Gaining and keeping competitive advantage for businesses has never been more challenging. At the same time, though, there have never been more opportunities to build and scale a company with global reach. With the internet as the platform for innovation and the emergence of the information-fueled economy, technology is both a strategic requirement and a strategic advantage. You’ve probably read about, or even experienced, a big business disruption, and for that to survive you must go through a “digital transformation.”

Frankly, digital transformation sounds ominous or even a little scary, and in general it is an overused buzz phrase. That doesn’t mean that there’s not something important for businesses in the underlying ideas that are the actual change drivers. Before we talk about that in more detail, though, let’s get the definition of digital transformation out of the way. I’m going to oversimplify a bit, but for this discussion the following definition should suffice. “Digital transformation is the application of technology to modernize business processes, activities, models and strategies with the intent of making the company more competitive and/or more profitable.”

There are at least five major business areas that can be modernized using the combination of technology and data: two related directly to strategy, and three that are more process- and activity-based. Those areas are:

• Business models
• Business strategy
• Workforce
• Customer interactions
• Business operations

Each of the three activity areas are unique, but they are all interdependent, so modernizing one often requires efforts in the other two. Business models and strategies drive modernization in the three activities directly. The modernization process can start at the business model and move to strategy, or can be a change/modification of the strategy that then drives the three activity areas. It’s also important to note that technology changes should be accompanied by process change to optimize the impact of the changes.

Business-model innovation is arguably the most impactful change that the growth of the internet has enabled: There are any number of examples from Amazon to Uber. While the most obvious examples are from companies that were started and grew up as “digital native” companies, there are also good examples from “pre-internet” companies that have used business-model innovations as a means to drive the overall change in the business. There are also some high-profile examples of pre-internet companies that did not adjust to the disruptions and make the shift to a modern, digital business model or strategy.

A simplified example of a company that is modernizing a business unit might help understand the concepts better. General Electric (GE), a company that is nearly 140 years old, has been on a digital transformation or business modernization path for several years now. That path has touched all business units and changed GE’s workforce, the way it engages and interacts with customers, and its business operations.

Take its aviation division, which manufactures jet engines as one product line. The legacy business model was based on selling jet engines to airlines. To modernize this model, GE has shifted to what it calls “executing critical outcomes” for its customers. What this actually means is that instead of selling a jet engine, it sells guaranteed up time. That shift drove massive changes in the aviation division.

For example, GE is now one of the largest companies in digital platforms, software development, artificial intelligence (AI) and internet of things (IoT). To accomplish the shift, it retooled its workforce by adding software developers and technology experts across a range of skills. It also built and launched a digital platform, Predix, as the underpinning of the new strategy. To sell outcomes rather than engines, the company had to find new ways to ensure asset reliability through increased monitoring using IoT, analysis by leveraging AI, and predictive actions. The change also came with business operations changes to support a subscription financial model over the old transaction-based model, as well as a long list of system changes to manage customer interactions, workforce activities, etc.

Digital transformation, then, is driving business change and technology innovation. Over the past few years, several technology trends have emerged or accelerated as a result of the modernization needs of businesses. For 2018, G2 Crowd research is tracking a list of these tech trends, and we’ve identified four broad technology areas that serve as the anchors or framework for ongoing analysis of technology trends, and their impact on modernizing businesses.

These technology anchors are:

•  AI
•  IoT
•  Cybersecurity
•  Digital Platforms

There are a lot of other technologies, of course, but they are taking a supporting role for these four anchors in many use cases. The rapid evolution of digital platforms and the broadening of what they include is a big factor in the modernization efforts. Many functions are moving into the foundation as the technology landscape is increasingly cloud-based and composed of smaller building blocks called microservices. The modular platform provides services for the other technologies and is the underpinning to a new flexible and adaptable business system architecture. The platform can provide utility services and can provide access to transactional frameworks such as blockchain, AI and other analytics, as well as system management. With the concept of “serverless,” there is a move to reduce or eliminate the IT management overhead by moving it out to the cloud platform vendor, freeing up business resources to concentrate on adding business value.

AI, which includes machine learning, deep learning, neural networks, image recognition and natural language processing, is having an impact across all of the software stack and leading quickly to “intelligent systems.” The rapid increase in the use cases and the need for more complex algorithms have created a skills shortage which will be a short-term limiting factor, but will generate a lot of opportunity as well. AI is quickly moving past simple automation, predictions and decision support to prescriptive systems and autonomous action. There will be many issues to resolve around that shift, as autonomous vehicles become common and self-programming systems evolve, including the necessary ability for the AI system to document and explain its own code and actions. All of the evolution of AI, though, is dependent on the accuracy and availability of data. AI enables and relies on moving from “big data” to smart and small data to properly execute its models.

IoT is an enabler of all sorts of innovations that involve the need for data from remote physical entities. Edge computing is evolving around the growth of IoT sensors to move much of the processing nearer to the sensors themselves to increase efficiency. The GE example relies on IoT as the source of much of the data needed by the Predix platform to manage risks and provide the desired business outcome.

In the midst of all the high-profile security breaches and issues in 2017, cybersecurity is, more than ever, a business topic of great interest. 2018 will see cybersecurity broadly incorporating AI to attempt to level the playing field created by an exploding number of bad actors, including those that are state-sponsored. Intelligent security has potential to, at a minimum, keep businesses on some par with the growing number of black hats.

In 2018, G2 Crowd research will provide ongoing insight on digital transformation, these four anchor technologies and a long list of supporting technologies. Check back often for more updates and a deep look at the emerging stars in each of the key tech categories.

For many years, like most analyst and/or analyst firms I know, I’ve published some sort of annual “predictions” at the start of the new year.  This year, in my new role (I can still say “new” for the rest of January, my 1 year anniversary isn’t till tomorrow) I’ve put together a small team (Rob Light and Tom Hardin) and we have been working on our own G2 Crowd 2017 trends list.  On the G2 Crowd Blog we are posting a whole series of posts (11 to be exact), but I’ll try to summarize the trends here as well.

Digital transformation continues to dominate a lot of tech conversations but it gets really interesting when the focus goes deeper and you look at what companies are doing to transform their businesses. At a high level most of the hot trends that are accelerating underneath the “digital transformation” label are related to a few technologies, including artificial intelligence (AI), Internet of things (IoT), data, cloud and digital platforms, and advances in methods to interact with technology. AI in particular is showing up everywhere, and there are some use cases that are getting the attention of businesses. Embedding AI into applications, what we’re calling intelligent applications, provides the ability to automate simple tasks to free up employees for more critical activities and the ability to digest massive amounts of data and provide insights to support more effective decision making. Embed AI in security and you get intelligent security, or move your security efforts from reactive to a combination of proactive and reactive. AI can provide the ability to analyze the massive amount of log and event data, and act on it with threat detection algorithms. AI plus IoT gives you intelligent things, and brings a wealth of sensor data to augment and support intelligent applications.

Those six broad themes; AI, IoT, data, cloud, digital platforms and new UI models, form the foundation for the trends we have identified for 2017. The top trends for 2017 are:

1. Intelligent Applications:  Embedding AI inside applications can enable many capabilities, but generally they fall into 2 broad categories – 1. The ability to automate tasks to relieve workers of more mundane tasks, freeing up more time for higher value activities and 2. Take large datasets and find the relevant data in some defined context, supporting business decisions.
2. Operational agility: Competing in business today requires the flexibility to react to threats more quickly, change strategies and methods, and to adjust or change underlying systems to support the changing requirements. Operational agility can take many forms, but a big part of it is the ability to take a strategic decision and quickly configure and build systems that can support the changes. That means that new digital platforms, which are also covered in these trends, are essential and that IT must be able to respond to the new requirements quickly. DevOps, agile development and other IT changes can help organizations support the required changes more effectively.
3. Intelligent things: The rapid growth of connected things via the IoT, opens up the opportunity to take the data produced by these sensors and provide systems that can make real time use of that data. IoT also offers enhanced capabilities beyond just remote monitoring, and, although it varies by the type of system involved, intelligent apps can use the data to drive remote action and task automation.
4. New UI models – Virtual Reality (VR) / Augmented Reality (AR) / conversational systems: Interacting with a computer was for many years mostly limited to keyboard, mouse or trackpad. Mobile, with the introduction of the iPhone, brought touch into the mainstream and accelerated the use of voice. AR and VR use has expanded beyond niche use to mainstream in consumer apps, and will have big impacts on businesses over the next few years as the use cases expand. Conversational UI’s, particularly with AI based systems saw a big increase over the past few years with consumers. Mobile phones and computers have found expanding use for virtual assistants like Apple’s Siri. Amazon has taken the conversational system to the smart home movement, followed closely by Google and others. The use cases in business systems will grow this year.
5. Digital marketing and micro-targeting: Digital marketing, or the delivery of marketing through online channels, is a significant part of most marketing programs. Over the past few years several technologies have converged to create highly targeted and individually impactful messaging. This new capability, micro-targeting, uses big dusts through data clouds and other rapidly growing data sources, advanced analytics, behavioral modeling and a variety of delivery channels ranging from social networks to proximity based sensors and is highly effective. The accuracy of the models and the broad use of micro-targeting is moving main stream.
6. Marketplaces: Online marketplaces evolved out of some of the earliest online business models pioneered by B2C companies like eBay and Amazon. From consumer roots though, it expanded to B2B with companies like Ariba, CommerceOne and others. Today the model is disrupting businesses in transportations, hospitality and commerce. Marketplaces are an important distribution channel for an increasing number of B2B products an services.  
7. Data clouds: The massive growth of data, and the increasing business need for more and more diverse data sets led to the growth of data aggregators using the Internet as a distribution method and a subscription business model. While data aggregators aren’t new, the Internet created both an easier way to collect more relevant data, and a more effective method for distributing that data.
8. The Next generation Cloud: The next generation of cloud applications and infrastructure is emerging and includes the use of many new technologies ranging from containers to in-memory computing. The digital platform is being redefined to include more services and the entire “stack” is being broken apart into smaller and more granular micro-services.
9. Digital platforms: The platform to support the new nimble and flexible business is evolving into a more modular set of services that can rapidly be configured and reconfigured to support changing competitive initiatives and strategies. The combined platform contains many more services embedded into the platform layer (like collaboration, security, storage, analytics, AI, etc.) and provides a rapidly configurable foundation for flexible business processes and models.
10. Intelligent security: The past few years have shown that anything connected to the Internet is vulnerable using existing security methods and tools. The need for inherently smart technologies to both react and predict in an increasingly complex IT environment and with high risk threats that are more intelligent, have more resources and are potentially government supported is a business imperative. Combining AI into new security solutions have the potential of offering a more effective approach to increasing overall security for businesses.

Roper Technologies operates a group of technology companies that serve distinct vertical or niche technology markets. The current portfolio serves the healthcare, education, food, academic research, energy and water industries and includes medical and scientific imaging, energy systems and controls, RF technology and software, and industrial technology like valves, pumps, test and measurement equipment, automatic meter reading (AMR) equipment and a variety of other items. Since its founding in 1983, Deltek has built and acquired software that serves businesses with project delivery at their core and serves the government contracting, professional services, architectural, engineering and construction, project manufacturing, and marketing and advertising services industries. With its deep expertise and focus on project based businesses, Deltek seems like a natural fit for the Roper Tech portfolio.

Deltek, once a traditional on premises software vendor, started a journey several years ago to reinvent itself as a modern cloud services company. The path from one business and architectural model to another is never easy, as software industry giants like Oracle and Microsoft would attest. Both of those companies have spent years in the transition to a cloud and subscription based company, a project that is still ongoing. Deltek, with investment and support as a private company in the Thoma Bravo portfolio, has aggressively moved down the transition path, and now has a significant amount of its business from subscription based software as a service (SaaS) offerings. Much product work has already completed and several key initiatives are set to roll out in the near future. One of the early decisions has supported Deltek’s growth very effectively, as they chose to partner with Amazon Web Services (AWS) for cloud infrastructure instead of building expensive data centers and building out their own cloud infrastructure.

2016 has continued to be an active acquisition year for the business software sector. Multiples for SaaS companies varied, but in general continued to be strong, particularly for companies that had previously been public. The $2.8B spent for Deltek is slightly over a 6X multiple on 2016 revenue, if my estimates are correct (Deltek is private and does not publicly release earnings. Today Roper projected Deltek 2017 revenue at $535M). By comparison the multiple for Marketo, acquired earlier this year by private equity, was around 8X revenue, and Salesforce paid $2.8B to acquire Demandware on 2015 revenue of $237M. Revenue isn’t the only story of course, growth is also a key indicator of health, and while Deltek’s growth isn’t public either, I would guess that subscription revenue is growing in double digits (year over year %). So all in all, the deal seems like a reasonable one, especially taking into account Thoma Bravo’s 2012 price of $1.1B, although you would also have to take into account the investments that were made since then in acquired companies, including HRSmart and several others.

What does this mean for Deltek customers? Based on what I know so far, and the public statements, there should be no negative impact for customers. The acquisition is good news for current customers for several reasons:

• It ends any speculation on Deltek’s future while owned by private equity
• It provides a larger and stable home for Deltek in a company that has a track record of investing in growth in its portfolio businesses
• With more financial support its likely that Deltek with be able to accelerate its transformation to the cloud, which is simply executing the current plan with more resources and support.
• The same team continues to execute the current strategy and operating plan, which provides consistency and maintains current relationships.

[corrected for purchase multiple based on new revenue data]

Einstein is an AI platform and a set of AI enabled features embedded into other applications. As a “product”, the platform it is not sold independently, but operates inside the overall App Cloud Platform and thus can be surfaced in any application built on the platform. AI isn’t new of course, and Einstein takes advantage of a lot of the consumer side work done previously, for example Amazon – predictive recommendations, Apple – natural language processing (NLP), Google – deep learning and Facebook – machine learning. Applying AI to enterprise problems provides two basic approaches, 1. Providing relevant data to the person or team that needs it, when it is needed and with the proper context, and 2. Automating simple routine daily tasks that take time away from more value add activities. Both of these approaches take advantage to the rapidly growing mass of structured and unstructured data that can be accessed by companies. This is important for several reasons, but maybe the most compelling is to reduce somewhat, the reliance on scarce data scientists by performing some of the modeling work through AI.

Salesforce Einstein, at a high level is built to, 1. Capture real-time data, 2. Learn through predictive analytics, NLP and machine and deep learning, and 3. Connect with customers by providing a “better” customer experience (CX). Embedded inside the Salesforce App Cloud platform Einstein has access to a broad set of customer data, both transactional and data sources from online sources through listening and through data integrations/partnerships. Using that data Einstein, according to Salesforce, is designed to: discover insights, predict outcomes to support better decision making, recommend the next best actions to maximize interactions and automate tasks to allow employees to focus on the customer. To capture the diverse data set needed to support the embedded AI, Einstein connects to many sources, here are a few:
• CRM: account, contact, lead, opportunity and custom objects
• Calendar: salesforce calendar, google calendar, iCal
• Email: salesforce inbox, gmail, yahoo mail, Apple mail
• Social: Twitter, Facebook, LinkedIn, Google+ and others

There are many ways to apply Einstein inside the current portfolio of Salesforce Clouds. Here are a few that were shown during the conference:
• Sales Cloud – guide reps to the best leads and opportunities
• Service Cloud – Proactive service by helping customers find their own answers and recommending the correct content to agents
• Marketing Cloud – help marketers build predictive journeys, offering up the right content at the right time based on prospect behaviors
• Commerce Cloud – personalized shopping experiences by recommending products and offers that are relevant
• Community Cloud – personalize experiences by recommending content and people to answer specific questions
• Analytics Cloud – automate and prioritize insights
• IoT Cloud – automate recommendations from the “best” sensors and predict events
• App Cloud Platform – build intelligent apps

The Salesforce UX tool Lightning, which has been around for a couple of years, is the core of a few announcements this year. Last year at Dreamforce the Lightning framework added some community templates to help improve the Community Cloud UX. This year those template concept was extended in a product called Lightning Bolt Framework to enable Salesforce ISV partners to built Bolt solutions that have business logic and workflow built in. In support of these ISV’s Salesforce also announced a Lightning Bolt section of the AppExchange, showcasing partner solutions built using Lightning Bolt. The announcement was summed up as extending the experience, providing the capability to build Lightning UX on the Apps Cloud and starting to build out an ecosystem around the Lightning tool.

Salesforce has had a very active year in acquisitions, including several important pieces of the Einstein offering, but one acquisition that was announced in august, Quip, generated a lot of buzz. Quip is a mobile first (with desktop and web clients as well) productivity tool that provides teams with conversational documents, or said another way, the ability to collaborate on content across a team, having conversations without email. It was seen as a competitive move against Microsoft Office, perhaps in retaliation of Microsoft aggressively pushing its own Dynamics CRM. It’s probably good to note that Salesforce and Microsoft are in the frenemy category at this point. It could also be seen as a competitive move against Alphabet / Google. Competing against both companies is good I guess, but the real strength of Quip to me, is in the team collaboration capabilities, and, after playing around with it a little, I’d say, at least for content centric teams, it is a viable alternative to tools like Slack. At Dreamforce the app was an integral part of the mobile strategy for the event, and of course getting people using it has the potential to boost the adoption quickly.

Quip isn’t the only new conversation tool though, Salesforce LiveMessage was announced, rebranded from the September acquisition of HeyWire. It fits well into the Service Cloud umbrella for now, and is a communication platform that integrates a variety of other tools as well as tightly integrated with the agent console. The app is mobile on the customer side of course, and helps the agents manage multiple conversations, keep good records of the conversation for later and provides additional capabilities like automated outbound messaging.

The Commerce Cloud, which I mentioned in the 1st Dreamforce post, was a big focus this year. The offering, which is rebranded Demandware and unifies all Salesforce commerce offering, provides an integrated experience across online and store, with Einstein built in to provide personalization. This smarter commerce capability with Einstein greatly enhances the CX by providing relevant offers and recommendations in a highly personalized (or is that individualized) manner.

Salesforce provides custom mobile apps for its customers through the Salesforce1 platform, and has for a few years. Custom mobile apps are extremely important to the CX strategy in many companies, particularly consumer focused industries. The one (big) limitation with Salesforce1 has been the ability to white label the apps, providing consistent branding and experiences across mobile an web. Most companies that provide these types of apps want to control the branded experience. Now they can with the new My Salesforce1, which provides the much needed ability to build white labeled mobile apps.

During Dreamforce this year the “trailhead”, “trailblazer” themes continued as a backdrop for planning each individual’s experiences. I took a look at two specific trails, the SMB trail and the dedicated area for developers (DevOps) that took up all of Moscone West exhibition area. The SMB trail included a SMB “Lodge” that provided individual stations for customers to meet with sales engineers and ask questions. I spent some time there and was very impressed with the idea. The sessions, which were designed to provide assistance and mutual learning seem quite valuable, at least from the general feedback. There were some account executives and marketing people in the background, but were there as observers to learn more about the problems that the SMB customers are facing.

This year for developers on the Salesforce App Cloud and underlying development platforms of Force and Heroku, there was a full schedule of activities and opportunities to interact and learn. From a role perspective Salesforce had stations set up for developers, admins, and even CIO’s and IT executives. Everything was very hands on, and many participants built apps or functionality with Salesforce assistance.

No story about Dreamforce would be complete without also mentioning the strong presence of several charities and the inclusion of them in the Main Stage keynotes. It’s well known that Salesforce and its founders are big supporters of giving back, and have had a 1:1:1 policy since the company was founded. This year will.i.am joined Marc Benioff on the stage and talked about education, its dire need and impact, and a program to adopt a school. His foundation, i.am.angel, provides scholarships and created the i.am.STEAM program to encourage elementary and high school students to become involved in tech, engineering, math and sciences. The charity organization Red was present through the conference and Red CEO Deborah Dugan joined Marc on stage during his keynote to talk about the global fight against AIDs.

As I’m headed into another Dreamforce event I plan to keep you updated on the happenings this week. As usual Dreamforce promises to be a whirlwind of activity and a high energy event. I attend a lot of conferences and while it can be a grind sometimes, Dreamforce is usually a high point in the conference season.

Since things are just opening up today I will give you a bit of a pre-conference look, since Salesforce has already hit us with three major announcements that will most likely be a big part of the keynotes and discussions. This three announcements are:

1. Einstein AI platform and applications
2. The launch of the Commerce Cloud
3. The acquisition of Krux

I recently published a post on intelligent applications that provides some background for the Einstein announcement, as Einstein fits the definition of an intelligent applications platform and applications. Officially announced a couple of weeks ago, the Einstein project is several acquisitions (MetaMind, PredictionIQ, RelateIQ, Tempo and a few others) and a lot of data science work in the making, and launched an AI platform, the first set of AI enabled applications and established a new research lab. The approach is similar to Salesforce’s other broadly applicable technologies, embedded into the App Cloud platform, which makes it available for use by developers building any other application on the App Cloud. Using machine learning, natural language processing (NLP), predictive analytics and other AI related capabilities, apps built with Einstein embedded inside can use the technology to support decisions like next best offer or when to take a specific sales action like providing relevant content, but perhaps even more importantly can automate functions that are today labor intensive like scheduling meetings or updating forecasts. In a time of talent scarcity in data scientists embedding tools that facilitate deep analysis and insight can help companies bridge the gap. Look for more use cases and intelligent application announcements this week.

The announcement of the commerce cloud is essentially the rebranding and integration of Salesforce’s recent acquisition of Demandware placed in the broader context of the full Customer Success Platform. For many years retail organizations have struggled with providing a consistent and coherent experience across on and offline interactions with customers. The Commerce Cloud will combine digital commerce experiences across mobile, social, web and physical store to provide a more intelligent and personalized buying experience. Using Einstein the Commerce Cloud enables onsite recommendations, email personalization and other features leveraging predictive intelligence.

The acquisition of marketing data management platform (DMP) startup Krux was announced yesterday for a rumored $700M. The latest in a year that has seen an even higher number of Salesforce acquisitions than “normal”, Krux’s Intelligent Marketing Hub provides deeper insights on customers by mining and analyzing data across the web. Krux uses embedded AI to provide more effective targeting for marketing and ad campaigns. The Intelligent Marketing Hub becomes a part of the Marketing Cloud.

This short list of pre-announcements will be topics of interest and will certainly get much more showcasing this week in the keynotes and on the conference floor. You can expect some additional surprises during the week of course, I’m pretty certain that some additional announcements of interest have been held back for tomorrow afternoon’s Marc Benioff keynote.

(Disclosure: Salesforce is a G2 Crowd client and covered my expenses to attend Dreamforce)

If the current time is called the “information” age, as opposed to the industrial age, then it stands to reason that business content, the thing that moves that information around and makes it “consumable” would be very important to businesses. It also stands to reason that if business content is important (some would say critical) to doing business that it would need special systems to produce it, manage it, distribute it, and store it. Those systems would also be important and to be optimized for daily business use they would have some very specific requirements and functions. At this point in the post, I suspect a lot of you are thinking that I’m describing systems that have been around for a very long time and are very commonplace in businesses, called content management systems. On the other hand you also might be thinking that I’m about to go off in a completely different direction, or at least a direction that is different from the traditional. Now if you’re thinking content management, and you haven’t abandoned me by now because the topic isn’t that interesting, you should know that I am heading in a somewhat different direction. Not that I won’t get into content management systems for a brief few minutes though, because they also are a part of this discussion, if maybe one that is not quite suited to solve the type of problems we will discuss.

Enterprise content management systems (ECM) were designed to “manage” content, thus the name, and they’re good at managing or controlling content. From a compliance, security, control and even pre-formatted routing of content, the existing systems, while of course somewhat variable among each one, do a very credible job of all those tasks. The systems thrive on structure and control. In a world with structured information / content and the need to have strict control over it, that’s all goodness. The problem though, is when you start to think of the entire business as information driven, and you understand that sharing information and working together in a more collaborative model is the “new” way of doing business in this post-industrial, information age, control is counter to much (not all) of getting work done. In the industrial business you often heard that “information is power”, but now, in an ultra connected business environment that has shifted to “information sharing is power.”

Operating a business and making the ongoing operating decisions that are required of all employees requires data at the point of need, at the needed time and with the needed business context around that information. For several years I’ve talked about the need for decision support systems, which by definition are the conduit for business content to the teams and individuals that need that content. Business content management (BCM) systems are designed to be a critical part of a decision system by approaching the way content is managed and disseminated in a collaborative process. These BCM tools are designed to function differently than ECM tools and have very different use cases. The software tools that make up that category came from several other categories and evolved into the current feature set over time. Some came from the file sync and share category, some from storage and a few from ECM, but they all have evolved to include:
• The ability to store business content across multiple file types
• Provide a preconfigured open environment for users to access, sync, edit and share content
• Embedded and prominent collaboration tools centered around the content
• Integration to ECM and digital asset management (DAM) software through APIs

For a more detailed definition you can take a look on the G2 Crowd site here. You can also see the list of products that we include in that category here.

In some businesses you will find all three types of tools, ECM, BCM and DAM, while in others there will be only one or two. Each have strengths and specific use cases that make sense based on your purpose. This also varies somewhat by industry, with ECM often necessary in highly regulated industries or industries with unusual compliance issues. More and more though, you will find BCM tools in use, even in situations where the business itself isn’t providing an “official” solution. BCM is one of the most often “bring your own” types of software since it is so important in supporting business decisions and in getting work done. Many of us use BCM personal editions for our own content management needs and that experience bleeds across into businesses. Because of that it is a wise IT organization that embraces and provides an enterprise class BCM tool, since it is very likely that one or more of this type of tool is in use already in your business. Standardizing on a tool that meets proper security and compliance requirements while providing all the functionality of a BCM tool is very important and a much more realistic approach to IT management than either ignoring the need or even trying to block BCM tools in favor of existing ECM implementations. They are not the same and are being used to meet very different needs and requirements.

Software does change processes (or at least fuels a business to change its processes), and when well designed probably does improve the flow of the work and might increase the output of some of the users. Most systems though, create work to save work, and so, for some users anyway, they actually decrease productivity. In other words systems require interaction including data entry, and the work of “feeding” the system, as well as manipulating the system to make it do other things, increases administrative overhead. Administrative overhead is bad. Maybe that’s oversimplification but face it, doing activities that are only required because you’re using a specific software solution, and not because it increases revenue, reduces costs or increases innovation is not how you want your employees investing their time. The problem then is getting as much value from a system (as in it supports one of the key functions listed, revenue, margin or innovation) while minimizing the investment in “managing” the system. There are several traditional software solutions that have high administrative overhead for many or at least for some key users of the systems. One common example is project management. The traditional approach to project management software led to a system that provided a lot of information for managers, executives and even resource planners but offered a minimal amount of value to the employees who had to execute the project work (and keep the system up to date on status, hours worked, moneys spent, etc.). Over the past 10 years or so new systems that focus on helping the workers get the work / tasks done gained popularity. The same is true for sales force automation (SFA) systems, the traditional systems focused more on reporting up (as in the forecast) and managing quota than in helping salespeople sell. Newer add ins to SFA provide sales intelligence type data to make the sales professional more productive. That’s the general path for many 3rd generation management systems, get tools into the hands of the people doing work to help with the work, not just facilitate reporting status.

Newer computing capabilities are available to make systems more adept at “helping” workers get work done. This is particularly interesting when the new technologies enable the system to take actions for the worker with only some oversight offered by the human. This is not an idea to replace workers but a concept that would free up employees from some types of work (including feeding systems) to focus on other, higher value activities. Artificial intelligence (AI) and cognitive computing are two terms that are growing in use and seem to offer some opportunity to improve outcomes and change activities for workers. AI is the overall category and cognitive computing is simply a subset or specialized type of AI.

AI in simple terms is the intelligence exhibited by software / machines, and through machine learning has the capability to improve itself over time. AI functions in different ways depending on the specific application of the technology, but in general it has some form of sensory input (like a neural net, or even just data mining large data sets) and provides some self improving output based on the sensory input. Cognitive computing focuses on assistive applications of AI, providing some processing of data and a suggested course of action as an output. This has been particularly useful in healthcare by providing diagnostic aid to physicians by managing the massive amount of input that is available to the physician, and offering a suggested set of potential diagnosis. The ultimate decision on diagnosis and treatment is still left to the human though.

Applying AI to existing types of applications has a great deal to offer. An AI based “agent” could manage tasks like scheduling meetings for example. I’ve participated in a beta of an agent like this from a company called x.ai that very effectively functions as a personal assistant to help manage your schedule. Managing calendars, meetings, email (prioritizing and maybe even providing simple responses to some types of email), many simple tasks could be managed with an AI agent.

Inside applications, or I suppose calling it embedded AI would be appropriate, there are even more types of activities that could be managed, or at least processed to a point that a human decisions could be made. Tasks that require the analysis of a merged dataset, like assigning resources to a preliminary project plan, or even developing a project plan that could be approved would speed up processes in professional services businesses. For people working remotely or out of the office the embedded AI could provide needed information in the context of a calendar or schedule, or even work order or other system input. A sales representative could then be provided with needed intelligence about a prospect, a deal, inventory, staffing, order status…any number of items of information all with the correct context and timing. Customer service organizations in some industries are using chat support bots that interact with people looking for assistance and mimic the experience of a live chat agent. The bot is connected to all the information available to a customer support agent including knowledge base for potential solutions, customer data, transaction data, etc. and also can open support tickets or escalate issues that it cannot resolve.

AI applications are starting to provide a variety of simple outputs based on some data / input. For example news organizations use AI to write simple stories like financial news summaries, sports updates, etc. It is not providing in-depth analysis of complex events of course, at least not yet. Applications that require input from visual sensors like security, handwriting analysis, facial recognition or aural sensors for voice recognition can take that input and coordinate a variety of outputs from alarms to purchase orders. AI capabilities are starting to enable the next generation of applications to incorporate new and unique types of “automation” that can change the way you interact with a system and greatly increase the usefulness of many types of applications. Intelligent applications are becoming more available, although many only offer minimal use of AI at present. The next generation of many systems is just starting to become available though, and that availability and the breadth of use of the embedded AI, should accelerate over the next few years. Once employees start using and seeing the substantial benefits from systems that use AI they will find it difficult to go back to older approaches.

1. If you play “Stairway to Heaven backwards it’s a prayer to Satan”
2. Oracle buys Netsuite.
3. If you swallow gum it takes 7 years to digest.

You guessed it? Well, this morning the wires are a buzz with tales of the next big Oracle acquisition, Netsuite. This is an old rumor that has surfaced a few times every year, at least for the last 6 or 7 years anyway. I always had a good set of arguments as to why it wasn’t going to happen then. Well this year’s rumor mill started up a few weeks ago, and got very strong yesterday. I suppose I’m lucky that I wasn’t asked the question yet, I’m not sure how I would have answered, and of course any answer now is colored by the fact that I know the answer anyway. Most of my reasons were more about finances, need, and how it fit into the strategy for both companies, even though there is certainly near complete overlap in products between the 2 companies. The product overlap argument was and isn’t an issue, as Oracle has proven over and over with its acquisitions. So why and why now?

There are several reasons why Oracle buys companies, well, not just Oracle but most software companies in general. It could be because there is a functionality / process gap in current offerings, particularly in industry vertical applications (like Retek for retail or Opower for utilities). Or it could be that there is a gap in cloud offerings specifically (RightNow for cloud customer service). It could also be about customers (buying them that is), talent and knowledge (engineering or sales), market share, or even just taking out a competitor. Most acquisitions are driven by more than 1 factor though. In this particular deal there are a few likely reasons. While functionally the two companies are mostly overlapping there is a difference in the size of company that is the usual customer. Oracle is 1^st and foremost an enterprise focused company. It does sell in the mid market too, but the Oracle sales force focuses on enterprise and industry verticals and generally leaves mid market sales to partners. Netsuite, which started as small and medium business solutions, has moved up market some over the past few years, but still is mostly sold into the mid market. For enterprise sales Netsuite has targeted with some success, departments or operating units in large enterprise companies. These entities often behave like mid sized companies. Netsuite’s products are particularly good for companies that need a complete ERP + Commerce capabilities that is pre-integrated and complete. You could do this with Oracle’s products but it is a bit more complex to assemble.

From an industry vertical perspective Netsuite would provide some cloud coverage that is incremental to Oracle’s offerings and would open up some additional opportunities to sell into the customer base. Specifically Netsuite is strong in high-tech (particularly software companies), commerce, manufacturing, wholesale distribution, professional services (in several micro-segments including IT consulting, embedded professional services, advertising and marketing agencies and consulting) and retail. Oracle has some coverage in all those verticals but there are some verticals that the Netsuite product provides a much better fit than existing Oracle cloud products. Manufacturing, for example, since Oracle Fusion originally did not focus on manufacturing at all and is only now gaining that functionality. The Netsuite professional services offerings are much better suited to the size and functionality needs of most firms in those micro segments I listed above. In retail, a strength of Oracle’s, there are complementary offerings that can be sold into the Netsuite base. In all the Netsuite customer base there is an opportunity for Oracle’s customer experience solutions, marketing cloud solutions, HCM/Talent management solutions and supply chain solutions.

Perhaps the more interesting opportunity for Oracle is in the “tribal” knowledge of a company that has been in SaaS / cloud computing from the very beginning of the idea (or maybe you could argue before the idea was really completely formed). Oracle has made progress with its cloud products, and has had a strong push for the sales and sales support resources to move from traditional perpetual licenses and on premises implementations to subscription and cloud, but that particular journey has not been without bumps (some would argue those bumps were more the size of mountains). It can only benefit from the knowledge and experience of a sales force that has only sold in the cloud/subscription model.

The last “reason” could simply be that adding 30K cloud customers to Oracle’s growing cloud customer numbers benefits it in its war with Salesforce, SAP, Microsoft, etc. This is the time to gain share, or at least it continues to be that time. All of the transitioning companies (from on premises to cloud offerings) have heavily marketed the growth of cloud share, which makes sense when you’re competing against cloud only companies that have been around for 10-15 years and have a significant head start. To be fair Oracle’s cloud / subscription customer numbers have grown quickly over the past few years, but will still benefit from the extra 30K going forward. And of course that’s all added to its reported ARR, so that’s good as well.

Oh and the details of the deal: Oracle’s offer is $9.3B or $109 per share, a 19% premium over the July 27^th market close price. Then deal should close this year, with all the normal disclaimers around regulatory review and shareholder approval, although remember that Ellison, his family and holding control about 40% of those shares.

We live in an information flooded world fueled by a constant state of “connected”. Businesses have tried for quite a long time to become “data driven”, so with the avalanche of data and ubiquitous connectivity you would think that supporting business decisions would be a routine approach at most companies. But, as all of you who just gasped loudly at the word “routine” know, we are far from being able to get the “right” data to the “right” person or team at the “right” time and with the “right” context.  I’ve written about this subject before, and about decision systems so I’m not going to rehash that here, but instead I want to move on to a different part of this subject, how companies could use data and get relevant data to help in software buying decisions.

Software is critical to operating a modern business, which also means that the types and sources of that software have multiplied to an almost incomprehensible point. The way software is delivered is changing, and the transition from older to newer models adds to the complexity by creating hybrid solutions that need to be integrated. The idea of sourcing all business software from one or even only a few software vendors is an enigma from a different time, at least for large businesses. Suites, or more appropriately “clouds”, still exist of course, but they are hierarchal sets of nested clouds, wade up of many modules. For the mid and small business if is common to find the deployment of a broad set of functionality in a cloud suite, but even then there are many other specialty products needed to meet changing functional needs, and deeper vertical capabilities than horizontal suites can  handle without complex customizations. I guess you could sum this paragraph up by saying simply that business technology is critical and often complex.

Dealing with this growing level of complexity for the business software landscape is a challenge for any size business, although the process does tend to vary by business size segment. Even with that variation of process the supporting data is fairly common across all the size segments. The basic flow of a selection process should have common elements, but the actual decision is related to organizational complexity and company governance and compliance guidelines. The steps should include, at a minimum:

• Awareness of business issue(s)
• Business needs analysis / research and identify potential solutions
• Develop functional and technical requirements
• Research solutions mapped to requirements
• Short list candidate solutions
• Evaluate short list
• Selection

These “steps” are relatively generic by design, so as to present a broad enough framework to accommodate a variety of requirements and processes. There are more need/gap analysis activities post selection, but that gets into implementation so not really the subject of this post. A few other observations, first, this is not a linear process at all, so businesses will move forward and backward inside the steps as necessary. Second, you’ll see that I put research in twice, I’m not really sure if I should just call all 4-5 of the steps up to selection research, but calling it out in the way I have at least focuses on the activity that should be driving the whole set of activities. Third, don’t get caught up in the name of the step, nor in some artificial timeline, timing varies by business size, problem complexity, internal process, culture, governance controls, etc. In a small business the whole process could be handled by the “owner”, or in a mid-size or large business a team of many people, depending on the system being evaluated.

With that sort of simple process in mind, let’s look at data to support the process. The first part of the analysis can be initiated from almost anywhere, so the potential data sources there are very broad. It could be kicked off by a system replacement cycle, a change in a process, a need for greater efficiency, a breakdown in strategy or new competitive threats…the list is endless. The conversation around this is back to the decision system I mentioned above and wrote about several times over the past couple of years. Once you understand your problem or opportunity (all issues aren’t just created by a problem of course, it could be a new business opportunity that requires some system changes to support them), then you can start to use external data sources to identify ways to solve the issue(s).

When you’re looking for external data the list of criteria is fairly simple, it has to be from a trusted source, it has to be relevant to your current situation, and it has to be accessible at the right time. Trusted source is ultimately based on a personal decision but there are factors that can influence that trust. One factor that is pretty common to people in general is the idea of “like me”. In other words we look for advice and tend to trust opinions from people that are to some degree similar to us. Offline we have sought out people like us for ages, you join groups that have similar interests, are culturally similar, have similar experiences, etc., so it’s no surprise that our behavior online is modeled after this same concept. Trust is very high when the online source is from that ”like me” category. The PR/Marketing firm Edelman does an annual global survey and report on trust called the Edelman Trust Barometer. In that report they look at trusted sources of information and advice, including where people go for purchase support information. In general peer generated media are 2 of the 3 most trusted sources of information, search and social. When looking at categories of “spokespersons”, “people like me” was 3^rd in 2016 at 63% (up from 57% in 2015), only behind academic expert 64% and technical expert 67%. What’s more telling though, is what is lower than people like me and how wide the gap is between the top 3 and the rest: financial industry analyst 53%, employees 52%, CEO 49%, NGO representative 48%, board of directors 44% and at the bottom, government official at 35%. That gap is very telling.

To support the data needs of researching solutions to an issue or opportunity, you can look to several sources that would meet the criteria of trusted, relevant and accessible. I talked about influence before, particularly in this post, so I’m not trying to rehash that topic. Instead I will look at some potential matches to the criteria for decision support data. Here are a few ideas:

• Public social networks: This is a good source of information if a bit variable depending on how connected you are to people that meet the criteria of course. Assuming that you tend to connect with people like you, then you’d assume that at least a part of your network would meet that criteria. Relevancy might be a little harder to find but again, I’d assume that it could be met, and of course accessibility isn’t likely an issue. I’d rate this as a highly useful source with a few caveats.
• Media (online and traditional): This is a bit more complicated since you’re searching and could have a little more difficulty getting to information that would meet all 3 criteria, but not impossible. Most of us have a few media sources that we already trust so it’s a matter of searching for content that is relevant to your specific set of questions. Accessibility might be an issue if the media was behind a paywall that you did not or chose not to support. I’d rate this one as medium useful as long as accessibility is dealt with.
• Industry organizations: A relatively good source of data, at least at the exploratory level of the research. This would depend on the level of relevancy and detail of the data, which varies by organization. Accessibility is restricted but if you’re planning on using it as a source I’d assume that you have or plan to solve the access issue. Since the nature of the organization is to be “like you”, at least at the industry level, this should be a medium to highly useful source.
• Industry analysts: For most industries there is a healthy set of analyst firms that provide service to that industry as well as the technology focused firms. Access depends on several factors but usually comes down to a paid relationship. The research will likely be helpful at some level but perhaps the more useful option is inquiry with an expert, and as the Trust Barometer shows, experts are very trusted. The information varies by firm, analyst, research methodology and experience so going into the inquiry with as much background on the firm and analyst is wise. You will probably not find complete alignment on the “like me” part of the data search, but if you have a trust relationship with the analyst the information would most likely be highly useful.
• Consultants: Many of the same caveats and observation apply to consultants as well. Both analysts and consultants should spend some time learning about your business and the issues before offering advice and information. With consultants generally the methodology allows more time for this phase of gaining understanding about your specific issues than with analysts. The usefulness and appropriateness varies by company and individual needs and always remember that there are a lot of consultants out there (and analysts too) so quality can vary so best to do a through bit of research yourself before engaging them. The outcomes can be highly useful, tempered with some common sense on your part.
• Vendor sponsored resources: There’s often a lot of valuable content and data available on vendor web sites. The issue may be one of trust however. Fairly consistently in surveys the vendor web site and sponsored content scores somewhat less trustworthy than independent resources. Even so, you can get useful information from the vendor and should check it if, and when you have learned enough to know possible solutions (in other words when you know the vendor is relevant to your situation). The variability of the resources will be great and you have to exercise an amount of critical thinking when you’re using them but they can be at least of medium usefulness.
• Vendor sales professionals: This is a difficult one since you inherently (at lest in a broad generalization) don’t trust sales reps. In a recent G2 Crowd survey (March 2016) 62% of the respondents reported that they only contact the vendor sales rep after they have made the purchase decision, which is certainly way after any data they would find could be used. I guess this is not very useful although it really depends on the relationship and the sales rep, not the generalization.
• Vendor reference companies: Vendor references can be vey helpful in the decision process if they are enough like you, and are trustworthy. It’s useful to understand the motivation behind taking the call. It can simply be that the solution is working and the person wants to share that, or in some cases there can be more to the arrangement…bet to be careful. In my experience the biggest issue is getting the “right” references and the “right” individuals to participate. If it meets the criteria then it can be highly useful, if not, then probably not very useful at all. Also remember that talking to 1 or 2 data sources is pretty limiting in the overall scheme of data collection.
• Peer review communities: Well I’m a little biased here since I work for one, but there is a lot of valuable data to be gleaned from peer reviews. From a references standpoint the crowdsourced nature of the reviews, which are in reality in-depth surveys, the information is much more likely to meet all 3 criteria. I do have some criteria for establishing trust of course, refer to this post for those. That post also does a good job of covering the types and use of information on leading review sites so I won’t go back through it here. I will just say that the better peer review sites do met all 3 criteria and are highly useful.

I didn’t spend much time on search engines, even though they are still rated very high on the Trust Barometer. That’s not a slight, certainly search will prove invaluable to your research, although I would caution that there is a lot of sponsored content there too, so use some discretion and common sense. I’m sure I’ve left out a bunch of other resources so if you think of one/some pls leave them in the comments.

You went to bed with the rumor or woke up to the announcement that Verizon is buying Yahoo (well, most of Yahoo anyway, the so called “core” business) for $4.83 billion. Left behind will be, for now anyway, a nameless investment company with shares in Alibaba and Yahoo Japan, all Yahoo’s cash and convertible notes, some minority investments and the Excalibur patent portfolio which includes all non-core patents, about 2600 in total. The Excalibur portfolio is also for sale, with an estimated value somewhere over $1 billion. The Yahoo brand goes with the sale to Verizon, whether it will continue going forward is anyone’s guess, but there is precedent for keeping the acquired brand active as Verizon has done with AOL, acquired last year. The deal will likely close in Q1 2017.

If you’re scratching your head a bit on this match, there are a couple of reasons why this makes sense for Verizon beyond the purple logo and “!”. The addition of Yahoo is intended to bolster Verizon’s content creation and curation efforts and extend Verizon’s ad network (and of course there is the nostalgic component of bringing AOL and Yahoo together in one portfolio, sort of a trip back to the 1990’s. The funny aside of course is that AOL had tried and failed over 2 years ago to put the 2 companies together, prior to the Verizon AOL acquisition.). At the time of the AOL deal Verizon stated that it’s intent was to create a “cross-screen” experience for consumers, content creators and advertisers. That seems to be a pretty good summary of this deal as well.

Verizon, which has struggled on the content side as it’s cable / ISP business felt the pressure from content publishers like Netflix and Hulu, on mobile/digital advertising as it owned the infrastructure (the so called “dumb pipe”) but not the ad network with Google, Facebook and a host of emerging players from Pinterest to Instagram, has been on an acquisition spree for the past few years. It added several online video streaming properties lately in addition to AOL. While struggling along for the past 10+ years, Yahoo does bring an ad network with scale, and scale is essential for successfully competing with the momentum of Google and Facebook. For digital advertising revenues Yahoo was 5th worldwide in 2015 behind Google, Facebook, Baidu, and Alibaba; and 7th so far in 2016 with Tencent and Microsoft slipping ahead. Added to Verizon’s existing ad share the acquisition essentially doubles digital advertising share and in the US makes Verizon a (distant) third behind leader Google and at about half of Google’s share, Facebook.

Verizon gets more than the ad network of course, Yahoo still has search, mail (don’t laugh, a lot of people don’t know it’s not cool to have Yahoo or AOL mail addresses), and content including Yahoo News, Finance and Sports, and of course acquisitions like Tumblr and Flickr. The Yahoo content properties are added to The Huffington Post, AOL.com, MAKERS, Endgadget and TechCrunch, giving Verizon a fairly wide set of content channels.

In her letter to Yahoos, CEO Marissa Mayer said she would stay on through the transition, but there is no mention of her joining Verizon. There is some speculation that she would take the reins of the yet unnamed investment company that remains, but that’s definitely a unknown. Mayer, who joined Yahoo from Google on 2012 to turn the business around, will leave Yahoo with a (according to an SEC filing) severance package of ~$55M.

Like all acquisition announcements there is still a lot of unknowns, but those holes will get filled in over the next 2 quarters as the deal progresses through its approvals by Yahoo shareholders and a host of regulatory reviews.