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.
BI and AI are are becoming an inseparable dynamic duo. This makes sense when you consider that both disciplines support the business in taking critical business and operational decisions. This duo is also fueled by the same thing: (big) data. Although BI and AI each have their own goals, applications, and issues, we're seeing several converging trends that will dominate the twenties. Our top 9 of technology trends combines business intelligence trends with big data trends and data analysis trends. Trend 1. "AI-first" strategies will dominate
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.
In a recent poll on our website about Business Intelligence, we asked visitors to give us their opinion about the following statement: "A business intelligence track can't succeed if...". Below, you'll find the results of this poll. Respondents indicated that the chance of a BI track failing are the biggest when there are too many KPIs and the top management isn't actively involved with BI. These two options were far ahead of the other three, which were: lack of a data warehouse, wrong tool choice, or the project is in the hands of the ICT department.
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.
The worldwide business intelligence and analytics software market revenue will reach 22.8 billion US Dollars by 2020, according to Statista. In 2008, the revenue for this market was only around 7 billion USD. Organizations use Business Intelligence software to perform thorough analyses to steer the course of their business operations. The demand for this software hasn't suffered from any economic crises, according to market research company Forrester. Organizations need the analyses provided by these programs, especially during economic downturns. Medium-to-small companies will purchase more BI Software. Currently, 30% of the revenue comes from these companies. Forrester expects that number to grow to 36% in the next five years.
Big Data has many potential advantages. It can provide new insights into consumer behavior, show you in which areas the organization can work more efficiently, predict future changes, and much more. However, many companies forge ahead into Big Data without being adequately prepared, and charge straight into a pitfall. Research shows that about 60% of Big Data projects stumble out of the starting blocks. How can you make sure that your organization doesn't become a statistic?
The demand for data scientists is rapidly growing. This function is becoming increasingly important within organizations and its salary is growing to match its importance. Research by the McKinsey Global Institute shows that the lack of analytical and management talent in successfully implementing Big Data is one of the biggest challenges the USA is facing. The McKinsey Global Institute estimates that there are four to five million openings for data scientists in 2018 alone. The hunt for data scientists has been opened. This jack of all trades in your organization should possess many talents in order to help shape and direct the explosive amount of new possibilities provided by big data. But is this realistic?
Over the past 20 years, responsibility for Business Analytics has shifted within organizations. First BI was placed with the IT manager, later it was given to a CIO or CFO. And sometimes it was given to the CMO (Chief Marketing Officer). Now the idea is that BI & analytics will end up with the Chief Data Officer (CDO). One of the research agencies predicted two years ago that 90% of larger organizations will have a CDO in 2019. An enquiry amongst our larger customers makes it clear that this percentage is not even close to being reached.
Ethics is a discipline in philosophy primarily concerned with discerning what forms of human behaviour are acceptable and those that are not. More generally, ethics (or moral philosophy) is a sub-section of philosophy that deals with recording, defending and supporting the concept of good and bad conduct. Generally speaking, this implies that ethics deal with questions such as: How people can best live together in society?
An old term from 1960 revived. It develops really fast, powered by all applications of such major data masters as Google, Uber, Amazon, LinkedIn, Instagram and Facebook. The algorithms were there, but the large amounts of data were missing. Artificial Intelligence never managed to get rid of this. And forecasts were not always as reliable. Now, since more and more pictures, videos, blogs, posts, and sensor data gets available, AI acquires its actual added value. Photography becomes the new universal language (Heiferman, 2013). This sounds intriguing. Every day, 1.2 billion images are taken worldwide. They are shared and distributed. This new language also raises some questions. Isn’t the first impression of a photo too dominant? Numerous studies, including the one conducted by the Yale University, show that people are bad in making good decisions, especially at first impressions or under pressure. Such effects as priming, group polarization, the opinion of the majority or the confirmation bias throw spanner in the works.
Business analytics improves the process of value creation in organizations in two ways: There are managers who use business analytics to achieve strategic goals or solve vital problems of their organization. Other managers involve business analytics specifically to create new opportunities for their organizations, by encouraging necessary innovations through Big Data and necessary changes made in time.
Minne van der Sluis has been working as an Associate Partner at Passionned Group since April 1, where he devotes himself to issues related to Big Data, Data Science and Data Warehousing. "Big Data allows clients to improve their approach to their customers, products, and employees resulting in better processes and their outcomes and, ultimately, to make the leap to innovation. These are the actions I want to take."
In this article I’m going to share some of my experiences from the SAS Analyst Conference, May 27 - 29 this year in Marbella. The first slide Jim Davis, executive vice president of SAS, showed had instant impact: their revenue growth over the last 39 years. Year over year a steady growth without any big difficulties, now earning a revenue of more than 3 billion dollars a year. Foremost I was wondering what the driving forces are behind that continuous growth and success. I think there might be lessons for all businesses in that, especially for the companies that are operating in the BI and Analytics arena.
The first five of the fifteen process steps of the Business Analytics cycle are usually fully executed by computers. This paragraph takes a closer look at the various processors - the most important stakeholders of Business Analytics – that play a role in the last ten process steps. Figure: a variety of processors within an organization process information and knowledge and keep the organization on track.
In recent years, Business Intelligence and Analytics has become more relevant and as a result gained a much larger audience. In the past managers formed the main audience but nowadays we see that many Business Intelligence applications are also being created for knowledge workers. There are two reasons for this change: firstly, the booming information democracy and secondly, the fact that knowledge workers, to an ever greater extent, are urged to make independent decisions and solve problems quickly. In this article, we examine the benefits of Business Intelligence for managers and knowledge workers from various angles.
Decisions need to be informed and accurate in order to run an organization well. The main advantage of Analytics - besides many other benefits which we will discuss here – is that the people within an organization are able to make better decisions faster, both at the level of the knowledge worker and at the level of the Board of Directors. Using effective BI / Analytics the organization is not only guided by the management, but also – and increasingly so – by knowledge workers and operators and in many cases, directly by customers and suppliers.
It sometime seems that Business Analytics is the only game left in town. The familiar term ‘business intelligence’ or BI seems to have been changed everywhere with ‘search and replace’ into ‘business analytics’. The Silver Bullet that puts an end to all problems. If only life were that simple. From thorough and extensive Passionned Group research, it seems that successfully applied Business Intelligence has a number of interconnected critical success factors. These can be placed in the higher organizational concepts; All-round vision, Agility, Alignment and - indeed - Analytics.
Last year TIAS, a separate division for executive teaching of Tilburg University, started an executive programme for IT Consulting. Of the 10 modules, one is dedicated to Business Intelligence. Prof. Piet Ribbers asked Passionned to take on the lion's share of the BI sessions. “A wonderful challenge and a great honor” according to Daan van Beek. TIAS offers a number of programmes that are IT-related. The students are typically in their thirties with about 5 years of work experience who want to gain an extra MSc degree or another diploma valued in practice. The IT Consulting programme is aimed at those who want to become proficient in this area. The programme was created in addition to the existing IT Auditing programme. There is a joint intake for IT Auditing and IT Consulting, after about a year both groups split.
Predictive analytics is a powerful instrument for many organizations. It helps them to create competitive advantage and make their business processes more effective. Insurance companies and credit card issuers for example use it to detect fraud, cops use it to catch criminals, sometimes even before they commit a crime, and car dealers apply analytics to predict the chance that someone responds to a campaign. From our experience we strongly believe that there is a lot of added value in predictive analytics. But, prediction is very difficult - especially if it's about the future (Niels Bohr) - and making automated predictions is even more difficult. There are many pitfalls on the road to success. We think this are the five most important pitfalls:
Even though there are many standardized tools, specific solutions will always have a place. New challenges demand tailor-made solutions. A cotton manufacturer wanted to work more efficiently and reduce waste. Critical questions spurred that desire on. The production team wondered: why do we do things the way we do them? Why are we throwing away so much good material? Can we do better? Most problems only become interesting once the money runs out. Or when the margins shrink, or revenue keeps decreasing. That's when alarm bells start going off. In many cases, that's too late to turn the tide. The solutions which were thought up under pressure are no longer executable. If only you'd taken action sooner. If only you'd listened to the data.
We use dashboards, reports and interactive analysis to enable us to see general and simple relationships within business operations – for example, more customer visits lead to an increase in cross-selling which leads to better financial results – visible. The smaller, more specific and more complex relationships surface when we use data mining. "Data mining is the uncovering of hidden (unknown) relationships or segments in large data collections that have a predictive value for a specific part of the business operations".
The importance of business analytics (BA) was revealed in a recent Bloomberg survey, which found that BA has been effective in decision making for three out of four enterprises. Among the improvements are increased profitability, reduced cost, faster decision making, and critical performance improvements. BA refers to technologies, applications, skills, and practices for the investigation of past business performance to improve insight into this past performance. BA is much more than merely providing simple data to a business. By using BA tools, meaning can be found in data, which results in a business improving its business intelligence. Some examples of Business Analytics include statistical analysis, decision processes, web analytics etc.