By now, most of you have read about Apple's powerful public statement of refusal to comply with a court order compelling the firm to help the FBI gain access to the data stored in the San Bernardino shooter’s iPhone 5. Specifically, the FBI requires Apple’s help disabling the device’s data auto-erase function after 10 incorrect password attempts, and Apple is refusing to modify the software to enable this.
Over the past three years, Apple has hitched its brand wagon to privacy, because the firm believes that a) customers care enough about privacy to vote with their dollars and b) as the steward of people’s most personal, sensitive data, Apple has an obligation to serve their best interests. While this isn’t the first time that Apple has found itself targeted by regulators over privacy, this is the firm’s staunchest defense yet against government intrusion.Forrester believes that, with this move:
Apple is putting its money where its mouth is. Until recently, there has been plenty of debate about whether Apple has simply been paying lip service to privacy.But this move — along with its recent shuttering of the iAds business — proves that Apple is making serious product and business model changes in support of user privacy. Tim Cook is holding fast on the line he drew in the sand last year at the EPIC’s (Electronic Privacy Information Center’s) Champions of Freedom event in Washington, DC, where he said:
Last week, I participated in a roundtable during a conference in Paris organized by the French branch of DAMA, the data management international organization. During the question/answer part of the conference, it became clear that most of the audience was confusing data management with data governance (DG). This is a challenge my Forrester colleague Michele Goetz identified early in the DG tooling space. Because data quality and master data management embed governance features, many view them as data governance tooling. But the reality is that they remain data management tooling — their goal is to improve data quality by executing rules. This tooling confusion is only a consequence of how much the word governance is misused and misunderstood, and that leads to struggling data governance efforts.
So what is “governance”? Governance is the collaboration, organization, and metrics facilitating a decision path between at least two conflicting objectives. Governance is finding the acceptable balance between the interests of two parties. For example, IT governance is needed when you would like to support all possible business projects but you have limited budget, skills, or resources available. Governance is needed when objectives are different for different stakeholders, and the outcome of governance is that they do not get the same priority. If everyone has the same objective, then this is data management.
I don't know about you, but I'm getting way too many unwelcome solicitations from LinkedIn. I love this website when I need to look up someone I'm meeting on the phone. But I don't pay for it despite LinkedIn's repeated pitches on premium this and high-value that. Doubt I ever would. After all, I can just ask the person and usually do.
But LinkedIn's solicitations have begun to reach fever pitch, roughly one every other day coming into my inbox. And then I realized in the last three months, LinkedIn's stock has dropped 59% down to $103 today from $254 in November. The headlines stress slowing growth.
A lightbulb went off. Does marketing get its spam marching orders when the CEO is anxious about growth? Is that how it works? Does more spam mean slower growth?
I started thinking about other frenetic pitches I've been getting lately. AT&T, Verizon, Flipboard, Strava, even Facebook have been loading up my inbox with screed I didn't ask for and don't need. Are their growth plans suspect, too?
Can't say this is analysis, but it's a hypothesis worth researching.
The Chinese social media landscape is unique and evolving rapidly. Since the publication of our first report on benchmarking social marketing in China, marketers have continued to invest in social marketing. My latest report, Benchmarking Social Marketing Efforts In China In 2015, tells how their social marketing efforts now stack up against the competition.
The key findings are:
WeChat is marketers’ social darling; LinkedIn is stepping into a market dominated by local players. WeChat has replaced Weibo and dominates both marketers’ adoption and satisfaction: A whopping 92% of the marketers we surveyed use WeChat, and two-thirds report being satisfied or very satisfied with it. And for the first time, a Western social platform is gaining popularity in China; LinkedIn has become an essential platform for social marketers there.
Effort and satisfaction vary among four types of social tactics and platforms. Based on marketers’ adoption and satisfaction ratings, we have categorized the social tactics and platforms into four groups: essential, optional, undervalued, and overvalued (see the figure). Focus your efforts on essential ones, such as WeChat, and undervalued ones, such as placing ads in online communities.
We’re now only a week away from the Mobile World Congress 2016 to be held again in Barcelona. As the excitement builds and we plan our schedules, it serves us to reflect back on last year’s event and to explore what we expect this year.
Mobile World Congress remains the pre-eminent event of the mobile industry and now one of the largest global events across all industries – a fact which illustrates an ambiguity in the meaning itself of “mobile industry.” Last year, over 94,000 people attended the event – a 10% increase from the 2014 event but a 30% increase over the 2013 event. Interest in “mobile” continues to grow – for now. But the most interesting stat about past attendees is diversification. Yes, the event continues to draw representatives from mobile operators, device manufacturers, network equipment providers, software vendors, and other usual suspects. But representation from other industries is growing. Last year almost ¼ of attendees came from industries other than telecom and technology, including 4% from finance, 3% from government and others from automotive, pharmaceutical, retail, education, and entertainment. I expect even more diversity this year.
A CMO and a CIO walk into a hotel bar (Let’s call them Tom and Dick). After ordering a drink, Tom says, “Dick, I really need to start working with a DMP this year, and I want your help selecting one.” Dick says, “A DMP? My enterprise architecture team is building a near real-time, self-service data management platform. We’ll be done by the end of the year. You’re going to love it in 2017!” With an absent look on his face, Tom says “A DMP is a piece of AdTech that we can use to quickly target tailored audiences with our ad campaigns. It’s not a back-office data warehouse”. Dick laughs and says, “Ad campaigns? Didn’t you just buy a campaign management tool from one of those so-called marketing cloud vendors? You know, our CRM system has a campaign module, not to mention an enormous customer database.” Tom’s response: “You’re not getting it. Cross-Channel Campaign Management is a MarTech tool, not CRM. And a DMP is not a customer database.” Exasperated, Dick shouts, “What the hell is the difference between MarTech and AdTech anyway!”
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
The transfer of power from companies to the customer is driving a wide variety of changes: some small and targeted and some that are far-reaching and fundamentally change the trajectory of companies (and careers, I may add).
I had the pleasure of moderating a video webinar last week that explored the customer dynamic, specifically looking at how it will play out in 2016. We also looked at how companies sense and respond to this dynamic change: how well companies are reading the tea leaves and taking action and what actions seem to matter to compete and win in a customer-led market.
I had a blast moderating this panel with Sharyn Leaver, Michelle Moorehead, and Harley Manning. If you saw it live or on-demand, I hope you had a blast as well and took something away that can make a difference for your company and yourself.
We captured the webinar through a thought-illustration that provides an artistic touch to a great conversation.
It’s complex, right? There are a lot of moving pieces, big ideas, and really big decisions. So let’s break it down:
I am kicking off a research stream which will result in the "Text Analytics Roles & Responsibilities" doc. Before I finalize an RFI to our clients to see who/how/when/where they employ for these projects and applications, I'd like to explore what the actual roles and responsibilities are. So far we've come up with the following roles and their respective responsibilities
Business owner. The ultimate recipient of text analytics process results. So far I have
Customer intelligence analyst
Customer service/call center analyst
Competitive intelligence analyst
Product R&D analyst
Linguist/Data Scientist. Builds language and statistical rules for text mining (or modifies these from an off-the-shelf-product). Works with business owners to
Create "golden copies" of documents/content which will be used as base for text analytics
Works with data stewards and business ownes to define corporate taxonomies and lexicon
Data Steward. Owns corporate lexicon and taxonomies
Architect. Owns big data strategy and architecture (include data hubs, data warehouses, BI, etc) where unstructured data is one of the components
Developer/integrator. Develops custom built text analytics apps or embeds text analytics functionality into other applications (ERP, CRM, BI, etc)