Four Big Themes from #GartnerBI

| March 21, 2016

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I spent this week at Gartner’s Business Intelligence & Analytics Summit. It was the first time that I’ve gone to a Gartner event, let alone spoken at one, and found the couple days that I was there to be both interesting and fun. For those of you who were not there, I thought that there were four recurring themes that routinely worked their way into many of the talks and analyses:

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Dun & Bradstreet data and insights help improve your business performance. Delivered through the Dun & Bradstreet Data Cloud and our market-leading solutions, our data and insights help you accelerate revenue, manage risk, lower cost and transform your business. Global businesses of all sizes rely on our data, insight & analytics. Visit us at www.dnb.com for more information.

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Living upto Learn, Re Learn and Unlearn

Article | March 23, 2021

Learn, re Learn and Unlearn The times we are living in, we have to upgrade ourselves constantly in order to stay afloat with the industry be it Logistics, Traditional business, Agriculture, etc.. Technology is constantly changing our lives the way we used to live, living and will live. Anyone who thinks technology is not their cup of tea then I would say he /she will have no place in the world to live. It’s a blessing or curse on human race, only time will tell but effects are already surfacing in the market in the form of Job cut, poverty, some roles are no longer needed or replaced with. Poor is getting poorer and rich is getting richer. Covid19 has not only brought the curse on human race but it has been a blessing in disguise for Tech giants and E-commerce. Technology not only changing the business but every human’s outlook towards life, family structure, the globalization of talents etc. It is nerve wrenching to imagine just what the world will look like in coming 20 years from now. Can all of us adapt to learn, re learn and unlearn quote? Or we have to depend upon countries/Governments to announce Minimum Wage to sustain our basic needs? Uncertainties are looming as the world is coming closer due to technology but emotionally going far. It’s sad to see children, colleagues communicating via emails and messages in the same home and office. Human is losing its touch and feel. Repercussion to resists of learning, unlearning and relearning can bring down choices to none in the long run. Delay in adapting to change can be increasingly expensive as one can lose their place in a world earlier than one think. From 1992, where fewer people used to have facility of internet around , People used to stay in jobs for life but same people are now not wanted in the jobs when they go for interview as they lack in experience just because they have been doing what they were doing in one job without exposing themselves to the world’s new requirement of learn , re learn and unlearn. Chances of this group, getting a job will be negative. World has thrown different types of challenges to people, community, jobs, businesses , those people used to be applauded for remaining On one job for life ,same group of people are looked differently by corporate firms as redundant due to technology. So should people keep changing jobs after few years to just get on to learn, re learn and unlearn or continue waiting for their existing companies to face challenges and go off from the market? Only time and technology will determine what is store for human race next. According to some of the studies, its shown the longer the delay in adopting technology for any given nation, the lower the per capita income of that nation. It shows extreme reliance on Technology but can all of us adopt to the technology at the same rate as its been introduced to us? Can our children or upcoming next generations adopt technology at same scale? Or future is Either Technology or nothing, in Short Job or Jobless there is no in between option? Stephen Goldsmith, director of the Innovations in Government Program and Data-Smart City Solutions at the John F. Kennedy School of Government at Harvard University, said that in some areas, technological advancements have exceeded expectations made in 2000. The Internet also has exploded beyond expectations. From 2000 to 2010, the number of Internet users increased 500 percent, from 361 million worldwide to almost 2 billion. Now, close to 4 billion people throughout the world use the Internet. People go online for everything from buying groceries and clothes to finding a date. They can register their cars online, earn a college degree, shop for houses and apply for a mortgage but again same question is arising , Can each one of us at the same scale use or advance their skill to use technology or we are leaving our senior generations behind and making them cripple in today’s society? Or How about Mid age people who are in their 50s and soon going to take over senior society , Can they get the job and advance their skill to meet technology demands or learn, unlearn and re learn or Not only pandemic but even Technology is going to make human redundant before their actual retirement and their knowledge, skill obsolete. There should be a way forward to achieve balance, absolute reliance on Technology is not only cyber threat to governments but in long term, Unemployment, Creating Jobs or paying minimum wage to unemployed mass will be a huge worry. At the end of the day, humans need basic and then luxury. Technology can bring ease of doing business, connecting businesses and out flows, connecting Wholesalers to end users but in between many jobs, heads will be slashed down and impact will be dire. Therefore Humans have to get themselves prepared to learn, unlearn and re learn to meet today’s technology requirement or prepare themselves for early retirement.

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How Artificial Intelligence Is Transforming Businesses

Article | February 11, 2020

Whilst there are many people that associate AI with sci-fi novels and films, its reputation as an antagonist to fictional dystopic worlds is now becoming a thing of the past, as the technology becomes more and more integrated into our everyday lives.AI technologies have become increasingly more present in our daily lives, not just with Alexa’s in the home, but also throughout businesses everywhere, disrupting a variety of different industries with often tremendous results. The technology has helped to streamline even the most mundane of tasks whilst having a breath-taking impact on a company’s efficiency and productivity.However, AI has not only transformed administrative processes and freed up more time for companies, it has also contributed to some ground-breaking moments in business, being a must-have for many in order to keep up with the competition.

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Data Analytics Convergence: Business Intelligence(BI) Meets Machine Learning (ML)

Article | July 29, 2020

Headquartered in London, England, BP (NYSE: BP) is a multinational oil and gas company. Operating since 1909, the organization offers its customers with fuel for transportation, energy for heat and light, lubricants to keep engines moving, and the petrochemicals products. Business intelligence has always been a key enabler for improving decision making processes in large enterprises from early days of spreadsheet software to building enterprise data warehouses for housing large sets of enterprise data and to more recent developments of mining those datasets to unearth hidden relationships. One underlying theme throughout this evolution has been the delegation of crucial task of finding out the remarkable relationships between various objects of interest to human beings. What BI technology has been doing, in other words, is to make it possible (and often easy too) to find the needle in the proverbial haystack if you somehow know in which sectors of the barn it is likely to be. It is a validatory as opposed to a predictory technology. When the amount of data is huge in terms of variety, amount, and dimensionality (a.k.a. Big Data) and/or the relationship between datasets are beyond first-order linear relationships amicable to human intuition, the above strategy of relying solely on humans to make essential thinking about the datasets and utilizing machines only for crucial but dumb data infrastructure tasks becomes totally inadequate. The remedy to the problem follows directly from our characterization of it: finding ways to utilize the machines beyond menial tasks and offloading some or most of cognitive work from humans to the machines. Does this mean all the technology and associated practices developed over the decades in BI space are not useful anymore in Big Data age? Not at all. On the contrary, they are more useful than ever: whereas in the past humans were in the driving seat and controlling the demand for the use of the datasets acquired and curated diligently, we have now machines taking up that important role and hence unleashing manifold different ways of using the data and finding out obscure, non-intuitive relationships that allude humans. Moreover, machines can bring unprecedented speed and processing scalability to the game that would be either prohibitively expensive or outright impossible to do with human workforce. Companies have to realize both the enormous potential of using new automated, predictive analytics technologies such as machine learning and how to successfully incorporate and utilize those advanced technologies into the data analysis and processing fabric of their existing infrastructure. It is this marrying of relatively old, stable technologies of data mining, data warehousing, enterprise data models, etc. with the new automated predictive technologies that has the huge potential to unleash the benefits so often being hyped by the vested interests of new tools and applications as the answer to all data analytical problems. To see this in the context of predictive analytics, let's consider the machine learning(ML) technology. The easiest way to understand machine learning would be to look at the simplest ML algorithm: linear regression. ML technology will build on basic interpolation idea of the regression and extend it using sophisticated mathematical techniques that would not necessarily be obvious to the causal users. For example, some ML algorithms would extend linear regression approach to model non-linear (i.e. higher order) relationships between dependent and independent variables in the dataset via clever mathematical transformations (a.k.a kernel methods) that will express those non-linear relationship in a linear form and hence suitable to be run through a linear algorithm. Be it a simple linear algorithm or its more sophisticated kernel methods variation, ML algorithms will not have any context on the data they process. This is both a strength and weakness at the same time. Strength because the same algorithms could process a variety of different kinds of data, allowing us to leverage all the work gone through the development of those algorithms in different business contexts, weakness because since the algorithms lack any contextual understanding of the data, perennial computer science truth of garbage in, garbage out manifests itself unceremoniously here : ML models have to be fed "right" kind of data to draw out correct insights that explain the inner relationships in the data being processed. ML technology provides an impressive set of sophisticated data analysis and modelling algorithms that could find out very intricate relationships among the datasets they process. It provides not only very sophisticated, advanced data analysis and modeling methods but also the ability to use these methods in an automated, hence massively distributed and scalable ways. Its Achilles' heel however is its heavy dependence on the data it is being fed with. Best analytic methods would be useless, as far as drawing out useful insights from them are concerned, if they are applied on the wrong kind of data. More seriously, the use of advanced analytical technology could give a false sense of confidence to their users over the analysis results those methods produce, making the whole undertaking not just useless but actually dangerous. We can address the fundamental weakness of ML technology by deploying its advanced, raw algorithmic processing capabilities in conjunction with the existing data analytics technology whereby contextual data relationships and key domain knowledge coming from existing BI estate (data mining efforts, data warehouses, enterprise data models, business rules, etc.) are used to feed ML analytics pipeline. This approach will combine superior algorithmic processing capabilities of the new ML technology with the enterprise knowledge accumulated through BI efforts and will allow companies build on their existing data analytics investments while transitioning to use incoming advanced technologies. This, I believe, is effectively a win-win situation and will be key to the success of any company involved in data analytics efforts.

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Taking a qualitative approach to a data-driven market

Article | February 18, 2021

While digital transformation is proving to have many benefits for businesses, what is perhaps the most significant, is the vast amount of data there is available. And now, with an increasing number of businesses turning their focus to online, there is even more to be collected on competitors and markets than ever before. Having all this information to hand may seem like any business owner’s dream, as they can now make insightful and informed commercial decisions based on what others are doing, what customers want and where markets are heading. But according to Nate Burke, CEO of Diginius, a propriety software and solutions provider for ecommerce businesses, data should not be all a company relies upon when making important decisions. Instead, there is a line to be drawn on where data is required and where human expertise and judgement can provide greater value. Undeniably, the power of data is unmatched. With an abundance of data collection opportunities available online, and with an increasing number of businesses taking them, the potential and value of such information is richer than ever before. And businesses are benefiting. Particularly where data concerns customer behaviour and market patterns. For instance, over the recent Christmas period, data was clearly suggesting a preference for ecommerce, with marketplaces such as Amazon leading the way due to greater convenience and price advantages. Businesses that recognised and understood the trend could better prepare for the digital shopping season, placing greater emphasis on their online marketing tactics to encourage purchases and allocating resources to ensure product availability and on-time delivery. While on the other hand, businesses who ignored, or simply did not utilise the information available to them, would have been left with overstocked shops and now, out of season items that would have to be heavily discounted or worse, disposed of. Similarly, search and sales data can be used to understand changing consumer needs, and consequently, what items businesses should be ordering, manufacturing, marketing and selling for the best returns. For instance, understandably, in 2020, DIY was at its peak, with increases in searches for “DIY facemasks”, “DIY decking” and “DIY garden ideas”. For those who had recognised the trend early on, they had the chance to shift their offerings and marketing in accordance, in turn really reaping the rewards. So, paying attention to data certainly does pay off. And thanks to smarter and more sophisticated ways of collecting data online, such as cookies, and through AI and machine learning technologies, the value and use of such information is only likely to increase. The future, therefore, looks bright. But even with all this potential at our fingertips, there are a number of issues businesses may face if their approach relies entirely on a data and insight-driven approach. Just like disregarding its power and potential can be damaging, so can using it as the sole basis upon which important decisions are based. Human error While the value of data for understanding the market and consumer patterns is undeniable, its value is only as rich as the quality of data being inputted. So, if businesses are collecting and analysing their data on their own activity, and then using this to draw meaningful insight, there should be strong focus on the data gathering phase, with attention given to what needs to be collected, why it should be collected, how it will be collected, and whether in fact this is an accurate representation of what it is you are trying to monitor or measure. Human error can become an issue when this is done by individuals or teams who do not completely understand the numbers and patterns they are seeing. There is also an obstacle presented when there are various channels and platforms which are generating leads or sales for the business. In this case, any omission can skew results and provide an inaccurate picture. So, when used in decision making, there is the possibility of ineffective and unsuccessful changes. But while data gathering becomes more and more autonomous, the possibility of human error is lessened. Although, this may add fuel to the next issue. Drawing a line The benefits of data and insights are clear, particularly as the tasks of collection and analysis become less of a burden for businesses and their people thanks to automation and AI advancements. But due to how effortless data collection and analysis is becoming, we can only expect more businesses to be doing it, meaning its ability to offer each individual company something unique is also being lessened. So, businesses need to look elsewhere for their edge. And interestingly, this is where a line should be drawn and human judgement should be used in order to set them apart from the competition and differentiate from what everyone else is doing. It makes perfect sense when you think about it. Your business is unique for a number of reasons, but mainly because of the brand, its values, reputation and perceptions of the services you are upheld by. And it’s usually these aspects that encourage consumers to choose your business rather than a competitor. But often, these intangible aspects are much more difficult to measure and monitor through data collection and analysis, especially in the autonomous, number-driven format that many platforms utilise. Here then, there is a great case for businesses to use their own judgements, expertise and experiences to determine what works well and what does not. For instance, you can begin to determine consumer perceptions towards a change in your product or services, which quantitative data may not be able to pick up until much later when sales figures begin to rise or fall. And while the data will eventually pick it up, it might not necessarily be able to help you decide on what an appropriate alternative solution may be, should the latter occur. Human judgement, however, can listen to and understand qualitative feedback and consumer sentiments which can often provide much more meaningful insights for businesses to base their decisions on. So, when it comes to competitor analysis, using insights generated from figure-based data sets and performance metrics is key to ensuring you are doing the same as the competition. But if you are looking to get ahead, you may want to consider taking a human approach too.

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Dun & Bradstreet

Dun & Bradstreet data and insights help improve your business performance. Delivered through the Dun & Bradstreet Data Cloud and our market-leading solutions, our data and insights help you accelerate revenue, manage risk, lower cost and transform your business. Global businesses of all sizes rely on our data, insight & analytics. Visit us at www.dnb.com for more information.

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