Guiding Technology Decisions from Know to Yes
It’s not news that times have changed and Microsoft with it. Instead of looking to destroy Linux and FOSS, Microsoft CEO Satya Nadella has embraced it.
Microsoft has begun to meld with the FOSS community, creating Linux-Windows combinations that were unthinkable in the Ballmer era.
In just the past few years Microsoft has:
My name is Lynne Baer, and I’ll be covering the world of data science software for Amalgam Insights. I’ll investigate data science platforms and apps to solve the puzzle of getting the right tools to the right people and organizations.
“Data science” is on the tip of every executive’s tongue right now. The idea that new business initiatives (and improvements to existing ones) can be found in the data a company is already collecting is compelling. Perhaps your organization has already dipped its toes in the data discovery and analysis waters – your employees may be managing your company’s data in Informatica, or performing statistical analysis in Statistica, or experimenting with Tableau to transform data into visualizations.
But what is a Data Science Platform? Right now, if you’re looking to buy software for your company to do data science-related tasks, it’s difficult to know which applications will actually suit your needs. Do you already have a data workflow you’d like to build on, or are you looking to the structure of an end-to-end platform to set your data science initiative up for success? How do you coordinate a team of data scientists to take better advantages of existing resources they’ve already created? Do you have coders in-house already who can work with a platform designed for people writing in Python, R, Scala, Julia? Are there more user-friendly tools out there your company can use if you don’t? What do you do if some of your data requires tighter security protocols around it? Or if some of your data models themselves are proprietary and/or confidential?
All of these questions are part and parcel of the big one: How can companies tell what makes a good data science platform for their needs before investing time and money? Are traditional enterprise software vendors like IBM, Microsoft, SAP, SAS dependable in this space? What about companies like Alteryx, H2O.ai, KNIME, RapidMiner? Other popular platforms under consideration should also include Anaconda, Angoss (recently acquired by Datawatch), Domino, Databricks, Dataiku, MapR, Mathworks, Teradata, TIBCO. And then there’s new startups like Sentenai, focused on streaming sensor data, and slightly more established companies like Cloudera looking to expand from their existing offerings.
Over the next several months, I’ll be digging deeply to answer these questions, speaking with vendors, users, and investors in the data science market. I would love to speak with you, and I look forward to continuing this discussion. And if you’ll be at Alteryx Inspire in June, I’ll see you there.
On April 9th and 10th, Amalgam Insights attended the fifth Cloudera’s Industry Analyst and Influencer Conference (which I’ll self-servingly refer to as the Analyst Conference since I attended as an industry analyst) in Santa Monica. Cloudera sought to make the case that it was evolving beyond the market offerings that it is currently best known for as a Hadoop distribution and commercial data lake in becoming a machine learning and analytics platform. In doing so, Cloudera was extremely self-aware of its need to progress beyond the role of multi-petabyte storage at scale to a machine learning solution.
Blockchain looks to be one of those up and coming technologies that is constantly being talked about. Many of the largest IT companies – IBM, Microsoft, and Oracle to name few – plus a not-for-profit or two are heavily promoting blockchain. Clearly, there is intense interest, much of it fueled by exotic-sounding cryptocurrencies such as Bitcoin and Ethereum. The big question I get asked – and analysts are supposed to be able to answer the big questions – is “What can I use blockchain for?”
Recommended Audience: CIOs, Enterprise Architects, Data Managers, Analytics Managers, Data Scientists, IT Managers Vendors Mentioned: Trifacta, Paxata, Datameer, Datawatch, Lavastorm, Alation, Tamr, Unifi, 1010Data, Podium Data, IBM, Domo, Microsoft, Information Builders, Board, Microstrategy, Cloudera, H20.ai, RapidMiner, Domino Data Lab, Dataiku, TIBCO, SAS, Amazon Web Services, Google, DataRobot. In case you missed it, I just finished…
API management is a necessary but boring practice. As developers make use of a mix of public cloud, purchased or open source libraries, and homegrown services, the number of APIs used by developers quickly renders pouring through documentation impractical.
Microservices, usually accessed via RESTFul APIs, cause API calls to rapidly proliferate. Even modest-sized microservices-based systems experience API overload quickly. Agile development can exacerbate the problem of understanding and using APIs. The rapid pace of Agile, especially Scrum, leaves little time for proper documentation of APIs. Documentation often takes a back seat to continuous deployment.
Continue reading “As API Management Problem Grows, Informatica Jumps into the Market”
On the week of September 25th, 2017, Microsoft made a huge announcement at its annual Ignite and Envision conference. Microsoft has become one of a small number of companies that is demonstrating quantum computing. IBM is another company that is also pursuing this rather futuristic computing model.
For those who are not up-to-date on quantum computing, it uses quantum properties such as superposition and entanglement to develop a new way of computing. Current computers are built around tiny electron switches called transistors that allow for two states, which represent the binary system we have today. Quantum computers leverage quantum states that give us ones, zeros, and combinations of one and zero. This means a single qubit, the quantum equivalent of a bit, can represent many more states than the bit can. This is, of course, a gross oversimplification but quantum computing promises to deliver more dense and exponentially faster computing.
There are a number of problems with practical quantum computing. The hardware is still in a nascent stage and must be cooled to a temperature that is quite a bit colder than deep space. This makes it much more likely that quantum computing will be purchased via a cloud model than on-premises. The other inhibitor is that there is no standard programming model for quantum computing. IBM has demonstrated a visual programming model that shows how quantum computing works but is clearly not going to be a serious way to write real programs. Microsoft, on the other hand, showed a more standard looking curly bracket programming language. This application layer makes quantum computing more accessible to existing programmers who are more used to the current model of computing.
When quantum computing becomes practical – I would predict that is at least 5 years away, perhaps longer – it won’t be for everyday computing tasks. The current model is already more than adequate for those tasks. It’s also unlikely that the capabilities of quantum computers, especially the information dense qubit, and costs will have much a place in transactional computing. Instead, quantum computing will be used for analyzing very large and complex data sets for simulation and AI. That’s fine because the AI and analytics market is still new and the future needs are not yet completely known. That future computing needs is what quantum computing is meant to address. Even today’s big data applications can stretch computing capabilities and force batch analytics instead of real-time for some use cases.
Microsoft’s entry into what has been an otherwise esoteric corner of the computing world signals that quantum computing is on the path to being real. It has a long way to go and many obstacles to overcome but it’s no longer just science fiction or academic. It will be years but it is on the way to becoming mainstream.
Note: This post was originally posted on Tom’s Take
This past week (September 25 – 27, 2017) Microsoft held its Ignite and Envision Conferences. The co-conferences encompass both technology (Ignite) and the business of technology (Envision). Microsoft’s announcements reflected that duality with esoteric technology subjects such as mixed reality and quantum computing on equal footing with digital transformation, a mainstay of modern business transformation projects. There were two announcements that, in my opinion, will have the most impact in the short-term because they were more foundational.
The first announcement was that machine learning was being integrated into every Microsoft productivity and business product. Most large software companies are adding machine learning to their platforms but no company has Microsoft’s reach into modern businesses. Like IBM, SAP and Oracle, Microsoft can embed machine learning in business applications such as CRM. Microsoft can also integrate machine learning into productivity applications as can Google. IBM can do both but IBM’s office applications aren’t close to having the market penetration of Microsoft Office 365. Microsoft has the opportunity to embed machine learning everywhere in a business, a capability that none of their competitors have.
Continue reading “Microsoft Infuses Products with Machine Learning and the Social Graph”
Note: To read Part 1 of Amalgam’s coverage of Microsoft’s ASC 606 adoption, please check how Microsoft Early Adopts New ASC 606 Revenue Recognition Standard.
Recommended Audience: CFO, Chief Revenue Officers, CIOs, COOs, IT Finance, Sales Operations seeking to understand how ASC 606 revenue recognition changes will affect their responsibilities.
On August 3rd, 2017, Microsoft held an investor metrics conference call led by:
This call was focused on its implementation of new accounting standards, including ASC 606 for revenue recognition and ASC 842 for lease accounting.
There have been multiple acquisitions and announcements in the revenue recognition space as IT vendors ensure that they can support the ASC 606 standard including: