Quarterly Enterprise Storage Systems Tracker, was published on March 4, 2019. It showed vendor revenue in the worldwide enterprise storage systems market is still increasing: 7.4% year over year to $14.5 billion during the fourth quarter of 2018 (4Q18). Total capacity shipments were up 1.7% year over year to 92.5 exabytes during the quarter. The total All Flash Array (AFA) market generated just over $2.73 billion in revenue during the quarter, up 37.6% year over year; and the Hybrid Flash Array (HFA) market was worth slightly more than $3.06 billion in revenue, up 13.4% from 4Q17.

 

The Revenue generated by the group of original design manufacturers (ODMs) selling directly to hyperscale datacenters (public cloud) did decline 1.5% year over year in 4Q18 to $2.7 billion due to significant existing capacity. The report noted the increasing trend to hybrid clouds as enterprise customers place a higher priority on ensuring that storage systems support both a hybrid cloud model as well as increasingly data thirsty on-premise compute platforms. OEM vendors selling dedicated storage arrays are addressing demand from businesses investing in both on-premises and public cloud infrastructure. The move to hybrid storage means that enterprises are starting to look at their total storage environment and looking at the operational aspects of their data to maximize their business outcomes.

 

As a result, the revenue misses reported this week by Pure and NetApp were not surprising.

 

On Wednesday May 22, 2019, Pure Storage announced disappointing Q1 results and reduced their fiscal year guidance downward. The stock has tumbled down more than 20% in after-hours and early next-day trading following the release of the report. Pure Storage is simply that: purely storage and their prospects are directly tied to the storage market as that is the only thing they sell. It is even more restricted in that it is an all flash play which is less than 19% of the 14.5B enterprise storage market in 4Q 2018. As companies start to look at their total data environments, pure play companies such as Pure will not be as relevant to customers in the future.

 

After the market close on May 22, 2019, NetApp announced disappointing Q4 and full fiscal year 2019 results, missing on consensus revenue estimates, consensus earnings per share estimates, and providing lower-than-expected guidance for both revenue and EPS for the upcoming quarter. NetApp blamed their revenue performance on a variety of issues - sub-optimal sales resource allocation, declining OEM business, decreased ELA renewals – but also currency and macroeconomic headwinds, extended purchase decisions and sales cycles. While NetApp has a broader portfolio than Pure, it is still primarily a midrange storage play with a lot of legacy storage in the market.

 

Customers expect more than a place to store their data. While a faster flash storage array can shave milliseconds off an I/O response time, it doesn’t help your bottom line if the right data is not in the right place at the right time. The fact that enterprises are extending their purchase decisions, thinking twice about purpose built OEM solutions, and evaluating hybrid storage solutions, indicates that they realize that their problem is not about storing data, but about unlocking the information that exists in the data they have. This takes DataOps.

 

DataOps is needed to understand the meaning of data as well as the technologies that are applied to the data so that data engineers can move, automate and transform the essential data that data consumers need. Hitachi Vantara offers a proven, end-to-end, DataOps methodology that lets businesses deliver better quality, superior management of data and reduced cycle time for analytics. At Hitachi Vantara we empower our customers to realize their DataOps advantage through a unique combination of industry expertise and integrated systems.

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AI and Solomon's Code

There once was a king of Israel named Solomon, who was the wisest man on earth. One day he was asked to rule between two women, both claiming to be the mother of a child. The arguments on both sides were equally compelling. How would he decide this case? He ordered that the child be cut in halve so that each woman would have an equal portion of the child. One mother agreed while the other mother pleaded that the baby be spared and given to the care of the other women. In this way King Solomon determined who was the real mother.

 

If we had submitted this to an AI machine would the decision have been any different?

 

Solomon’s Code is a book that was written by Olaf Groth and Mark Nitzberg and published in November of last year, so it is fairly up to date on the recent happenings in the AI world. “It is a thought provoking examination of Artificial Intelligence and how it will reshape human values, trust, and power around the World.” I greatly recommend that you read this book to understand the potential impact AI will have on our lives, for good or bad.

 

The book begins with the story of Ava who is living with AI in the not too distant future. AI has calculated her probability of developing cancer like her mother and has prescribed a course of treatment tied to sensors in her refrigerator, toilet, and ActiSensor mattress. Her wearable personal assistant senses her moods. The Insurance company and her doctor put together a complete treatment plan that would consider everything from her emotional well-being, her work activities, and even the friends that she would associate with. Her personal assistant makes decisions for her as to where she goes to eat, what music she listens too, and who she calls for support.  

 

As we cede more of our daily decisions to AI, what are we really giving up? Do AI systems have biases? If AI models are developed by data scientists whose personality, interests and values may be different than an agricultural worker or a factory worker, how will that influence the AI results? What data is being used to train the AI model? Does it make a difference if the data is from China or the United Kingdom?

 

The story of Solomon is a cautionary tale. He built a magnificent kingdom, but the kingdom imploded due to his own sins and it was followed by an era of violence and social unrest. “The gift of wisdom was squandered, and society paid the price”

 

The Introduction to this book ends with this statement.

 

‘Humanity’s innate undaunted desire to explore, develop, and advance will continue to spawn transformative new applications of artificial intelligence. The genie is out of the bottle, despite the unknown risks and rewards that might come of it. If we endeavor to build a machine that facilitates our higher development -rather than the other way around – we must maintain a focus on the subtle ways AI will transform values, trust, and power. And to do that, we must understand what AI can tell us about humanity itself, with all its rich global diversity, its critical challenges, and its remarkable potential.”

                                                                                                                                                           

This book was of particular interest to me since Hitachi’s core strategy is around Social Innovation.Where we will operate business to create three value propositions: improving customer’s social values, environmental values, and economic values. In order to do this we must  be focused on understanding the transformative power of technologies like AI for good or bad.                     

                                   

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Forget the Rules, Listen to the Data

 

Rule-based fraud detection software is being replaced or augmented by machine-learning algorithms that do a better job of recognizing fraud patterns that can be correlated across several data sources. DataOps is required to engineer and prepare the data so that the machine learning algorithms can be efficient and effective.

 

Fraud detection software developed in the past have traditionally been based on rules -based models. A 2016 CyberSource report claimed that over 90% of online fraud detection platforms use transaction rules to detect suspicious transactions which are then directed to a human for review. We’ve all received that phone call from our credit card company asking if we made a purchase in some foreign city.

 

This traditional approach of using rules or logic statement to query transactions is still used by many banks and payment gateways today and the bad guys are having a field day. In the past 10 years the incidents of fraud have escalated thanks to new technologies, like mobile, that have been adopted by banks to better serve their customers. These new technologies open up new risks such as phishing, identity theft, card skimming, viruses and Trojans, spyware and adware, social engineering, website cloning and cyber stalking and vishing (If you have a mobile phone, you’ve likely had to contend with the increasing number and sophistication of vishing scams). Criminal gangs use malware and phishing emails as a means to compromise customers’ security and personal details to commit fraud. Fraudsters can easily game a rules-based system. Rule based systems are also prone to false positives which can drive away good customers. Rules based systems become unwieldy as more exceptions and changes are added and are overwhelmed by today’s sheer volume and variety of new data sources.

 

For this reason, many financial institutions are converting their fraud detection systems to machine learning and advanced analytics and letting the data detect fraudulent activity.Today’s analytic tools with modern compute and storage systems can analyze huge volumes of data in real time, integrate and visualize an intricate network of unstructured data and structured data, and generate meaningful insights, and provide real-time fraud detection.

 

However, in the rush to do this, many of these systems have been poorly architected to address the total analytics pipeline. This is where DataOps comes into play. A Big Data Analytics pipeline– from ingestion of data to embedding analytics consists of three steps

 

1. Data Engineering: The first step is flexible data on-boarding that accelerates time to value. This requires a product that can ETL (Extract Transform Load) the data from the acquisition application which may be a transactional data base or sensor data and load it using a data format that can be processed by an analytics platform. Regulated data also needs to show lineage, a history of where the data came from and what has been done with it. This will require another product for data governance.
2. Data Preparation: Data integrationthat is intuitive and powerful. Data typically goes through transforms to put it into an appropriate format, this can be called data engineering and preparation. This is colloquially called data wrangling. The data wrangling part requires another set of products.
3. Analytics: Integrated analytics to drive business insights. This will require analytic products that may be specific to the data scientist or analyst depending on their preference for analytic models and programming languages.

 

A data pipeline that is architected around so many piece parts will be costly, hard to manage and very brittle as data moves from product to product. 

 

Hitachi Vantara’s Pentaho Business Analytics can address DataOps for the entire Big Data Analytics pipeline with one flexible orchestration platform that can integrate different products and enable teams of data scientists, engineers, and analysts to train, tune, test and deploy predictive models.

 

Pentaho is open source-based and has a library of PDI (Pentaho Data Integration) connectors that can ingest structured and unstructured data including MQTT (Message Queue Telemetry Transport) data flows from sensors. A variety of data sources, processing engines, and targets like Spark, Cloudera, Hortonworks, MAPR, Cassandra, GreenPlum, Microsoft and Google Cloud are supported.  It also has a data science pack that allows you to operationalize models trained in Python, Scala, R, Spark, and Weka.  It also supports deep learning through a TensorFlow step.  And since it is open, it can interface with products like Tableau, etc. if they are preferred by the user. Pentaho provides an Intuitive drag-and-drop interface to simplify the creation of analytic data pipelines. For a complete list of the PDI connectors, data sources and targets, languages, and analytics, see the Pentaho Data Sheet.

 

Pentaho enables the DataOps team to streamline the data engineering, data preparation and analytics process and enable more citizen data scientists that Gartner defines in “Citizen Data Science Augments Data Discovery and Simplifies Data Science” . This is a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics. Pentaho’s approach to DataOps has made it easier for non-specialists to create robust analytics data pipelines. It enables analytic and BI tools to extend their reach to incorporate easier accessibility to both data and analytics. Citizen data scientists are “power users” who can perform both simple and moderately sophisticated analytical tasks that would previously have required more expertise. They do not replace the data science experts, as they do not have the specific, advanced data science expertise to do so, but they certainly bring their individual expertise around the business problems and innovations that are relevant.

 

In fraud detection the data and scenarios are changing faster than a rules based system can keep track of, leading to a rise in false positive and false negative rates which is making these systems no longer useful. Machine Learning can solve this problem since it is probalilistic and uses statistical models rather than deterministic rules. The machine learning models need to be trained using historic data. The creation of rules is replaced by the engineering of features which are input variables related to trends in historic data. In a world where data sources, compute platforms, and use cases are changing rapidly, unexpected changes in data structure and semantics (known as data drift) require a DataOps platform like Pentaho Machine Learning Orchestration to ensure the efficiency and effectiveness of Machine learning.

 

You can visit our website for a hands on demo for building a data pipeline with Pentaho and see how easy Pentaho makes it to “listen to the Data.

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Cisco and Hitachi Vantara: The Power of Two

It’s been just over two months since our strategic partner Cisco and Hitachi Vantara announced the further strengthening of our 15+ year relationship with the launch of our jointly developed Cisco and Hitachi Adaptive Solutions for Converged Infrastructure.

I thought it would be good to provide you with some recent updates as well as answer some questions that have come up from our customers and partners.

Why this solution now?

Well, with all sincerity, it’s all about you. At Hitachi Vantara, we realize that to best serve our customers and enable them to meet their IT and business and data management objectives, we must embrace and recognize complementary technologies.

In this case, we chose to partner with Cisco for its industry-leading technologies, combined with our customer-proven Hitachi Virtual Storage Platform (VSP) all-flash and hybrid arrays and AI operations software - the result is a comprehensive converged solution for truly demanding virtualized workloads and enterprise applications.

It’s this “Power of Two” philosophy that also encompasses a company-wide and executive commitment in the partnership to benefit our customers, for the long term.

This is especially critical in the dynamic business environment that our customers are facing today – from compliance demands, resource limitations, and resource constraints.

According to Enterprise Strategy Group research, “38% of organizations have a problematic shortage of existing skills in IT architecture/planning”

In a previous blog I wrote about our Continuous Business Operations capability that enables customers to achieve strict zero RTO/RPO requirements.

We’ve extended this capability to Cisco and Hitachi Adaptive Solutions for Converged Infrastructure, specifically for VMware vSphere virtualized environments.

We’ve enabled the disaster recovery orchestration to be much easier for customers via Hitachi Data Instance Director, which removes the complexity and simplifies Global Active Device deployment to a series of clicks (vs complex CLI and scripting).

Cisco and Hitachi Adaptive Solutions for Converged Infrastructure. Meet in the Channel For Flexibility

Cisco and Hitachi Vantara have a select group of channel partners that can customize this solution to specific customer requirements, thereby enabling you to choose the validated Cisco networking, Cisco servers, and Hitachi Virtual Storage Platform configurations that best fit your needs, all with the assurance of a fully qualified and supported solution by Hitachi and Cisco.

l invite you to join my colleague, Tony Huynh, our solutions marketing manager for Hitachi Vantara, team up with the Enterprise Strategy Group for an upcoming webinar on May 15^th, 2019 at 9AM PST to 10AM PST where we will discuss this and other items of interest.

Webinar Registration:

https://www.brighttalk.com/webcast/12821/357216?utm_source=Webinar&utm_medium=Email&utm_campaign=Sales

ESG Analyst Report:

Read this ESG white paper to know how Cisco and Hitachi Adaptive Solutions for Converged Infrastructure can help organizations achieve digital transformation milestones on a reliable, secure infrastructure that ensures access to their data.

https://www.hitachivantara.com/en-us/pdf/analyst-content/cisco-hitachi-adaptive-solutions-for-converged-infrastructure-esg-whitepaper.pdf

More information on Cisco and Hitachi Adaptive Solutions for Converged Infrastructure

https://www.hitachivantara.com/en-us/products/converged-systems/cisco-hitachi-adaptive-solutions-for-converged-infrastructure.html

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International Women of Hitachi Vantara

Last year around this same time, I posted a blog about the women of Hitachi Vantara, and featured four of the women that I worked with on a regular basis here in Santa Clara. This year, I thought I would like to introduce three other women who I have known and worked with internationally. While these three women represent different countries and cultures, they all share the same attributes of the four that I profiled last year. They all know how to lead, innovate, and succeed.

 

Merete Soby has been very successful as the Country Manager for Hitachi Vantara in Denmark for the past 11 years. When she joined, what was then, Hitachi Data Systems-Denmark, we were a solid storage company with 15 -20% market share. Within 5 years, under her leadership, HDS Denmark was able to grow market share to 45-50% and became the number 1 storage vendor in the Danish market. Over the years, the Hitachi Vantara team in Denmark has won many big named accounts and created a strong winning culture in the company. The journey continued with new solutions, expanding beyond storage to converged solutions, solution for unstructured data as HNAS and Object solutions, analytics solutions, and REAN cloud services.

 

When I visit Denmark and talk to people in the industry, they always have great things to say about our team in Denmark. The first thing they comment on is the team’s commitment to customer support and engagement. A lot of credit is given to Merete who is described as an engaging, passionate, involved executor who empowers people to become better at what they do. When one of her team members that I worked with fell ill, Merete sought me out at a busy conference to assure me of that team member’s recovery, showing her awareness and concern for people and relationships.

 

I asked Merete if she ever felt limited in her career because she was a woman. She replied that she did not feel limited. “I believe that due to my relative young age, first as sales manager (26 years old) and later on as country manager in HDS (32 years old) I felt I needed to be a bit better and more prepared in every aspect of my business, but not directly because of my gender.”

 

Merete is a mom to three kids, 11 year old twins and an 8 year old boy. Her children have made her very focused on having the right work life balance, which she feels has increased her performance at work. She says that she does not mentor her children directly, “I show them how to behave and act in life by my own behavior. I show them to prioritize family and our values, by living them myself.” I believe that same philosophy extends to her leadership at work.

 

Basak Candan joined the Hitachi Vantara team in Turkey two and half years ago as Office Manager and Marketing Coordinator. Last September she was promoted to be the Field Marketing Manager for Turkey and Middle East. She has the awesome responsibility to drive the end-to-end Field Marketing planning and execution in Turkey and the Middle East working very closely with the sales teams to win new business and grow revenue. She is also taking the lead in the Emerging Marketing team to support Brand Leadership Programs to ensure that we are building consistent and relevant messages across Emerging EMEA markets for our entire portfolio.

 

I recently worked with Basak when I was invited to participate in the World Cities Congress in Istanbul. She helped me prepare for my panel discussions at the conference and arranged for me to visit customers in Istanbul and Ankara. She was very helpful in helping me understand the marketing environment in Turkey. On the day before I was to fly to Ankara, she noticed that I did not have a top coat and she expressed concerned for my well-being. That evening, much to my surprise, the hotel concierge delivered a top coat to my room that they loaned to me for my trip to Ankara. I was very touched by Basak’s concern, creativity, and attention to detail.

 

Basak told me that If anyone had asked her as a child what she wanted to be when she grew up, it probably would not have been anything to do with technology. Before joining Hitachi, she had different sales and marketing roles in a number of large luxury hotel chains for more than 7 years. Her formal education was in hospitality and marketing, but she has been able to transfer those skills into a technology career.

 

Thanks to some strong, positive, influential women in her life who steered her in that direction, the real transformation started for her when she began working at Hitachi Vantara. She said working with Hitachi Vantara on storage, cloud, IoT, and Big Data Analytics, was like discovering a new planet. With the help of her Hitachi Manager, she applied to Boğazici University, which is among the top 200 universities in the world and was accepted into a Digital Marketing and Communication Program. There she worked on a project analyzing JetBlue Airways’ marketing campaigns on how they could digitally transform their marketing. Her project was judged by a jury and won a special prize. That gave her encouragement to grow and show her power in technical marketing. Basak typifies the type of person who is a self-starter. Someone who is capable of recognizing and seizing new opportunities. Self-starters immerse themselves in new endeavors and remain passionate about pursuing their vocation and honing their skills.

 

When I need help in understanding the tough technical question about 3 data center disaster recovery or the latest mainframe features for Geographically Dispersed Parallel Sysplex™, I call on the expert, Ros Schulman, and she is always up on the latest technologies and business processes for disaster recovery.

 

Ros has been with Hitachi Data Systems and Hitachi Vantara for over 20 years. In the last 9 years she has filled director level roles in product management, technical sales support, business development, and technical marketing with extensive skill sets around Data Protection (Replication and Backup), Business Continuity and Resiliency. Her experience in analyzing customer requirements, technologies and industry trends have helped to maximize revenue growth in these areas. She is always in demand to speak at customer events and industry forums.

 

Ros was born in London and went to school there. She started her career as a computer operator at the age of 18 in local government and later became a system programmer on MVS at a time when very few women were in that field. She later moved to the United States and continued her technical career working for both the vendor and customer sides. When I joined Hitachi Data Systems, Ros was already recognized as the technical expert in operating systems and disaster recovery. She is passionate about our storage and systems technology and is generous in sharing her experience and insights with others. She is not shy. I have seen this petite lady going toe-to-toe with several heavyweight MVS systems programmers debating the benefits of different systems.

 

When I asked her what her advice would be for women considering a technical career, she said, “It’s something you have to be passionate about. I still believe it’s much harder to move ahead, so you have to be willing to love what you do. I would also recommend that you take some business classes, as in today’s digital age, you need a lot more than just technical skills. My motivation is learning and growing, this industry fascinates me, when I started, we used disk drives that were 20MB in size and MF had less than 2GB memory and look where we are today. I do not know of another career where things have changed so radically and continue to change and have now been embraced in every facet of our lives.”

 

It is one thing to have the knowledge and skills to be technical. However, it requires passion and enthusiasm to excel in a technical area; and be recognized as the go-to expert. Ros Schulman is my go-to expert.

 

Hitachi Vantara recognizes the value of diversity. The Women of Hitachi play an important role in defining our culture and contributing to our success as a technology company. Women are well represented in our sales and marketing organizations, as well as in product management and technical support roles. Our CIO, CFO, and Chief Human Resource Officer are women. Women account for more than 25% of our IT team – just over the industry average – according to CIO Renée McKaskle.

 

A recent Wall Street Journalblog reports that:

 

“She (Renee McKaskle) credits the Hitachi Inc. subsidiary’s “double” bottom-line goal, saying “a healthy bottom line is important but doing what is right for society is important, too.”

To that end, she said, the company supports several global and local diversity initiatives, including women’s summits and mentoring programs.

“These programs have been critical to forging the diversity we have in place today, with positive indicators that this will continue to increase,” she added.”

 

One of the things I enjoy most about my job is the ability to work with wide variety of people, and see them in action, celebrate their successes, and hear their stories. I hope you enjoyed hearing about these women who have inspired me and will perhaps inspire you as well.

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DataOps and Hitachi Vantara

According to the Harvard Business Review, "Cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or used at all. More than 70% of employees have access to data they should not, and 80% of analysts’ time is spent simply discovering and preparing data. Data breaches are common, rogue data sets propagate in silos, and companies’ data technology often isn’t up to the demands put on it." That was in a report back in 2017. What has changed since then?

 

Few Data Management Frameworks are Business Focused

Data management has been around since the beginning of IT, and a lot of technology has been focused on big data deployments, governance, best practices, tools, etc. However, large data hubs over the last 25 years (e.g., data warehouses, master data management, data lakes, Hadoop, Salesforce and ERP) have resulted in more data silos that are not easily understood, related, or shared. Few if any data management frameworks are business focused, to not only promote efficient use of data and allocation of resources, but also to curate the data to understand the meaning of the data as well as the technologies that are applied to the data so that data engineers can move and transform the essential data that data consumers need.

 

Introducing DataOps

Today more customer are focusing on the operational aspects of data rather than on the fundamentals of capturing, storing and protecting data. Following the success of DevOps (a set of practices that automates the processes between software development and IT teams, in order that they can build, test, and release software faster and more reliably) companies are now focusing on DataOps. DataOps can best be described by Andy Palmer, who coined the term in 2015, “The framework of tools and culture that allow data engineering organizations to deliver rapid, comprehensive and curated data to their users … [it] is the intersection of data engineering, data integration, data quality and data security. Fundamentally, DataOps is an umbrella term that attempts to unify all the roles and responsibilities in the data engineering domain by applying collaborative techniques to a team. Its mission is to deliver data by aligning the burden of testing together with various integration and deployment tasks.”

 

At Hitachi Vantara we have been applying our technologies to DataOps in four areas: Hitachi Content Platform, Pentaho, Enterprise IT Infrastructure, and REAN Cloud.

 

• HCP: Object storage for unstructured data through our Hitachi Content Platform and Hitachi Content Intelligence software. Object storage with rich meta data, content intelligence, data integration, and analytics orchestration tools enable business executives to identify data sources, data quality issues, types of analysis and new work practices needed to use those insights

 

• Pentaho: Pentaho streamlines the entire machine learning workflow and enables teams of data scientists, engineers and analysts to train, tune, test and deploy predictive models.

• IT Infrastructure: Secure Enterprise IT Infrastructure that extends across edge to core to Cloud, based on REST APIs for easy integration with third party vendors. This gives us the opportunity to not only connect with other vendor’s management stacks like ServiceNow, but also apply analytics and machine learning and automate deployment of resources through REST APIs.

 

 

• REAN Cloud: A cloud agnostic managed services platform for DataOps in the cloud. Highly differentiated offerings to migrate applications to the cloud, modernize applications to leverage the cloud offerings for data warehouse modernization, predictive agile analytics, and real time IoT. REAN Cloud also provides ongoing managed services.

Summary

• Big Data systems are becoming a center of gravity in terms of storage, access and operations.
• Businesses are looking to DataOps, to speed up the process of turning data into business out comes.
• DataOps is needed to understand the meaning of the data as well as the technologies that are applied to the data so that data engineers can move, automate and transform the essential data that data consumers need.
• Hitachi Vantara provides DataOps tools and platforms through
  • Hitachi Content Platform,
  • Pentaho data integration and analytics orchestration,
  • Infrastructure analytics and automation
  • REAN Cloud migration, modernization, and managed services.

 

Grad Student Katie Bouman uses DataOps to capture first picture of a black hole.

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