How do these weapons deliver the desired effect? How will the enemy and others respond to this effect? Questions to support lethality require answers at the speed of relevance if decision making left-of-boom is to be effective. Decision space has collapsed and so our processes must adapt to keep pace with the speed of war. All too often, answers are channeled up to decision makers through a sequential, slow, and often highly editorialized request-for-information process.
With a request for information, one military unit requires information to which they do not have access from other units that might possess this data. They draft a request for that information and send it to a multitude of units.
This arcane process further requires humans in the loop to laboriously recognize the request, review their information holdings, and respond, even when there is little incentive to do so.
Often, positive responses take days because humans cannot operate at the speed and efficiency of digital systems. Fortunately, information can be represented by digital bits that travel through fiber optic cables at the speed of light. Writing in Business the Speed of Thought , Bill Gates offered the world many examples of how good information systems can dramatically speed up production. He proposed a critical distinction between bit-oriented information processes on the one hand and atom-oriented efforts constrained by the physical environment on the other.
According to Gates, the most successful organizations will reduce all information to highly structured bits of data, write algorithms to manipulate this data according to organizational objectives, and then seamlessly connect analysts and decision makers to this information to outpace their competition. Gates implored industry leaders to study all their information processes and integrate them into a digital nervous system, significantly reducing the time required for any bit-oriented efforts.
Understanding the power of good information, many leaders in defense, industry, and the U. Congress have argued for increased government spending to support information fusion centers, big-data analytics, machine learning, artificial intelligence, and cloud computing.
These are all noble efforts that partially contribute to information dominance, but are considerably less effective if a more fundamental problem for the Department of Defense—that it is data rich and information poor—is not defeated first.
It seems proponents of these initiatives see the power of shiny solutions without fully understanding the problem of the Department continuing on a path of garbage in, garbage out. Appropriating additional funding to the proposed solution does not fix the problem when the underlying data is garbage, or the data is stored in thousands of garbage dumps as inaccessible stovepipes of information. To fully understand and correctly address the problem of being data rich and information poor, it is first necessary to recall what the cognitive domain has to say about data and information.
As represented above, data must first be processed to create usable information that supports learning and ultimately decision making. Data is often represented by points that are seemingly meaningless until processed. Examples include locations, temperatures, or specific times. Processing these data points together by synthesizing matched attributes yields information about the temperature of a particular location at a specific time. Applying individual learning to this information would provide a basic level of knowledge that could be useful to a decision maker.
Information becomes considerably more valuable when multiple records are fused together to build even broader knowledge. However, correctly moving up the cognitive domain from factual premises to inductive conclusions becomes a challenge when data cannot be accessed or points are not correctly structured.
July 20, Article 5 min read. How much do you spend on maintenance, defects, and lost production? Most companies have been running their ERP systems for many years now and have amassed a wealth of data. Start with the end in mind: Data design leads to faster insights. When implementing your costing system, keep these seven important considerations in mind: Ensure financial and production information are accurate, clean, and simplified to a level of detail that aligns with your overhead cost groupings.
For example, expense detail and overhead cost groupings should align where possible. If you have a large machining overhead pool, you should have a large machining supply or labor expense account to match. Also, bill of materials, production routing, and work center master files must be structured to fit the business and contain accurate, complete information. Implementing a new cost system requires companywide involvement.
Review currently available reporting with a diverse, cross-functional team of users. Bring in others when appropriate.
The best insights can come from those with different or fresh perspectives which could lead to implementing a new cost system like a center-led purchasing model.
Leverage your test system. Now is the time to set one up. IT processes and systems should be in place to easily refresh the test system for another round of validation without impacting live production.
Where significant master data changes are being made, see if an upload process can be used to streamline the process. Review results from the updated data and modified reporting again with the cross-functional team. Get sign-off from all stakeholders. Costing is where operations and finance intersect, and all related departments must be aligned.
Low-cost satellite IoT will making agricultural water use more efficient and less environmentally harmful. While organizations around the world are waking up to their obligation to protect the environment, progress on decarbonization, water quality and other green initiatives is slow because,.
Save my name, email, and website in this browser for the next time I comment. Data Rich and Information Poor. June 16, Connected Industry. Data is useless unless it provides context and getting to this contextual state will be made easier if we begin focusing on the following… Integrate Workflow Across the IoT Ecosystem If your IoT efforts are not integrated with internal workflow processes overall, value and ROI is nearly impossible.
Eliminate Silos At the end of the day, this is all about eliminating silos of data that are collected from hardware and building a solution that has the power to integrate multiple IoT data streams from a number of sources. Pair Automation with Machine Learning With a goal of gaining more insight into the current state of IoT deployments, my team and I have spoken to a number of IT executives.
About the Author This article was written by Angie Sticher , Co-Founder, Chief Product Officer, and Chief Operating Officer of UrsaLeo, an enterprise software company that enables users to visualize and interact with realtime operational data in a photorealistic 3D representation of their facility or equipment.
Facebook Twitter LinkedIn. November 8, Smart Agriculture. November 4, So transforming the mess of data that we routinely collect into a useful asset really has to be a process. It has to be a very focused and considered process.
The challenge is taking a lot of data that they are probably already accumulating in their operations and sifting through it to properly drive operational efficiencies and achieve an even higher level of service and support to customers. Software maker Tableau is helping companies harness all that data into something that can become useful.
They have come out with a pretty simplistic way of helping manufacturers use data to transform their companies, saying there are four basic ways a company can revolutionize its industry with data:. Tableau suggests looking at the way electric vehicle maker Tesla Motors Designs used self-service analytics—meaning they gave its employees the ability to explore their own data—to discover some things about its own production improvement process.
Another example of taking a hard look at big-data analytics is Trane, maker of air conditioning systems and equipment. Tableau showed how Trane went from just using spreadsheets to using data visualizations with customer service data to get a better feel for how they were satisfying their customers. We want to react directly by responding to our customers.
We also want to react as a business and strategically determine what is important to our customers.
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