Privacy considerations in mobile app development for real estate

Real estate mobile app developers, and the brokerages that deploy these apps, will gain a competitive advantage and consumer trust by adopting privacy aware best practices in their app development processes. As I detail here discussing a recent guidance document published by the California Attorney General, state and federal regulatory oversight will continue to increase with respect to privacy issues in mobile apps. Thus, brands that incorporate privacy best practices will more easily pass regulatory muster going forward. Similarly, as consumers become more aware as to how their personally identifiable information (PII) is shared, leased, and sold, they will trust brands that make privacy controls a priority, easily accessible, and understandable within mobile apps.

Photo credit: codepo8

Big data corporate culture fosters success

According to this Harvard Business Review article, leveraging big data positions a company to make big revenue gains and achieve higher customer satisfaction. The HBR article noted five management challenges for those companies pursuing a data-driven decision making culture:

Leadership
Simply having more data, better data does not necessarily make a company successful. Rather, companies that have leaders who know how to ask the right questions, set clear business objectives, have a clear vision, and facilitate a corporate culture that embraces data-driven decision making will thrive.

Talent management
Data scientist are an important team asset, but so are designers skilled at data visualization. Most importantly, data scientists and data artists must be comfortable “speaking the language of business and helping leaders reformulate their challenges in ways that big data can tackle.”

Technology
Firms will have to embrace open source software as a core component of their tech infrastructure and strategy.

Decision making
Cross-functional cooperation is key to success. Team members that know how to collaborate is important as well as team members who have requisite problem solving techniques.

Company culture
A data-driven company does not ask “What do we think?” Rather, it asks “What do we know?”

Are modern real estate brokerages in the data business and just happen to be selling real estate? This question is similar to how Zappos describes itself as being in the customer service business and just happens to sell shoes (and, now, many other types of products). Obviously, as a core element of long-term success, brokerages must deploy traditional means to empower a more delightful and meaningful home buying and selling experience for both its clients and agents. But when looking ahead will the most successful brokerages thrive when their systems and support infrastructure becomes more data-driven in addition to their leadership team becoming more data minded? I think the answer is yes.

Photo creditSebastian Sikora

How big data and statistical teams support brands that operate as media publishers and producers

David Armano of Edelman Digital, in this post, makes a compelling argument that brands will have to create and nurture internal team structures that resemble big media companies so as to deliver compelling and meaningful marketing in the future.

In his post, Armano describes what he calls “the social-creative newsroom” and discusses how Oreo has incorporated this concept:

Oreo has done with its Daily Twist initiative, where in honor of the cookie’s 100th anniversary, agency teams get together daily to decide how to riff off of relevant, often newsworthy, subjects that, by day’s end, produce a new piece of clever, highly shareable visual content that’s sent out into the digital ecosystem.

This social-creative newsroom process that Armano describes has synergies with SCRUM software development. Indeed, the social-creative newsroom process–as embodied in the Oreo use case–is essentially a “SCRUM creative development” process.

Armano identifies three core roles of the creative newsroom: community managers, editors, and creative producers. I would suggest adding a big data and statistical (“BDS”) team to support the creative output. The BDS team is responsible for delivering the necessary daily or hourly input that the creative team needs so as to make logical, informed, and timely decisions in their daily creative SCRUM.

For example, the BDS team could provide input such as semantic analysis, characterization, and categorization of Twitter hashtags, which could aid the creative SCRUM team in responding to positive, negative, or neutral brand sentiment as it relates to a branding campaign. Similarly, the BDS team could perform spatial-temporal data analysis of social media, which could support immersive mobile experiences delivered via a native app. For example, refer to section 7.6.4 in the latter cited paper and imagine a series of rich media delivered to users via NFC on their smartphones when the user is at a particular locus (e.g., Big Ben or the Palace of Westminster). Now imagine if the rich media content was personalized based on a user’s identified interest categories (e.g., a military history buff would receive rich media content tailored to his or her interest).

The possibilities and applications of a BDS team are virtually limitless, which is why the creative-social newsroom (or nerve center) needs to drive the creative SCRUM process. The newsroom provides guidance, context, meaning, and consumer relevance to the input provided by the BDS team.

Related posts: Creating agile entrepreneurial teams promotes creativity and innovation , Creating a culture of creativity and innovation

Photo credit: aussiegall

 

Big data hyper-hypo-hyperbole or reality?

Is “BIG DATA” hype? Have we over-sugared ourselves with too much big data candy? I’ll dodge the answer and instead present you with four interesting resources addressing this issue.

First up is a great Big Data intro video shown at the 2012 SAS Analytics conference. What I like about this video (even though it could have been cut by 30 seconds) is that it really frames the issues well.

Second, is an excellent article on big data recently published by the Harvard Business Review. This article points out that big data will make an impact, but not in the traditional sense. “Traditional” big data analytics focuses on prediction, but in the future big data will have more transformative impact on areas such as mobile-location analytics, personalized medicine, and artificial intelligence.

Third, in this blog post on big dataJason Rushin notes that

In this era of digital everything, nearly every marketer has access to more data than they can reasonably handle. A single web visit by a single customer can result in thousands of data points across items viewed, locations, durations, browser, referral, clickstream, frequency, etc.  Couple that with device, payment methods, demographic data, product attributes, not to mention data across your other channels, and any retailer is quickly drowning in data.

Rushin points that regardless of the size of your data set, your inability to act on this data set is what matters. He advises you to look for solutions that can readily supply BI value and insights.

Finally, I encourage you to spend 40 minutes and watch this video presentation by Jim Stogdill on how corporations will evolve leveraging big data (tasty tidbit: hear how a corporation is compared to a nematode).