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Tag Archives: Intelligence

Transforming Big Data into Actionable Intelligence

March 23, 2021   Sisense

Attempting to learn more about the role of big data (here taken to datasets of high volume, velocity, and variety) within business intelligence today, can sometimes create more confusion than it alleviates, as vital terms are used interchangeably instead of distinctly. However, when investigating big data from the perspective of computer science research, we happily discover much clearer use of this cluster of confusing concepts.

Before we dive into the topics of big data as a service and analytics applied to same, let’s quickly clarify data analytics using an oft-used application of analytics: Visualization!

Looking at the diagram, we see that Business Intelligence (BI) is a collection of analytical methods applied to big data to surface actionable intelligence by identifying patterns in voluminous data. As we move from right to left in the diagram, from big data to BI, we notice that unstructured data transforms into structured data. The implication is that methods of data analytics are applied to big data, the methods of data preparation and data mining for example, to bring us closer and closer to the goal of distilling useful patterns, knowledge, and intelligence that can drive actions in the right hands. 

Hopefully this clarifies these complex concepts and their place in the larger analytics process, even though it’s common to see pundits and outlets tout BI or big data as if they were ends in themselves.

AI-driven analytics is a complex field: The bottom line is that datasets of all kinds are rapidly growing, causing these organizations to investigate big data reporting tools or even approach companies whose whole business model can be summed up as “big data as a service” in order to make sense of them.  If you’ve got big data, the right analytics platform or third-party big data reporting tools will be vital to helping you derive actionable intelligence from it. And one of the best ways to implement those tools is to embed third party plugins.

the data journey infographic blog cta banner 770x250 1 Transforming Big Data into Actionable Intelligence

Big data challenges and solutions

When you have big data, what you really want is to extract the real value of the intelligence contained within those possibly-zettabytes of would-be information. To best understand how to do this, let’s dig into the challenges of big data and look at a wave of emerging issues.

For starters, the rise of the Internet of Things (IoT) has created immense volumes of new data to be analyzed. IoT sensors on factory floors are constantly streaming data into cloud warehouses and other storage locations. 

These rapidly growing datasets present a huge opportunity for companies to glean insights like:

  • Machine diagnostics, failure forecasting, optimal maintenance, and automatic repair parts ordering. Intelligence derived from these systems can even be fed to HR teams to improve service staffing, which further feeds to enterprise HR management and performance solutions (AI-based analytics reporting to ERP solutions)
  • Assembled products shipped also feed directly to ERP on updating supply chain solutions, improving customer awareness and experience

To put it bluntly, the challenge we face is that no cloud architecture yet exists which can accommodate and process this big data tsunami. How can we make sense of the data wont fit in the enterprise service bus (ESB)? (ESB is a middleware component of cloud systems which will be overwhelmed if a million factories were to all try to extract intelligence from their sensors all at once.)

One  solution with immense potential is ”edge computing.” Referring to the conceptual “edge” of the network, the basic idea is to perform machine learning (ML) analytics at the data source rather than sending the sensor data to a cloud app for processing. Edge computing analytics (like the kind platforms like Sisense can perform) generate actionable insights at the point of data creation (the IoT device/sensor) rather than collecting the data, sending it elsewhere for analysis, then transmitting surfaced intelligence into embedded analytics solutions (eg. displaying BI insights for human users).  

The pressure to adopt the edge computing paradigm increases with the number of sensors pouring out data. Edge computing solutions in conjunction with a robust business intelligence big data program (bolstered by an AI-empowered analytics platform) are a huge step forward for companies dealing with these immense amounts of fast-moving and remote data.

Big data analytics case study: SkullCandy

SkullCandy, a constant innovator in the headset and earbud space, leverages its big data stores of customer data regarding reviews and warranties to improve its products over time. In a twist on typical analytics, SkullCandy uses Sisense and other data utilities to dig through mountains of customer feedback, which is all text data. This is an improvement over previous processes, wherein SkullCandy focused on more straightforward performance forecasting with transactional analysis. 

Now that SkullCandy has established itself as a data driven company, they are experimenting with additional text analytics that can extract insights from reviews of their products on Amazon, BestBuy, and their own site. Teams also use text analytics to benchmark their performance against their competitors. 

SkullCandy’s big data journey began by building a data warehouse to aggregate their transaction data, reviews. A breakthrough insight/intelligence in product development occurred thanks to the text analysis of warranties through which SkullCandy was able to distinguish between product issues and customer education. The fact that AI-based analytics can delineate between product and  education in a text message is groundbreaking. A common pattern was that clients were returning a product as broken when in fact they simply didn’t know how to use bluetooth connectivity.

Data-driven product development also benefitted: Big data analytics allowed SkullCandy to analyze warranty/return data that showed that one of their headsets, which was used more during workouts than previously thought, was being returned at a higher than normal rate. It turned out that sweat was causing corrosion in terminals, leading to the returns. The outcome was to waterproof the product.  

Among the many successes SkullCandy achieved, we also see a pattern of value derived from big data.

Big Data as a Service: Empowering users, saving resources

Strictly speaking, “big data analytics” distinguishes itself as the large-scale analysis of fast-moving, complex data. Implicit in this distinction is that big data analytics ingests expansive datasets far beyond the volume of conventional databases, in essence combining advanced analytics with the contents of immense data warehouses or lakes.

In order to get a handle on these huge amounts of possible-information, the AI components of a big data analytics program must necessarily include procedures for inspecting, cleaning, preparing, and transforming data in order to create an optimal data model that will facilitate the discovery of actionable intelligence, identify patterns, suggesting next steps, and supporting decision making at key junctures.

Intelligence drawn from big data has real potential to transform the world, from text analysis that reveals customer service issues and product development potential to training financial models to detect fraud or medical systems to detect cancer cells. Savvy businesses will empower users, analysts, and data engineers to prepare and analyze terabyte-scale data from multiple sources — without any additional software, technology, or specialized staff.

Fortunately, it is now possible to leverage all of these potentials and to avoid the cost and time of in-house development, by embedding expert third party analytics. Recognizing the tremendous task of big data analytics in conjunction with the value of outcomes, the natural propensity exists to use it as a service, and thereby reap the benefits of big data as a service as quickly as possible.

big data in healthcare blog cta banner 770x250 1 1 Transforming Big Data into Actionable Intelligence

Chris Meier is a Manager of Analytics Engineering for Sisense and boasts 8 years in the data and analytics field, having worked at Ernst & Young and Soldsie. He’s passionate about building modern data stacks that unlock transformational insights for businesses.

Tags: Big Data | data analytics

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Free Training – How to Leverage Sales Intelligence to Improve Sales Performance

March 10, 2021   CRM News and Info

Join us for this free training session and learn how to leverage sales intelligence technology to improve sales performance.

Date: March 18th 
Time: 12-1pm Eastern

Research has proven that incorporating sales intelligence in your sales process significantly improves sales performance for B2B sellers.  Harvard Business Review found that top-performing sales teams cite intelligence as a key driver fueling sales growth:

  • 41% improvement in targeting
  • 40% improvement in forecasting
  • 34% improvement in lead quality
  • 27% reduction in time spent looking for data
  • 20% improvement in won opportunities
  • Be our guest for an informative session and learn how to incorporate sales intelligence into your sales cycle.

During this session you will learn: 

  • What is sales intelligence?
  • The cost of missing & bad data to sales
  • How to uncover deep information about your prospects & customers
    • Annual Revenue, ownership, employee count, and more
    • Key contacts with verified email and telephone
    • Technologies in use
    • Industry info. and similar companies
  • Automatically update prospect & customer info
  • Day-in-the-life of sales using intelligence
All Attendees will receive 30 days of access to InsideView Insights, a leading B2B sales intelligence platform

CLICK HERE to register

About the Author: David Buggy is a veteran of the CRM industry with 18 years of experience helping businesses transform by leveraging Customer Relationship Management technology. He has over 17 years of experience with Microsoft Dynamics CRM/365 and has helped hundreds of businesses plan, implement and support CRM initiatives. To reach David or call 844.8.STRAVA (844.878.7282) To learn more about Strava Technology Group visit www.stravatechgroup.com

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Artificial intelligence and the antitrust case against Google

October 22, 2020   Big Data
 Artificial intelligence and the antitrust case against Google

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Following the launch of investigations last year, the U.S. Department of Justice (DOJ) together with attorney generals from 11 U.S. states filed a lawsuit against Google on Tuesday alleging that the company maintains monopolies in online search and advertising, and violates laws prohibiting anticompetitive business practices.

It’s the first antitrust lawsuit federal prosecutors filed against a tech company since the Department of Justice brought charges against Microsoft in the 1990s.

“Back then, Google claimed Microsoft’s practices were anticompetitive, and yet, now, Google deploys the same playbook to sustain its own monopolies,” the complaint reads. “For the sake of American consumers, advertisers, and all companies now reliant on the internet economy, the time has come to stop Google’s anticompetitive conduct and restore competition.”

Attorneys general from no Democratic states joined the suit. State attorneys general — Democrats and Republicans alike — plan to continue on with their own investigations, signaling that more charges or backing from states might be on the way. Both the antitrust investigation completed by a congressional subcommittee earlier this month and the new DOJ lawsuit advocate breaking up tech companies as a potential solution.

The 64-page complaint characterizes Google as a “monopoly gatekeeper for the internet” and spells out the reasoning behind the lawsuit in detail, documenting the company’s beginning at Stanford University in the 1990s alongside deals made in the past decade with companies like Apple and Samsung to maintain Google’s dominance. Also key to Google’s power and plans for the future is access to personal data and artificial intelligence. In this story, we take a look at the myriad of ways in which artificial intelligence plays a role in the antitrust case against Google.

Search

The best place to begin when examining the role AI plays in Google’s antitrust case is online search, which is powered by algorithms and automated web crawlers that scour webpages for information. Personalized search results made possible by the collection of personal data started in 2009, and today Google can search for images, videos, and even songs that people hum. Google dominates the $ 40 billion online search industry, and that dominance acts like a self-reinforcing cycle: More data leads to more training data for algorithms, defense against competition, and more effective advertising.

“General search services, search advertising, and general search text advertising require complex algorithms that are constantly learning which organic results and ads best respond to user queries; the volume, variety, and velocity of data accelerates the automated learning of search and search advertising algorithms,” the complaint reads. “The additional data from scale allows improved automated learning for algorithms to deliver more relevant results, particularly on ‘fresh’ queries (queries seeking recent information), location-based queries (queries asking about something in the searcher’s vicinity), and ‘long-tail’ queries (queries used infrequently).”

Search is now primarily conducted on mobile devices like smartphones or tablets. To build monopolies in mobile search and create scale insurmountable to competitors, the complaint states, Google turned to exclusionary agreements with smartphone sellers like Apple and Samsung as well as revenue sharing with wireless carriers. The Apple-Google symbiosis is in fact so important that losing it is referred to as “code red” at Google, according to the DOJ filing. An unnamed senior Apple employee corresponding with their counterpart at Google said it’s Apple’s vision that the two companies operate “as if one company.” Today, Google accounts for four out of five web searches in the United States and 95% of mobile searches. Last year, Google estimated that nearly half of all search traffic originated on Apple devices, while 15-20% of Apple income came from Google.

Data at scale

Exclusive agreements that put Google apps on mobile devices effectively captured hundreds of millions of users. An antitrust report referenced these data advantages, stating that “Google’s anticompetitive conduct effectively eliminates rivals’ ability to build the scale necessary to compete.”

In addition to the DOJ report, the antitrust report Congress released earlier this month frequently cites the network effect achieved by Big Tech companies as a significant barrier to entry for smaller businesses or startups. The incumbents have access to large data sets that give them a big advantage, “especially when combined with machine learning and AI,” the report reads. “Companies with superior access to data can use that data to better target users or improve product quality, drawing more users and, in turn, generating more data — an advantageous feedback loop.”

Network effects often come up in the congressional report in reference to mobile operating systems, public cloud providers, and AI assistants like Alexa and Google Assistant, which improve their machine learning models through the collection of data like voice recordings.

One potential solution the congressional investigation suggested is better data portability to help small businesses compete with tech giants.

Google Assistant

One part of maintaining Google’s search monopoly, according to the congressional report, is control of emerging search access points. While Google searches began on desktop computers, mobile is king today, and fast emerging are devices like smartwatches, smart speakers, and IoT devices with AI assistants like Alexa, Google Assistant, and Siri. Virtual assistants are using AI to turn speech into text and predict a user’s intent, becoming a new battleground. An internal Google document declared voice “will become the future of search.”

The growth of searches via Amazon Echo devices is why a Morgan Stanley analyst previously suggested Google give everyone in the country a free speaker. In the end, he concluded, it would be cheaper for Google to give away hundreds of millions of speakers than to lose its edge to Amazon.

The scale afforded by Android and native Google apps also appears to be a key part of Google Assistant’s ability to understand or translate dozens of languages and collect voice data across the globe.

Search is primarily done on mobile devices today. That’s what drives the symbiotic relationship between Apple and Google, where Apple receives 20% of its total revenue from Google in exchange for making Google the de facto search engine on iOS phones, which still make up about 60% of the U.S. smartphone market.

The DOJ suit states that Google is concentrating on Google Nest IoT devices and smart speakers because internet searches will increasingly take place using voice orders. The company wants to control the next popular environment for search queries, the DOJ says, whether it be wearable devices like smartwatches or activity monitors from Fitbit, which Google announced plans to acquire roughly one year ago.

“Google recognizes that its ‘hardware products also have HUGE defensive value in virtual assistant space AND combatting query erosion in core Search business.’ Looking ahead to the future of search, Google sees that ‘Alexa and others may increasingly be a substitute for Search and browsers with additional sophistication and push into screen devices,’” the DOJ report reads. “Google has also harmed competition by raising rivals’ costs and foreclosing them from effective distribution channels, such as distribution through voice assistant providers, preventing them from meaningfully challenging Google’s monopoly in general search services.”

In other words, only Google Assistant can get microphone access for a smartphone to respond to a wake word like “Hey, Google,” a tactic the complaint says handicaps rivals.

AI like Google Assistant also features prominently in the antitrust report a Democrat-led antitrust subcommittee in Congress released, which refers to AI assistants as efforts to “lock consumers into information ecosystems.” The easiest way to spot this lock-in is when you consider the fact that Google prioritizes YouTube, Apple wants you to use Apple Music, and Amazon wants users to subscribe to Amazon Prime Music.

The congressional report also documents the recent history of Big Tech companies acquiring startups. It alleges that in order to avoid competition from up-and-coming rivals, companies like Google have bought up startups in emerging fields like artificial intelligence and augmented reality.

Finishing thoughts

If you expect a quick ruling by the DC Circuit Court in the antitrust lawsuit against Google, you’ll be disappointed — that doesn’t seem at all likely. Taking the 1970s case against IBM and the Microsoft suit in the 1990s as a guide, antitrust cases tend to take years. In fact, it’s not outside the realm of possibility that this case could still be happening the next time voters pick a president in 2024.

What does seem clear from language used in both US v Google and the congressional antitrust report is that both Democrats and Republicans are willing to consider separating company divisions in order to maintain competitive markets and a healthy digital economy. What’s also clear is that both the Justice Department and antitrust lawmakers in Congress see action as necessary based in part on how Google treats personal data and artificial intelligence.


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The Social Impact of Artificial Intelligence and Data Privacy Issues

September 8, 2020   BI News and Info

The current era of AI and Big Data is already considered by many as the start of the fourth industrial revolution that will reshape the world in years to come. Google searches, Map navigation, voice assistants like Alexa, personalized recommendations on portals like Facebook, Netflix, Amazon, and YouTube are just a few examples where artificial intelligence is already playing an important role in our day to day lives. Perhaps people do not even realize this. In fact, a report suggests that the AI market will reach a whopping $ 169.41 Billion by the year 2025.

But there is a negative aspect of AI as well, which poses great privacy and social risk. The risk is associated with how some organizations are collecting and processing a vast amount of user data in their AI-based system without their knowledge or consent, which can lead to concerning social consequences.

word image 27 The Social Impact of Artificial Intelligence and Data Privacy Issues

(Source)

Is your Data Private?

Every time you go to the internet for searches, browsing websites, or when you use mobile apps, you do not even realize that you are giving away your data either explicitly or without your knowledge. And most of the time you allow these companies to collect and process your data legally since you would have clicked “I agree” button of terms and conditions of using their services.

Apart from your information that you explicitly submit to your websites like Name, Age, Emails, Contacts, Videos, or Photo uploads, you also allow them to collect your browsing behavior, clicks, likes and dislikes. Reputed companies like Google and Facebook use this data to improve their services and do not sell this to anyone. Still, there have been instances where third party companies have scrapped sensitive user data by loopholes or data breaches. In fact, the sole intention of many companies is to collect user data by luring them into using their online services, and they sell this data for vast amounts of money to third parties.

The situation has worsened with the surge in malicious mobile apps whose primary purpose is to collect even that data from the phone for which it did not seek permission. These are primarily data collection apps disguised as a game or entertainment app. In today’s world, smartphones contain very sensitive data like personal images, videos, GPS location, call history, messages, etc., and we do not even know that our data is getting stolen by these mobile apps. Every now and then, such malware apps are removed from Play Store and Apple Store but not before they have already been download millions of times.

Why are Artificial Intelligence and Data Privacy a Big Concern?

People are becoming increasingly aware that their data is not safe online. However, most of them still do not realize the gravity of the situation when AI-based systems process their social data in an unethical manner.

Let us go through some well-known incidents to understand what type of risk we are talking about.

The Cambridge Analytica-Facebook Scandal

word image 28 The Social Impact of Artificial Intelligence and Data Privacy Issues

(Source)

In 2018, news broke out that a data analytics firm Cambridge Analytica had analyzed the psychological and social behavior of users through their Facebook likes and targeted them with Ad campaigns for the 2016 US Presidential election.

The issue was that FB does not sell its users’ data for such purpose. It was revealed that a developer created a FB quiz app that utilized the loophole in a FB API to collect data of users and their friends as well. He then sold it to the Cambridge Analytica firm who was accused of playing an important role in the outcome of the 2016 US Presidential elections, by unethically obtaining and mining users’ data from Facebook. And the worst thing is that users who had used the quiz app would have blindly given all permission to the app without knowing they are exposing their friends’ data also.

Clearview Face Recognition Scandal

word image 29 The Social Impact of Artificial Intelligence and Data Privacy Issues

(Source)

Clearview is an artificial intelligence company that created a face recognition system to help police officers identify criminals. They claim that their software has helped law enforcement agencies to track down many pedophiles, terrorists, and sex traffickers.

But in January 2020, The New York Times covered a long story about how Clearview made the mockery of data privacy by scraping around three billion photos of users from social media platforms like Facebook, YouTube, Twitter, Instagram, and Venmo to create the AI system. Its CEO, Mr. Ton-That, claimed that its system only scraped public images from these platforms. Shockingly in an interview with CNN Business, the software fetched the images from the Instagram account of the show’s producer.

Google, Facebook, YouTube, and Twitter sent cease and desist letters to prevent Clearview from scrapping photos from their platform. However, the images that you uploaded online may be included in that AI software without your knowledge. If this software gets into the hands of a rogue police officer, or if the system itself gives false positive, many innocent people might fall prey to police investigations.

DeepFakes

word image 30 The Social Impact of Artificial Intelligence and Data Privacy Issues

DeepFake puts the face of a person to another’s body (Source)

Images and videos that are created using deep learning and contain a real person acting or saying things they didn’t do or say are called Deepfakes. If used for entertainment purposes, Deepfakes are fun, but people are creating Deepfakes for fake news and information and, worse, Deepfake porn.

In 2019, an application called DeepNude was launched where you could upload any image of women, and it would generate a real-like nude image from it. It is quite disturbing that anyone could exploit woman images available online by creating nude images from DeepNude. After too much controversy, it was shut down. Still, it is just a matter of time that someone can again misuse Deepfake technologies by taking your publicly available videos or photos.

word image 31 The Social Impact of Artificial Intelligence and Data Privacy Issues

word image 32 The Social Impact of Artificial Intelligence and Data Privacy Issues

(Source)

Mass Surveillance in China

word image 33 The Social Impact of Artificial Intelligence and Data Privacy Issues

(Source)

In recent years, China has received severe criticism due to mass surveillance of its people without their consent. They use over 200 million surveillance cameras and facial recognition to keep a constant watch on their people. China also mines their behavioral data captured on the cameras.

To make it worse, China implemented a social credit system to rate the trustworthiness of its citizens and give them ratings accordingly based on their surveillance. People with high credit get more benefits and low credits loose benefits. But the worse part is that all this is being determined by AI-based surveillance without people’s knowledge and consent.

How to prevent misuse of data with AI

The above case studies should have made it clear that the unethical processing of private data with artificial intelligence can lead to very dangerous social consequences. Let us see how we can play our part to stop this malpractice of private data misuse with artificial intelligence.

Government Responsibility

Now many countries have created their own data regulation policies to bring more transparency between these online platforms and the users. Most of these policies are centered around giving users more authority in what data they can share and be informed about how the platform would process their data. A very well known example of this is the GDPR law that came into existence a couple of years back for the EU countries. It gives EU people more control of their personal data and how it is processed by the companies.

Company Responsibility

Large companies like Google, Facebook, Amazon, Twitter, YouTube, Instagram, and LinkedIn literally own a majority of users’ social data across the world. Being, reputed giants, they should be extra careful not to leak any data to malicious people either intentionally or unintentionally.

AI Community Responsibility

The people of AI communities, especially the thought leaders, should raise their voices against the unethical use of AI on the personal data of the users without their knowledge. And also they should educate the world that this practice can lead to such a disastrous social impact. Already many institutes are teaching AI ethics as a subject and also offering it as course.

User’s Responsibility

Finally, we should remember that, despite government regulations, these are just policies and the responsibility lies with us as individuals. We have to be careful about what data we are uploading on social platforms and mobile apps and always inspect what permissions we are giving them to access and process our data, let us not merely “accept” anything blindly in “terms and conditions” that comes our way on these online platforms.

Conclusion

There are many concerns around the ethics in AI within the artificial intelligence community due to the social biases and the prejudices it can create. But processing personal data with AI without people’s consent and further misusing it raises the concerns of AI ethics to the next level. AI emergence in the real world is still in nascent age, and we all have to step up to make sure that our creating AI by misusing personal information should not become a regular occurrence in the future.

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Artificial Intelligence, Real Enhancements, Better Insights

August 6, 2020   Sisense

Sisense News is your home for corporate announcements, new Sisense features, product innovation, and everything we roll out to empower our users to get the most out of their data.

Ashley Kramer is our new chief product and marketing officer. She began her career as a software engineering manager at NASA, worked at Oracle, Amazon, and elsewhere before moving to Alteryx, serving as senior vice president of product. She recently spoke with SearchBusinessAnalytics about her vision for the Sisense platform and what it’s like being one of the few women helping shape software development at a major business intelligence and analytics vendor.

SearchBusinessAnalytics: What led you to the decision to leave Alteryx and move to Sisense to head the development of its analytics platform?

Ashley Kramer: What was really interesting to me about Sisense was the approach they took from the beginning, which was to give agility to customers. That means being able to do analytics the way that they need — the Periscope acquisition means it can be code first or drag-and-drop — and being able to allow people to leverage analytics wherever they need, whether that’s embedded deeply within a product, whether that’s in an internal portal, or whether that’s a dashboard that you deploy.

In addition, I have a lot of cloud experience, and I understand what works and what doesn’t, and Sisense took a really unique approach here a year ago to say, “Let’s rewrite our products to be more of a Linux microservices-based architecture,” so that as companies scale in the cloud and as they leverage different complex data scenarios, we can grow with them. We can meet them where they are today, and we can grow with them as they go into the future, and that was truly unique to me and super exciting.

SBA: What is the dynamic between you as chief product officer and chief technology officer Guy Boyangu?

AK: He deeply understands the architecture, both where it was and where it’s come, and what we collaborate on is around the AI space. We have something called the Knowledge Graph that gathers all kinds of intelligence so we can give our customers smartness out of the box when we deliver our analytics, so what Guy and I collaborate on is on the under-the-hood side of where Sisense goes next.

I’m the product strategist and visionary working with the entire team, and Guy is able to take that and say, “OK, technology-wise, this can be our strategy.” It’s a really good partnership that we have together.

SBA: As you come to Sisense, what is your vision for where the analytics platform will be in two or three years?

AK: There are a lot of places we can go, and we need to focus those efforts so we can make sure we’re delivering what customers need. But I can really break this down into further investments in three different areas.

First, how will we continue to scale across clouds? If you think about [many] of the other vendors in this space, they’re mostly part of a cloud ecosystem — Looker and Google, Tableau and Salesforce, Power BI always being part of Microsoft — and we’re a cross-cloud platform, we can work with data analytics cross-cloud, so we want to continue innovating there and scaling to make sure we work seamlessly with all of the different services that all of the different clouds provide. The second is more intelligence. How do we continue to do deeper smartness within the platform, so automated recommendations and automated decisions, alerting, those types of things. And of course we have natural language querying so you type a sentence and get your results. There are many more things we can do to make analytics easier for everybody involved and get more people involved in analytics.

And that brings me to the last one, which is if you look at statistics on the different usage and adoption of analytics it tends to be pretty low in general within organizations, and I truly believe that’s for one reason — most BI platforms assume people will unnaturally leave their everyday workflows and processes and look at a dashboard and then come back. With our API-driven platform and approach, we can bring analytics to the salesperson that spends their entire day in whatever sales platform or CRM (customer relationship management) platform they use, and for someone like me that’s always on the go, send it to my cellphone. By continuing to innovate in that area, I think we can start to see adoption go up and really start seeing organizations make data-driven decisions, and then of course as an end result have better business outcomes.

si whitepaper banner data deduplication 092720191 Artificial Intelligence, Real Enhancements, Better Insights

SBA: What are some trends you see in BI and analytics that Sisense is responding to and are helping drive Sisense’s roadmap?

AK: The first trend is we’re finally starting to see people migrate their data to the cloud. It’s been something people have been talking about, but we’re really seeing it come to life. The second is adoption — different statistics say that only about a third of an organization are actually using the analytics to make decisions. And the next one is a huge trend around AI and augmented analytics, so how do we not just allow people to make a decision about what’s happening right now, build a dashboard and see sales are down in the west, but how do we actually start to help them predict what to do next and prescribe how to take the action. A lot of the things we’re doing with our Knowledge Graph and a lot of the features we’ve released and have planned as part of our roadmap will start getting people out of just descriptive and taking them to those next two steps of predictive and prescriptive analytics.

SBA: How did your time at Oracle, Tableau, Alteryx, and other organizations prepare you for your new role at Sisense?

AK: My early career at Oracle and NASA was valuable because I was a programmer, so I understand technology pretty deeply, and I understand how sometimes a product team can dream up something but it might actually be a hard engineering challenge. That was helpful for me to understand how to be a better product leader. My time at Amazon helped me understand the crazy importance of the cloud. They were the early innovators in that area. Tableau was really interesting because Tableau started coming into the market with cloud analytics right at the cusp of when it was happening, so I got to learn a lot about the early market and then see how it evolved. And of course when you’re one of the earliest to arrive you don’t get everything right, and that’s okay. You learn lessons and figure out how to do it better next time. And at Alteryx it was trying to understand what the more data-science part of the world needs.

As a product leader it’s super important to understand what is possible for engineering and what is not, understanding the cloud is important because we’re cross-cloud here at Sisense, and then understanding where people want to go next with analytics by actually scaling it across the organization and taking that next step to be predictive. So, this really was the perfect role.

SBA: As chief product officer at Sisense you are now one of the few women helping shape the product development of a leading BI vendor. Did you face barriers on your path to Sisense?

AK: I was always interested in technology growing up — even in the early days, the Oregon Trail days. Fast-forward to high school and I was in the very first computer science class taught by my physical education teacher, which is crazy when you think about it. It doesn’t make any sense, but at least my school was thinking about offering computer science. I was a senior and the only woman in a class of 10 people. I was very fortunate to have people to encourage me to continue on this path. Move on to college, and in my graduating class there were three to five women in the class. Since then, I think what the world has done a better job of is have things like Girls Who Code, different programs to push the importance of STEM and make girls and women be a part of that. The world has changed and evolved, but it wasn’t really there when I was coming to my professional stage of my career.

SBA: What have you found following school in your professional life?

AK: As far as the glass ceiling, I feel like I’ve had a lot of great mentors along the way, some of them were women and some of them were men, and I think sometimes we undervalue the importance of what we can do for each other. Women can get together and I can go to every women leadership CPO dinner that exists, but we need men there too supporting us. We need them to be part of the conversation. Something that’s super attractive to me about Sisense is that not only is 50% of our core operational team reporting to the CEO women, but also [CEO] Amir Orad’s wife is a CEO [of LimitScreen Inc.]. He definitely understands the value of women in these leadership roles. Everyone has an equal voice, and I think that’s really powerful.

It’s been interesting. I’ve seen a trend with my generation and there are other women CPOs — there aren’t many, but even at my last company there was a woman chief strategy officer and we were really big supporters of each other. There was a historic thing in the past that there could only be one woman in the room, but that didn’t exist at any of the companies I’ve been. I feel fortunate to not have to have lived in that world. The more women in the room at leadership levels, the better, so I don’t feel like I have to speak up or be the alpha woman because that doesn’t exist anymore.

Editor’s note: This Q&A has been edited for clarity and conciseness.

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Isima emerges from stealth with $10 million to apply intelligence to real-time data

August 4, 2020   Big Data
 Isima emerges from stealth with $10 million to apply intelligence to real time data

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Isima today emerged from stealth with $ 10 million in funding to launch a data convergence platform called BiOS. The company asserts its solution can reduce or even eliminate disparate databases while improving overall speed and reliability.

IDC expects the global big data analytics market to be worth $ 274.3 billion by 2022, and Forbes found in 2017 that over 50% of companies were adopting big data. This trend is likely attributable to the return on investment (ROI) adopters report. Entrepreneur notes that businesses using big data experience a profit increase of 8-10%, on average. But obstacles remain, with 40.3% of respondents to a Statista survey suggesting big data usage was held up by a lack of organizational agility.

With BiOS, organizations can collate business intelligence and AI-driven applications into a single platform to derive insights from real-time data. The “telco-grade” platform enables developers to build apps by adding data sources and deploying to production in a semi-automated fashion.

BiOS embeds software development kits (SDKs) within microservices, or systems that structure apps as collections of maintainable, testable, loosely coupled, and independently deployable services. It replaces third-party telemetry trackers with Isima’s first-party tracker to assure users that brands aren’t sharing their data without permission, and it offers “replica-free” insights that enable data analysts across a range of departments to collaborate on interdepartmental data without burdening relational database management systems. Off-the-shelf change-data-capture mechanisms wrap SDKs provided by BiOS to ingest changes.

Early adopters of the BiOS platform include PharmEasy, an Indian online pharmacy and medical supply store. Isima also claims to have subscription revenue from several Fortune 500 customers.

Isima was founded in 2016 by Monish Suvarna, who previously headed product development and portfolio licensing at private equity firm Intellectual Ventures. Cofounder Darshan Rawal is the former senior director of products at DataStax, where he led product management for DataStax’s commercial offerings based on open source projects like Apache Cassandra, SOLR, Spark, Titan, and OpsCenter.

Sway Ventures and Engineering Capital led the $ 10 million seed round in Silicon Valley-based Isima.

Isima competes with companies like Dremio, which has raised about $ 115 million for tools that help streamline and curate data, and Incorta, whose hyperconverged analytics platform helps companies garner insights by crunching vast swathes of data from across their cloud-based applications. Isima has another rival in Quantexa, which uses AI and machine learning tools to uncover risk and opportunities by providing a view of data in a single place, solving challenges across financial crime, customer intelligence, credit risk, and fraud.

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Yann LeCun and Yoshua Bengio: Self-supervised learning is the key to human-level intelligence

May 3, 2020   Big Data
 Yann LeCun and Yoshua Bengio: Self supervised learning is the key to human level intelligence

Self-supervised learning could lead to the creation of AI that’s more human-like in its reasoning, according to Turing Award winners Yoshua Bengio and Yann LeCun. Bengio, director at the Montreal Institute for Learning Algorithms, and LeCun, Facebook VP and chief AI scientist, spoke candidly about this and other research trends during a session at the International Conference on Learning Representation (ICLR) 2020, which took place online.

Supervised learning entails training an AI model on a labeled data set, and LeCun thinks it’ll play a diminishing role as self-supervised learning comes into wider use. Instead of relying on annotations, self-supervised learning algorithms generate labels from data by exposing relationships among the data’s parts, a step believed to be critical to achieving human-level intelligence.

“Most of what we learn as humans and most of what animals learn is in a self-supervised mode, not a reinforcement mode. It’s basically observing the world and interacting with it a little bit, mostly by observation in a test-independent way,” said LeCun. “This is the type of [learning] that we don’t know how to reproduce with machines.”

But uncertainty is a major barrier standing in the way of self-supervised learning’s success.

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Distributions are tables of values — they link every possible value of a variable to the probability the value could occur. They represent uncertainty perfectly well where the variables are discrete, which is why architectures like Google’s BERT are so successful. Unfortunately, researchers haven’t yet discovered a way to usefully represent distributions where the variables are continuous — i.e., where they can be obtained only by measuring.

LeCun notes that one solution to the continuous distribution problem is energy-based models, which learn the mathematical elements of a data set and try to generate similar data sets. Historically, this form of generative modeling has been difficult to apply practically, but recent research suggests it can be adapted to scale across complex topologies.

For his part, Bengio believes AI has much to gain from the field of neuroscience, particularly its explorations of consciousness and conscious processing. (It goes both ways — some neuroscientists are using convolutional neural networks, a type of AI algorithm well-suited to image classification, as a model of the visual system’s ventral stream.) Bengio predicts that new studies will elucidate the way high-level semantic variables connect with how the brain processes information, including visual information. These variables are the kinds of things that humans communicate using language, and they could lead to an entirely new generation of deep learning models.

“There’s a lot of progress that could be achieved by bringing together things like grounded language learning, where we’re jointly trying to understand a model of the world and how high-level concepts are related to each other. This is a kind of joint distribution,” said Bengio. “I believe that human conscious processing is exploiting assumptions about how the world might change, which can be conveniently implemented as a high-level representation. Those changes can be explained by interventions, or … the explanation for what is changing — what we can see for ourselves because we come up with a sentence that explains the change.”

Another missing piece in the human-level intelligence puzzle is background knowledge. As LeCun explained, most humans can learn to drive a car in 30 hours because they’ve intuited a physical model about how the car behaves. By contrast, the reinforcement learning models deployed on today’s autonomous cars started from zero — they had to make thousands of mistakes before figuring out which decisions weren’t harmful.

“Obviously, we need to be able to learn models of the world, and that’s the whole reason for self-supervised learning — running predictive models of the world that would allow systems to learn really quickly by using this model,” said LeCun. “Conceptually, it’s fairly simple — except in uncertain environments where we can’t predict entirely.”

LeCun argues that even self-supervised learning and learnings from neurobiology won’t be enough to achieve artificial general intelligence (AGI), or the hypothetical intelligence of a machine with the capacity to understand or learn from any task. That’s because intelligence — even human intelligence — is very specialized, he says. “AGI does not exist — there is no such thing as general intelligence,” said LeCun. “We can talk about rat-level intelligence, cat-level intelligence, dog-level intelligence, or human-level intelligence, but not artificial general intelligence.”

But Bengio believes that eventually, machines will gain the ability to acquire all kinds of knowledge about the world without having to experience it, likely in the form of verbalizable knowledge.

“I think that’s a big advantage for humans, for example, or with respect to other animals,” he said. “Deep learning is scaling in a beautiful way, and that’s one of its greatest strengths, but I think that culture is a huge reason why we’re so intelligent and able to solve problems in the world … For AI to be useful in the real world, we’ll need to have machines that not just translate, but that actually understand natural language.”

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Business Intelligence and Dynamics 365

April 8, 2020   Microsoft Dynamics CRM
crmnav Business Intelligence and Dynamics 365

In this blog, we will be discussing two of Microsoft’s top products, Power BI and Dynamics 365. We will touch upon what Power BI is, how we can integrate these two products and the many benefits that come from this integration! So, let’s begin! 

What is Power BI? 

In layman’s terms, Power BI is a newer term coined by Microsoft to put a name to all their business intelligence (BI) endeavors. It is a collection of business analytic tools that enable organizations and individuals to organize and display data in a meaningful way.  

Microsoft’s Power BI is designed to enhance an organization’s data reporting and analysis capabilities, and it supports multiple sources. For this post, however, we will be solely discussing Dynamics 365. The integration of Power BI and Dynamics 365 was indeed inevitable, as together, these two services drastically increase an organization’s competitive advantage!  

How can we use these two together, you ask? Well, there are many ways to use Power BI and Dynamics 365 simultaneously. In the case of Power BI, use the following steps: 

  1. Log in to your Power BI
  2. Go to your data sources
  3. Add Dynamics 365 

Your data will be synced automatically, and you will now have multiple visualizations readily available.  

In the case of Dynamics 365, use the following steps: 

  1. Open the Dynamics 365 settings
  2. Navigate to the Reporting Tab
  3. Check Allow Power BI visualization embedding.  

Now you have embedded your Power BI tiles to your Dynamics 365 dashboard! That is some cool stuff going on there!  

Why integrate Dynamics 365 with Power BI? 

Now, you may wonder why you need to integrate your Dynamics 365 with Power BI. There are numerous benefits of doing so and we will be going over each one of them in detail. 

Powerful and User-Friendly Workspace 

Dynamics 365 allows users to execute routine tasks more effortlessly and effectively by empowering them with action hubs. They can use action hubs to create new activities, manage priorities, and analyze relationship insights. The user-friendly interface makes it easier for employees to understand and move about the workspace, which boosts employee productivity. 

Furthermore, the combination of Dynamics 365 and Power BI allows companies to interact with customer data on the go and get meaningful insights. These insights then help companies develop and maintain positive business relationships with clients. 

This powerful workspace digs deeper into analytics with empowering data features and allows for better task management. 

It also helps develop better communication via email messages and shows the reactions and responses of contacts. 

Additionally, it allows for customization through out-of-the-box features, thus helping employees and organizations create their own tailored analytics. With these powerful workspaces and the multiple insights, you can nurture and enhance all business relations.

Power BI integration allows you to augment your operations 

The fact that Dynamics 365 and Power BI allow seamless integration makes it quite popular amongst other analytics options. There are multiple (pre-built and custom) content pack options that you can choose from in order to enhance your reporting capabilities. You can choose to make your custom content packs available for your customers as well! 

You will be enjoying a complete view of all business users and important metrics through one unified dashboard. This data will be updated in real-time and will be easily accessible from all connected devices. Power BI online will allow you to build dashboards which means that you will always enjoy complete visibility into your data and reports. Furthermore, you will have many options to explore and analyze data in the dashboard using the many tools Power BI integration will bring you!  

For example, you can use the SQL server database to build analytics and publish them into content packs! 

Personalized Customer Experiences 

You know what they say, getting new customers is far more costly than retaining old ones! Dynamics 365 contains multiple targeted applications and is also equipped with deep integration of Cortana Intelligence and Azure Machine Learning. All this allows for positive user-experience and highly nurtured relationships with clients.  

With this integration, companies can focus on their customer relations by receiving reminders on their personal devices regarding meetings. Companies can also track sales orders, view information on invoices in real-time, and stay up to date on all customer operations. Additionally, intelligent customer analytics and predictive analytics allows companies to better understand their customers through relevant insights and trends. On top of that, all of this is maintained in one single dashboard! With this, you can put your customer first and ensure that none of their needs are ignored. Having happy, loyal customers was never this easy!  

Safeguard Continuous Progress  

Dynamics 365 is a suite of apps that are built with a plethora of powerful and intelligent tools. From the Cortona Intelligence suite to Power Apps, from Power BI to Machine Learning, Dynamics 365 has got you covered. With Dynamics 365, you get to truly understand your business and make data-driven decisions. By combining Power BI and Dynamics 365, companies empower themselves to make every touchpoint relevant so that they can respond with preemptive insights into the needs of their clients. 

This allows for cost-saving and time efficiency which is a plus for all businesses! Resources are freed up so that the organization can focus on scaling thus safeguarding continuous growth for the company. 

The purpose of this post was to enlighten you on the benefits of integrating your Dynamics CRM software with Power BI so that your company can gain a competitive edge and achieve all your goals easily. The combination of these two products is indeed powerful and allows companies to surface to the top with intelligent tools and many features. It is indeed a must-have! If you would like to know more about the pricing and technicalities of these products, visit the official websites! And if you have any questions for us, feel free to leave a comment below! We are more than happy to answer them! 

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DAX Time intelligence and the 29th of February – #PowerBI

February 3, 2020   Self-Service BI

Yesterday I visited a client and was asked – how do the time intelligence functions handle the fact that February has 29 days in 2020.

Well – in fact there was a few surprises depending on what you select from you date table.

Let’s look as some examples – I will use the following Internet Sales Amount from the years 2011-2013 from Adventure Work Database where we in February 2012 have 29 days.

 DAX Time intelligence and the 29th of February – #PowerBI

As you can see, we have the year 2012 where we have 29 days.

SAMEPERIODLASTYEAR()

In order to calculate Internet Sales Amount LY – I use the following

Internet Sales Amount LY = CALCULATE([Internet Sales Amount];SAMEPERIODLASTYEAR(DimDate[Date]))

Which works fine

 DAX Time intelligence and the 29th of February – #PowerBI

But notice the behavior if we put dates or days numbers on the rows

 DAX Time intelligence and the 29th of February – #PowerBI

SURPRISE – Internet Sales Amount LY will show the value for the 28th of February 2011 instead of a blank value as you perhaps would expect

If you select year 2013 we will see this

 DAX Time intelligence and the 29th of February – #PowerBI

The 29 of feb 2012 will “disappear” but the total for February will include the number.

DATEADD() – last year

If we use the function DATEADD instead – it will work exactly the same way.

IAS LY = CALCULATE([Internet Sales Amount];DATEADD(DimDate[Date];-1;YEAR))

 DAX Time intelligence and the 29th of February – #PowerBI

DATEADD() – same day last year

If you want to compare the same Saturday (the 29th of feb 2020 is a Saturday) last year – which is the 2nd of march we can do this by using the same DATEADD function but with different parameters

IAS LY same weekday = CALCULATE([Internet Sales Amount];DATEADD(DimDate[Date];-364;DAY))

 DAX Time intelligence and the 29th of February – #PowerBI

This will compare the same day 52 weeks ago (52 * 7 = 364) and there by giving us the value from the 29th of feb 2012 on the 27th of feb 2013.

DATESMTD()

Now what about the function DATESMTD()

ISA MTD = CALCULATE([Internet Sales Amount];DATESMTD(DimDate[Date]))

ISA MTD LY = CALCULATE([Internet Sales Amount LY];DATESMTD(DimDate[Date]))

These functions will calculate the running total for the month for the given day number

 DAX Time intelligence and the 29th of February – #PowerBI

Notice that the ISA MTD works fine in 2012 for the 29th and the LY measure will show the same result for the 28th and 29th in 2012 – and in 2013 it will actually for the 28th show the sum of both the 28 and 29th 

Conclusion

You might find that some users find it difficult to understand how the calculations works when the look at dates instead of month totals especially in the case where they will get the same value for LY on both the 28 and 29th in 2012/2020.

If you compare cross years on calendar dates I find the result that SAMEPERIODLASTYEAR() returns makes better sense than leaving it empty/blank but what do you or your users think. Let me know in the comments.

Hope you find this little walkthrough useful.

And remember to ALWAYS use a datetable in your model if you do time intelligence calculations in DAX.

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Weaving Business Intelligence into the fabric of the organization with Microsoft Power BI

November 5, 2019   Self-Service BI

Our customers recognize that in today’s digital economy, data is everywhere – enabling every single employee in their organization to harness the power of all of that data is critical to their competitive advantage.

The Power BI team has a single goal: to enable organizations to embrace a data culture by allowing everyone to make data-driven decisions through beautiful, friendly, and easy to use analytics.

As we kickoff Ignite in Orlando this week, I’m really excited to announce a set of new capabilities in Power BI that help our customers weave business intelligence deep into the fabric of their organizations.

World-class Microsoft security capabilities help protect data

Power BI plays a key role in bringing data insights to everyone in an organization. However, as data becomes more accessible to inform decisions, risk of accidental oversharing or misuse of business-critical information can increase.

Microsoft has world-class security capabilities to help protect customers from threats. Over 3,500 security researchers along with sophisticated AI models reason every day over 6.5+ trillion signals globally to help protect customers against threats at Microsoft.

We are excited to announce new data protection capabilities in Power BI that build on Microsoft’s strengths in security and enable customers to empower every user with Power BI and better protect their data no matter how or where it is accessed.

  • Classify and label sensitive Power BI data using the familiar Microsoft Information Protection sensitivity labels used in Office.
  • Enforce governance policies even when Power BI content is exported to Excel, PowerPoint, or PDF, to help ensure data is protected even when it leaves Power BI.
  • Monitor and protect user activity on sensitive data in real time with alerts, session monitoring, and risk remediation using Microsoft Cloud App Security.
  • Empower security administrators who use data protection reports and security investigation capabilities with Microsoft Cloud App Security to enhance organizational oversight.
New protection metrics in the Power BI admin center

Now in public preview, the capabilities are engaged when Power BI is paired with Microsoft Information Protection and Microsoft Cloud App Security.

These updates, combined with the just announced data lineage view, show the strategic importance of incorporating Enterprise Information Management (EIM) capabilities into Power BI.

Power BI integrated with Azure Synapse Analytics and large models public preview

Today, Microsoft announced Azure Synapse Analytics, the next generation of Azure SQL Data Warehouse. With Azure Synapse, big data and SQL data professionals can collaborate, manage, and analyze your most important data with ease—all within the same service.

We’re excited to announce that Power BI is natively integrated into Azure Synapse Analytics.

  • Customers can link Power BI and Azure Synapse workspaces together providing them with a single pane of glass to develop and manage their end to end analytics infrastructure.
  • BI professionals can create enterprise grade semantic models from the Synapse workspace through integration with Power BI Desktop – which helps customers define their KPIs and business logic, apply role based security, and provides a blazingly fast in-memory cache.
  • Customers can build Power BI reports directly from within the Synapse workspace and make these reports available to their end users.
Power BI integrated with Azure Synapse Analytics

We’re also announcing the availability today of the public preview of large in-memory semantic models. As customers work with massive data volumes they will be able to create data models that go up to 400GB of compressed data, taking advantage of all the memory they have in their Power BI Premium capacity. This increase in model sizes unleashes endless possibilities for enterprise models and in conjunction with certified and promoted datasets, enables organization to have one, enterprise-grade version of the truth.

New AI driven augmented analytics and machine learning experiences

Power BI is a pioneer when it comes to bringing AI experiences into business intelligence with capabilities that empower end users, business analysts, and data scientists. Today we are announcing new experiences directly built into Power BI and Power BI Premium with no additional purchase necessary.

Decomposition Tree

Our community voted and we’ve acted – the decomposition tree idea has over 4,500 votes on ideas.powerbi.com. We are super excited to announce the preview of the decomposition tree visual will ship in the November Power BI Desktop release next week. This visual helps users understand the root cause that contributes to a high or low KPI value broken down by one or more dimensions. The AI component of the decomposition tree is the choice to automatically find the most interesting splits and conduct the root cause analysis for you, instead of splitting manually by dimensions.

The new decomposition tree visual in action

Automated ML General Availability

Last week, Automated ML in Power BI was made generally available. With Automated ML, business analysts can build machine learning models to solve business problems that once required data scientist skillsets. Power BI automates most of the data science workflow to create the ML models, all while providing you full visibility into the process.

The Automated ML model selection screen in Power BI

Cognitive Services in Power BI Desktop (Preview)

The integration with Azure Cognitive Services allows users to better engage with unstructured data by detecting language, scoring sentiment, extracting key phrases, and identifying objects in images. Cognitive Services is available today in Power BI Premium and can be accessed from Power BI dataflows. As of next week, these capabilities will accessible directly from Power BI Desktop.

Cognitive Services menu in the Power BI Desktop Query Editor

Power BI integrated more deeply than ever with Microsoft Teams

Effortless communication using data enables everyone to make fast and accurate decisions. Today, we’re excited to announce deeper integrations with Microsoft Teams are coming soon. They’ll help your organization harness data effectively as individuals work together to achieve shared goals.

  • Starting in late 2019, we will ship a new tab for Microsoft Teams that supports the new workspace experience and reports in organizational apps. This gives all your team members access to data without leaving Microsoft Teams
  • In 2020 we’ll go further by releasing rich previews of Power BI reports directly within Teams channels and chats. We’ll include action button so everyone can quickly act on the data.
Teams and Power BI new experiences for cards and tabs

SQL Server 2019 Analysis Services and Reporting Services

With the general availability announcement of SQL Server 2019, SQL Server 2019 Analysis Services and Reporting Services are now generally available. This release is packed full of new features for both products, including calculation groups in SSAS for calculation reusability, governance settings to protect server resources, and enhanced accessibility options for SSRS. The full list of features in the SQL Server 2019 release, including those for both SSRS and SSAS, are available in the What’s New in SQL Server 2019 documentation online.

See you at Ignite!

With all these exciting innovations, the Power BI team and I are looking forward to seeing many of our customers and community members at Ignite this week. If you are attending Ignite, please attend the “Microsoft Power BI: Business intelligence strategy, vision, and roadmap update”. You can find the full set of Power BI sessions here.

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