No. The buy side market is nowhere near maturity and will continue to be a greenfield opportunity to many BI vendors. Our research still shows that homegrown shadow IT BI applications based on spreadsheets and desktop databases dominate the enterprises. And only somewhere between 20% and 50% of enterprise structured data is being curated and available to enterprise BI tools and applications.
The sell side of the market is a different story. Forrester’s three recent research reports are pointing to a highly mature, commoditized and crowded market. That crowded landscape has to change. Forrester is making three predictions which should guide BI vendor and BI buyer strategies in the next three to five years.
As digital disruption continues its unstoppable march, digital engagement is rapidly evolving and customers’ expectations that they will get what they want during moments of digital interaction continue to grow. Now more than ever, firms need to understand their customers during and across these moments — and use this understanding to surprise, delight, and personalize. To do this, firms and their insights pros need to cultivate digital intelligence (DI), which Forrester defines as:
The practice of developing a holistic understanding of customers across digital touchpoints for the purpose of optimizing and perfecting the experiences delivered and decisions made by brands during moments of engagement.
To build a holistic understanding — and synchronize engagement optimization — across a growing digital customer engagement edge, firms have procured a plethora of DI tech to deliver capabilities such as web analytics, mobile analytics, behavioral targeting (personalization) capabilities, and more. Initially, the tech was procured in isolation by various relevant teams, including those for web, digital, marketing, mobile, and products. But leading practices have reached a tipping point; they are starting to mature their DI strategies to the point of coordinating the adoption and integration of this tech. The result is that the last 18 months have shown a growth in interest and adoption of platformsthat deliver multiple DI capabilities.
As the product development process and product usage creates higher volumes of data, PLM is a necessary tool to consolidate disparate sources of product information. From this repository, engineering can use product usage data to inform next generation products, operations can improve product development processes, and business stakeholders can focus on linking products to holistic customer experiences. These opportunities reveal the benefit of opening PLM up to stakeholders beyond the product development organization, thus bringing the customer closer to product ideation and development.
A catalyzing functionality in this democratization of PLM are role-based applications which open once-complicated PLM software solutions to new users across the organization. These applications improve usability, solution adoption, time-to-market, and collaboration by incorporating more cross-functional input to the product development process. PLM vendors, large and small, are rolling out role-based application modules for customers, and end user buyers say they are beginning to get requests from their internal constituents for this type of functionality.
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.