Artificial Intelligence is a hot topic. Applications based on machine learning make the news on a near-daily basis. Smart police cameras steered by algorithms can register drivers holding cell phones with great precision. Algorithms can dynamically determine the real-time prices for taxi rides, hotel rooms, airplane seats, and so on. High-frequency traders are getting rich as they sleep by letting their secret algorithms do the work.
Over the past two years, the teachers of the Master of Data Science masterclass have taught dozens of learners the essential principles of data science. The experiences, feedback, and personal ambitions of our students have painted a fairly consistent picture of the data science issues plaguing the workplace. Collating all of their feedback, we've distilled eight data science success factors that we'd love to share with you. The key take-away: let the data work for you instead of vice versa.
Self-learning algorithms are making more and more business decisions independently while also becoming an ever-growing part of our private lives. Decisions made by governments, credit card companies, and banks are can now be solely based on algorithms. "Computer says no" usually means end of discussion, because the algorithm doesn't explain itself. The public debates about the use of algorithms and AI trends occur along the intersection of IT, legislation, and ethics.
The recent wave of acquisitions and the shrinking BI market, as described in our previous BI Tools news alert, has continued to develop: in early August, HPE announced that it would be taking over all the assets of MapR, which was in financial hot water. In the wake of this news, Cloudera announced that it had received the go-ahead to acquire Arcadia Data's technology and assets. Tech companies that have access to (patented) technology to acquire valuable insights from big data analytics in an accessible way continue to be interesting acquisition targets in a consolidating market that seems to be more and more focused on self-service BI and everything that comes with that.
Data analysis, and data-driven working in particular, enables organizations to make the most of their improvement potential. But in order to live up to their fullest potential, some proverbial sacred cows will have to be sacrificed on the altar of progress. So says Daan van Beek, founder of Passionned Group and author of management books such as Data Science for Decision-Makers. The idea of BI as a separate department, for example, is a thing of the past. "But that's okay, because change releases new energy," according to Van Beek.
During the month of May, the market for BI platforms and analytics carried on like business as usual, but early June brought with it breaking news. The BI community was shaken up by the acquisition of two promising BI Tools vendors, and there were some high-profile failures at two well-known specialists of big data solutions. Let's start with the acquisitions. Firstly, the Santa Cruz-based niche player Looker was acquired. Looker, mostly known for LookML as an alternative to SQL, disappeared into the Google cloud for 2.6 billion USD. Hardly a moment passed before Salesforce announced they were absorbing the Seattle-based market leader Tableau for 15.7 billion USD in stocks. Are customers going to have any choice left?
In our regular BI Tools News feature, Passionned Group, publisher of the popular Business Intelligence Tools Survey 2019, walks you through a selection of the most interesting announcements made by BI vendors over the last two months. This is the May 2019 edition of our BI Tools News Alert. During the SAS Global Forum 2019 in Dallas, SAS made several announcements. Key among these is the company's praiseworthy (if not entirely selfless, of course) attempt to democratize the field of (big) data analytics further. The software vendor is offering free AI software to teachers; launching a new analytics simulation game (at a price); and awarding AI certificates and badges. SAS is also investing in the Boys & Girls Clubs to teach kids the tricks of the programming trade.
Software developers and analysts are experts at coming up with new acronyms and exotic-sounding names for their platforms, BI tools, features, plug-ins, and add-ons. NLG, VBD, MOLAP, BIaaS, federated analytics, augmented intelligence, the Prep Conductor, Vizzes, smart analytics, the list goes on. End-users have the unenviable task of looking past the jargon and trying to judge all new announcements on their own merits. A critical attitude, focusing on the promised functionality, will get you far.
Software developers and analysts are experts at coming up with new acronyms and exotic-sounding names for their platforms, features, plug-ins, and add-ons. NLG, VBD, MOLAP, BIaaS, federated analytics, augmented intelligence, the Prep Conductor, Vizzes, smart analytics, the list goes on. End-users have the unenviable task of looking past the jargon and trying to judge all new announcements on their own merits. A critical attitude, focusing on the promised functionality, will get you far.
Not everyone gets excited about the prospect of discussing information entropy, shadow IT, and technical debt. But for Martijn Evers, it's all in a day's work. We had an animated discussion about holistic data management and the art of taming bulls. Together with Ronald Damhof, Martijn Evers, co-founder of i-Refact, started an online movement dedicated to perhaps the ultimate job of the future: full-scale data architect. It's a job that has to suit you. "Usually, you're born a data architect", the self-appointed data missionary says. In other words: abstract thinking has to be in your genes. That's why organizations usually call on people with real passion to fill this key role.
In practice, Martijn Evers, co-founder of i-Refact, believes there's a desire for data architects with a holistic vision (read part 1 of our interview here). Architects that can effortlessly switch between various modes. He jokingly refers to the contrast between a gorilla architect, who is assertive and supported by the direction, and a guerrilla architect, who doesn't have a wide base of support in the organization due to all kinds of politically sensitive matters, and thus is forced to operate under the radar.
AI may be currently dominating the discourse, but BI (Business Intelligence) is proving to be a term with staying power through 2019, both for BI tool vendors and end-users. Nevertheless, BI vendors dating back to the early days of the field are having a hard time staying out of the danger zone. Microsoft, on the other hand, is rising above the pack with Microsoft Power BI. This product has the potential to become the de facto BI standard. These are the results of the updated Business Intelligence Tools Survey 2019, which Passionned Group has been publishing as the Passionned Parabola™ since its early beginnings.
As we recently pointed out, data literacy is a hot-button issue. Only 24% of the decision-makers in international business spheres indicate that they have complete confidence in their own ability to read data, work with it, analyze it, and discuss it. This alarming conclusion was drawn by the worldwide Data Literacy Project. Spoiler alert: 2019 and beyond could be the year of data literacy. The concept of literacy is a lot broader than most people think, according to Wikipedia. It's the ability to work with information, understand it, and use it with purpose. In the Netherlands, Princess Laurentien has been working tirelessly to promote literacy since 2001. At her behest, the Foundation for Reading & Writing was founded in 2004. Although the foundation achieved successes in its fight against illiteracy, the war is far from won.
Change is a fact of life. New software, processes, and system updates cause minor upheavals on a daily basis. And that's to say nothing of the digital transformation. Without learning about the psychology behind change processes, without clear communication, and without investing in human, "warm", contacts, such projects are doomed to fail. Those are the steadfast beliefs of Ericka Petrignani, associate partner with Passionned Group. With her refined stakeholder approach, she's helped many organizations out of a tight spot. What's the "secret sauce" she uses in change projects?
Artificial Intelligence is a hot topic. Algorithms can determine the notes of a new perfume. The high-art world has been scooped by a portrait painted by an algorithm and signed with the code: minG maxD Ex[log(D(x))]+Ez[log(1-D(G(z)))]. These are two random examples of relatively innocent, yet surprising, applications of AI. However, algorithms can also inspire fear. Crashing self-driving cars, smart speakers that take over the entire house, algorithms that thoughtlessly dismiss job applicants based on gender or sentence suspects without mercy. Can algorithms be used for the good of mankind? Algorithms are essentially no more than a recipe, a simple set of instructions. Computers are algorithm machines, modeled to save data, apply mathematical formulas to it, and deliver new information as output. A simple example of a so-called If This, Then That algorithm is: "If the temperature in a house falls below a certain threshold, then the heating will turn on." But what about more advanced forms of AI? Because there's a general lack of understanding surrounding algorithms, we're debunking six common misconceptions.
As project leader, you're cradling an ambitious BI project, and you're looking for theoretical and (especially) practical frameworks. You know that you can achieve better results using the right management information. Googling "Business Intelligence" returns 527 million results. "BI Tools" narrows it down a bit, but still returns 855,000 hits. Discussions are spreading out in all directions and it's hard not to get dizzy. If you want to get serious about working with BI, it's easy to end up in a roller coaster. You'll want to get both feet on the ground again as soon as possible. But BI is dreaming, daring, and especially doing. But you don't have to start from scratch; you can learn from the growing pains that others have already experienced. The road to hell is paved with good intentions. after all. Learn to recognize these symptoms and avoid them, or deal with them. This stories are based on the goals and ambitions of the hundreds who have taken our Business Intelligence training course.