WHY IT MATTERS: Digital Transformation

An agile approach to overhauling data architecture improves speed, flexibility, and innovation.

The coronavirus pandemic and the protests sparked by the May 25 murder of George Floyd have been the defining events of 2020 so far, and in both cases one 17-year-old has played a major role online: Avi Schiffmann, the creator of the web’s preeminent covid-19 case tracker and, more recently, a protest tracking site.

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Phone location heat maps track social distancing, but they raise a serious issue.

Expert data scientists and other professionals working in data science roles require capabilities to source data, build models and operationalize machine learning insights. Significant vendor growth, product development and myriad competing visions reflect a healthy market that is maturing rapidly.

The home surveillance company owned by Amazon bragged on Instagram about taping millions of kids going door to door.

Technology is the future of customer experience. These statistics show the grow of new technology and how it impacts everything about the future of customer experience.

Finding shabby abodes like these and making them respectable is the load-bearing wall of Amherst’s strategy. Amherst depends on humans to find cities, towns, and neighborhoods where fixer-uppers can become profitable, then relies on automation to pick individual homes. Negri, 31, heads the human team. He spends 150 days a year on the road overseeing Main Street ­Renewal’s operations from Atlanta to Denver, searching for “sweet spot” neighborhoods that combine affordable rents with a strong middle-income employment base.

Road traffic injuries are a leading cause of death worldwide. Proper estimation of car accident risk is critical for appropriate allocation of resources in healthcare, insurance, civil engineering, and other industries. We show how images of houses are predictive of car accidents. We analyze 20,000 addresses of insurance company clients, collect a corresponding house image using Google Street View, and annotate house features such as age, type, and condition. We find that this information substantially improves car accident risk prediction compared to the state-of-the-art risk model of the insurance company and could be used for price discrimination. From this perspective, public availability of house images raises legal and social concerns, as they can be a proxy of ethnicity, religion and other sensitive data.

There are a number of approaches to measurement, depending on the important metrics for your business. Google’s Jeremy Freedman walks you through the steps toward better attribution.

In this report, TDWI uncovers deeper insights that give users new perspectives on business questions. AI will transform BI and the way people make decisions and act. Rather than start with a hypothesis, data analysts will begin with an AI-driven insight. Instead of querying data to prove or disprove their hypothesis, users will query data to expand or validate a machine-generated insight or recommendation—or they might act on the AI-based insight at face value. But to get to that point, AI-infused BI tools will need to gain people's trust by consistently delivering accurate, relevant, and transparent insights within the context of a business user’s existing workflow.
In the future, AI-infused BI tools will go beyond just surfacing insights; they will recommend ways to address or fix issues, run simulations to optimize processes, create new performance targets based on forecasts, and take action automatically. And yes, machines will make some decisions for us—especially operational decisions in real-time environments. We see this today with fraud detection and online trading systems, but it will become more pervasive.

In this report, TDWI uncovers deeper insights that give users new perspectives on business questions. AI will transform BI and the way people make decisions and act. Rather than start with a hypothesis, data analysts will begin with an AI-driven insight. Instead of querying data to prove or disprove their hypothesis, users will query data to expand or validate a machine-generated insight or recommendation—or they might act on the AI-based insight at face value. But to get to that point, AI-infused BI tools will need to gain people's trust by consistently delivering accurate, relevant, and transparent insights within the context of a business user’s existing workflow.
In the future, AI-infused BI tools will go beyond just surfacing insights; they will recommend ways to address or fix issues, run simulations to optimize processes, create new performance targets based on forecasts, and take action automatically. And yes, machines will make some decisions for us—especially operational decisions in real-time environments. We see this today with fraud detection and online trading systems, but it will become more pervasive.

More than 1,300 people mainly working in the tech, finance and healthcare revealed which machine-learning technologies they use at their firms, in a new O'Reilly survey.

The golden era of AI is here. But how can organizations best harness the technology and integrate it seamlessly into their CRM? This MIT report explores the next-gen powers of AI for CRM. Download the whitepaper for a deep dive on how Salesforce Platform, embedded with Einstein, turns customer data into predictive insights to deliver the most personalized, intelligent experiences for both customers and employees.

Gartner has recognized ThoughtSpot as a Leader in the 2019 Magic Quadrant for Analytics and BI Platforms. ThoughtSpot’s search and AI-driven analytics platform makes it easy for anyone to get insights in seconds.

When a leasing team reviews leases set to expire in the next quarter or year, it should study the universe of potential tenants to fill the pipeline: current tenants that might be better off occupying a different unit within the mall, tenants that are in the company’s other malls but not in this one, and any potential new tenants that have expressed interest in leasing a unit.

To succeed in the digital age, mall operators will need to instill a culture of fact-based decision making throughout the organization. In addition to implementing advanced-analytics tools, they should invest in collecting more of the valuable data that will inform their business decisions. For instance, they can deploy new technologies (such as beacons, granular Wi-Fi, and facial-recognition cameras) to capture behavioral data. They can launch mallwide loyalty programs to gather individual transaction data and generate insights into the customer journey across the entire mall ecosystem. They can also pursue partnerships with tenants—for instance, by negotiating preferred rents in exchange for data sharing. Armed with robust data and advanced analytics tools, malls have the potential to revitalize and revolutionize not just their own business performance but that of the rest of the retail industry as well.

That said, a handful of forward-thinking malls are leading the way in advanced analytics. They’re using prescriptive and predictive analytics—built into user-friendly tools with strong data-visualization capabilities—to make smarter business decisions. In this article, we home in on how malls are using advanced analytics in an especially critical part of their business: revenue management. They’re determining the best mix of stores, understanding and planning store adjacencies that drive higher consumer spending and longer mall visits, and engaging in more-informed rent negotiations with tenants. It’s paying off: malls using these tools have increased their leasing revenues by double-digit percentages.

Cutting-edge technology gives a glimpse into the future of how things will get made, and what manufacturers must do to stay relevant.

Securitas 2018 investor update conference presents the strategy for the future.

Daisy Intelligence is one such artificial intelligence vendor. The Toronto-based company focuses on retail merchandise planning: promotion, pricing and demand forecasting optimization. We spoke with Gary Saarenvirta, CEO of Daisy Intelligence with the intention of finding answers to the following questions:
Why might grocery vendors or supermarkets need AI?
How can retailers leverage AI for optimizing merchandising decision making?

Check out the top-performing retailers and ecommerce companies as evaluated by Sailthru's experts in retail personalization and customer experience.

Lors de la soirée du Débat des chefs, bien des choses ont été dites sur Internet, par les électeurs qui suivaient le débat, par les analystes, les sympathisants et même, les partis eux-mêmes. Voici une analyse de ces propos produites par les gens de Semeon Analytics pour chacun des chefs présent lors du débat de jeudi…

This post explains why traditional marketing analytics tools can't deliver the results CMO’s demand and what you can do to overcome their limitations.