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Get ready! Top Dynamics 365 Trends Coming your Way in 2021

January 15, 2021 CRM News and Info

xget ready top dynamics 365 trends coming your way 2021 625x316.jpg.pagespeed.ic.Id 3PmwwB3 Get ready! Top Dynamics 365 Trends Coming your Way in 2021 2020. A year we will remember. A year that was not quite as we expected it to be – to say the least! Still, the Earth keeps spinning, the software world keeps evolving, and you don’t want to fall behind.

There are several Dynamics 365 trends out there and you should keep an eye on them. Why are they important? These trends will shape your professional landscape in 2021 and beyond, so insights into what to expect and where to invest your efforts can make a difference.

Let’s have a look at our top 6 and provide specific examples as we go along. That’s the best way to get to grips with the latest tech and understand what it means in a living, breathing business context.

What Are the Essential Dynamics Trends for 2021?

2021 has plenty of exciting innovations in store, many of which are just beginning to reveal their full potential. To keep your finger on the pulse, let’s get started and focus on the top tech trends for Microsoft Dynamics users in 2021:

Read on and make sure that you take the right turns as the new year unfolds!

Dynamics 365 Trends #1 – Use Blockchain to Prove a Document’s Authenticity

Exciting and futuristic, Blockchain has been a buzzword for some years now. Above all, people associate Blockchain with Bitcoin and other cryptocurrencies. As Blockchain use evolves, experts predict that it will become less about cryptocurrency and more about using it for a different aim: prove a document’s authenticity.

Did you even know you can use Blockchain technology’s security and distributed features to prove a document is untampered? Well, it turns out that you can, this is available on the market now. And the good thing about it is that your files and documents become trustworthy data. In other words, your files become a sound base for your company’s strategic business decisions. That makes all the difference.

Stefano Tempesta, Microsoft Regional Director and advisor to Australia’s National Blockchain Roadmap, believes that “With blockchain, you can imagine a world in which every agreement, every process, every task, and every payment would have a digital record and signature that could be identified, validated, stored, and shared. Intermediaries like lawyers, brokers, and institutions like notaries might no longer be necessary. Individuals, organizations, and machines would freely transact and interact with one another with little friction.”. In an article on the future of Blockchain, he discusses possible uses of one such solution for SharePoint documents that is already out on the market and that can be easily integrated with Dynamics 365. We will certainly hear more about that during 2021.

Dynamics 365 Trends #2 – AI, the Slow but Strong Trend

AI has been a promising trend for 30 years or more now. Don’t you have the feeling we keep hearing AI is going to be the next big thing? As we enter 2021, I think it is time to realize AI is not a “big burst” type of trend. It is a continuously growing trend that goes into more and more areas of our daily lives. We currently see significant improvements in machine vision, natural language processing (NLP) and automated speech recognition (ASR). These will facilitate the structuring of unstructured data such as images or emails.

In the big world of Dynamics 365, we see AI coming into very distinct areas, from Dynamics 365 Fraud Protection to Dynamics 365 Virtual Agent for Customer Service. The area where we expect the most significant impact of AI will be in Dynamics 365 Customer Insights, with AI helping to define segments and predictive models.

Dynamics 365 Trends #3 – Integrate with Microsoft Teams for Better User Adoption

Not even Microsoft would have guessed Teams would reach 115 million daily active users like it did back in October 2020. Microsoft Teams has become ubiquitous, thanks in large part to need for remote work and remote events.

This makes the integration with Microsoft Teams a plus for other products. Moreover, it is also a way to help drive other products’ user adoption. Within Microsoft 365 business you can see all Office apps fully links with Teams and that also happens with SharePoint. The level of integration for Dynamics 365 is not the same just yet, but the trend is for Dynamics 365 to be more and more integrated with Teams. Currently, you have the Dynamics 365 app available for Teams. Besides the normal use of the app within Teams, you can “chat” to get the D365 information you need from within Teams. You can also add a Dynamics 365 tab into a Microsoft Teams channel and access customer engagement app records there. This way, you and your team members can collaborate on one or multiple records (Accounts, Contacts, Opportunities, among others).

If you have custom applications that are important in your business daily routine, you can take this one step further and consider surfacing them in Teams. This is a new and evolved means of getting new users to use those custom applications, therefore driving user adoption. It might even help existing users get into the habit of using those same apps more.

Dynamics 365 trends #4 – IIoT meets Dynamics 365

Dynamics 365 offers organizations across different industries the ability to know their customers as well as their own organizations. The idea is always to enable predictive insights and, therefore, smarter decisions.

Next on our big rundown of Dynamics trends for 2021 is the trend to enable this in manufacturing and industrial settings. How? By connecting Dynamics to shop floor information, leveraging data from machines and sensors. This gets the information to where the organization needs it – in 2021, we will be evolving from reactive decisions to proactive analytics that can support the decision process.

Yes, this is definitely the most industry-specific trend on the list. Nonetheless, for this industry, this trend is a game-changer. By using Dynamics to make these analytics and insights accessible throughout an industrial organization, great things will happen.

There are many interesting use cases for combining IIoT with business software like Dynamics:

Go for predictive maintenance and prevent machine breakdowns by monitoring your machines’ condition and performance during regular operation and spotting early signs of the problem.
Implement remote monitoring and stay informed (real-time) by viewing the information you need with the appropriate detail level.
Improve production planning by having all the relevant information right where you need it, and you can finally make production planning more information-based and much more straightforward.

As you can see, IIoT meeting Dynamics covers a range of possibilities and concepts, giving it various use cases. As such, it’s one of the 2021 trends you can’t afford to ignore.

Dynamics 365 trend #5 – Guarantee Security for Software Integrations

The next trend in our rundown is security related. There is an increasing need to share data while maintaining privacy and security. That is NOT going to stop in 2021.

In our hyper-connected digital age, the trend for 2021 is to guarantee security when software integrations are involved. Truth be told, it is natural to assume that there is no danger in integrating two separate software pieces if you are already using them both. The only problem is that that assumption is wrong.

Let’s have a look at an example. Say you use Microsoft Dynamics 365 and Microsoft SharePoint. One day you discover that Microsoft provides an integration between the two. Cool! You can have the documents you previously had piled up in Dynamics automatically transferred to SharePoint but still readily accessible from Dynamics. It sounds like a brilliant idea!

You could very easily overlook the fact that the privileges for the documents that you had carefully set up in Dynamics are not going to be transferred to SharePoint. Everyone can see everything from the SharePoint side! Unknowingly, you would have created a security and privacy threat.

Luckily, the fact that there are more integrations than ever also means the solutions for these integration security problems are showing up – and this trend will be stronger throughout 2021.

For example, for the Dynamics and SharePoint integration problem above, you now have a solution. Moreover, you also have an add-on to that solution that ensures an automatic organization into folders of those documents on SharePoint.

Dynamics 365 trends #6 – Beware of your Dynamics 365 Storage Options

The last in our rundown of Dynamics trends for 2021 is storage. It was already common for Dynamics 365 admins to look for alternative storage for documents and attachments. The motivation for this was saving on costs and increasing performance. Alternative storage locations were frequently SharePoint or Azure Storage.

In 2021, we perceive that security is becoming one of the primary motivators for using alternative storage. As a result, we are already seeing new providers emerging in a market that felt consolidated up to now.

For example, in German-speaking countries, Dracoon is getting strong and will release the possibility of integrating with Dynamics 365 still in this quarter.

Takeaways

By embracing the prevailing trends in 2021 and using them to your advantage, you will accelerate your business’s growth in an ever-changing landscape.

You can hit the ground running as a new year unfolds!

Talk with Connecting Software’s experts if you want help putting these trends to practice in your organization. They will be more than happy to answer any questions you might have or jump on a quick call to show you how all this can work.

Get ready! Top Dynamics 365 Trends Coming your Way in 2021

By Ana Neto

Software engineer since 1997, she is now a technical advisor for Connecting Software.

Connecting Software is a producer of integration and synchronization software solutions since 2004. We operate globally and we are also a proud “Top Member 2019” at CRMSoftwareBlog.

Get ready! Top Dynamics 365 Trends Coming your Way in 2021

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Visualize 5 Cool Insights on Holiday Tree Trends Over Time

December 31, 2020 TIBCO Spotfire

Reading Time: 3 minutes

Did you know Thomas Edison’s assistants proposed putting electric lights on Christmas trees? There’s a long and rich history surrounding holiday trees, in America and around the world. According to the History Channel, symbolic traditions involving evergreen trees in winter began in ancient Egypt and Rome and continue to take on new meaning today.

New Holiday Traditions: Annual Analytics

Here at TIBCO, we’ve started our own holiday tradition involving the classic festive trees: using our data visualization software to understand trends in holiday tree data. Last year, we shared our analysis and “treemap” visualization (quite literally a treemap of trees) via TIBCO Spotfire^®. This year, we dived even deeper into the data, using the new Spotfire Mods functionality to design custom apps for greater interactivity. Here’s what we found:

Top Tree Producing States: All 50 states contribute to the holiday tree industry, but our analysis shows the greatest production occurs in Oregon, North Carolina, Michigan, and Pennsylvania. Also interesting is that while Oregon and North Carolina are top producers overall, states like Ohio and Michigan definitely over-index for total tree producing counties as a percentage of their total land area.

Visualize 5 Cool Insights on Holiday Tree Trends Over Time — *Immersive, interactive exploration of a bubble “tree-map” visualization Mod alongside county-level Spotfire geoanalytics [*source: USDA census data]*

Artificial vs. Real Tree Sales: As you can see below, artificial tree sales have been on the rise over the last decade, with 162 percent growth between 2004 and 2018. Artificial trees are taking over. Actually, 81 percent of the trees on display, whether in storefronts, businesses, or homes, in 2019 were of the artificial variety. But what does that mean for the global economy when China produces 80 percent of artificial trees worldwide and given that artificial trees cannot be recycled like real trees?

Rising Average Price of Real Trees: According to an article in the Hustle, “During the recession in 2008, ailing farmers planted too few trees. As a result, prices have been much higher since 2016.” The article also cites the National Christmas Tree Association as stating that the average retail price for a real tree in 2019 was $ 75. Obviously, this is a huge market, but one that continues to shift with economic and social changes—which makes us wonder just how different our analysis next year will look.

Consumer Demand Lower in 2019: In the area chart visualization above, we see that sales for natural trees still account for a larger share of the market. However, the artificial tree category set new high marks for sales in each year progressively from 2016 to 2018. Why could this be? One hypothesis might be that as Baby Boomers retire as “empty-nesters” and downsize their homes, they are buying fewer trees, but let us know your thoughts on this surprising find.

The More the Merrier? Multiple Trees: According to a survey by the American Christmas Tree Association, the number of households in the United States that display more than one Christmas tree has grown by 10 percent from 2014 to 2019. In 2019, approximately 16 percent of American households display multiple trees. But will this trend continue or, as with the overall tree sales, will the number of trees per household decrease in the coming years?

We’ve started our own holiday tradition involving the classic festive trees: using our data visualization software to understand trends in holiday tree data. Click To Tweet

A New Tradition: Immersive Yourself in Custom Analytics Applications

But this is just one festive story you could tell around data trends. What about shopping trends this year, will there be an increase in small business online sales? What will be the top gifted items in 2020?
You tell us! Join our tradition, and read our whitepaper to learn how the immersive qualities of Hyperconverged Analytics will create new value for your business. For a closer look at all of “What’s New in Spotfire^®” including visualization Mods, watch our 20-minute intro webinar on demand.

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9 trends in enterprise database technology

December 30, 2020 Big Data

The database has always revolved around rock-solid reliability. Data goes in and then comes out in exactly the same way. Occasionally, the bits will be cleaned up and normalized so all of the dates are in the same format and the text is in the same character set, but other than that, nothing should be different.

That consistency is what makes the database essential for any enterprise — allowing it to conduct things like ecommerce transactions. It’s also why the database remains distinct from the data warehouse, another technology that is expanding its mission for slower-twitch things like analysis. The database acts as the undeniable record of the enterprise, the single source of truth.

Now databases are changing. Their focus is shifting and they’re accepting more responsibilities and offering smarter answers. In short, they’re expanding and taking over more and more of the stack.

Many of us might not notice because we’ve been running the same database for years without a change. Why mess with something that works? But as new options and features come along, it makes sense to rethink the architectures of data flows and take advantage of all the new options. Yes, the data will still be returned exactly as expected, but it will be kept safer and presented in a way that’s easier to use.

Many drivers of the change are startups built around a revolutionary new product, like multi-cloud scaling or blockchain assurance. For each new approach to storing information, there are usually several well-funded startups competing to dominate the space and often several others still in stealth mode.

The major companies are often not far behind. While it can take more time to add features to existing products, the big companies are finding ways to expand, sometimes by revising old offerings or by creating new ones in their own skunkworks. Amazon, for instance, is the master at rolling out new ways to store data. Its cloud has at least 11 different products called databases, and that doesn’t include the flat file options.

The other major cloud providers aren’t far behind. Microsoft has migrated its steadfast SQL Server to Azure and found ways to offer a half-dozen open source competitors, like MySQL. Google delivers both managed versions of relational databases and large distributed and replicated versions of NoSQL key/value pairs.

The old standards are also adding new features that often deliver much of the same promise as the startups while continuing support of older versions. Oracle, for instance, has been offering cloud versions of its database while adding new query formats (JSON) and better performance to handle the endless flood of incoming data.

IBM is also moving dB2 to the cloud while adding new features like integration with artificial intelligence algorithms that analyze the data. It’s also supporting the major open source relational databases while building out a hybrid version that merges Oracle compatibility with the PostgreSQL engine.

Among the myriad changes to old database standards and new emerging players, here (in no particular order) are nine key ways databases are being reborn.

1. Better query language

SQL may continue to do the heavy lifting around the world. But newer options for querying — like GraphQL — are making it easier for front-end developers to find the data they need to present to the user and receive it in a format that can be dropped right into the user interface.

GraphQL follows the standard JavaScript format for serializing objects, making it easier for middle- and front-end code to parse it. It also hides some of the complexity of JOINs, making it simpler for end users to grab just the data they need. Developers are already adding tools like Apollo Studio, an IDE for exploring queries, or Hasura, an open source front-end that wraps GraphQL around legacy databases like PostgreSQL.

2. Streaming databases follow vast flows

The model for a standard database is a big ledger, much like the ones clerks would maintain in fat bound books. Streaming databases like ksqlDB are built to watch an endless stream of data events and answer questions about them. Instead of imagining that the data is a permanent table, the streaming database embraces the endlessly changing possibilities as data flows through them.

3. Time-series database

Most database columns have special formats for tracking date stamps. Time-series databases like InfluxDB or Prometheus do more than just store the time. They track and index the data for fast queries, like how many times a user logged in between January 15 and March 12. These are often special cases of streaming databases where the data in the streams is being tracked and indexed for changes over time.

4. Homomorphic encryption

Cryptographers were once happy to lock up data in a safe. Now some are developing a technique called homomorphic encryption to make decisions and answer queries on encrypted data without actually decrypting it, a feature that vastly simplifies cloud security and data sharing. This allows computers and data analysts to work with data without knowing what’s in it. The methods are far from comprehensive, but companies like IBM are already delivering toolkits that can answer some useful database queries.

5. In-memory database

The original goal of a database was to organize data so it could be available in the future, even when electricity is removed. The trouble is that sometimes even storing the data to persistent disks takes too much time, and it may not be worth the effort. Some applications can survive the occasional loss of data (would the world end if some social media snark disappeared?), and fast performance is more important than disaster recovery. So in-memory databases like Amazon’s ElasticCache are designed for applications that are willing to trade permanence for lightning-fast response times.

6. Microservice engines

Developers have traditionally built their code as a separate layer that lives outside the database itself, and this code treats the database as a black box. But some are noticing that the databases are so feature-rich they can act as microservice engines on their own. PostgreSQL, for instance, now allows embedded procedures to commit full transactions and initiate new ones before spitting out answers in JSON. Developers are recognizing that the embedded code that has been part of databases like Oracle for years may be just enough to build many of the microservices imagined by today’s architects.

Jupyter notebooks started out as a way for data scientists to bundle their answers with the Python code that produced it. Then data scientists started integrating the data access with the notebooks, which meant going where the information was stored: the database. Today, SQL is easy to integrate, and users are becoming comfortable using the notebooks to access the database and generate smart reports that integrate with data science (Julia or R) and machine learning tools. The newer Jupyter Lab interface is turning the classic notebook into a full-service IDE, complete with extensions that pull data directly from SQL databases.

7. Graph databases

The network of connections between people or things is one of the dominant data types on the internet, so it’s no surprise that databases are evolving to make it easier to store and analyze these relationships.

Neo4j now offers a visualization tool (Bloom) and a collection of data science functions for developing complex reports about the network. GraphDB is focusing on developing “semantic graphs” that use natural language to capture linguistic structures for big analytic projects. TerminusDB is aimed at creating knowledge graphs with a versioning system much like Git. All of them bring efficiency to storing a complex set of relationships that don’t fit neatly into standard tables.

8. Merging data storage with transport

Databases were once hidden repositories to keep data safe in the back office. Delivering this information to the user was the job of other code. Now, databases like Firebase treat the user’s phone or laptop as just another location for replicating data.

Databases like FaunaDB are baking replication into the stack, thus saving the DBA from moving the bits. Now, developers don’t need to think about getting information to the user. They can just read and write from the local data store and assume the database will handle the grubby details of marshaling the bytes across the network while keeping them consistent.

9. Data everywhere

A few years ago, all the major browsers began supporting the Local Storage and Indexed Storage APIs, making it easier for web applications to store significant amounts of data on the client’s machine. The early implementations limited the data to 5MB, but some have bumped the limits to 10MB. The response time is much faster, and it will also work even when the internet connection is down. The database is not just running on one box in your datacenter, but in every client machine running your code.

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Anti-Predictions: 8 Future-proof Analytics Trends for 2021 and Beyond

December 12, 2020 TIBCO Spotfire

Reading Time: 3 minutes

This is not one of those “crystal ball” new year prediction blogs. In the wake of a most unpredictable year full of black swan events, now is the time for data scientists and business analysts to humbly take the hard-learned lessons of 2020 and move forward.

So, rather than chase buzzword mysticism of sage prognostication, let us default back to a tried-and-true model for charting a future-proof course forward: industry research trends.

The Eight Trends That Will Matter Most

Professional research fields often seek validation of directional trends with a minimum of three data points. It’s the magic number, and the basis for establishing the beginning of a true “pattern.” As such, the following is an index of these rolled up trends as themes which echoed persistently from industry analysts as top analytics trends for 2021. And check out companies that are ahead of the curve in each category and next steps for your business!

Intelligent, adaptable business…

Very complex global market conditions will demand more shifts and pivots again in 2021, and organizations will need to be lean and agile enough to respond in real time. If 2020 taught us anything: in business climates with rapidly changing global market conditions, decisions must be data-informed, but perhaps most importantly, nimble enough to adapt to both risk as well as new opportunity as they develop—on much shorter frequencies and turnaround.

Ahead of the curve: CargoSmart. WATCH

More AI-driven automation in the workplace…

With as many as one in every four remote workers supported by new forms of automation (Robotic Process Automation (RPA), etc.), more artificial intelligence (AI) Proof-of-Concept projects will continue to roll off the line and progress into production environments. While there will be more automation in decision environments, humans will remain a vital cog in the decision-making process.

Ahead of the curve: Bayer Crop Science innovating with image analysis and more. READ

Growth on The Edge…

Computing continues to get closer to where data is actually generated. As a result, real-time analysis of those edge events is being built into models sooner—when and where decision makers need it. With the explosion of sensors and connected smart devices, ever-increasing Internet of Things (IoT) data exhaust will be the fuel for optimization loops to predict and improve future performance.

Ahead of the curve: Siemens Mobility. WATCH

Trust and ethics: more accountable AI…

Trust will remain the top spot reason why executive decision makers don’t believe AI-recommendation systems. While 94 percent of leaders who’ve adopted AI trust their data, only 64 percent of those trust it to be reliable enough for business change. Further operationalization of “responsible AI” will be underpinned by the growing discussion of digital ethics and bias.

Next step: For more, read APEX of Innovation blog on AI.

Closed-loop decision models…

Businesses will need to leverage learnings and harness collective knowledge as a shared asset in 2021. Using closed-loop decision models to accomplish this, organizations can accelerate knowledge sharing and develop pipelines to support learning.

Ahead of the curve: Mercedes-AMG Petronas F1’s industrial optimization model. READ

Convergence of analytics technology continues…

Whether machine learning, data management, or governance, these formerly discrete market categories will continue to intersect. This new, tightly-integrated approach, what we call Hyperconverged Analytics, brings together visual analytics, data science, and streaming capabilities together in a seamless experience, delivering immersive, smart, and real-time business insights.

Ahead of the curve: WATCH how AA Ireland uses the Hyperconverged Analytics solution for real-time pricing.

Interactive discovery is now table stakes…

With Natural Language Query (NLQ) and similar capabilities making great strides, these features and functionality have now become commoditized and are no longer differentiating in the market. “Analytics culture” will remain a top barrier to full realization and adoption of augmented intelligence.

Next step: For more, read about Interactive AI in TIBCO Spotfire^®.

More momentum in network graph analytics…

Interest in graph and network analysis will continue to grow in future years. Organizations seeking connections across datasets will implement network graph analytics to identify the most valuable relationships and explore unknowns.

Next step: How Hyperconverged Analytics is enabling immersive, smarter network analysis. READ

With rapidly changing global market conditions, decisions must be data-informed, but perhaps most importantly, nimble enough to adapt to both risk as well as new opportunity as they develop—on much shorter frequencies and turnaround. Click To Tweet

For more top analytics landscape trends, watch our webinar with Doug Henschen of Constellation Research: Analytics in 2021: Move from Insight to Predictions and Real-time Action.

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Fear! Excitement! Trends disrupting your career in the 2020s!

December 3, 2020 Tableau

Do you feel left behind? If you are witnessing today’s unprecedented speed of technological change, a sense of apprehension would not be surprising.

In a hyperspeed environment, individuals cannot gain expertise quickly enough. By the time you learn and master a topic, that expertise already seems obsolete. From a corporate perspective, a lack of technology talent pushes companies toward other options, one of which is automation to reduce reliance upon human workers.

Our current automation trend will eliminate a large chunk of today’s jobs. However, that same disruption causes many more opportunities to emerge for the right individuals. Do not be careless and allow your career to be destroyed in the 2020s. Be aware of technology trends, prepare, and pivot to a new place of success.

Here are five areas you should watch in the next few years.

CLOUD

Motivated by competitive and cost-saving reasons, companies are migrating on-premise applications and data onto cloud platforms such as Amazon Web Services, Microsoft Azure, and Google Cloud. Companies benefit by eliminating the significant overhead private data centers, hardware, and support services. Plus, they gain market agility by being able to scale digital assets almost immediately.

Companies will eliminate the jobs associated with legacy, on-premise support. With talent at a premium, companies may retrain these individuals for other roles, but the cold reality is most management might instead eliminate long-time legacy employees and replace them with new talent.

Software vendors will reduce costs by moving to cloud-only solutions, eliminating the need to install and support on-premise applications. Field technical staff who performed these roles will no longer be needed; only a smaller core group for the centralized cloud support will remain.

AUTOMATION

Companies will continue automation, combining software with artificial intelligence and machine learning, to reduce costs, gain competitive advantages, and increase revenue. With smart automation, firms will replace many individuals who perform repeatable tasks in controlled environments. It’s a simple decision: automated work can be performed 24×7 without stoppage and at lower costs than humans.

Vendors will provide automated tools to accelerate the movement from on-premise applications to the cloud platforms. During the next few years, you will see a mad rush to push business applications onto cloud platforms.

NEW COMPETITORS

New endeavors with new technology will emerge and disrupt legacy businesses. One advantage is they do not have the baggage of legacy platforms, bureaucracy, and long-time employees. These nimble barbarians will attack the fortresses of established empires, speeding the decline of well-known companies.

DISPOSABLE TECHNOLOGY

Rapid technological improvements mean that older technology needs to be thrown away sooner. Ongoing changes, new competition, along with lack of talent pushes companies to speed their elimination of legacy applications and old ways of doing business.

When everything becomes a paid cloud service, companies need fewer technical support employees. However, individuals who can train others in emerging technology will be important. Because modern tools change quickly these tech trainers must quickly pivot and learn. Instead of working for one company, these individuals may provide global online services, generating both active and passive income. To meet demand, online courses and certifications will grow.

CELEBRITY TALENT

While automation will eliminate many legacy jobs, rapidly changing technologies and lack of resources will provide a Wild West goldrush for savvy individuals in the 2020s. Some will become solopreneurs with a strong social media presence, causing firms to find a new HR model, other than their legacy comand-and-control methods designed to restrict employee behaviors.

Instead of dependency upon full-time employees, companies will leverage project-based, remote talent who can be shuffled in and out as needed. Firms will need to develop the culture and skills for working with free agents. A corporate initiative will begin to resemble the effort of producing a blockbuster movie using contracted talent during development.

As a result of a smaller talent base, corporate work conditions will change. Work-from-home will gain even more acceptance and individuals will not need to live within an hour commute of a downtown office building. Instead, technology will be securely available from cloud platforms and workers will spread out, with less clustering in mega-urban centers. Headquarters will become occasional convention sites for talent community-building, celebration, and edification events.

Depending on your worldview, the technology trends of the 2020s are either fear-inducing dangers or exciting opportunities. Make sure you wield this double-edge sword properly. Contact me at Doug@kencura.com.

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Five Technology Trends in Capital Markets

November 19, 2020 Microsoft Dynamics CRM

From an industry perspective, it’s all too common to lump all things money-related into a single pool and call it FSI – or the Financial Services industry. But those working in this channel know all too well about the myriad differences – from subtle to substantial – between, say, banking and insurance, or between capital markets and credit cards. Yes, they all deal with money…

Source

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Recognizing data points that signal trends for the future of business post-pandemic

October 18, 2020 Big Data

Planning for a post-COVID-19 future and creating a robust enterprise strategy require both strategic scenario planning and the ability to recognize what scenario planners call “news from the future” — data points that tell you whether the world is trending in the direction of one or another of your imagined scenarios. As with any scatter plot, data points are all over the map, but when you gather enough of them, you can start to see the trend line emerge.

Because there are often many factors pushing or pulling in different directions, it’s useful to think of trends as vectors — quantities that are described by both a magnitude and a direction, which may cancel, amplify, or redirect each other. New data points can also show whether vectors are accelerating or decelerating. As you see how trend vectors affect each other, or that new ones need to be added, you can continually update your scenarios.

Sometimes a trend itself is obvious. Twitter, Facebook, Google, and Microsoft each announced a commitment to new work-from-home policies even after the pandemic. But how widespread will this be? To see if other companies are following in their footsteps, look for job listings from companies in your industry that target new metro areas or ignore location entirely. Drops in the price or occupancy rate of commercial real estate, and how that spills over into residential real estate, might add or subtract from the vector.

Think through possible follow-on effects to whatever trend you’re watching. What are the second-order consequences of a broader embrace of the work-from-home experience? Your scenarios might include the possible emptying out of dense cities that are dependent on public transportation and movement from megacities to suburbs or to smaller cities. Depending on who your workers and your customers are, these changes could have an enormous impact on your business.

What are some vectors you might want to watch? And what are examples of news from the future along those trend lines?

The progress of the pandemic itself. Are cases and deaths increasing or declining? If you’re in the U.S., Covid Act Now is a great site for tracking the pandemic. This suggests that pandemic response won’t be a “one and done” strategy, but more like what Tomas Pueyo described in his essay “The Hammer and the Dance,” in which countries drop the hammer to reduce cases, reopen their economies, see recurrences, and drop the hammer again, with the response increasingly fine-grained and local as better data becomes available. As states and countries reopen, there is a lot of new data that will shape all of our estimates of the future, albeit with new uncertainty about a possible resurgence (even if the results are positive).

Is there progress toward treatment or a vaccine? Several vaccine candidates are in trials, and new treatments seem to improve the prognosis for the disease. A vector pushing in the other direction is the discovery of previously missed symptoms or transmission factors. Another is the politicization of public health, which began with masks but may also extend to vaccine denial. We may be living with uncertainty for a long time to come; any strategy involving a “return to normal” needs to be held very loosely.

How do people respond if and when the pandemic abates? Whatever comes back is likely to be irretrievably changed. As Ben Evans said, sometimes the writing is on the wall, but we don’t read it. It was the end of the road for BlackBerry the moment the iPhone was introduced; it just took four years for the story to play out. Sometimes a seemingly unrelated shock accelerates a long overdue collapse. For example, ecommerce has been growing its share for years, but this may be the moment when the balance tips and much in-person retail never comes back. As Evans put it, a bunch of industries look like candidates to endure a decade of inevitability in a week’s time.

Will people continue to walk and ride bikes, bake bread at home, and grow their own vegetables? (This may vary from country to country. People in Europe still treasure their garden allotments 70 years after the end of World War II, but U.S. victory gardens were a passing thing.) Will businesses have the confidence to hire again? Will consumers have the confidence to spend again? What percentage of businesses that shut down will reopen? Are people being rehired and unemployment rates going down? The so-called Y-shaped recovery, in which upper-income jobs have recovered while lower-income jobs are still stagnant, has been so unprecedented that it hasn’t yet made Wikipedia’s list of recession shapes.

Are there meaningful policy innovations that are catching on? Researchers in Israel have proposed a model for business reopening in which people work four-day shifts followed by ten days off in lockdown. Their calculations suggest that this would lower transmissibility of the virus almost as well as full lockdown policies, but allow people in many more occupations to get back to work, and many more businesses to reopen. Might experiments like this lead to permanent changes in work or schooling schedules? What about other long-discussed changes like universal basic income or a shorter work week? How will governments pay for the cost of the crisis, and what will the economic consequences be? There are those, like Ray Dalio, who think that printing money to pay for the crisis actually solves a long-standing debt crisis that was about to crash down on us in any case. Others disagree.

Are business models sustainable under new conditions? Many businesses, such as airlines, hotels, on-demand transportation, and restaurants, are geared very tightly to full occupancy. If airlines have to run planes with half as many passengers, will flights ever be cheap enough to attract the level of passengers we had before the pandemic? Could “on demand” transportation go away forever? Uber and Lyft were already unprofitable because they were subsidizing low prices for passengers. Or might these companies be replaced as the model evolves, much as AOL yielded online leadership to Yahoo!, which lost it in turn to Google? (My bet is that algorithmic, on-demand business models are still in their infancy.)

These topics are all over the news. You can’t escape them, but you can form your own assessment of the deeper story behind them and its relevance to your strategy. Remember to think of the stories as clustering along lines with magnitude and direction. Do they start to show patterns? More importantly, find vectors specific to your business. These may call for deep changes to your strategy.

Also remember that contrarian investments can bring outsized returns. It may be that there are markets that you believe in, where you think you can make a positive difference for your customers despite their struggles, and go long. For O’Reilly, this has been true of many technologies where we placed early bets against what seemed overwhelming odds of success. Chasing what’s “hot” puts you in the midst of ferocious competition. Thinking deeply about who needs you and your products and how you can truly help your customers is the basis for a far more robust strategy.

The audio problem:

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Better Analytics Through AI: Our Take on Gartner’s AI Trends

September 6, 2020 Sisense

AI and machine learning are the future of every industry, especially data and analytics. In Growing Up with AI, we help you keep up with all the ways these pioneering technologies are changing the world.

Reading through the Gartner Top 10 Trends in Data and Analytics for 2020, I was struck by how different terms mean different things to different audiences under different contexts. We hear a lot about AI and analytics not only in internal conversations, but also from our customers and prospects. But what do we really mean when we talk about these issues?

Seeing as how they will only become more important to our world, I thought it would be worthwhile, as Sisense’s head of AI research (AIR), to dive into 7 of the 10 trends on the list and give my views on each.

The article starts with a big statement about AI starting to operationalize, moving the requirements for data and analytics infrastructure to accelerate the development and adoption phase:

“By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.”

This is a major change in the way AI has been used in the past alongside data and analytics, making both more powerful and effective. Let’s dive into these trends and see what else is on the horizon.

Trend 1: Smarter, faster, more responsible AI

Gartner:

“Within the current pandemic context, AI techniques such as machine learning (ML), optimization and natural language processing (NLP) are providing vital insights and predictions about the spread of the virus and the effectiveness and impact of countermeasures.

“Significant investments made in new chip architectures such as neuromorphic hardware that can be deployed on edge devices are accelerating AI and ML computations and workloads and reducing reliance on centralized systems that require high bandwidths. Eventually, this could lead to more scalable AI solutions that have higher business impact.”

My take:

Augmentation and reinforcement learning are much more powerful than out-of-the-box solutions, and this is what’s guiding us along the way. Planning for every feature starts with questions about how the user will be able to play around with and modify the input to see how it affects the result. It was only natural for us here at Sisense to put significant investment into knowledge graphs, NLP, and automated machine learning. Together, they enable users to actively engage with the system, enjoying recommendations along with analysis. These features also facilitate a positive feedback loop, using engagement to strengthen what works and get rid of what doesn’t.

One result is that systems become much more intuitive: Users can take advantage of the “Simply Ask” feature to check “what are my sales next two months” and receive chatbot messages with projected visualizations and suggestions for further exploration routes. In a similar way, the forthcoming “Explanations” feature provides users with possible drivers of the movements in the data automatically, using knowledge graphs to go beyond the boundaries of their charts. This can turn the problem definition environment to multidimensional and learn from the user interaction with the system to personalize and match the results.

From Forecast to Trends to natural language querying, we are completely transparent about the technology behind and the statistical characteristics of the output. Whatever you’re seeing when you use Sisense, you can easily dig into the systems behind it.

Trend 2: Decline of the dashboard

Gartner:

“Dynamic data stories with more automated and consumerized experiences will replace visual, point-and-click authoring and exploration.”

My take:

At Amazon, everyone in a meeting sits down at the beginning and reads a full write-up, and then the discussion begins, rather than sitting through an endless PowerPoint presentation during the whole meeting. They focus on real storytelling rather than bullet points. We expect something similar to happen with dashboards: fetching insights-driven digests just in time, but also accompanying the daily routines with an “agent” supporting business flows in various tools.

Do you like to see what you missed first thing in the morning? Be alerted on significant movements? Is an executive summary enough to start the ball rolling, knowing you can always do a deep dive and ask for more? Using your favorite task management solution? The world is moving from the static, rigid experience to the data-, insight-, and personalization-driven assistant that knows how you want specific analytics to be served.

In order to make that work, a number of moving parts need to come together as one well-oiled machine: embedded interfaces (on-the-go via your device, in your email, chat, or in-app), pretrained analytics services and training pipeline, the vehicle to facilitate the data model creation, and the right visualization and narration to make the results digestible, trustable, and learning.

This is what keeps Sisense AIR busy: dashboard automation research and our knowledge graph, which has incorporated the behavior of thousands of past users.

Trend 3: Decision intelligence

Gartner:

“By 2023, more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling.”

“It provides a framework to help data and analytics leaders design, model, align, execute, monitor, and tune decision models and processes in the context of business outcomes and behavior.”

My take:

Decision-making automation requires a lot of steps: First you document the process, then configure it based on the result, then automate the possible parts. My take on it is that if you can automate the loop from data to analysis to decision back to data, it is not analytics, it’s robotic process automation. There’s an argument to be made that once decision-making on a use case becomes predictable, it should be moved from BI to a part of the back office.

But that kind of thinking comes from the world we used to know, a world that was less volatile and more manageable, more influenced by the proximity ecosystem than by world events and climate. Today, the world changes at a speed that’s hard to fathom, so decision-making needs to be adjusted based on insights coming from data, accompanied by recommended actions. “Survival of the fastest” is the rule today.

Trend 4: X analytics

Gartner:

“Gartner coined the term ‘X analytics’ to be an umbrella term, where X is the data variable for a range of different structured and unstructured content such as text analytics, video analytics, audio analytics, etc.”

My take:

The world is wider than the traditional BI tabular data. It’s visual, it’s spoken, it’s audible. Why use just one of the senses and limit your perspective?

Sisense recently used our ecosystem of ML service providers to help scan and surface the medical crowd wisdom of COVID treatments from piles of textual data from a site called G-Med. There was no point in reinventing the wheel to build our own video, image, speech, and text analysis tools — there are plenty of those on the market already.

How exactly is all that data going to talk to each other and come together to provide the end-to-end analysis? Knowledge graphs will be the base of how the data models and data stories are created, first as relatively stable creatures and, in the future, as on-demand, per each question.

Trend 5: Augmented data management

Gartner:

“Augmented data management uses ML and AI techniques to optimize and improve operations. It also converts metadata from being used in auditing, lineage and reporting to powering dynamic systems.”

My take:

The Gartner article doesn’t go beyond lineage or workload automation. That’s important, but that’s only what’s going on today. Fetching calculation results ahead of the question improves performance, but it’s still limited to the data model or dimensional paradigm of the single individual in the organization. Do they have the required perspective to include hurricane data for the supply chain dashboard for East Asia? Domain experts would likely decide to include that information after reading about losses in the news. What if the relevant data could be added to the context to tell the data story without humans needing to take action themselves? Data exchanges will play a more significant role in the future, extending their offerings to data modeling.

Trend 6: Cloud is a given

Gartner:

“By 2022, public cloud services will be essential for 90% of data and analytics innovation. As data and analytics moves to the cloud, data and analytics leaders still struggle to align the right services to the right use cases, which leads to unnecessarily increased governance and integration overhead.”

My take:

Cloud is here to stay. I witnessed the mainframe/PC/cloud/personal graphics processing unit evolution. To me, the tipping point of cloud analytics will be in the “context as a service” combination of data and logic components served based on user questions. With offerings like AWS Outposts, it couldn’t be easier to start the cloud journey.

In the analytics world, it’s crucial to stay up to date, implementing “continuous integration/continuous delivery” systems and A/B testing for better performance and experience. This is only possible with cloud services. Cloud combined with compliance with General Data Protection Regulation and SOC are vital to gain customers’ trust. Data-hungry calculations will be costly to perform in the cloud if data is on-premises due to data gravity and latency. Adjusting a system’s architecture can make all the difference quickly, meaning you can easily pull insights from large datasets.

Trend 7: Data and analytics worlds collide

Gartner:

“Data and analytics capabilities have traditionally been considered distinct entities and managed accordingly. Vendors offering end-to-end workflows enabled by augmented analytics blur the distinction between the two markets.

The collision of data and analytics will increase interaction and collaboration between historically separate data and analytics roles. This impacts not only the technologies and capabilities provided, but also the people and processes that support and use them. The spectrum of roles will extend from traditional data and analytics roles in IT to information explorer, consumer, and citizen developer as an example.”

My take:

I agree that new roles are required. As new data and analytics products are built and every product begins to have data and analytics elements in it, data/knowledge product managers will emerge. These specialists will understand data and be able to run and create queries and transformations but will also be knowledgeable about the applications running on top of those data streams.

Regarding data and tools, “extract, transform, and load” (ETL) will become ETLT. The “T” stands for the “transformation pipelines” either bringing data from the exchanges or pre-trained ML services or training pipelines for both structured and unstructured data. Software developers and data scientists can use these same pipelines to deploy their parts of the application, and analytics workflows can be automated to the point where business users can even trigger them without outside help.

AI and analytics: Building the future together

If you have data, odds are you have a lot of it. You’ve probably got more than you can handle. Alone, that is. Only AI will be able to help humans make sense of the huge datasets being generated every day by countless individuals and devices. AI systems will play greater and greater roles in our personal and business worlds, so whatever you’re building, start thinking about the ways AI can help your product, service, colleagues, and customers be better. And whatever you’re working on, build boldly.

Inna Tokarev-Sela, Sisense’s Head of AI Research, has over 15 years’ experience in the tech industry. She spent the last decade at SAP, driving innovations in cloud architecture, in-memory products, and machine learning video analytics. A frequent speaker at industry events like IBC, NAB, Wonderland AI, and Media Festival, Inna holds a BS in physics and computer science, an MBA, and an MS in information systems, having written her thesis on neural networks.

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Alexa and Google Assistant execs on future trends for AI assistants

July 17, 2020 Big Data

Find all the Transform 2020 sessions on-demand.

Businesses and developers making conversational AI experiences should start with the understanding that you’re going to have to use unsupervised learning to scale, said Prem Natarajan, Amazon head of product and VP of Alexa AI and NLP. He spoke with Barak Turovsky, Google AI director of product for the NLU team, at VentureBeat’s Transform 2020 AI conference today as part of a conversation about future trends for AI assistants.

Natarajan called unsupervised learning for language models an important trend for AI assistants and an essential part of creating conversational AI that works for everyone. “Don’t wait for the unsupervised learning realization to come to you yet again. Start from the understanding that you’re going to have to use unsupervised learning at some level of scale,” he said.

Unsupervised learning uses raw, unlabeled data to draw inferences from raw, unclassified data. A complementary trend, Natarajan said, is the development of self-learning systems that can adapt based on signals received from interacting with a person speaking with Alexa.

“It’s the old thing, you know: If you fail once, that’s OK, but don’t make the same failures multiple times. And we’re trying to build systems that learn from their past failures,” he said. Members of Amazon’s machine learning team and conversational AI teams told VentureBeat last fall that self-learning and unsupervised learning could be key to more humanlike interactions with AI assistants.

Another continuing trend is the evolution of trying to weave features into experiences. Last summer, Amazon launched Alexa Conversations in preview, which fuses together Alexa skills into a single cohesive experience using a recurrent neural network to predict dialog paths. For example, the proverbial night out scenario involves skills for buying tickets, making dinner reservations, and making arrangements with a ridesharing app. At the June 2019 launch, Amazon VP of devices David Limp referred to Amazon’s work on the feature “the holy grail of voice science.” Additional Alexa Conversations news is slated for an Amazon event next week.

Natarajan and Turovsky agreed that multimodal experience design is an another emerging trend. Multimodal models combine input from multiple mediums like text and photos or videos. Some examples of models that combine language and imagery include Google’s VisualBERT and OpenAI’s ImageGPT, which received an honorable mention from the International Conference on Machine Learning (ICML) this week.

Turovsky talked about advances in surfacing the limited number of answers voice alone can offer. Without a screen, he said, there’s no infinite scroll or first page of Google search results, and so responses should be limited to three potential results, tops. For both Amazon and Google, this means building smart displays and emphasizing AI assistants that can both share visual content and respond with voice.

In a conversation with VentureBeat in January, Google AI chief Jeff Dean predicted progress in multimodal models in 2020. The advancement of multimodal models could lead to a number of benefits for image recognition and language models, including more robust inference from models receiving input from more than a single medium.

Another continuing trend, Turovsky said, is the growth of access to smart assistants thanks to the maturation of translation models. Google Assistant is currently able to speak and translate 44 languages,

In a separate presentation earlier today, Turovsky detailed steps Google has taken to remove gender bias from language models. Powered by unsupervised learning, Google introduced changes earlier this year to reduce gender bias in neural machine translation models.

“In my opinion, we are in the early stages of this war. This problem could be seemingly simple; a lot of people could think it’s very simple to fix. It’s extremely hard to fix, because the notion of a bias in many cases doesn’t exist in an AI environment, when we watch it learn, and get both training data and train models to actually address it well,” Turovsky said. Indeed, earlier this year researchers affiliated with Georgetown University and Stanford University found racial automatic speech detection systems from companies including Amazon and Google work better for White users than Black users.

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5 Critical CRM Trends For 2020-2021

June 23, 2020 CRM News and Info

The world of sales and customer relationship management has been changing rapidly, just like everything else around. Here are the CRM trends to get prepare for this and next year.

Photo by Clay Banks on Unsplash

Mobile CRM functionality

Lucky are those who can forget about work once they cross the office door. But the reality is – many professionals keep on accomplishing tasks beyond regular 9 to 5 and from any place, also while commuting. Another thing is that even office workers aren’t necessarily in the office all the time – as the saying goes, “if your salespeople are in the office, they are not selling”. This puts a huge emphasis on mobile devices. And more CRM functionality on portable devices required by customers’ increasing need for connectivity.

A pitfall here though is security. What if a mobile phone is lost or stolen? Will it put customer data or correspondence at risk? CRM systems producers need to prepare to mitigate the risks. And employees of high-security enterprises like financial institutions or the public sector meanwhile can leverage technologies that give access to the mailbox but restrict sensitive information outside the local network.

CRM Integration with Everything

Marketers use up to 12 different sources of data for customer relationship management. And a typical employee makes 134 copy-and-paste actions each day, stats say.

In 2020, we can’t possibly waste so much time switching between apps and doing other unnecessary things. Integration is another hot trend for CRM which simplifies and streamlines the production process through the day.

CRM systems can provide much better results faster when integrated with:

Project management software
Storage/warehouse
Supplier Systems and Purchase Order systems (POs)
ERP system
An inventory management system
A product lifecycle management system (PLM).
A data warehouse
Sales orders.

You can custom develop such integrations or use an integration platform with pre-built connectors to your systems. The second option allows us to minimize the integration time and maintenance of your updated systems.

Check an example of an elaborate integrated system including Dynamics CRM of an energy service provider in Switzerland.

CRM going into the cloud

Another side of being connected 24/7 and accessing your data from any place and device means deploying the customer relationship management in the cloud.

63% of businesses already prefer cloud-hosted CRM systems over on-premise applications, and the trend is here to stay.

But what if you are stuck in the 90-s with the on-prem systems?

There are tools that help you to migrate your CRM system from on-prem to the cloud or synchronize them smoothly in real-time.

CRM and the IIoT

The Internet of Things has been giving huge revenues to those who can leverage data via CRM. But let’s take a step further to the bigger brother – the Industrial Internet of Things and automation of the processes.

IIoT devices can transfer information about performance issues and maintenance to the equipment producer. Similarly, IoT-connect CRM software can help companies proactively detect product performance issues and identify potential problems with customer satisfaction – thus providing predictive maintenance, a huge advantage for all parties involved.

Get yourself familiar with how to make sense of machine data in your CRM.

CRM security and reporting with blockchain

Financial and legal audits usually cause a lot of eye twitching to business managers. They cost a lot of money, preparation for audits require a lot of effort, and also it usually interrupts the workflow. A new solution by Connecting Software leverages blockchain to provide security for documents in CRM systems and increase trust in business. With a click of a button, you seal a document with a blockchain stamp. And any time later check if the document stayed intact or was changed – with another click of a button. The technology is simple to use – it is inbuilt in a CRM system as an add-on, and it is much cheaper than any other authenticity certificate. Similarly, the blockchain stamps protect your CRM documents against fraud and help reach compliance with the GDPR and other data protection regulations.

Take away

Time and technology are changing, so is CRM. But unlike other fickle trends, mobile connectivity, integration and cloud tech, the Internet of Things, and new ways of proving authenticity are here to stay with us in 2020 and develop even more in 2021. So make sure to keep your customer relationship management up with the flow.

***

Written by Anastasia Mazur from Connecting Software, a producer of synchronization and integration software for business needs. For more stories, visit our blog.

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Tag Archives: trends

What Are the Essential Dynamics Trends for 2021?

Dynamics 365 Trends #1 – Use Blockchain to Prove a Document’s Authenticity

Dynamics 365 Trends #2 – AI, the Slow but Strong Trend

Dynamics 365 Trends #3 – Integrate with Microsoft Teams for Better User Adoption

Dynamics 365 trends #4 – IIoT meets Dynamics 365

Dynamics 365 trend #5 – Guarantee Security for Software Integrations

Dynamics 365 trends #6 – Beware of your Dynamics 365 Storage Options

Takeaways

New Holiday Traditions: Annual Analytics

A New Tradition: Immersive Yourself in Custom Analytics Applications

1. Better query language

2. Streaming databases follow vast flows

3. Time-series database

4. Homomorphic encryption

5. In-memory database

6. Microservice engines

7. Graph databases

8. Merging data storage with transport

9. Data everywhere

VentureBeat

The Eight Trends That Will Matter Most

Intelligent, adaptable business…

More AI-driven automation in the workplace…

Growth on The Edge…

Trust and ethics: more accountable AI…

Closed-loop decision models…

Convergence of analytics technology continues…

Interactive discovery is now table stakes…

More momentum in network graph analytics…

CLOUD

AUTOMATION

NEW COMPETITORS

DISPOSABLE TECHNOLOGY

CELEBRITY TALENT

Trend 1: Smarter, faster, more responsible AI

Trend 2: Decline of the dashboard

Trend 3: Decision intelligence

Trend 4: X analytics

Trend 5: Augmented data management

Trend 6: Cloud is a given

Trend 7: Data and analytics worlds collide

AI and analytics: Building the future together

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