The Woes of Train Life: Get Out of My Way, Move your Feet and Give me a Seat

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For centuries we have associated trains and train stations with romance and glamour. At least that’s the image we get from movies. However, if you’ve ever ridden the city train, you know the experience is anything, but glamorous.

Railways are an increasingly important part of urban society, and for decades, they have tracked and used large volumes of operational data. Yet, for some reason, as it is in many business environments, these theoretical benefits of analyzing and storing this data are still falling short of reality; trains are still over filled, people still stand instead of sit on train runs, especially those during traffic rush…and well, let’s just say, we don’t see much improvement in the overall operations, even though railroads collect large amounts of data using global positioning systems, automatic equipment identification readers, electronic data, video inspections, field tablets and more. It seem that management just doesn’t know what to do with these huge amounts of data.

This same scenario doesn’t just happen to rail systems, but to businesses in varying industries.  The key lies in organizing this data, narrowing large amounts of unstructured statistics into usable data.

Data is Valuable

IDC, a nationally recognized marketing firm, suggest that big data is expected to grow to $16.9 billion in 2015. If nothing else, this statistic shows how important data is to corporate or business decisions.

But structuring this big data seems to be the challenge most businesses face. Added to this, structuring large amounts of data can be a very expensive process, requiring large amounts of man-power hours, engineering resources and investment, unless you implement database report writing configurations on your server. These costs can be reduced through the use of professional analyst services and specific data organizational tools, such as microsoft sql report writer. These help businesses  make sense of their data, converting it to both visual and readable content, thus taking far less time to analyze by all corporate departments.

How to Make Sense of It All

Still, knowing that data must develop from unstructured data to a structured format, allowing for important decision making, is very different from implementing the procedure. For many, the solution has been to hire a data analyst, also known as a data scientist. His role is to implement important database report writing configurations , then analyze data and help the business come up with new opportunity and growth strategies. But all too often, the analytics project is confined to the person who holds this position or to the few members that make up this isolated department. This results in a lack of cooperation from other departments and a lack of needed tools to organize the data.

By not involving the entire team in the structuring, planning and use of the data, an organization may be hindering the potential benefits of the information it holds. Organizing unstructured data into scalable data, which offers solutions for the business, requires a team commitment; one where skills from the different departments are used to determine the feasibility of an actionable decision based on the data. The scalability functions much like this:

  1. The Analysis of General Data – a mistake often made here is when a company leaves this step to be analyzed and narrowed by the data analyst. In reality there are four teams that should be involved in this process: operations, IT, engineering or equipment analysts and the data analyst team. Together these teams should focus on developing the data into possible solutions.
  2. Feasibility of Solutions – once team members have analyzed the data and reported possible solutions, adding new analytical team integration is appropriate. At this stage, the Training and Development department, financial department and even the HR department can add significant input as to the feasibility of a growth, product or marketing decision.
  3. Deployment – once the feasibility of a particular change is analyzed and determined effective by professionals with all of these skill sets then the deployment of a decision change or implementation starts.

Bottom Line

The key to using big data efficiently lies in structuring this data with the help of several skill sets within the business environment, and with database report writing configurations. This combination of skills and tools, compensate for areas where the analyst’s knowledge may fall short. As the data becomes more structured through the analytical process other team members become crucial in determining the feasibility of a decision implementation.

 

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