Why your mobile-first strategy must expand

To harness the power of mobile apps, organizations must get on board with the Internet of Things (IoT) and contextual computing.

Person using Smartphone and typing on a PC

Mobile-first apps drive employee engagement.

It’s been 10 years since Apple’s iPhone launched what would become the ubiquitous mobility era, but we’re still struggling to realize the vision of a mobile-first world.Newer consumer apps take advantage of mobile’s unique features, such as location awareness and voice control, but enterprise software still has a long way to go. Most companies work within the confines of applications and experiences that were designed in the 1980s.

The challenges of embracing mobile-first aren’t just about technology maturity. A mobile-first strategy requires companies to commit to overhauling business processes and workflows to take advantage of new data and device functionality. And it requires more than just focusing on mobile.

More than mobile

The term mobile-first seems out of place today. After all, would anyone today build a new app or service that only runs on a PC? I think not. A company may create a cloud-based service, but that runs on any device with a browser. What of the burgeoning internet of things (IoT) market? Should apps become IoT-first? No.

In several years, we won’t even talk about mobility. Everything that we build will be designed to work across mobile, PCs and a variety of connected devices. The new IT world assumes we’ll embrace and expand upon all of the mobile and cloud computing concepts developed over the past decade. In 2017, next-generation computing should deliver apps, services and business workflows that have four qualities:

  • They’re built to operate and move seamlessly across devices. The best experiences allow a person to start a workflow or transaction on one device and seamlessly transfer it to another device. Apple and Microsoft both offer this type of portability through their Continuity and Windows Continuum features, respectively.
  • They’re adaptable to the user and device context. Context, in this caseĀ ,could refer to device size or to the availability of input mechanisms such as keyboard, voice, stylus, touch and gesture. Apps also need to sense what functions are available — such as camera, GPS and biometric sensors — and provide different options for actions the user can take based on these capabilities. Context-aware apps can also show different information based on location, such as bringing up certain notes or launching Microsoft PowerPoint when the user enters a meeting room in a specific building.
  • They’re designed to collect and act on new data sources. Smartphones ushered in a new wave of sensors such as accelerometers and gyroscopes. Wearables and IoT devices add opportunities for gleaning sensor data such as heart rate and humidity. Next-generation computing requires deep integration with a wide range of connected devices. Wearable apps can collect data from sensors, for example, to provide more context for what the user is doing or feeling at a given moment — and provide in-app options that react to that context.
  • They can learn and make predictions. Mobile brought to IT the concept of personalized services based on an understanding of user behavior. End-user computing in 2017 will take advantage of big data storage, analytics and machine learning to deliver services that provide users with the right information at the right time.

We’re living in a mobile- and cloud-first world that relies on a diverse set of devices and ways to access business data. If you haven’t embraced this approach, you’re behind. The only question is, will you change your mobile-first strategy to take advantage of these tools? If not, you’ll be even further behind when the next wave of change — IoT, augmented and virtual reality, and artificial intelligence.

This post was originally published on TechTarget. You can read the latest about Lopez Research content, events and more by signing up for our newsletter here.

How eBay Uses Big Data and Machine Learning to Drive Business Value

Digital transformation, while not new, has changed tremendously with the advent of new technologies for big data analytics and machine learning. The key to most company’s digital transformation efforts is to harness insights from various types of data at the right time. Fortunately, organizations now have access to a wide range of solutions to accomplish this goal.

How are leaders in the space approaching the problem today? I recently had a discussion with Seshu Adunuthula, Senior Director of Analytics Infrastructure at eBay, to discuss this matter. eBay was always a digital business, but even IT leaders of companies that were born as digital businesses are embracing the latest digital technologies to enhance their existing processes and build new experiences. According to Adunuthula, “Data is eBay’s most important asset.” eBay is managing approximately 1 billion live listings and 164 million active buyers daily. Of these, eBay receives 10 million new listings via mobile every week . Clearly, the company as large volumes of data, but the key to its future success will be how fast it can turn data into a personalized experience that drives sales.

Designing and updating a technical strategy

The first challenge eBay wrestled with was finding a platform, aside from its traditional data warehouse, that was capable of storing an enormous amount of data that varied by type. Adunuthula stated that the type of data, the structure of the data and the required speed of analysis meant the company had to evolve from a traditional data warehouse structure to what it calls data lakes. For example, the company needs to keep roughly nine quarters of historical trends data to provide insights on items such as year over year growth. It also needs to analyze data in real-time to assist shoppers throughout the selling cycle.

The ability to support data at the scale of an internet company was a key consideration in the selection of technologies and partners. The company chose to work with Hortonwork’s Hadoop product because it offered an open source platform that was highly scalable and the vendor was willing to work with eBay to design product enhancements. With a foundation of Hadoop and Hortonworks, the other two components of eBay’s data platform strategy are what it calls streams and services.

A big technical challenge for eBay and every data-intensive business is to deploy a system that can rapidly analyze and act on data as it arrives into the organization’s systems (called streaming data). There are many rapidly evolving methods to support streaming data analysis. eBay is currently working with several tools including Apache Spark, Storm, Kafka, and Hortonworks HDF. The data services layer of its strategy provides functions that enable a company to access and query data. It allows the company’s data analysts to search information tags that have been associated with the data (called metadata) and makes it consumable to as many people as possible with the right level of security and permissions (called data governance). It’s also using an interactive query engine on Hadoop called Presto. The company has been at the forefront of using big data solutions and actively contributes its knowledge back to the open source community.

eBay’s current big data strategy represents a few of the potential combinations and options that are available to companies seeking to process a large volume of data that aren’t similar in format and combinations of data that may need to be analyzed in real-time or stored for analysis at a later date. Of course, the selection of big data solutions depends on what you are trying to accomplish as a business.

Using a big data and machine learning platform to deliver business value
In the case of eBay, the company is using big data and machine learning solutions to address use cases such as personalization, merchandising and A/B testing for new features to improve the user’s experience. For example, eBay models personalization on five quarters of structured (e.g. one billion listings, purchases, etc.) and unstructured (behavioral activity synopsis, word clouds, badges etc.) data. Merchandising improved by using analytics and machine learning to help recommend similar items on key placements on site and mobile. Items, such as deal discovery, uses machine learning to find patterns in structured data. eBay’s also creating predictive machine learning models for fraud detection, account take-over, and enabling buyer/seller risk prediction. Clearly, eBay has spent enormous time and resources attaining this level of expertise in data processing and business workflow enhancement. For eBay and many others, the journey is far from over. The company wants to continue to optimize streaming analytics and enhance data governance.

What should you do next?

For those companies that are getting started, Adunuthula offered a few words of sage advice. The biggest challenge is data governance and preventing it from becoming the wild west. A business can’t just dump everything into a system and worry about the governance later. If you’re building a data strategy today, start with the governance.

Examples of this could include defining the process for allowing access to different people and how to enable PCI compliance in the data sets for retailers. The strategy should outline how to make data discoverable and how to evolve the process. He noted that there are new solutions, such as Atlas and Navigator, emerging today. However, the landscape continually changes. If you are starting the journey today, a business can put data governance in place before building massive datasets, data warehouses, and data lakes. It’s easier to add data governance at the beginning of the process.

From discussions with my clients, I’ve learned there are several important steps in building a big data strategy that includes:

  • Defining a quick win and a longer term use case. Building a tightly scoped use case is essential for acquiring funding and demonstrating immediate value from your data strategy efforts. For example, many companies define a use case that involves connecting and analyzing new data sources to understand buying behaviors. Selecting a narrow use case allows data analysts to test new technologies and deliver new insights to the business.
  • Evaluating what you need in a data partner. eBay has a sophisticated engineering team and knows what it was trying to achieve. The company was looking for a partner to help deliver scale and assistance in improving open source solutions. A company might also need their partner to provide more training, consulting services and reference architectures based on industry.
  • Building the right ecosystem. There isn’t one data storage and analytics solution that will solve all of a company’s use cases. In some areas, a company’s existing data warehouse solutions work perfectly. In other cases, you’ll need streaming analytics. Similarly, there isn’t a single tool or vendor that will provide everything you need. Today’s data analysis world requires an ecosystem of tools and partners. Look for partnerships between vendors that will ease integration challenges.
  • Looking for new use cases. Instead of replicating what you have, a business should look for ways that new data can be acquired and analyzed to improve your business processes. Part of the benefit of these new data and analytics tools is discovering patterns, anomalies and new insights that didn’t exist in your legacy data analysis system. Business leaders should work with IT to look for ways that new data storage and analytics solutions can answer questions that weren’t easy to answer in the past.