Notes about Cutting-Edge Technologies and Everything

According to Gartner: “By 2022, organizations using multiple go-to-market approaches for digital commerce will outperform noncommerce organizations by 30 percentage points in sales growth." Covid-19 has forced many brands to accelerate their digital-first commerce strategies, sooner than they had planned, in an effort to keep up with customer demands and drive revenue.    Whether your brand was an early adopter, or is still struggling to implement a robust digital commerce strategy, the Gartner Predicts 2021:  COVID-19 Drives Accelerated Shift to Digital and Commerce Model Evolution report will help you prepare for 2021. We believe, in this report you will discover:  5 key digital commerce predictions for 2021 and beyond The market implications of these predictions  How you can embrace new market realities to propel your business Get the report >>>

DataOps techniques can provide a more agile and collaborative approach to building and managing data pipelines. The pandemic has accelerated the need for data and analytics leaders to deliver data and analytics insight faster, with higher quality and resiliency in the face of constant change. Organizations need to make better-informed and faster decisions with a focus on automation, real-time risk assessment and mitigation, continuous value delivery and agility. The point of DataOps is to change how people collaborate around data and how it is used in the organization  As a result, data and analytics leaders are increasingly applying DataOps techniques that provide a more agile and collaborative approach to building and managing data pipelines. What is DataOps? Gartner defines DataOps as a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization. “The poi...

Adoption is the use of a new technology. Engagement is the amount of involvement with a technology. This small semantic difference is the key to unlocking the full potential of new applications. Some applications will have inherently higher engagement than others. For example, a frontline healthcare worker will have a high level of engagement with an electronic medical record because it contains essential information for treating patients. There are other technologies we introduce to make processes easier and faster, even if they are not required. For example, a data analyst may or may not choose to use a metadata management application to learn about the data they use every day. While using the application will make their work easier and faster, they can choose to do their work without it. Engagement is about utilization — increasing the likelihood that people will use the application. Continue reading >>>

The Gartner Hype Cycle highlights the 29 emerging technologies CIOs should experiment with over the next year. Today, companies detect insurance fraud using a combination of claim analysis, computer programs and private investigators. The FBI estimates the total cost of non-healthcare-related insurance fraud to be around $40 billion per year. But a maturing emerging technology called emotion artificial intelligence (AI) might make it possible to detect insurance fraud based on audio analysis of the caller. Some technologies will provide “superhuman capabilities” In addition to catching fraud, this technology can improve customer experience by tracking happiness, more accurately directing callers, enabling better diagnostics for dementia, detecting distracted drivers, and even adapting education to a student’s current emotional state. Though still relatively new, emotion AI is one of 21 new technologies added to the Gartner Hype Cycle for Emerging Technologies, 2019. Original article ...

Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product. Becoming a data-driven organization remains one of the top strategic goals of many companies I work with. My clients are well aware of the benefits of becoming intelligently empowered: providing the best customer experience based on data and hyper-personalization; reducing operational costs and time through data-driven optimi...

The analytics center of excellence has a new mandate for making the entire organization proficient in generating and leveraging automated insights. Data and analytics leaders should include a broad spectrum of organizational, project, data, educational and technological capabilities in their ACE. Key Challenges Data and analytics leaders often struggle to define, establish and communicate the range of analytics capabilities their teams can offer the organization. IT leaders such as CIOs regularly contend that they want to "get out of the report writing business" and expand the notion of analytics from BI application development to enable the entire organization to benefit from data and analytics. Tactical business intelligence competency centers (BICCs), having formed within and emerged from IT organizations, are too limited in scope and technology-focused to provide broad-spectrum analytic enablement. Leading enterprises in most industries generally are more successf...

In a previous blog we discussed how you enable a highly collaborative and data driven organization through the concepts of multi-speed or bi-modal IT.  We then expanded on this through a discussion on the overall information and analytic lifecycle and the interaction with five persona across that lifecycle. You can read those blogs here:  Multi-speed IT drives fast business experiments and empowered citizen analysts Enabling a highly collaborative and data-driven organization Interestingly enough, Forrester Research recently published a report titled “The False Promise of Bimodal IT” which was referenced in an article on CIO.com . Forrester argues this paradigm is fundamentally a mistake as it creates a two class system with the implication that you have a slow moving entity focused on back office systems (IT) with a second group focused on fast roll out of digital products. From an organizational perspective the arguments being made are valid, but when I th...