Blogs from Judith Hurwitz Mon, 21 Aug 2017 23:39:42 +0000 Joomla! - Open Source Content Management en-gb A tribute to Marcia Kaufman: A Woman of Valor

Marcia and I would always tell people that we met in high school when we were both 15 years old. But that doesn’t tell the story of a friendship and a business partnership that began in 2003. I was an entrepreneur at a crossroads of my career. I had lost the company I had started in 1992 and walked away from a company that I started in 2002. I wasn’t sure what I wanted to do next and I will admit that I was afraid that I would fail. Then one day I got a phone call from Marcia. We had been in touch off and on over the years. Marcia too was at a crossroads. She had recently left a job as an industry analyst and was also trying to decide what to do next.

She said to me, “If you are thinking about starting another company I am interested.” To be truthful, I wasn’t sure that I was ready for what I knew would be difficult. But I agreed that we should meet for coffee. Despite my misgivings, and perhaps because of Marcia’s infectious optimism, I decided that it made sense to give it a try.

And I was right. It was hard. In the beginning we struggled to find projects and position our very tiny company. We taught each other a lot about working as a team, about technology, and about having fun while working hard. I could tell you hundreds of stories about our adventure over 13 years. There was the time that we worked all Christmas day so that we could finish a research paper. I could tell you about the one time that Marcia yelled at the top of her lungs at a freelance researcher who was working with us on a project. It was unusual because Marcia could get along with everyone – except this one very annoying writer. I could tell you about all the times that we would meet at the airport to go to conferences in Las Vegas. While I dreaded going to Vegas, it energized her. She made these trips fun. She loved the concerts that took place during the shows. She loved to dance and sing. I often left early to get some sleep. But Marcia never seemed to tire and stayed long after I went to bed.

I would often push us to take on new projects, such as the many books we wrote together. She would look at me as though I was crazy (which I probably was) but she would never say no. Even when she was sick, we worked together on the hardest writing project we ever undertook – cognitive computing and big data analytics. It was a wonderful book and a testament to Marcia’s brilliance and perseverance.

Over the last three years, it became harder and harder for Marcia to work. This made her angry, because she loved researching, learning, and writing. Over the years, she became a master writer. She was widely respected and deeply loved. She would tell me in moments when the two of us were together how very sick she was. She was quite aware of her condition, but she continued to work. When she couldn’t come into the office, she would work at home. Her doctor was shocked that she was still working. In fact, I remember Marcia telling me that her doctor expected her to stop working and just take care of herself. She continued to work until the disease finally made it impossible. With every setback she would first say to me, ‘”Oh, Judith, I just got such bad news. And in the next sentence she would tell me, “…but I am going to beat it.” In fact, the last time I visited Marcia when she was in rehab two weeks before she died, she told me that she had come to accept what was happening to her. But in characteristic Marcia fashion, her next words were, ‘”But I am still going to fight.”

This is at least the 6th draft of this note I that I have written, trying to capture the Marcia knew I loved Marcia as a friend and colleague. I miss her strength, her honesty, her intensity, her kindness, her elegance, and her love of life. I don’t think that I will ever meet another person like Marcia. She held onto life with such fervor.

As brokenhearted as I am, I know that Marcia lived life as fully as anyone I have ever known. I will miss you forever and you will always be in my heart.

Read more]]> (Judith Hurwitz) Uncategorized Sun, 26 Mar 2017 14:31:47 +0000
Can IBM Take Machine Learning Mainstream?

It is clear that Watson, IBM’s cognitive engine, is becoming embedded in just about every tool and application that IBM is selling these days. In its most recent announcement, IBM is now making the Watson Machine Learning (ML) engine available on the mainframe in order to bring machine learning to transactional databases.

This ML engine will be available both for on premises use and as a private Cloud implementation. IBM’s announcement had a vast array of capabilities too numerous to mention in a single blog, I would like to mention some of the aspects that I found especially important.

The following are my five top take aways.

  1. While Watson has commonly known to support unstructured data in Natural Language Processing (NLP) solutions, the underlying engine provides sophisticated machine learning engine. This machine learning engine is applicable to advanced analytics on structured data as well as unstructured data.
  2. Mainframe transactional databases are the mainstay of many of IBM’s large financial services, retail, and airline customers. Given the complexity, value and the scale of this data it makes sense to provide advanced analytics based on machine learning to support the need to better understand this data. Being able to detect patterns and anomalies in this data provides a valuable tool for customers. Equally important is the ability to execute these advanced algorithms close to the data rather than moving the data to an external platform.
  3. One of the interesting capabilities of the machine learning engine is its ability to provide productivity assistance to the developers. Experienced data scientists are expert at building models and selecting the right algorithms. However, there are simply not enough data scientists. The benefit of being able to provide a cognitive assistance to help a less experienced developer take advantage of machine learning is extremely important. IBM’s Machine Learning engine provides this capability. In essence, once a model is built, the system learns from the ingested data and recommends an algorithm that best matches the task. Once the algorithm is trained on the data, the system may suggest an alternative algorithm. Being able to provide the developer with help in selecting the most effective algorithm, or part of an algorithm, will make machine learning much more approachable. Through Cognitive Assist for Data Science (CADS), the system sends the testing data to all the 200 algorithms and starts calculating to determine which algorithm or combinations of algorithms provides the highest score and reliability.
  4. Flexibility and productivity are important aspects of IBM’s announcement. First, the engine enables developers to use the tools they are already familiar with and have made investments in. For example, developers can take advantage of the 55 SPSS algorithms. This is especially important for the large number of organizations that have used SPSS for years. In addition, the ML engine supports many of the languages widely used for machine learning including R, Java, Scala, and Python. Developers have a choice of execution engines including Hadoop and Spark.
  5. Improving on the user experience is another aspect of the ML engine. IBM has invested in creating a dashboard interface called the visual model builder that assists developers in building models – one of the most complex aspects of machine learning.


It is not a surprise that IBM would make its ML engine available first on the System z. Customers are reluctant to move their crown jewels of data off the mainframe onto other platforms – yet the requirement to bring advanced analytics to this core data is going more urgent. IBM plans to bring this same engine to the Power System in the near future. I liked the pragmatism of this approach to applying cognitive computing and machine learning to complex transactional data. Combine this will the ability to reuse SPSS, open source tools, languages, and algorithms should make this offering attractive. In addition, the ability to combine analytics on a combination of structured and unstructured data will be important element that will have important advantages for customers.

Read more]]> (Judith Hurwitz) IBM Mon, 27 Feb 2017 20:14:17 +0000
Welcome to Digital Transformation: Dragging Retailers to Adopt Emerging Technologies

I had the opportunity to attend the National Retail Federation Conference this year. Attending sessions and meeting with a variety of retailers and technology vendors I came away with some observations about the requirements for retailers in the future to be successful. My overriding observation is that retailers who are successful in the future will be those companies that are able to get creative in their use of technology. Retailers need to focus on the intersection of cloud, analytics, customer experience, and security. It is a difficult challenge. First, margins for retailers are quite low so that investing in technology can cause heartburn. Second, retailers are used to buying packaged software with packaged reporting and therefore aren’t ready for technology innovation and change.

Here are five observations about technology requirements for successful retailers

One. Retailers need to focus on security. I was fascinated by the lack of discussion about security. In fact, I did not see a single vendor focused on security on the show floor. Without a well-planned security software strategy, it will be difficult for retailers to succeed in a world that is increasingly moving online.

Two. Retailers don’t understand how to build customer loyalty on and off line. The retail market is in turmoil with the advent on online giants such as Amazon. Retailers are struggling with how to provide a great customer experience and intimacy so that customers don’t buy only on price.

Three. Flexible cloud-based solutions will be the key for the future. Technology products for retailers tend to fall into two categories: packaged turnkey solutions that cannot be easily modified and updated as new modular technology advances; or completely customized offerings from systems integrators. Customers need a combination of both to be future proofed. Software as a Service offerings that are configurable are the answer.

Four. Advanced Analytics is a requirement for retail success. I saw a lot of focus on packaged reports rather than analytics. Analytics is difficult to achieve but mandatory if retailers are going to understand how their customer’s buying patterns are changing. Leveraging cognitive computing and machine learning will be imperative and will separate the retailers that are prepared for the future and those that are left behind.

Five. Customization will be the important to the new generation of retailers. Many CEOs of emerging retailers are focused on providing customized offerings to customers. This is especially true for clothing where one size doesn’t fit all. One of the biggest problems for clothing retailers is that they can’t anticipate how well their clothing will fit those consumers walking in the door or ordering online. Technology solutions are the only answer and it is still quite early. Marrying computer vision combined with excellent customer experience and advanced analytics may hold the key to success.

Next Steps

Vendors selling products and services into the retail market need to help management understand the value of emerging technologies. They need to be able to demonstrate that advanced analytics and cloud services can lead to disruption and transformation in a highly competitive market. There is a clear need to educate this market and explain the value of emerging technologies with a step by step approach needed to gain buy-in. Retailers need to think outside the box and understand how they can keep ahead of the competition. It is no longer enough to simply buy a rigid software package. Retailers need to pay attention to the opportunities to beat the emerging retail players with technology weapons.

Read more]]> (Judith Hurwitz) Vendor Strategy Mon, 30 Jan 2017 16:32:44 +0000
2017 Predictions from the Hurwitz Team

Last year we predicted that it would be a year of consolidation and a time for businesses to absorb emerging technologies. We think that this trend wil continue.

This year we are digging in around seven different areas. Each area will have three predictions from our impressive analyst team. It is a lot to look at but we are hopeful that you will find these predictions interesting and insightful. We want your feedback. Engage in a dialog with us in the new year.

Getting the job done in 2017

The focus for 2017 is on getting the job done. Therefore, the key trends will be around pragmatic ways of implementing emerging and innovative technologies in a way that delivers fast, predictable, and well-managed solutions. It may not sound sexy but the pressure is on businesses to deliver differentiated services to customers faster and more effectively than ever.

We have divided our 2017 predictions into seven areas:

DevOps -- Making DevOps the way to continuously change and manage applications to support business change

Analytics & Cognitive -- Transforming data in new ways so that data can be used to transform the customer experience, leverage data to augment the ability to develop solutions to problems that have been unsolvable in the past.

Hybrid Cloud -- Providing the appropriate delivery models to satisfy customers

IoT -- Leveraging IoT data to deliver game changing results for industrial businesses

Servers & Storage -- Making infrastructure scale to the needs of the business

Security -- Creating environments that are well protected with advanced security and governance

Social Business --Bringing Digital business into a new level of maturity will change how businesses interact with their best customers.


What our analysts have to say:

Dan Kirsch on DevOps

DevOps began as an experiment – fusing development organizations with operations. Now many organizations have adopted DevOps to help meet the increasing expectations of both internal business users and end-user customers. Below are our 2017 predictions for DevOps:

Prediction #1. As IT organizations move towards an IT as a Service model, a marketplace of tools for developers supported by standardized APIs will change the way developers must collaborate with IT operations. IT organizations will need to support the requirements of the business or business units will continue


Prediction #2. Containers and micro services are beginning to become a pervasive approach to developing and deploying more flexible and agile applications.  We expect a micro services industry to emerge in 2017.


Prediction #3. Open source vendors will have to move from a support model to a solutions creation model if they are to survive. Too many open source vendors are unable to gain enough revenue to sustain themselves in a highly competitive market. We expect those that only provide support without a significant ecosystem of partners and solutions will not survive the year.


Dan Kirsch on Analytics & Cognitive

Just a few years ago, analytics experts and data analysts were trying to convince business users that predictive analytics could be trusted. The market has shifted and now C-level executives and asking how analytics can be applied in nearly every part of the business. Below are our 2017 predictions for analytics and cognitive computing.

Prediction #1. The emergence of embedded analytics into a broad array of applications will significantly impact the growth of the standalone analytics tools market. Increasingly analytics are being built into increasingly more business and consumer applications. Embedded analytics allow users to make fast data-driven decisions without the need to run complex queries, seek assistance from data analysts or move to a separate BI / Analytics application. Therefore, vendors of standalone tools will have to move to an industry focus to differentiate themselves.


Prediction #2. Artificial Intelligence and machine learning algorithms are becoming pervasive across both horizontal and vertical applications. The initial impact will be subtle (i.e., better targeting of customers). The significant impact will come from cognitive solutions that provide breakthrough solutions to difficult problems in vertical markets such as healthcare, manufacturing, and finance.  


Prediction #3. The role of the chief data officer is going to become an increasingly strategic in companies. These professionals will be in charge of driving digital transformation as companies realize that data is the only way to take on emerging threats.



Judith Hurwitz on Hybrid Cloud

While it is clear that the cloud market is maturing. Customers are beginning to understand the use cases for public versus private clouds in a much more pragmatic way than only a few months ago. We are seeing public cloud vendors moving to offer their cloud services on premises. We are also seeing private cloud vendors branching out to public clouds.   We expect to see a lot of change in hybrid cloud in 2017


Prediction #1. Businesses are considering private cloud services that are not connecting to the internet because of security fears. 2017 will be a year of contradictions and fear. Companies with sensitive workloads will decide to disconnect from the internet through a private cloud appliance.


Prediction #2. While security has always been the top issue for companies moving to the cloud, a larger percentage of businesses will select vendors based on their security infrastructure. This could be the year of hybrid cloud security.


Prediction #3. Finally, we will see a large number of hybrid cloud management solutions hit the market. As more businesses increase their reliance of a variety of

cloud based infrastructure, platform, and application services, the need to understand what and how various services are used will be a demand. Management will expand to cost considerations and capabilities based on the use case.


Judith Hurwitz on IoT

While sensors and its semi-structured data has been around for decades, we are finally getting to the point where IoT data is being coupled with high power clustering technologies such as Spark and scalable converged systems. Applying advanced analytics in maturing tools is beginning to turn a collection of data elements into a sophisticated data analytics platform that can be applied to real solutions to pragmatic problems. We expect to see a slew of solutions ranging from smarter traffic, to smarter factories, and smarter device management. Given this evolution what do we expect to happen in 2017?


Prediction #1. Our first prediction for IoT is that the growth will be in industrial sector rather than the consumer sector.


Prediction #2. We expect to see a huge number of startups focused on real time data for the IoT market. These startups will primarily focus their attention on the Spark Framework as the foundation for offerings.


Prediction #3. There will be more and more attention paid to the security side of IoT given some of the opportunities for mischief – i.e. hacking into cameras and sensor based devices. While there will be few startups in the IoT security market this year, we will expect to see security offerings customized for IoT from established security vendors.


Jean Bozman on Hardware

Servers and storage vendors find themselves in a highly competitive marketplace, with many new entrants in the hardware marketplace selling systems at commodity prices. This dynamic is causing large systems vendors to offer built-in differentiated features, and to highlight their services for IT transformation. We are seeing more integrated systems that combine servers and storage into converged systems for quick installation, workload consolidation and IT simplification. We are seeing more systems with automation to speed deployment, improve IT flexibility and reduce operational costs. In 2017, the traditional categories of servers and storage are giving way to definitions based on system capabilities to support specific workloads.

Prediction #1. More servers, storage devices and converged systems (combining servers and storage) will go all-flash in 2017.

Flash-enabled systems – those using solid-state disks (SSDs) in place of hard-disks (HDDs) – will see rapid growth in 2017. Using flash storage in these systems will give them high performance and low latency, while reducing data-center space requirements. This impacts business workloads, databases and analytics, providing faster time to results than older hard drives using mechanical, spinning disks. In 2017, more systems will go “all-flash.” Drivers for the all-flash designs include: reduced price per storage device, increased durability and more converged or hyper-converged systems using flash storage.


Prediction #2. Management software will become a key differentiator for systems vendors.

Orchestration and management of workloads are the key software tools that make software-defined infrastructure (SDI) a practical option for enterprises. Offering customers an integrated platform, with virtualized hardware, advanced management software and automation, helps customers to transform their data centers and achieve greater IT flexibility and operational efficiency. In 2017, the prospect of gaining unified views of datacenter systems – and better IT staff productivity – will drive more vendor focus on easy-to-use management software.


Prediction #3. High Performance Computing (HPC) and analytics are converging – via a common infrastructure of Linux servers.

Inside enterprise data centers and cloud data centers, HPC and analytics are being deployed on the same infrastructure – usually running Linux on low-cost, scale-out servers. Many customers are finding that HPC data, such as weather data, can inform broader analytics workloads—improving the accuracy of business forecasts for retail store sales, oil/gas exploration, agriculture and manufacturing. By running HPC and analytics on the same hardware, IT personnel can manage both workloads, redirecting processing to available servers and storage. In 2017, more customers will find that leveraging HPC data in analytics brings better business outcomes.


Chris Christiansen on Security

For 2017, passengers on cybersecurity flights better fasten their seat belts low and tight because we expect severe turbulence. The 45th President of the United States (POTUS) is going to demand that technology firms cooperate with law enforcement and government intelligence agencies. This will set a precedent for governments worldwide, but their balkanized interpretations will be driven by unique social, political, and religious situations. IoT explodes as a target and a vector for security threats. In a dramatic reversal, public cloud will become a refuge for many enterprises and SMB that are out-gunned by their attackers and unlikely ever to achieve parity let alone superiority.

Prediction #1. The 45th POTUS explodes the cybersecurity market. Data security becomes critical as law enforcement and government agencies get carte blanche for internal and external mass surveillance. Privacy laws are Balkanized according to religious, social, and political beliefs by the US and other countries. Governments increasingly regulate encryption and source code. Major countries use social network profiling to control immigration, business licenses, job prospects, and social services.


Prediction #2. IoT drives an exponential growth in security threats. Consumer and surveillance cameras spawn record-breaking DDOS and DNS attacks. National security threatened as cyber war against critical national infrastructure heats up old and cold wars.


Prediction #3. Cloud becomes the secure refuge for companies worldwide. Next Generation (N2G) security radically alters infrastructure as appliances are virtualized and hybridized across private and public cloud-based data centers. Except at the high-end enterprise and carrier level, hardware becomes a Kleenex-like disposable. Many small business’ find security too difficult and expensive so they eliminate on-premise appliance and moves entirely to cloud-based managed and SaaS security services for predictable costs, lower risk, and better user experience.

Vanessa DiMauro, Predictions for Digital Transformation

The digital discipline is maturing and practitioners are becoming more accountable and more tightly integrated into the lines of business. The age of experimentation with digital is ending. There is a clear imperative to reach customers earlier in their buyer journey through online strategies. Below is what I foresee the future holds for marketing leaders, digital officers and community builders everywhere.

Prediction #1: A big shift toward selective customer intimacy

Companies are beginning to understand that all customers are not created equal. We predict al huge movement towards targeting digital strategies where there are campaigns designed to meet the needs and expectations of important customers. In 2017, we’ll see a dramatic rise in private customer communities, velvet-rope customer retention programs, and tiered social selling programs focused on “elite” customers.


Prediction #2: The end of acceptance of vanity metrics and unclear digital outcomes

Increasingly stakeholders expect digital transformation leaders to provide a clear return on their investment. While 2016 marked the dawning of digital accountability, 2017 will bring formalized accounting standards and practices for accurately measuring business across industries (think GAP analysis for Digital Accounting).


Prediction #3: Digital impact will become strategically integrated into core operations. It is now clear that digital campaigns have to move beyond “Facebook likes”. The focus for 2017 is moving to a digital transformation strategy that support the firm’s strategic directions.

Read more]]> (Judith Hurwitz) Uncategorized Thu, 15 Dec 2016 17:16:44 +0000
The Lie of Digital Transformation

I’m not going to mess around. If I hear one more person talk about a digital transformation strategy I think I might scream. But I digress. What do company executives really mean when they say that they want to move to a digital transformation strategy? I think that if you talk to 10 executives you will probably get 10 different answers. The answer to the question is a lot more complex than simply expecting your company to be the “uber” in your market.

So, let me get to my point. Digital transformation really means a forward-looking strategy. Many well-established companies whether we are talking about transportation, manufacturing, retail, or electronics have a difficult time changing. The answer isn’t that mysterious. How can you take your intellectual property, your customer base, and your established brand in the market and translate it into a new generation? It turns out to be more difficult than it looks. Think about the companies that come up first on the radar when discussing digital transformation. They are companies like Uber, Airbnb, and Amazon. They all have one factor in common: they came to the market with no legacy products and revenue tied to those products. They didn’t have to invest in physical inventory or complex infrastructure to support their customers and their business. This is both a strength and a weakness. There are hundreds maybe thousands of digital transformation companies that fail and fail hard. The ones that succeed do so because they have mastered the management of complex data.

Does that mean that traditional companies are lost in an era of digital transformation? Do these companies have to throw it all away and start from scratch? No. I maintain that to be successful traditional business leaders have to do two things. First, they need to take a hard look at what has made them successful in the past. What products and services do customers love? What is the underlying intellectual property that has taken the company this far? Who are the customers and what are they looking for next that they can’t get today? Second, businesses need to be willing to walk away from products or services that do not produce growth. Just because you have always offered a particular product doesn’t mean that it will sustain you into the future.

True transformation requires bravery and imagination to see beyond your traditional ways of doing business. It is not a quick answer to a complex problem. Transformation requires a roadmap that moves you forward in order to take your knowledge and codify it into data. Transformation means that you have to take risks that are often outside of your comfort zone. Don’t simply ask customers what changes they want to see in existing products. Help them to work with you and look beyond what exists to what is possible.

If you take the roadmap steps of moving forward with data and out of the box thinking then you can begin your journey towards transformation. Ironically, established companies with well-known brands, loyal customers, and an innovative set of ideas can leapfrog the competition if they follow the data. By following this path you will be prepared to future proof your business and become a disruptor and not a victim.

Read more]]> (Judith Hurwitz) Vendor Strategy Tue, 13 Dec 2016 20:09:44 +0000
Can Open Source Companies Be Successful?

Open source is dramatically changing the IT landscape on many levels. For example, open source helps existing software companies jump-start their entry into new markets. Rather than trying to convince customers and partners to adopt a proprietary platform, creating an offering on open source platforms allows users to leverage tools they are familiar with. In addition, open source platforms often have the support of active user communities and are continually evolving to support new technical and business capabilities. While there are clearly benefits in the use of open source, there are issues that you need to keep in mind. Companies that attempt to commercialize an open source platform as the primary focus of their offerings do not fare well. The notable exception to that rule is Red Hat, which has had tremendous success in providing support for open source Linux as well as a number of open source middleware services. When I meet with open source vendors, many executives point to Red Hat’s success as a blueprint for profitability. Very few of these optimistic vendors have been able to replicate Red Hat’s model. The question is why is commercial open source so difficult? It is not a deep mystery.

Once customers and partners know that they can use a technology without payment, they are reluctant to pay. In fact, when I look at the many of vendors that have created a support model for open source, the majority have experienced anemic growth. In many situations, vendors discover that customers are actually happy to use an open source tool but less eager to pay for support. Ironically, the better the software tool, the less likely it is that the commercial open source company will be successful. Many smaller software companies depend on systems integrators to leverage their technology to increase their adoption and revenue. Unfortunately for these companies, even when systems integrators take advantage of an open source tool they are unlikely to need third party support. Integrators have enough internal resources to directly support customers that are using their offerings and don’t want to cut into profits by paying another vendor for open source support.

While there are risks for commercial open source vendors, there are also dangers for those customers that adopt open source tools. It is quite typical for individual developers to select an open source tool to support a new project. It is common for those developers not to purpose support services. When this developer leaves the company or moves on to a new project there is often no support for that existing development effort. If the results of the project are experimental there is little consequence. However, if the project becomes mission critical the lack of support can become a danger to the stability of the IT infrastructure. In addition, relying on open source tools without support may cause security risks because of a lack of testing and patching along with compatibility issues when updated are performed.

So, what is the answer? In my view, successful vendors are finding that an open source foundation is a critical ingredient in a software vendor’s strategy. Innovation from software vendors combined with open source resources provides customers with the best option. However, relying completely on open source is not sustainable for commercial software companies. Commercializing open source software needs to be a part of an overall software framework but it cannot stand-alone. Simply adding support and some management tools are not enough to get customers and partners to sign enterprise agreement.

What is the future of commercial open source tools? It is unlikely that independent vendors will find a model to simply sell open source tools with support without significant value add. However, open source in the context of unique value add has the potential for success.

Read more]]> (Judith Hurwitz) Vendor Strategy Tue, 29 Nov 2016 18:11:12 +0000
Why Tata Consultancy Services Created a Commercial Software Business


Over the past several weeks I have had some conversations with a new division of Tata Consultancy Services (TCS) that is offering digital software and solutions with a strong focus on data integration. While TCS itself provides solutions to customers through its consulting services, this new business unit is creating a self-sustaining organization based on analytics of customer and urban data.

Where does Digital Software and Solutions fit?

But first a little context. Tata Consultancy Services is a major business unit within the larger Tata Corporation. Like many multi-national companies, Tata offers a wide variety of products and services including everything from power generation and rail services, to insurance, chemical manufacturing, steel production and voice services. The company employees over 600,000 people and operates in more than 100 countries with revenue of $108.78 billion. This blog will focus not on the entire holding company but a single business unit within Tata Consultancy Services called Digital Software and Solutions Group. The business unit, headed by Seeta Hariharan, has been established as a group that is independent from the systems integration organization. The objective of the group is to sell licensed software including:

  • TCS Customer Intelligence and Insights (CII) for Banking and Financial Services
  • And TCS Intelligent Urban Exchange (IUX) that is intended to help clients accelerate the development of customer and citizen centricity initiatives.

The goal of the TCS Digital Software and Solutions organization is to focus on creating solutions targeting specific markets. Unlike a systems integrator, the Digital Software and Solutions group is creating a reusable software platform that is complemented by domain specific applications that are both vertical by industry and horizontal (data integration and analytics). Here is what I took away from these early discussions with TCS’s Digital Software and Solutions group.

  • TCS’s Digital Software and Solutions group is building a set of data-driven solutions. The first solutions are focused on city infrastructure, retail banking, and government agencies including water and transportation.
  • The vertical solutions are intended to concentrate on customer and citizen engagement
  • Operations management is based on an implementation of the Customer Intelligence & Insights platform
  • The focus will be on both unstructured data from sources like social media as well as structured data from transactional systems.
  • TCS’s Digital Software and Solutions offers a data integration platform that can aggregate a variety of third party tools and technologies. This integration is performed through a software development kit and published APIs. The base platform is an analytics engine and includes data ingestion and other data management engines.
  • The industry led solutions are designed in a modular fashion. TCS has the luxury of being able to create software in an era where modularity is the norm.  Therefore, TCS Digital Software & Solutions is building solutions as a set of modular software applications. Some of these applications industry specific and others that are common analytic services used as the foundation for all services.

The bottom line

One of the most complex tasks for traditional services led companies is to be able to make the transition to becoming a software company. While there are some successes there have also been failures. One of the strategies to succeed is to establish an independent organization with its own sales team, budget, and revenue targets. These revenue targets should be in-line with those of an established software company that is branching out into new territory. If TCS has the patience to keep progressing on this journey it has an opportunity for success.

Read more]]> (Judith Hurwitz) Vendor Strategy Tue, 26 Jul 2016 19:47:32 +0000
Turning its Strategy on its head: Why IBM is leading with cloud services

IBM’s journey to the cloud has been a complicated process. As an analyst, I have had a bird’s eye view of the evolution of IBM’s cloud strategy. Like many other enterprise technology companies, IBM has had to balance its need to preserve on- premises revenue with its desire to satisfy customers’ demands for cloud services. I recently attended IBM’s cloud analyst summit, where key executives put the evolving IBM cloud strategy into perspective. In the midst of massive changes in technology and customer requirements, Robert LeBlanc, Senior Vice President and leader of the cloud strategy, succinctly stated the overarching strategy: “Our solutions are cognitive, our platform is the cloud and our focus is industry.” In this blog, I will provide my perspective on what this strategy means for businesses.

What Does Cognitive Mean to Business?

Of all of the aspects of IBM’s strategy, cognitive business is probably the most complex. Typically, we have come to think of Cognitive computing as the technology behind the Watson platform. Watson is a set of underlying data driven technologies that incorporates machine learning, natural language processing, advanced analytics, and developmental APIs. Watson gains insights and learns based on both data and collaboration with subject matter experts. The environment combines dynamic learning, hypothesis generation and evaluation. Watson is delivered to clients as a cloud-based platform intended to create targeted and often industry-focused solutions.

The Insight X-Factor

More recently, with the use of machine learning and other advanced analytics techniques, it is now possible to bring unstructured and semi-structured data (i.e., IoT data) into the mix. Unstructured or semi-structured data exists in everything from emails, articles and books to sensors, images, videos, and even smells. A cognitive approach allows organizations to start to apply analytics and deep learning to these typically untapped, but highly valuable sources of data. When an environment can understand and learn from unstructured and semi-structured data, the system will begin to have the knowledge, insights, and intuition that subject matter experts with years of experience possess.

Smart CEOs have known for decades that, that if they could gain insights into the massive amounts of data inside their organizations as well as other data sources, they could gain a massive competitive advantage. It has long been understood that the winners in business are those organizations that can turn that data into differentiated strategies. Because of the massive amounts of compute power and storage required to understand all of a company’s available data, the bottom line is that without cloud computing companies could not afford to turn data into knowledge.

What Differentiates a Cognitive Cloud?

So what does it mean that IBM’s cloud environment is cognitive? If we take IBM at its word, a cognitive cloud could be profound because it is not just focused on compute and storage but also on advanced data analytics. It would surprise no one to say that nearly all businesses are run on their data and that the most successful businesses have a deep understanding of their data. Businesses have become pretty good at analyzing their structured data to better understand customers – who they are and what they buy. In recent years, with the advent of predictive analytics and near real-time capabilities, businesses have gotten much better at predicting customer behaviors and presenting customized offers.

Cloud as a Platform

While IBM has been a player in cloud computing for almost a decade, its relationship to the cloud has been complicated. Some of the common questions about IBM and the cloud that I’ve heard and sometimes asked have been: How much will IBM invest in the cloud? What will the impact of cloud be on hardware and on-premises software? Can cloud revenue grow fast enough to offset declining on-premises licensing fees? I am sure that there have been hundreds of meetings among IBM executives discussing these fundamental financial questions.

What was most intriguing about this recent analyst cloud meeting was LeBlanc’s assertion, “everything first gets delivered on the cloud.” IBM did not come to this decision lightly. However, faced with the growth of Amazon’s cloud business and the growth of Microsoft’s cloud offerings, IBM could not forego lucrative cloud revenue. The company also could not afford to ignore the reality that public cloud services affords IBM the most cost effective and pragmatic way to offer new business models and services. Likewise, without a solid cloud strategy, emerging companies would have abandoned any potential partnership with IBM in favor of vendors that have well-articulated cloud roadmaps.

This move to the cloud does not mean that IBM is abandoning on-premises solutions. Rather, based on customer demand and use cases, software will continue to be delivered on premises. There is plenty of evidence that many customers will continue to demand that core applications operate on premises. There are customers that are simply not ready for a wholesale move to the cloud (public or private). There are other customers that, because of security or regulatory concerns, will demand that certain applications run on premises. Still other companies are becoming vendors of cloud services for their own customers and want to control capital expenses, so they will run their own private cloud environments.

The Cloud Architecture

One of the interesting aspects of the cloud analyst briefing was the articulation of the cloud platform. In its early days, IBM’s cloud platform was a collection of offerings. Almost everything from servers to a variety of software offerings were labeled as cloud (much to the confusion of customers). That has changed under the direction of Bill Karpovich, the general manager of the cloud platform. He explains that the cloud platform is a single entity. That offers “a full spectrum of services from infrastructure to industry focused software.” The foundation of the IBM cloud is SoftLayer, the cloud platform company founded in 2005 and purchased by IBM in 2013. SoftLayer is best known for giving customers the ability to deploy on bare instead of only on virtualized images. Over the past several years, IBM has enhanced its platform with native data and business process integration services, support for containers and microservices, Platform as a Service based on Bluemix (a dev/ops platform based on cloud foundry), security and data privacy services. IBM also hinted that, with the recent acquisition of Gravitant, it will be introducing a hybrid cloud management suite.

IBM as an Open Source Cloud Player

What is behind this pronouncement? The cloud strategy is predicated on several key fundamentals, chief among them is the adherence to standards. Since the late 1980s, IBM has used open systems and standards as a defensive weapon. In that era IBM was knocked off its pedestal as the undisputed computing leader. To regain its footing, IBM embraced de facto standards such as Unix and TCP/IP. It took IBM several years to convince a skeptical market that it could move away from the proprietary world where they controlled every element to it controlled to a company based on openness. Remember that the development environment Eclipse was originally a propriety IBM development platform. Today, if we look at the foundation of IBM’s cloud offerings, openness is at the core; this includes the use of the Linux operating system, OpenStack, and open source tools such as Cloud Foundry, Chef, Puppet, and Jenkins. In addition, IBM has opened its own IP such as the Watson APIs so that partners can easily develop solution.

Leading with Public Cloud

One of the most surprising announcements was the focus on the public cloud. While IBM has dabbled with offering public cloud services for years, the primary focus has been on private cloud services. This seems to be changing. Not surprising, IBM feels pressure from Amazon Web Services to give customers what they want to buy – self-service public cloud resources. It will be interesting to observe how IBM is able to provide a cohesive set of offerings that can move back and forth between public (theirs and third party offerings) and private clouds. I suspect that this is why IBM is investing heavily in hybrid cloud management.

Solutions focus

IBM is going “all in” with industry-based solutions. This is a dramatic change from the early 2000s, when IBM determined that it would no longer offer packaged solutions and would instead focus on enablement and middleware. While we don’t expect IBM to create traditional, monolithic packaged applications, it will create modular services and will revitalize its services organization by offering IT as a Service based on deep industry expertise.

The Take Away

I would summarize IBM’s cloud strategy and the analyst meeting with five takeaways:

  1. IBM is betting big on solutions-focused clouds incorporating both public and private services, complete with its own integrated platform with open APIs.
  2. Cognitive services that add deep learning algorithms combined with industry-specific knowledge and data is IBM Cloud’s key differentiator.
  3. Hybrid cloud management will be the way that IBM brings its myriad of offerings together to support customer demands.
  4. IBM will focus on public cloud services to become more competitive line of business customers and emerging partners.
  5. Cloud based dev/ops will become one of the lynchpins of IBM’s Bluemix platform in order to draw developers into the IBM environment.

Read more]]> (Judith Hurwitz) Vendor Strategy Tue, 19 Jul 2016 18:19:55 +0000