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.
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:
It’s been a while since I’ve blogged; not because I’ve had nothing to say, but rather because I’ve been busy with my colleagues Ted Schadler, James McCormick, and Holger Kisker working on a new line of research. We wanted to examine the fact that business satisfaction with analytics went down 21% between 2014 and 2015, despite big investments in big data. We found that while 74% of firms say they want to be “data-driven,” only 29% say they are good at connecting analytics to action. That is the problem.
Ted Schadler and I published some initial ideas around this idea inDigital Insights Are The New Currency Of Business in 2015. In that report, we started using the phrase digital insight to talk about what firms were really after ― action inspired by new knowledge. We saw that data and analytics were only means to that end. We also found that leading firms were turning data into insight and action by building systems of insight ― the business discipline and technology to harness insights and consistently turn data into action.
When I read articles like today's WSJ article on mutual funds exiting high tech startups and triangulate the content with Forrester client interactions over the last 12 to 18 months (and some rumors) I am now becoming convinced that there will be some Business Intelligence (BI) and analytics vendor shake ups in 2016. Even though according to our research enterprises are still only leveraging 20%-40% of their entire universe of data for insights and decisions, and 50%-80% of all BI/analytics apps are still done in spreadsheets, the market is over saturated with vendors. Just take a look at the 50+ vendors we track in our BI Vendor Landscape. IMHO we are nearing a saturating point where the buy side of the market cannot sustain so many sellers. Indeed we are already seeing a trend where large enterprises, which a couple of years ago had 10+ different BI platforms, today usually only deploy somewhere between 3 and 5. And, in case you missed it, we already saw what is surely to be a much bigger trend of BI/analytics M&A - SAP acquiring mobile BI vendor Roambi. Start hedging your BI vendor bets!
Rule #1. Don't just jump into creating a hefty enterprise wide Business Intelligence (BI)
Business intelligence and its next iteration, systems of insight (SOI), have moved to the top of BI pros' agendas for enterprise software adoption. Investment in BI tools and applications can have a number of drivers, both external (such as regulatory requirements or technology obsolescence) and internal (such as the desire to improve processes or speed up decision-making). However, putting together a BI business case is not always a straightforward process. Before embarking on a BI business case endeavor, consider that:
You may not actually need a business case. Determining whether a BI business case is necessary includes three main considerations. Is it an investment that the organization must make to stay in business, should consider because other investments are changing the organization's IT landscape, or wants to make because of expected business benefits?
A business sponsor does not obviate the need for a business case. It may be tempting to conclude that you can skip making a business case for BI whenever there is a strong push for investment from the business side, in particular when budget holders are prepared to commit money. Resist this impulse whenever possible: The resulting project will likely suffer from a lack of focus, and recriminations are likely to follow sooner or later.
Do you ever feel like you’re facing a moving target? Whether it’s the latest customer requirements, or how to improve operations, or to retain your best employees, or to price your products, the context in which you are doing business is increasingly dynamic. And, so are the tools you need to better understand that context? Everyone is talking about the promise of big data and advanced analytics, but we all know that companies struggle to reach the Holy Grail.
Data and analytics tools and the skills required to use them are changing faster than ever. Technologies that were university research projects just last year are now part of a wide range of products and services. How can firms keep up with the accelerated pace of innovation? Alas, many cannot. According to Forrester's Q3 2015 Global State Of Strategic Planning, Enterprise Architecture, And PMO Online Survey, 73% of companies understand the business value of data and aspire to be data-driven but just 29% confirm that they are actually turning data into action. Many firms report having mature data management, governance, and analytics practices, but yesterday's skills are not necessarily what they will need tomorrow — or even today.
The same goes for data sources. We all know that using external data sources enhances the insights from our business intelligence. But which data and where to get it?
With the incredible popularity of big data and Hadoop every Business Intelligence (BI) vendor wants to also be known as a "BI on Hadoop" vendor. But what they really can do is limited to a) querying HDFS data organized in HIVE tables using HiveQL or b) ingest any flat file into memory and analyze the data there. Basically, to most of the BI vendors Hadoop is just another data source. Let's now see what qualifies a BI vendor as a "Native Hadoop BI Platform". If we assume that all BI platforms have to have data extraction/integration, persistence, analytics and visualization layers, then "Native Hadoop/Spark BI Platforms" should be able to (ok, yes, I just had to add Spark)
Use Hadoop/Spark as the primary processing platform for MOST of the aforementioned functionality. The only exception is visualization layer which is not what Hadoop/Spark do.
Use distributed processing frameworks natively, such as
Generation of MapReduce and/or Spark jobs
Management of distributed processing framework jobs by YARN, etc
Note, generating Hive or SparkSQL queries does not qualify
Do declarative work in the product’s main user interface interpreted and executed on Hadoop/Spark directly. Not via a "pass through" mode.
Natively support Apache Sentry and Apache Ranger security