How IT Companies Can Leverage Data Analytics

By: Dave Oswill, MATLAB Product Manager, MathWorks

Many organizations have realized the value in data that is collected from their products, services, and operations. They have created new executive positions, such as Chief Information Officer (CIO), whose main focus is on the proper use and protection of this new big data resource. The CIO subsequently enlists the information technology (IT) team to implement new policies and processes for data which includes:

· Governance: Ensure the integrity of the data by controlling the storage, access, and processing of data.
· Access: Make data available to engineering, operations, warranty, quality, marketing, and sales groups.
· Processing: If the data is large enough, a specialized processing platform becomes a necessity to eliminate delays in transferring data and decreasing the time to process data.

To comply with these new requirements, the IT organization is taking to new technologies and platforms for storing and managing these vast and ever-increasing sets of data.

Because of this, one needs to work closely with IT teams in order to gain access and setup a workflow that enables them to process the available data. In this new environment, using a software analysis and modeling tool that works with the systems that the IT teams are using to store, manage, and process big data, as well as one that they are familiar with, enables a person to effectively use this data in everyday activities.

Big Data Platforms and Applications

There are a number of platforms that IT organizations are adopting for the storage and management of big data. These platforms provide not only the infrastructure for storing big data, they also support a wide variety of applications that are used to process big data in different ways. These applications can be roughly categorized into two categories: batch processing of large, historical sets of data, and real-time or near real-time processing of data that is continuously collected from devices. This second case is often referred to as streaming and is found in most Internet of Things (IoT) applications.

For example, Hadoop is designed around distributed storage and distributed computing principles. It is comprised of two major subsystems that coexist on a cluster of servers, allowing it to support large data sets.
· HDFS: The Hadoop Distributed File System (HDFS) provides a large and fault-tolerant system for storing data.
· YARN: Yet Another Resource Negotiator (YARN) manages the highly scalable applications that run the Hadoop cluster and process data stored in HDFS.

Batch Applications and Creating Models
Batch applications are commonly used to analyze and process historical data that has been collected over long periods of time or across many different devices or systems. Having the ability to use these batch processing applications enables an engineer to look for trends in their data and develop predictive models that were not possible in the past with large sets of data.

Two of the more popular batch processing applications that operate on Hadoop include:
· Spark: A more generalized framework that optimizes in-memory operations, making it highly desirable for machine learning applications.
· MapReduce: A highly structured framework consisting of map and reduce functions, making it useful for large data analysis and data transformation applications.

Streaming Applications and Model Integration
Using models developed from sets of historical data, along with a streaming application such as Kafka or Paho can add more intelligence and adaptive capabilities to ones products and services. Examples of these differentiated capabilities include: predictive maintenance, which greatly reduces unnecessary maintenance as well as unplanned downtime; services that tune heavy equipment such as ships, locomotives, and commercial vehicles to better perform within its operating environment for better fuel economy and enhanced operation; and building automation systems that operate a building’s systems at the lowest cost possible, while still maintaining a comfortable environment.

In many cases, these kinds of services are usually developed in conjunction with enterprise application developers and system architects. But the challenge is how to integrate ones models into these systems in an effective way. Porting models to another language is time consuming and error prone, requiring extensive work each time an update is made to a model. Developing predictive models in typical IT languages is difficult. Engineers and scientists who have the domain expertise required for developing these models are not familiar with these languages, and these languages don’t always include the functionality needed to adequately process and create models from engineering and scientific data.

Enterprise application developers should look for a data analysis and modeling tool that are not only familiar to their engineers and scientists, but that also provides the domain specific tools they need. These tools must also scale for use in developing models and large datasets using Hadoop-based systems that provide capabilities such as a highly robust application server and code generation, enabling a direct path for deploying models into enterprise applications.

Other Applications for Data Access
There are many other applications that allow access to big data that are being adopted. Some of these include interfaces which allow access to the data stored in HDFS using database-type semantics. Hive and Impala are two such applications that allow data to be accessed and processed using the Structured Query Language (SQL). This is a well-established and widely used language for working with data in relational databases.

NoSQL databases, which have been architected to support different big data use cases, are also being adopted. Many times these databases support a subset of the SQL language for accessing and processing data, but may also have additional interface capabilities that traditional databases don’t offer.

As discussed above, various systems are used to store, manage, and process big data. By working closely with the IT team and utilizing a tool such as MATLAB, one can create a workflow that is familiar, enabling them to easily and efficiently work while gaining insight from a vast collection of data.

IT managers and solution architects can use modeling tools to enable the scientists and engineers in their organizations to develop algorithms and models for smarter and differentiated products and services. Simultaneously, one is also enabling the organization to rapidly incorporate these algorithms and models into the products and services by leveraging production-ready application servers and code generation capabilities that are found in these tools.

The combination of a knowledgeable domain expert who has been enabled to be an effective data scientist, along with an IT team capable of rapidly incorporating their work into the services and operations of their organization makes for a significant competitive advantage when offering the products and services that customers are demanding.