There is a reason why every company today is running towards collecting and gathering more and more data. In the 21st century, if there is anything that matters the most – it is ‘data’. Data is the key to unlock the solution of every problem that is hampering growth in the modern industrial world. Machine learning nowadays is highly influenced by data and poor data can completely rupture one’s machine learning process.
Data is responsible for driving analysis and helps one in predicting and speculating upcoming events of the future. Hence, one needs a valid and error-proof database to create a holistic and more fruitful operating environment.
With the evolution of the industrial world, the complexity of the problems has also increased and is now needed to be addressed more aptly and comprehensively. For this, one doesn’t only need one particular type of data; but more diverse and well-versed databases to plan effective and more comprehending models. Below is a list of steps that should be taken by the leadership to advance with an aggressive and rigorous approach for a decorated execution of a program.
One should have a very clear and cool head before he jumps into creating databases and using them to achieve his goals. Data can only be helpful if used in the right manner at right time and place. Data collected should also be able to support the cause and should be concrete enough to steer the entire transformation process and streamline the controls and systems.
Time plays a very crucial factor in any data management process. It has to be infused in the entire implementation plan to get the best and the most optimum results out of the project. One should take enough time to build efficient data quality fundamentals and execute them in the projects and its respective processes. Training and replicating the processes of the predictive model will help one in eliminating the root causes of errors.
Original training data keeps the processes in sync and helps one in bracing themselves for jumping between steps. Original data also helps in suggesting improvements in the predictive model and keeps it in the loop with the targets and numbers to be achieved in the process.
Distributing responsibilities is an effective way to engage the workforce in the entire process. It also assists in an effective execution of the project plan. Moreover, providing one with the charge of data quality after taking the model off the shelves helps in tracking its progress along with the effectiveness of the measures taken.
Quality assurance adds value to your product as well as provides the customer with the assurance that the processes involved in the product manufacturing is as per the standards and produce is trespassed through rigorous quality checks for a better and worth the money output.
Right data collection is extremely important and the key to success. Team Faber Infinite has been working with clients across industries, geographies and regions to collect the right data, build right dashboards and execute right improvement plans.
Written & Compiled By Faber Kishlay & Faber Mayuri