May i get a positive respond about the comment below.

Big data has recently attracted researches and practitioners because of its potential use in decision making processes. Big data analytics is becoming increasingly common among manufacturing companies as it helps to gain insights and make big data based decisions. There are also many obstacles in industrial supply chains to the implementation of big data analytic. It is therefore necessary for manufacturing companies to identify and examine the nature of each barrier. Data analytics, machine learning and artificialmanufacturing intelligence are not just propaganda. They allow cost savings and process optimization if done properly, using them calls for a professional approach. Most analytics projects fail because the level of sophistication involved is misunderstood by the stakeholders. Manufacturers should address these areas to prevent these situations are, Analytics strategy, Gradual and agile approach, Utilities and pipelines, AI experiments and Operationalised AI.Analytics strategy is the systems DNA and the principal point of orientation for the following regions. For all your different efforts , a simple overall raodmap on where you are will help you identify your objectives and control all required steps. Gradual and agile approach is when, gradual investment produces measurable benefits and eventually overcomes greater barriers. Utilities and pipelines is you need the data processing platform and hardware, as well as smart pipelines to centrally capture and store data. AI experiments is the rapid prototyping and agile development with AI provide inspiration and the best way to automate the system and operations. Many companies expect ready solutions to their every need, but for most industries plug and play solutions are uncommon, and applied AI solutions and applications are still under heavy study. Operatinalised AI is the successful experiments need to be operatinalised. The entire workload needs to be moved from experiment to a solution that is safe, managed and corporate in nature.