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Shawn Rogers - Blog


Cloud Based API Management - IronCloud

StrikeIron has overhauled its IronCloud solution and relaunched it this week. The solution is a fully hosted Cloud API Management platform. The solution had previously only been available to StrikeIron clients and partners is now open to any company seeking a better smarter way to manage their API based offerings. StrikeIron earned its stripes in this space by hosting its own API's for contact record validation and verification, mobile messaging and data enrichment. StrikeIron developed enterprise class security and infrastructure to host and deliver its service and has been in the API space for over 10 years.

IronCloud provides a simple yet powerful platform designed for business users and IT who are responsible for managing and delivering API powered assets. As enterprise application and data requirements evolve and become more diverse. API's are playing a significant roll in adding agility and flexibility to enterprise environments. Being able to leverage API's through common interfaces that are simple and manageable will be a key component of any smart business strategy. 

StrikeIron solutions are pre-integrated into Eloqua, Informatica, Oracle CRM On-Demand, Marketo and more.


Big Data and Health Care

An excellent story from the Huffington Post about Dr. Patrick Soon-Shiong. He's doing incredible things with data to solve cancer treatment challenges. The full article is available here -  "Meet Dr. Patrick Soon-Shiong, The LA Billionaire Reinventing Your Health Care". 


Vocabulary of America

An interesting visualization of how Americans differ in vocabulary and pronunciation.

SODA / POP / COKE from Atlantic Video on Vimeo.



Social Data Analytic Platform - Capabilities Pt. 2

Part one of this post helps to explain the foundation of enterprise class social data analytics platforms (SDAP). Below is a list of the type of analytic functions you should demand from the platform you are working with. Not all end users require all of these functions but if you are planning to support a wider array of users within your organization its critical to have a strong and flexible analytic foundation within your SDAP application.

Platform Analytic Functions

Geospatial Analysis - Leveraging the location data of customers, prospects and communities is a critical capability of a strong social data analytics platform. Understanding the location of a social data elements opens the door for creative applications in marketing and service for innovative companies. Examples would include Geo targeted add delivery, regionally focused next best offers, supply chain insight and more.

Sentiment Analysis - Understanding sentiment within social data is a sophisticated analytic function. Its dependent on the natural language processing (NLP) capabilities of the platform and can provide a candid view of how a social community or specific user "feels" about your product, brand or service. This insight can drive marketing and customer care strategies as well as alert you to growing trends around your service both positive and negative.

Influence Analysis - Influence is the currency of social data and its critally important to understand where influence is within your company's social sphere. SDAP's with influence analytic capabilities can surface who is seen as a community or topic leader and give a company insight into their own influence on topics important to their customers. Identifying influencers and monitoring corporate influence are both critical to successful social data analytical strategies.

Machine Data Analysis - Machine data is often defined as information that is produced by computer systems as processes or functions are executed. An example would be the log file entries that track which pages of a website a browser or group of browsers interact with. This machine data can provide the behavioral insights of a social network user helping to create a view of what content they are interested in, how long they interact with information or what advertisement they react to. Leveraging machine data for insight into social interaction and behavior is an exciting point of insight for a company striving to better understand customers and prospects.

Demographic Analysis - Simple demographic analysis is important especially to marketing professionals. Some solutions utilize available data via API's from the social networking platforms supplying the data others go beyond to leverage NLP technology to determine demographic details based on content and linguistic analysis. Capturing information on gender, age, ethnicity creates added value in social data. Many originating systems tie the social data to account information for the authors. when this is available for analysis it possible to build highly detailed master data on customers and prospects who are socially active.

Brand Affinity - Is a metric utilized by marketing professionals to measure the good will created with customers via a companies branded products. Its similar to sentiment data in that it measure how people feel towards a brand but in this case is more focused on marketing processes and campaigns. For many marketing professions Brand Affinity analytics is a foundational function to build strategies upon.

Text Analytics - Is a capability that builds on the output from Natural Language Processing. Once the unstructured social data has been parsed and organized text mining solutions can analyze the information to identify patterns and trends within the data. Text analytics isn't terribly different that data mining in that it strives to organize the data for analysis and once organized can produce insights otherwise too difficult for people to do manually. Text analytics opens the door for innovative analysis of social data.


Social Data Analytic Platform - Capabilities Pt. 1

There are many vendors in the social data analytic platform (SDAP) space. Each brings a somewhat unique set of features and functions to the game. Early entrants have been focused primarily on the needs of marketing and public relations teams and often focused on only monitoring the social landscape. As a company's social data analytics strategy evolves from "watching" to a more widely integrated approach a new set of capabilities is required to meet the needs of enterprise users. Platform functionality will enable a wide range of analytic and functional capabilities so its important to make sure the solution you choose for your company has the platform functionality required to support action, collaboration, integration with enterprise data and applications and can support advanced analytic functions.

Platform Functions

Alerting and Workflow - Social Data Analytic Platforms are gaining traction within enterprise companies and its critical as more stakeholders become involved that the platform help these professionals to understand the insights that are critical to their company and take faster action. Alerting and key performance indicator (KPI) driven metrics have long been included in traditional business intelligence solutions and they are now finding their way into SDAP solutions. Receiving an email alert or text message is a useful way to interact with social data insights but its even more successful if the platform combines alerts with workflow functionality that allows users to design business process functions based on the alerts or for alerts to automatically kick off processes based on thresholds and KPI's.

Collaboration - As much as KPI's and Alerts help users to take action; collaboration is a key function to adding value to workflows and decisions. Most vendors in the social data analytic space are still struggling to provide highly useful collaborative capabilities. As with traditional business intelligence bringing a diverse set of skills and insights to a business problem will most often result in a better decision for the company overall this is true with social workflows and decisions. 

Integration / API's - SDAP solutions can't impact the enterprise unless they can be highly and seamlessly integrated with existing analytic platforms and data sources. Many leading business intelligence vendors are incorporating social data analytic capabilities into their BI platforms as a strategy to bring the data and the decision making to a single integrated platform.

Natural Language Processing (NLP) - NLP combines linguistics and artificial intelligence (AI) to enable computers to understand human or natural language input. The business value of NLP is probably obvious. Social data is often information directly created by human input and this data is unstructured in nature making it nearly impossible to leverage with standard SQL. NLP can make sense of the unstructured data that is produced by social data sources and help to organize it into a more sturcutred model to support SQL based queries. NLP opens the door for sophisticated analysis of social data and supports text data mining and other sophisticated analytic functions.

These four platform function areas are key foundations for the analytic insights most companies will need to leverage with thier social data analytic platform. Alerting, Workflows, Collaboration, integration and API's and natural Language processing engines are important building blocks for strong platforms that strive to support enterprise class needs.

Look at part 2 of this post for details on the type of analytic functions that will help deliver success with social data.