By using big data in manufacturing processes, such as a tablet monitoring what is happening in a factory, manufacturers ensure greater efficiency and success.
Manufacturing has always needed large quantities of data to produce the complicated goods typical of a modern industrial society while using data for the time management of workers and general efficiency within a factory stretches back to the 19th Century. However, the rise of big data and the Internet-of-Things (IoT) have both made the data that manufacturers use much larger and more complex than was the case in the past. While this is excellent news for the most forward-thinking manufacturers, there is no doubting the challenges that data present for all those engaged in manufacturing products and in trying to assess the future demand for the various products they are making.
IoT means that every step in the manufacturing process can now be monitored, including the state of any machinery involved in the production process. Vast amounts of publicly available data also make it much easier to assess what is going to be the future demand for products by using advanced analytics processes to analyse the big data and help to streamline manufacturing processes, reduce costs, offer operational insights and increase productivity.
Getting the data into a data lake to properly analyse them without contaminating the lake and turning it into a mudpit is the first challenge that manufacturers will face. Essentially this means cleaning the information or data available in multiple platforms. Typically these platforms for manufacturers are an enterprise resource planning application and a customer relationship management system.
At Spotless Data we specialise in the data cleaning of any and all rogue data which are full of corruptions and inconsistencies. This means we can be very helpful to any kind of manufacturer who requires clean data fast in order to make their business more efficient, cost-effective, customer-friendly and profitable, as well as gaining a better understanding of the demand for the products they are manufacturing and of their plant performance, which can then be tweaked for improved output.
An Enterprise resource planning or ERP system can offer an integrated view of all the core processes going on by accessing the data stored in the various databases that make up a typical manufacturing business. These include the primary materials, the machinery that produce the products, cash flow, orders, and payments, such as for raw materials or paying staff. These will all typically be stored in separate databases and then integrated with the ERP.
A customer relationship management (CRM) is a much more customer focussed system and is typically found alongside an ERP within the data held by a manufacturer. It works by analysing the history of individual customers and is designed to keep the customer and to improve sales.
ERP and CRM systems typically involve multiple databases whereas a data lake is by definition a single repository where all the data which a manufacturer holds, whatever its nature, is stored. And when the data within the lake are all clean and consistent with each other, which is what Spotless does, then any manufacturer has a tremendously valuable resource which they can then use across the whole range of activities for which they require the data.
This is why using a data cleaning service is so vital for any manufacturer hoping to make the most of the masses of data they already have. There are many data cleaning software solutions on the market. Spotless differs from these in that there is no need to download any software onto multiple devices because our data cleaning solution is accessible on any devices through a web browser.
Arguably the best feature of the Spotless API is the report which we produce within a minute of when you upload your data to our secure API. This report points out what our own automated machine learning filters believe are the best procedures to clean your data then allowing you to set your specifications. However, when you are dealing with known dirty sources of data and after some experience of working with our API then even this can be automated to ensure a swifter than ever cleaning of your data, as we recognize the need for swiftness when it comes to having data which you can trust in.
Poor data quality or a lack of data validation is always going to create problems for manufacturers. This might be inefficient manufacturing processes resulting in your products costing more to make than the similar products of your rivals. Or perhaps miscalculating the demand so that you either produce too much and have to get rid of the stock at half price in a sale, or produce too little and leave your customers not merely dissatisfied but turning to your rivals who have calculated demand better due to having better data quality, meaning that they can indeed fill all the demand for a particular product. Or perhaps you have to halt production over several critical days in the run-up to producing goods for Christmas because while you had installed Internet-of-Things sensors into all your plant machinery, the data was so poor in quality that you failed to realise that a critical component was about to break down until it actually failed. Or perhaps you had to ask your bank for an emergency loan due to a cash flow issue the day you needed to pay all your staff because poor data quality meant you hadn't realised that you had also had to pay your suppliers the day before. Or it may just be that manufacturing your products cost more than you expected because your company failed to use your data to streamline the manufacturing processes for maximum efficiency.
The critical element is the data integration of all the data from different databases and sources into one seamless and pristine data lake where all the data, and indeed all the fields within all the data, are integrated in a seamless fashion so that they work to perfection. In cases where this integration appears to be impossible to Spotless' automated machine learning filters, they will quarantine the data and we will send you an email (at the details you gave us when you signed-up) to alert you to the problem so that between us we can resolve the issue. There is no question but that data which are thoroughly integrated because they have been scrupulously cleaned are an absolute pre-requisite to success in modern manufacturing. With the rise of the Internet-of-Things this is now more so than ever.
You can read our introduction to using our API and then try out our service on your My Filters page but, for this, you need to log in first. If you haven't already done so, you can sign-up using your email address, Facebook, Google or GitHub accounts. You may also view our video on data cleaning an EPG file, which also explains how to use our API.
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If your data quality is an issue or you know that you have known sources of dirty data but your files are just too big, and the problems too numerous to be able to fix manually please do log in and try now