I’ve been a part of several development organizations, and, for several of those teams, security was an afterthought to the development process. We’d secure databases and even implement field level encryption but we rarely had to consider many attack vectors as we were building internal apps for enterprises and the risks were there, but not as great.
Fast forward to the Mobile First world we live in and that lazy attitude is no longer acceptable. S&R teams have real concerns and actively work to protect their computing environments – both internal-facing and external-facing. Development teams work the other side of that and implement secure code as part of their daily activities (right?). With an appropriate level of trust between the two organizations, many use code scanning utilities to verify delivered code and hunt for vulnerabilities. There are many sources of vulnerabilities; it could come from code written by the company’s developers, code pasted in from Stack Overflow or even added through some third-party or open source library. In my experience, static code scanning tools are effective and can catch a lot of potential vulnerabilities but, from a developer behavior standpoint, what the ultimately do is simply teach developers how to get their code to pass the scans, not actually deliver more secure code.
Years ago, I worked at a large customer service vendor. Our CEO had tasked us to "eat our own dog food" - that is implement our own solutions for our customer service operations which comprised of 40 or so tier 1 and 2 customer service agents. With these marching orders, I put a group of consultants and business analysts together to get this done. And after several months, the project stalled; got restarted; stalled again; then finally died. We limped on with our old systems in place for many more years.
Why did this project fail? It was because of a mismatch between the complexities of the solution that we were trying to implement, and the company's business needs. The customer service company that I worked for made enterprise software solutions, suitable for large organizations, which was typically implemented in call centers of many hundreds, if not thousands of call center agents. These solutions offered robust case management, with very customizable workflows, queuing and routing rules. These solutions also offered complex knowledge management, email and chat engines that could support millions of interactions a month. Implementation tended to span many months, where professional services consultants dove into the business processes that agents followed, and then reproduced them in these enterprise solutions.
Yet these solutions - as powerful as there are - were too complex for our simple needs. There were no simple "out of the box" best practice process flows. There were no rapid deployment options to get a company up and running quickly. There were no simple ways of setting up FAQs or simple knowledge, or creating simple email and chat routing rules for a moderate volume of digital interactions. What we needed was a highly usable solution, with a quick time-to-value, which contained just the most common functions of the enterprise solution.
Digital intelligence (DI) is the practice of bringing together the big data that we have on our customers to analyze and generate insights in so as to deliver the best, optimal and/or the most relevant experiences during moments of their digital interaction. Firms that get it right have a major competitive advantage in the digital age of the customer (For more information on the digital intelligence approach, see the “Optimize Customer Experiences With Digital Intelligence ” Forrester report).
This hot topic is why I am excited to announce the publication of the brand new Forrester report entitled “TechRadar™: Digital Intelligence, Q2 2016 ”. In this report, I analyze and review the business success and growth of the 15 core technologies for digital data management, analytics, and experience optimization needed to deliver great digital intelligence capabilities.
Some of my findings include:
DI tech is really hot at the moment. Whether its technology to ingest, manage, and merge different customer data (e.g. tag management or data warehousing), or to generate digital insights (e.g. app analytics or spatial analytics), or that for optimizing digital interactions (e.g. online testing or behavioral targeting) we found all the core DI technologies are on a trajectory for delivering a moderate if not significant success.
Knowledge is power. And in a time where insights drive business differentiation, knowledge is also the origin of power. In our daily routines as consumers, search is probably the most common application we use to find knowledge, and it forms the basis of our personal systems of insight. But at long last, search in the enterprise is catching up. A new wave of search-based applications and search-driven experiences are now being delivered by companies who understand the need to empower their employees and customers with immediate, contextual knowledge in an easily-consumable format.
In our new research, Mike Gualtieri and I look at how the emerging landscape of cognitive search experiences are incorporating advanced analytics, natural language processing (NLP), and machine learning to enable organizations to see across wide arrays of enterprise data and stitch together insights hidden among them.
Today in the US, we are gearing up to celebrate Cinco de Mayo with lively music, ice-cold margaritas, colorful clothing — the works. But while many Americans use the day to revel in the trappings of Mexican culture, they often don’t realize that the holiday is actually met with little pomp and circumstance in Mexico itself.
Cinco de Mayo is one of many traditions that have been adopted — and appropriated — across country borders. But the holiday represents a larger concept that applies to people, too: As individuals relocate around the world, they spark cultural variations and build unique identities in their own right.
For example, Forrester’s Consumer Technographics® survey data shows that Mexican-born individuals who now live in the US develop distinct behaviors and attitudes: Not only do these longer-tenured US residents become more comfortable sharing sensitive data (like financial information) online, they also increasingly execute digital transactions:
It’s interesting to note that even though metropolitan Mexico and the US have similar mobile penetration rates, the device profile, technology attitudes, and digital behaviors that characterize Mexican consumers shift after they settle in the US.
Delivering broad access to data and analytics to a diverse base of users is an intimidating task, yet it is an essential foundation to becoming an insights-driven organization. To win and keep customers in an increasingly competitive world, firms need to take advantage of the huge swaths of data available and put it into the hands of more users. To do this, business intelligence (BI) pros must evolve disjointed and convoluted data and analytics practices into well-orchestrated systems of insight that deliver actionable information. But implementing digital insights is just the first step with these systems — and few hit the bull's eye the first time. Continuously learning from previous insights and their results makes future efforts more efficient and effective. This is a key capability for the next-generation BI, what Forrester calls systems of insight.
"It's 10 o'clock! Do you know if your insights support actual verifiable facts?" This is a real challenge, as measuring report and dashboard effectiveness today involves mostly discipline and processes, not technology. For example, if a data mining analysis predicted a certain number of fraudulent transactions, do you have the discipline and processes to go back and verify whether the prediction came true? Or if a metrics dashboard was flashing red, telling you that inventory levels were too low for the current business environment, and the signal caused you to order more widgets, do you verify if this was a good or a bad decision? Did you make or lose money on the extra inventory you ordered? Organizations are still struggling with this ultimate measure of BI effectiveness. Only 8% of Forrester clients report robust capabilities for such continuous improvement, and 39% report just a few basic capabilities.
In a recent blog post, "Why Millennials Struggle For Success", well known psychologist, author and MacArthur Fellow Angela Duckworth, explores the question many experienced business leaders and managers ask as well: What’s wrong with Millennials? Why do they keep changing jobs? Why do they complain when work needs to be taken home over the weekend? And so on. She asks if it’s because they don’t have enough grit. Duckworth believes the secret to outstanding success is not talent but rather a special blend of passion and persistence called grit. Duckworth developed a Grit Scale and now has scores from thousands of Americans where her data reveal that grit and age go hand and hand.
So what do we do with those Millennials? Today, Millennials make up one third of the workforce and in four years time they will be half. With the average age of the US B2B sales rep at 40 years old, millennial sellers are, or soon will be, the future revenue generators and business leaders for their firms. Smart sales and marketing leaders realize that status quo communication, management approaches and tools will fail to inspire, and are adapting their enablement strategies and tech stacks to resonate with this important group of employees.
We had a fantastic event last week in New York, as 750+ marketing leaders converged on Forrester’s Marketing 2016. The big theme this year was how to succeed as a marketer in a post-digital world.
What’s “post-digital” you ask? Well, we’re living in that world today and it’s time to acknowledge it. Digital technology is embedded in our daily lives as consumers, as professionals – as human beings. I opened the event describing how marketing evolves in three phases: The pre-digital era was characterized by a mass-media centric, one-to-many approach; the digital era ushered in a data-driven, one-to-one mantra; and today we’re in a post-digital world, and your success will be determined by your ability to adapt to one-to-moment marketing.
The digital distinction now dissolves into our daily lives. This raises the stakes for marketers because your customers aren’t just empowered by digital technology – they’re actually entitled. They think they deserve something, they want it now, and if you can’t provide it, they will quickly find it somewhere else. Forrester and industry speakers explored this phenomenon over the course of our 2-day forum. Here are the top 5 key takeaways from last week:
This year’s Microsoft Analyst Summit took place at the St. Regis hotel in Singapore, a prestigious place that hosted more than 90 analysts from the entire region. The Forrester team was impressed by Microsoft’s strategies in cloud, digital transformation and partnerships, and in particular, the main takeaway for us throughout the 2-day event was Microsoft’s innovation capabilities and ambition, especially in the APAC region.
HoloLens puts the spotlight on Mixed Reality. Unlike Augmented Reality, which is lightweight but has limited views and functionality, or Virtual Reality, which is very powerful but comes with bulkiness and dependence on a PC, Mixed Reality blends holograms with the real world to marry agility and powerfulness. HoloLens brings this concept to life, it is light enough for users to move around safely, and it is very powerful because it is a self-contained computer that doesn’t require tethering to another PC. There is even an emulator that allows developers to develop holographic apps for HoleLens without a device. HoloLens could drastically change the way people work, live or even think, we are all very eager to see if the first wave of HoleLens products will successfully establish an ecosystem that can sustain mass market deployments and future growth.
Are you lost in a confusing soup of vendor-speak about what their data analytics stack actually offers? Big data, data platforms, advanced analytics, data lakes, real-time everything, streaming, the IoT, customer analytics, digital intelligence, real-time interaction, customer decision hubs, new-stuff-as-a-service, the list goes on.
Recognize the convergence happening as vendors evolve their technologies from doing just one thing like predictive analytics or search to many things together. For example, data integration, data warehouse, and BI tools are typically sold separately, but breakout vendor Looker combines data integration, model governance, basic BI, and a runtime for data applications all in one software layer that sits on your data lake. As another example, consider predictive analytics vendor Alpine Data Labs or SAS Viya from SAS. These vendors have built out a lot of data management and insight delivery tooling into their platforms because without it users struggle to maximize value. Another trend is big data search vendors like Maana that now also include hooks for predictive model execution as well as more data management functions. Lastly, systems integrators are packaging their IP and offering it as a data management and analytics integrated product — for example, Saama’s Fluid Analytics Engine or Infosys’ Information Platform.
In fact, the list of innovative vendors blending data management, analytics, and insight execution technology is growing by leaps and bounds. To address this trend, I just published a report, Insight Platforms Accelerate Digital Transformation, in which I created a broad definition that labels this trend: