 All right, so good morning. Good morning. Good morning. Once again, everybody is a place we have to mute our microphones Yes, everything that exists as a life cycle. So those data human beings we have life cycle Animals they have the life cycle. So data is meant to have a life cycle as well So whenever we have we talk about data, we had data. What exactly comes to our mind? So a few points quickly on the chart. So this meant to just some gather some information around what we think of data Okay, raw fact. Okay, we think of data's numbers Just type just type on the chart. What do you think of data generally? You hear the word? What exactly comes to your mind first? Put that on the chart quickly I'm sure we all agree with me that Data is everywhere. Data is all around us. If you think about it every activity that we do as Element of data within it. In fact, it is one of those things that generates data. So if we begin to narrow this down Into different organizations organizations have their own ways of generating data and Because they run on certain systems, which have been summarized to be two Which I'm sure that we are all aware of and I would ask us to mention it in the moment So the systems are systems that Generate a lot of data for them. So for the moment, let's quickly have a review of that. What are the two systems that organizations? typically run on This is systems that organization run on. Let's post that on this chart quickly, please Okay, let's drop that on the chart Operational systems They are used to execute business processes. At Debran Consulting we have a number of processes we execute with our clients One of the systems that we use which is an operational system is the 1-page CRM So 1-page CRM will use that to manage our deals, to manage our pipelines We see our works are coming in to manage at what point we when it comes to all of these things But these systems They manage our processes, but we actually need to also evaluate those processes. Probably there are bottlenecks somewhere Probably there are certain recurrences within the systems or probably even the data that the system generates We need to analyze them. That is the essence of having analytic systems But before you can analyze whatever data is coming from your operational systems You actually do need to do some sort of data collection first Which is the starting point for the data lifecycle You need to collect data. You need to gather data. When you have collected the data that you need, you've gathered it It has to be stored somewhere Organizations store data differently. We have certain organizations who store data on-premise, which means the store data locally in the organization, the data sits within the organization Probably by using some external storage devices or using Some infrastructures, data cubicle, to manage the data we house internally. Some other organization, they subscribe to enterprise packages so that the data sits on the cloud Maybe with Microsoft Azure, Amazon Web Services All of this is so that they ensure that the data haven't gathered and collected Stored somewhere and they can access them at whatever point in time When we define data physically We say that data is unprocessed Information, it has to be processed. It has to be processed so that we can make some meanings out of it In the bid to get some information out of data, that is the reason why we visualize it We visualize our data in different ways Analysts within the organization will come up with different charts, come up with different tables to make meanings out of that data But when we look at the chart, for instance, maybe we are analyzing our training names Who could say that in the last one year we had we delivered 700 sessions Maybe we had five clients. Maybe some sort of information would come out whenever data is visualized But this information are not actually what we need. They don't have so much value They are only telling us what has happened within our business That is not the end goal. The end goal will be that okay now We have known that okay, we have Information about our training to say that we deliver this number of trainings. How were they distributed? What was the pattern? What was the trend? We could see when we begin to analyze. That's okay Maybe usually first quarters we don't deliver so much trainings Trainings begin at second quarter in the third quarter We have so much engagement with our clients, consulting jobs And this will now begin to drive some actions to say okay Since we have noticed that from our data, we don't usually Deliver so much trainings in the first quarter. Then what we need to do is that first quarter is easy to plan ahead Let's use it to prepare certain things. Maybe we need to do some course there Maybe we need to you know, just gather some knowledge base strengthen ourselves as to what we want to deliver to our client And that is where the insights begin to come in Peter Sundegard mentioned that information is the oil of the 24th century and analytics is the combustion engine Which implies that we cannot actually separate the two if you look at oil and automotive you can't separate it too Some really what we're saying is this the start point of whatever thing we're doing with data begins with the data garden and collection itself If data is properly gathered and collected We would observe that that would now drive certain things within the business, okay fine I want to work on it want to Get some information out of that data. We visualize the data The visualization would drive certain things We will be able to spot some patterns and trains and that's where we can now run some comparison We can do some analysis which we now inform certain decisions certain actions Which eventually if we work on those actions would propel our business take us into the next level That was so much anticipated to get to thank you very much everybody So that is the data life cycle begins with data Information and then get to inside so the inside is the goal and that should be what we aim at at every point in time Thank you very much everybody