Prescriptive analytics is about using data and analytics to improve decisions and therefore the effectiveness of actions. Isn’t that what all analytics should be about? A hearty “yes” to that because, if analytics does not lead to more informed decisions and more effective actions, then why do it at all? Many wrongly and incompletely define prescriptive analytics as the what comes after predictive analytics. Our research indicates that prescriptive analytics is not a specific type of analytics, but rather an umbrella term for many types of analytics that can improve decisions. Think of the term “prescriptive” as the goal of all these analytics — to make more effective decisions — rather than a specific analytical technique. Forrester formally defines prescriptive analytics as:
"Any combination of analytics, math, experiments, simulation, and/or artificial intelligence used to improve the effectiveness of decisions made by humans or by decision logic embedded in applications."
Prescriptive Analytics Inform And Evolve Decision Logic Whether To Act (not not act) And What Action To Take
Business intelligence (BI) is a runaway locomotive that keeps picking up speed in terms of enterprise interest, adoption, and spending levels. The result: Forrester now tracks 73(!) vendors in the segment. Their architectures and user interfaces vary, but they support similar use cases. Forrester started the original research with fewer than 30 vendors in 2014 and ended up with 73 in the current 2017 update. Expect this dynamic to continue for the foreseeable future. Even though the BI market is quite mature from the point of view of the number of players and breadth and depth of their functionality, it is still quite immature regarding business and technology maturity, adoption, and penetration levels in user organizations. Vendors will continue to seize this opportunity — new players will keep springing up, and large vendors will continue to acquire them.No market, even a
It’s a surprise because AppDynamics was one day away from its IPO, giving nary a hint of courting a suitor. That would be an awfully expensive and troublesome camouflage. And if it was camo, it was amazingly airtight in this notoriously leaky information age. (As I write this, several press outlets report the deal went from idea to agreement in three days.)
It’s not a surprise because:
· AppDynamics’ APM competitors have been rapidly broadening their monitoring to yield better analytics with fewer blind spots. Cisco gives AppDynamics an exceptionally clear view of network performance and AppDynamics gives Cisco a clear view of application performance. APM solutions must continue to expand their data ingestion to provide optimum value.
Stop! Before you invest even 10 minutes of your precious time reading this blog, please make sure it's really business intelligence (BI) governance, and not data governance best practices, that you are looking for. BI governance is a key component of data governance, but they're not the same. Data governance deals with the entire spectrum (creation, transformation, ownership, etc.) of people, processes, policies, and technologies that manage and govern an enterprise's use of its data assets (such as data governance stewardship applications, master data management, metadata management, and data quality). On the other hand, BI governance only deals with who uses the data, when, and how.
Since the term BI is often used to also include data management processes and technologies, let's assume that in your case you are only looking for expertise required to build reports and dashboards and it does not include
Data integration (ETL, etc) expertise
Data governance (master data management, data quality, etc) expertise
Data modelling (relational and multidimensional) expertise
Video conveys emotion unlike text and can show features and functionality unlike any picture. That’s why retailers see nearly triple the conversion rate on product pages that have video versus those that don’t. Entering what Facebook CEO Mark Zuckerberg calls “this new golden age of videos online,” companies and brands need an enterprise-class online video platform to deliver the video experiences that drive customer engagement.
Over the past few months, following publication of my "Customer Insights Center of Excellence" report , there’s been a significant uptick in questions by insights and analytics teams who want to talk to us about CoEs. That’s a positive sign that firms are feeling the crunch to get more value from their insights functions. What’s the evidence for that conclusion? What can we learn from who’s asking about insights CoEs? And most importantly, what really matters in how you organize?
Before we dig in to answers, let’s set the bar on what “great” looks like in truly customer obsessed organizations: they use data for insights to improve customer experience that matters most to business outcomes. As my colleagues James McCormick, Brian Hopkins, and Ted Schadler write in their recent report, "The Insights-Driven Business," customer obsessed businesses act on insights in closed loops, at speed, and at scale in all parts of the firm. They embed analytics and testing directly into operating teams. And, firms who implement these approaches run faster and fleeter than you. The pressure is on from insights-driven organizations.
Is your business digital? Like Domino’s Pizza, do you realize that you are not a product or service business, rather you are a software and data business that provides products or services? Do you exploit all of your customers' data to know them inside-out? Are customers flocking to you because you are driving every engagement with insight about them? If the answer to any of these questions is not a resounding, “Yes!”, then you are losing revenue and shareholder value.
In Forrester’s new report, The Insight Driven Business, my colleagues Ted Schadler, James McCormick and I identify a type of business that ignores the "data driven" hype. Instead, insights-drivenbusinesses focus on implementing insights - that is actionable knowledge in the context of a process or decision - in the software that drives every aspect of their business. This is a big shift from most firms that fret over big data and technology. Instead insights-driven businesses focus on turning insights into action. The big data and technology pieces come along naturally as a consequence.
To gauge the economic impact of insights-driven businesses, Forrester built a revenue model that conservatively forecasts insights-driven businesses will earn about $400 billion in 2016; however, by 2020 they will be making over $1.2 trillion a year due to an astonishing compound annual growth rate between 27% and 40%. Given that global growth is less than 4%, how will they pull this off? Plain and simple, they’ll do this by understanding customers more deeply and using that insight to steal them from their competition.
The questions below may sound familiar to you. I hear them from leaders of business insights teams of all kinds, from quant to qual, digital analytics to database marketing, customer analytics to voice of customer, market research to competitive intelligence, campaigns to customer service, behaviorial to predictive, B2C to B2B, CPG to pharma – you name it:
"I lead our [name the insights area[s] here] team. We’re struggling to get our business and operational areas to take action on insights – heck, sometimes we don’t even know what happens to the insights we provide. How do we change this?"
"Our insights teams work in silos that have built up over the years. The teams are good at what they do. But how do we pull together and combine our different flavors of insights to get more customer understanding? How should we organize?"
"I've been asked to re-organize [or, I'm new and I've taken over] our insights areas. I need to give a presentation to the C-team about what I'll propose. Any ideas on a framework I should use?"
The Background – Linux as a Fast Follower and the Need for Hot Patching
No doubt about it, Linux has made impressive strides in the last 15 years, gaining many features previously associated with high-end proprietary Unix as it made the transition from small system plaything to core enterprise processing resource and the engine of the extended web as we know it. Along the way it gained reliable and highly scalable schedulers, a multiplicity of efficient and scalable file systems, advanced RAS features, its own embedded virtualization and efficient thread support.
As Linux grew, so did supporting hardware, particularly the capabilities of the ubiquitous x86 CPU upon which the vast majority of Linux runs today. But the debate has always been about how close Linux could get to "the real OS", the core proprietary Unix variants that for two decades defined the limits of non-mainframe scalability and reliability. But "the times they are a changing", and the new narrative may be "when will Unix catch up to Linux on critical RAS features like hot patching".
Hot patching, the ability to apply updates to the OS kernel while it is running, is a long sought-after but elusive feature of a production OS. Long sought after because both developers and operations teams recognize that bringing down an OS instance that is doing critical high-volume work is at best disruptive and worst a logistical nightmare, and elusive because it is incredibly difficult. There have been several failed attempts, and several implementations that "almost worked" but were so fraught with exceptions that they were not really useful in production.[i]