Retail chains - Starbucks - Data analytics and Business Intelligence
Have you ever wondered how your favorite coffee shop uses data analytics and business intelligence (i.e. BI) to deliver to you the unique Starbucks Experience? This largest and the most recognizable coffee brand is one of the places where business and data analytics solutions meet in the real world.
I tried at my best to bring the retail coffee business across all of you.Request you all to please share your valuable and experienced suggestions......Regards,Ajit Singh
Significance of Predictive Data Analytics in Banking
Banking industries are rich with data. Used or unused, there is an excess amount of data in these sectors. Most banks are under pressure to stay profitable and simultaneously understand the needs, wants and preferences of the customers. Lately, many financial institutions have adopted new models that help them to compete. Banks need to go beyond their standard business reporting and sales forecasting to be able to identify a set of crucial factors relating to success.
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Multi Dimensional Visualization
This visualization technique for displaying multiple dimensions has been developed by a research group at the University of Athens. They are looking for feedback from colleagues in the field on its functionality and utility.
All feedback on the functionality and utility of this multi-dimensional visualizational tool will be used to further develop the application. The link provided also includes a test data set, but where possible please apply your own data.
Drowning in Data?
Find real value and insights in the intersections between small data and big dataData – a set of facts and statistics collected together for reference (https://en.oxforddictionaries.com).As I talk to many business leaders time and time I hear the same frustrations, “I’m drowning in Data, what I need are real insights to drive my decision-making”.Despite these frustrations and the volumes of data available, I am amazed at the degree to which decisions are still being taken on often a very small number of discreet data points, with businesses often using separate disconnected data sets from different vendors to make decisions.So is data a definitive thing, a trusted source, a fact or figure that can be referenced to reinforce or justify a statement or decision? If so then surely it doesn’t matter the size of the data set, if a necessary fact can be derived? Well, it’s not a Yes or No answer, in fact, its Yes and No!In daily life, when looking at any situation, do we look at just one side of an argument or issue? No, because in open freethinking societies we shift our viewpoints to consider other views. In practice, this means triangulating our understanding with many differing sources and views to get much richer information on which to make well-informed decisions. Therefore, our perspectives and rationales change dependent on varying factors and the information available at hand. Such an approach however requires a common context with as a minimum some aligned data points for consideration.If this is how we naturally make our daily decisions, why should our use of data to inform business decisions be any different? Seeing how we make informed decisions on a personal level, is there any surprise that there is a real desire to get deeper and richer perspectives and therefore real insights from the data sources we use at a corporate level?So with so much data available this should be easy, shouldn’t it? If everyone is drowning in data there must be enough relevant information to provide real insights, right? Sadly the real problem is the complexity to align available data to provide the triangulation and joined up views required. Different definitions, taxonomies ,standards and complex data integration makes finding actionable insights from data sources a real problem.Stepping away from ICT to show a real world example, Let’s use a topical issue right now in the EU, Brexit to make a point. The question remains, how many ill-informed choices were made on both sides of the argument with an inaccurate application of individual non-joined up facts preventing a well rounder argument to support decision making, How many non-fact based subjective views were fuelled by bias and prejudice? Well, the answer can be seen in the general confusion and frustration arising after the post-Brexit vote. Prime Minister Theresa May could easily be one of the business leaders quoted in my opening gambit, “I’m drowning in Data, what I need are real insights to drive my decision-making”.Returning to ICT sourcing intelligence, At Pivotal iQ we believe that value is derived best when we are able to use data like we should use information in daily life, looking through different dimensions of interconnected facts and figures to see different perspectives of a client, contract or opportunity to identify the subtleties behind a situation that will inform a decision. We believe real value is actually in the intersections of data.Let me provide an example of how value can be derived in this way using 3 seemingly unrelated Big Data points:Company A has an outsourcing contract with Company B due for renewal in 12 monthsCompany C has an outsourcing contract due for renewal in 10 monthsCompany D has an outsourcing contract with Company E in 12 monthsWhat we have here in isolation are a number of data points that individually are useful but don’t provide sufficiently rich insights on an opportunity. In fact we could look at each and make many assumptions.However, by building relationships between facts and interconnecting ‘small data’ we can start to build richer insights:Company A has an outsourcing contract with Company B due for renewal in 12 monthsCompany A isn’t very happy with Company B’s delivery performance.Company B just released poor financial resultsCompany B has just partnered with Company CJohn a CTO at company A has traditionally had good relations with Company CA service provider now looking at this opportunity may indeed decide to prioritise this opportunity as the clients dissatisfaction provides an opportunity for displacement. The service provider may also seek to partner with C or factor in this association into their sales strategy with company A.This type of in-depth data when combined produces actionable insights. Indeed, Forbes.com (2013) confirms “Data is meaningless unless it helps make decisions that have measurable impact. Unfortunately, many decision makers are ensnared rather than enlightened by Big Data, preventing data and insights from making it to the front lines in relevant and usable forms”.I recently caught up with a global ICT service provider that used the joined up approach I advocate to build a picture of an international customer’s installed technologies across its many sites. By joining company , spending and installed base data they were able to see across a company’s sites and installations to identify an opportunity for consolidation within the company that the global provider was well placed to fulfill. The positive outcome was a huge order win, made possible by the real insights provided by the ‘small data’ between the ‘big data’ points.At Pivotal iQ, our solution has always been to standardise, building and integrating data sources that allow for cross sectional views of companies, opportunities, installed technologies, transactions and announcements allowing ‘small data views’, Integrating several data facts in this way makes for much richer insights.We believe that What you see depends on what you look for. By combining a Big and Small Data approach, we allow you to see the opportunities others can’t by providing an ability for you to see value and insight in the intersections of data.I urge every business leader to challenge their data approach, to see how it can be improved using the Big and Small Data principals championed by Pivotal iQ to provide the richer sourcing insights they demand.
In the event that everybody is suffocating in data there must be sufficient applicable data to give genuine experiences. Unfortunately, the genuine issue is the intricacy to adjust accessible information to give the triangulation and signed up perspectives required. Various definitions, scientific categorizations, gauges and complex information mix makes finding significant bits of knowledge from information sources a genuine issue.
Top Trend in Analytics
The pace and evolution of business intelligence solutions mean what’s working now may need refining tomorrow. From natural language processing to the rise in data insurance, we interviewed customers and Tableau staff to identify the 10 impactful trends you will be talking about in 2018. Whether you’re a data rockstar or an IT hero or an executive building your BI empire, these trends emphasize strategic priorities that could help take your organization to the next level.Read more at https://www.tableau.com/reports/business-intelligence-trends#QZ5lKOrsPRsTiKj2.99https://www.tableau.com/reports/business-intelligence-trends?domain=yahoo.co.uk&eid=CTBLS000011076203&elqCampaignId=28134&elqTrackId=df07a602fd9948c0944bf2daa142366d&elqaid=26586&elqat=1&utm_campaign=Whitepaper%20-%20BI%20Trends%20-%20Prospect%20-%20EMEA%20en-GB%20-%202017-11-16&utm_medium=Email&utm_source=Eloqua&domain=yahoo.co.uk&eid=CTBLS000011076203&elqTrackId=df07a602fd9948c0944bf2daa142366d&elq=d48b17f551624380a22bdc07f4d95a6c&elqaid=26586&elqat=1&elqCampaignId=28134
How Walmart Uses Data To Drive Disaster Relief
Usually, when disaster strikes, whether it’s natural like a hurricane or fire, or a terrorist attack, it seems our best preparations fail. Take Hurricanes Sandy and Katrina, which left whole swathes of the US devastated and uninhabitable for far longer than expected. The earthquake in Haiti a few years ago is another prime example where it took resources far too long to reach those in need.https://www.forbes.com/sites/danwoods/2017/04/21/how-walmart-uses-data-to-drive-disaster-relief/
Chief Data and Analytics Officer Week
Coming to Mainland Europe for the first time, this year's Chief Data and Analytics Officer Week will explore how you as a data and analytics leader can deliver on insight-led decision-making and drive innovation within your business.https://coriniumintelligence.com/cdaoeurope/?gclid=EAIaIQobChMI1OmmqNT51QIVZTPTCh1wYgmkEAAYAiAAEgK_D_D_BwE
Sumo Logic lands $75 million Series F, on path to IPO
Sumo helps companies monitor log data, as well as event and time series data. It’s applying that data to security, threat detection and building a better understanding of what’s happening across your network, all in a cloud-native package.
The Maths behind Memes: Why numbers could help us predict an internet sensation
In today’s social media driven society an internet meme or online trend can explode in popularity overnight. Remember the Ice Bucket challenge?But why do some online trends take the world by storm before disappearing just as quickly as they arrive?Researchers at The University of Manchester’s School of Mathematics and data analysis company Spectra Analytics, are investigating internet trends and memes from the recent past to find out.From ‘planking’ and ‘cat beards’ to ‘hadokening’ and ‘the lying down game’, the team have been looking at 26 different internet memes and trends dating back to 2011, measuring their impact and longevity then collating the data for analysis.All the numbers have now been crunched and the team have identified and tested a mathematical model that accurately demonstrates why some fads will take off and even predicts how long it will last.‘Complex contagion’ is the model that best describes the spread of behaviours driven by online sharing according to Dr Thomas House, the study’s senior author. Complex contagion isn’t a new concept but here, and for the first time ever, Dr House and the study’s first author Dr Dan Sprague have used mathematics and data analysis to describe the complex contagion theory and provide empirical evidence for its action across society.Dr House explains: ‘Social influence can lead to behavioural “fads” that are briefly popular but then quickly die out. Various theories and models have been proposed to explain such behaviours, but empirical evidence of their accuracy as real-world predictive tools has been absent so far.’http://www.manchester.ac.uk/discover/news/the-maths-behind-memes-why-numbers-could-help-us-predict-an-internet-sensation/