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:
The tug of war between reason and emotion has fueled contentious debate since the days of Socrates. But, Socrates and subsequent thinkers didn’t anticipate the influx of data in our contemporary world. Today, our modern media saturation, infinite social connection, and sensor-laden bodies and buildings mean that we create, consult, and critique data more than ever before. How does the vast amount of information – that is now literally at our fingertips – actually influence our daily decisions, and why?
Forrester’s Consumer Technographics® survey data proves that individuals are steeped in information and are keenly aware of it. In fact, the insight shows that US online adults increasingly lean on data to make daily choices across spheres of life:
Most enterprises aren't fully exploiting real-time streaming data that flows from IoT devices and mobile, web, and enterprise apps. Streaming analytics is essential for real-time insights and bringing real-time context to apps. Don't dismiss streaming analytics as a form of "traditional analytics" use for postmortem analysis. Far from it — streaming analytics analyzes data right now, when it can be analyzed and put to good use to make applications of all kinds (including IoT) contextual and smarter. Forrester defines streaming analytics as:
Software that can filter, aggregate, enrich, and analyze a high throughput of data from multiple, disparate live data sources and in any data format to identify simple and complex patterns to provide applications with context to detect opportune situations, automate immediate actions, and dynamically adapt.
Forrester Wave™: Big Data Streaming Analytics, Q1 2016
To help enterprises understand what commercial and open source options are available, Rowan Curran and I evaluated 15 streaming analytics vendors using Forrester's Wave methodology. Forrester clients can read the full report to understand the market category and see the detailed criteria, scores, and ranking of the vendors. Here is a summary of the 15 vendors solutions we evaluated listed in alphabetical order:
The challenges of how to manage, ingest, store, analyze, and act upon data in the IoT are beginning to bear down on enterprises. The honeymoon talk of ‘billions and billions of devices’ is over and it’s time to get down to the dirt of how to generate value from all these connected devices. Streaming analytics platforms, already architected to handle IoT data as it streams into the data center, are being extended to deploy out to gateway devices (such as wireless access points) and even out to edge devices (such as manufacturing equipment) to extend the intelligence out to where data is generated and actions occur.
Forrester clients can read the full details of our analysis here and start the process of turning slow processes and weekly analytical batches into the immediate insights needed to support today’s dynamic business environment.
Businesses must focus on those activities that they can transform into digital business models. Not every industrial activity can become a digital business, but it will be impossible to succeed in digital transformation by developing a digital business and an industrial business and then operating them side by side indefinitely. GE sold 40% of its business activities because it felt that it could not transform them into digital businesses. For those industrial activities that can become digital businesses, executives need to be aware that:
Every industrial worker has to develop digital DNA. Industrial workers and mechanical engineers have to be comfortable interacting with digital systems. At GE, mechanical engineers have to design a locomotive in such a way that they can place a local data center inside it. Every industrial worker will have to have analytics skills, whether that’s the ability to create sensible and reliable data sets or to analyze and interpret these data sets.
Next time you find yourself wading through data points, sifting out patterns from the noise, hoping to catch the rare pearl of insight to affix to your business plan, know that you are not alone. Employees worldwide incessantly engage with data, and the companies they work for urgently execute on data-driven strategies in a race for better, faster results. Data pervades the workplace and continues to grow in terms of volume and variety: Research suggests that by 2020, the number of connected devices will more than triple, tens of thousands of data scientist jobs will be in high demand, and the majority of sales decisions will be data-driven.
But using data regularly doesn’t mean that employees truly understand it – or are comfortable with data practices. Specific obstacles prevent individuals – at the top and bottom of the organization – from eliciting effective insight. Forrester’s Business Technographics® and ConsumerVoices MROC data shows that while individuals rely heavily on data for decision-making, they still grapple with key challenges regarding the accuracy, volume, value, and security of the data they use:
This year’s big technology themes at Mobile World Congress (MWC) can be summarized as big data, Internet of Things (IoT), 5G, and virtual/augmented reality (VR/AR). These themes will be important for B2B players and especially for revolutionizing customer experiences, optimizing industrial and operational processes, and boosting service enhancements. My recently published report, “Brief: Observations From Mobile World Congress That Will Shape Your B2B Digital Transformation,” summarizes our observations from MWC 2016 and the key takeaways for developing B2B digital transformation strategies. We observed that:
The main MWC themes are increasingly intertwined. VR and AR will enhance user experiences on mobile devices and expand mobile moments. Big data will provide context-based, and more relevant, insights and use cases — including for VR and AR solutions.
Mobile data is driving digital customer experience. Enterprise apps are increasingly integrated with business processes. In turn, enterprise apps help generate data-derived insights from mobile objects and devices. This will help transcend app silos to generate a single view of the customer who benefits from a better end-to-end user experience.
Bigger is not necessarily better. MWC feels near its zenith in terms of visitor numbers and industry impact. In 2016, nearly 101,000 attendees from 204 countries made it to MWC — more than ever. Yet, for business users MWC still falls short of translating mobility into tangible business benefits for digital transformation.
You can't turn anywhere without bumping into artificial intelligence, machine learning, or cognitive computing jumping out at you. Our cars brake for us, park for us, and some are even driving us. Our movie lists are filled with Ex Machina, Her, and Lucy. The news tells about the latest vendor and cool use of technology, minute by minute. Vendors are filling our voicemail and email with enticements. It's all so very cool!
But cool doesn't build a business. Results do.
Which brings me to the biggest barrier companies have in adopting artificial intelligence. Companies are asking the wrong questions:
What is artificial intelligence (or insert: machine learning or cognitive computing)?
More than 100,000 people descended on Barcelona, Spain last week to be part of Mobile World Congress (MWC), one of the world’s largest annual technology events. My new report,IoT And Insights Are Two Sides Of The Same Coin, recaps some of the MWC 2016, including expectations for new 5G networks, the Internet of Things (IoT), and applications that will deliver value from the multitude of connected things — and people. A few of those highlights include:
5G Networks Promise Speed But Require Patience.
Telecom operators and network equipment providers eagerly discussed the faster speeds and lower latency of new 5G networks. And, fast it will be. While reports vary, network tests show download speeds peaking at more than 20 Gbps; average 5G speed is expected to be 100 times faster than current 4G networks. With that kind of speed, true video streaming becomes a reality for consumer and business uses. And, that reality can be with virtual or augmented: AR and VR were all over the exhibit hall. I successfully fought with a dragon but had to bail out of the helicopter I was flying as the experience got a little too real.
But alas, these good things only come to those who wait. The 5G standards will not be finalized before 2018; and commercial availability not before 2020 at the earliest. Large-scale network rollouts will likely take much longer. For now, we’ll all have to live with 4G reality as it is.
Interest In The Internet Of Things Is Exploding – Well Beyond Things.
One of the reasons for only a portion of enterprise and external (about a third of structured and a quarter of unstructured -) data being available for insights is a restrictive architecture of SQL databases. In SQL databases data and metadata (data models, aka schemas) are tightly bound and inseparable (aka early binding, schema on write). Changing the model often requires at best just rebuilding an index or an aggregate, at worst - reloading entire columns and tables. Therefore many analysts start their work from data sets based on these tightly bound models, where DBAs and data architects have already built business requirements (that may be outdated or incomplete) into the models. Thus the data delivered to the end-users already contains inherent biases, which are opaque to the user and can strongly influence their analysis. As part of the natural evolution of Business Intelligence (BI) platforms data exploration now addresses this challenge. How? BI pros can now take advantage of ALL raw data available in their enterprises by: