Big Data presents Organizational Challenges | Automation.com

Big Data presents Organizational Challenges

March 032014
Big Data presents Organizational Challenges

By Bill Lydon, Editor

Big data is a term used to describe large, complex collections of data sets. Big data is a hot topic in business, manufacturing, and IT circles because, if utilized correctly, it provides an opportunity for companies to improve efficiency and productivity. The challenge for many organizations is that in order for big data to be used most effectively, the organizational silos within companies need to cooperate.

Big data is a collection of data so large and complex that it becomes difficult to process using traditional analysis and database management tools. The software industry has been developing new software technologies for analysis that make this task easier. Big data momentum and investment are growing as indicated by International Data Corporation (IDC) forecasts. IDC predicts that the big data technology and services market will grow at a 27% compound annual growth rate (CAGR) to $32.4 billion through 2017 – that is about six times the growth rate of the overall information and communication technology (ICT) market. IDC commented that the benefits of big data are not always obvious.

Data historians in the process industries have become widely used in the automation industry. This trend has been growing as users have learned the value of using this information. The value derived from large volumes of historic data comes not from the data in its raw form, but from the insights, decisions, and deeper understanding that emerge from analysis of the data. This is the same situation with the broader application of big data across the entire business enterprise. The premise is that collecting and analyzing data across the value chain(s) of the entire business, including manufacturing operations, will provide more insights and enable improved results. Achieving this level of benefit requires the cooperation and collaboration of the entire company. In many companies, this collaboration will require integration of previously isolated organizational silos.

Industrial Focused Solutions

There are a couple of big data initiatives focused on industrial automation and control.

GE Intelligent Platforms (GE IP) is promoting their Proficy Monitoring & Analysis Software Suite as a tool to deal with big data. I interviewed Brian Courtney, General Manger Industrial Data Intelligence at GE IP, about their offering. Read more.

Seeq is a startup company that is developing software products to help manufacturing companies take advantage of big data. Read more.

Getting Started

At the Big Data Conference held on October 22-23, 2013 in Chicago, IL, Meta Brown, a consultant, speaker, and writer in the field of big data and business analytics, suggested these basic steps to get started:

  • Business goals must come first.
  • Start small.
  • Be thorough in identifying and involving all constituents.
  • Work backwards from the goal to outline a plan.
  • Tools come last.

Organizational Challenges

Collecting and analyzing data across the entire business will require cooperation across groups that have traditionally operated independently. Organizational change is always challenging, and the value of analytics may not be initially apparent to everyone. Therefore, senior leaders will need to make the case for cooperation and sharing big data throughout the organization. This effort will require strong organizational leadership. Leading companies typically define clear owners and sponsors for analytics initiatives and provide incentives for analytics-driven behavior to ensure data is incorporated into processes for key decision-making. In this environment, IT departments typically do not own big data but often play a critical role in providing and maintaining the infrastructure and tools required to store data and provide analytic tools.

The application of these big data concepts enables the enterprise and automation systems to operate as a coordinated system and achieve superior efficiency and productivity.

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