1. Assuming that data is of a high standard and completeness<\/strong><\/h3>\n\n\n\n\u201cIt is important to actually look at your data and understand it<\/strong>, not simply trust that \u2018someone\u2019 has managed it. We\u2019ve seen some worrying trends across the whole globe, not just in oil and gas but also across power generation where make, model, and manufacturing numbers for equipment have not been in place to levels anywhere between 50% to 5% population.\u201d
<\/strong><\/p>\n\n\n\n2. Lack of budget and time to plan and risk assess the transition<\/strong><\/h3>\n\n\n\n\u201cA data transition can either be a massive problem or a massive benefit depending on how you conduct it. We often see ambiguity in who actually owns the data<\/strong>, some see it as an IT job, others see it as a maintenance job, but in reality, everyone uses data. There’s finance, procurement, warehousing, and all sorts of people involved in the transition, and if this is not planned and risk assessed ahead of time then it can have detrimental effects on the whole business.\u201d
<\/strong><\/p>\n\n\n\n3. No shared understanding of the importance of data within the business<\/strong><\/h3>\n\n\n\n\u201cWe often see an absence of company KPIs set around data quality<\/strong>. It\u2019s important to think about how different departments interact within your business and ensure they share the same understanding of how important their roles are in the overall picture of managing data. Picture a rugby team on a pitch, there is not just one individual player holding the ball at all times, it\u2019s about how well you throw the pass between each other, and how well it is received. Data is much the same, you need to ensure you are handing information over to people in an effective way<\/strong>.\u201d<\/p>\n\n\n\n![\"Challenge](\"https:\/\/3825857.fs1.hubspotusercontent-na1.net\/hub\/3825857\/hubfs\/Challenge%203.png?width=304&name=Challenge%203.png\")
<\/p>\n\n\n\n
How to overcome these challenges <\/h1>\n\n\n\n1. Understand your current position and future aspirations<\/strong><\/h3>\n\n\n\n\u201cA good analogy to help clearly envision a data transition is to think of it like moving house. The first things you need to do are figure out what you have and what your aspirations are for the move<\/strong>. What are you going to take with you? Are you going to move yourself or pay for movers? From a business point of view, try to understand your business goals and what your current situation truly is. Do you want to lower emissions and aim for target zero? How do you prove that you\u2019re making progress? Are you going to use the circle economy and aim to recycle? Look at your asset and decide which ones could be reused for carbon capture<\/a>, what condition are they in, and are they fit for purpose? If you\u2019re not recording your data well then this all becomes a problem during the transition, knowing what and where to move data. <\/p>\n\n\n\n\u201cManagement consultant and author, Peter Drucker, famously said, \u201cThere is surely nothing quite so useless as doing with great efficiency what should not be done at all\u201d<\/strong>. Before you jump into your data transformation\/transition, you must understand your change driver. Why is this change in your data needed? For example:<\/p>\n\n\n\n\n- Do you have a new asset?<\/li>\n\n\n\n
- Are you merging with other entities?<\/li>\n\n\n\n
- Is there an update to your existing CMMS system?<\/li>\n\n\n\n
- Are you going through a recommissioning process? <\/li>\n<\/ul>\n\n\n\n
\u201cWhen you move from one master data system to another, you want to ensure that you\u2019re in a\u00a0better position than you were before<\/a>.\u00a0In terms of the moving house analogy, you wouldn\u2019t bring old clothes that you never wear, so do you need to bring all of your outdated and obsolete data?\u201d<\/p>\n\n\n\n2. Compile your Data Standards<\/strong><\/h3>\n\n\n\n\u201cBegin with reviewing your data standards and be honest<\/strong> with yourself about whether you have a complete Data Standard that covers all your assets or not. With monumental amounts of data now available, it is easy to find yourself drowning in a data lake filled with rubbish, so try and understand what you really need. <\/p>\n\n\n\n“Write down how you are going to use your data both overall and within each department. This will not only help when conducting data standards creation, review, and issuing, but it will also give departments a sense of ownership and responsibility over the master data<\/strong>.\u201d<\/p>\n\n\n\n![\"Mapping-01\"\/](\"https:\/\/3825857.fs1.hubspotusercontent-na1.net\/hub\/3825857\/hubfs\/Mapping-01.png?width=336&name=Mapping-01.png\")
<\/figure>\n\n\n\n3. Find a way to manage the data change<\/strong><\/h3>\n\n\n\n\u201cThinking back to the moving house analogy, ask yourself who is going to do the moving? Will you hire a van or use your own car to do it yourself, or will you hire movers and packers and hand that responsibility across. One way to efficiently manage a data change is to find an asset management strategy deployment tool such as EffioTM<\/sup><\/a> that can deliver:<\/p>\n\n\n\n\n- Storage of the data standard\/rules \u200b<\/li>\n\n\n\n
- Linking of data across departments\u200b<\/li>\n\n\n\n
- Understanding of data quality and gaps\u200b<\/li>\n\n\n\n
- Means to close gaps and monitor progress\u200b<\/li>\n\n\n\n
- Visibility of data to users and transition team\u200b<\/li>\n\n\n\n
- Log of changes, ensuring auditable trail is in place\u200b<\/li>\n\n\n\n
- Process management and recorded sign-off <\/li>\n\n\n\n
- Accessibility for all stakeholders\u200b<\/li>\n\n\n\n
- Secure and managed access<\/li>\n<\/ul>\n\n\n\n
<\/strong><\/p>\n\n\n\n
2. Lack of budget and time to plan and risk assess the transition<\/strong><\/h3>\n\n\n\n\u201cA data transition can either be a massive problem or a massive benefit depending on how you conduct it. We often see ambiguity in who actually owns the data<\/strong>, some see it as an IT job, others see it as a maintenance job, but in reality, everyone uses data. There’s finance, procurement, warehousing, and all sorts of people involved in the transition, and if this is not planned and risk assessed ahead of time then it can have detrimental effects on the whole business.\u201d
<\/strong><\/p>\n\n\n\n3. No shared understanding of the importance of data within the business<\/strong><\/h3>\n\n\n\n\u201cWe often see an absence of company KPIs set around data quality<\/strong>. It\u2019s important to think about how different departments interact within your business and ensure they share the same understanding of how important their roles are in the overall picture of managing data. Picture a rugby team on a pitch, there is not just one individual player holding the ball at all times, it\u2019s about how well you throw the pass between each other, and how well it is received. Data is much the same, you need to ensure you are handing information over to people in an effective way<\/strong>.\u201d<\/p>\n\n\n\n<\/p>\n\n\n\n
How to overcome these challenges <\/h1>\n\n\n\n1. Understand your current position and future aspirations<\/strong><\/h3>\n\n\n\n\u201cA good analogy to help clearly envision a data transition is to think of it like moving house. The first things you need to do are figure out what you have and what your aspirations are for the move<\/strong>. What are you going to take with you? Are you going to move yourself or pay for movers? From a business point of view, try to understand your business goals and what your current situation truly is. Do you want to lower emissions and aim for target zero? How do you prove that you\u2019re making progress? Are you going to use the circle economy and aim to recycle? Look at your asset and decide which ones could be reused for carbon capture<\/a>, what condition are they in, and are they fit for purpose? If you\u2019re not recording your data well then this all becomes a problem during the transition, knowing what and where to move data. <\/p>\n\n\n\n\u201cManagement consultant and author, Peter Drucker, famously said, \u201cThere is surely nothing quite so useless as doing with great efficiency what should not be done at all\u201d<\/strong>. Before you jump into your data transformation\/transition, you must understand your change driver. Why is this change in your data needed? For example:<\/p>\n\n\n\n\n- Do you have a new asset?<\/li>\n\n\n\n
- Are you merging with other entities?<\/li>\n\n\n\n
- Is there an update to your existing CMMS system?<\/li>\n\n\n\n
- Are you going through a recommissioning process? <\/li>\n<\/ul>\n\n\n\n
\u201cWhen you move from one master data system to another, you want to ensure that you\u2019re in a\u00a0better position than you were before<\/a>.\u00a0In terms of the moving house analogy, you wouldn\u2019t bring old clothes that you never wear, so do you need to bring all of your outdated and obsolete data?\u201d<\/p>\n\n\n\n2. Compile your Data Standards<\/strong><\/h3>\n\n\n\n\u201cBegin with reviewing your data standards and be honest<\/strong> with yourself about whether you have a complete Data Standard that covers all your assets or not. With monumental amounts of data now available, it is easy to find yourself drowning in a data lake filled with rubbish, so try and understand what you really need. <\/p>\n\n\n\n“Write down how you are going to use your data both overall and within each department. This will not only help when conducting data standards creation, review, and issuing, but it will also give departments a sense of ownership and responsibility over the master data<\/strong>.\u201d<\/p>\n\n\n\n<\/figure>\n\n\n\n3. Find a way to manage the data change<\/strong><\/h3>\n\n\n\n\u201cThinking back to the moving house analogy, ask yourself who is going to do the moving? Will you hire a van or use your own car to do it yourself, or will you hire movers and packers and hand that responsibility across. One way to efficiently manage a data change is to find an asset management strategy deployment tool such as EffioTM<\/sup><\/a> that can deliver:<\/p>\n\n\n\n\n- Storage of the data standard\/rules \u200b<\/li>\n\n\n\n
- Linking of data across departments\u200b<\/li>\n\n\n\n
- Understanding of data quality and gaps\u200b<\/li>\n\n\n\n
- Means to close gaps and monitor progress\u200b<\/li>\n\n\n\n
- Visibility of data to users and transition team\u200b<\/li>\n\n\n\n
- Log of changes, ensuring auditable trail is in place\u200b<\/li>\n\n\n\n
- Process management and recorded sign-off <\/li>\n\n\n\n
- Accessibility for all stakeholders\u200b<\/li>\n\n\n\n
- Secure and managed access<\/li>\n<\/ul>\n\n\n\n
<\/strong><\/p>\n\n\n\n
3. No shared understanding of the importance of data within the business<\/strong><\/h3>\n\n\n\n\u201cWe often see an absence of company KPIs set around data quality<\/strong>. It\u2019s important to think about how different departments interact within your business and ensure they share the same understanding of how important their roles are in the overall picture of managing data. Picture a rugby team on a pitch, there is not just one individual player holding the ball at all times, it\u2019s about how well you throw the pass between each other, and how well it is received. Data is much the same, you need to ensure you are handing information over to people in an effective way<\/strong>.\u201d<\/p>\n\n\n\n<\/p>\n\n\n\n
How to overcome these challenges <\/h1>\n\n\n\n1. Understand your current position and future aspirations<\/strong><\/h3>\n\n\n\n\u201cA good analogy to help clearly envision a data transition is to think of it like moving house. The first things you need to do are figure out what you have and what your aspirations are for the move<\/strong>. What are you going to take with you? Are you going to move yourself or pay for movers? From a business point of view, try to understand your business goals and what your current situation truly is. Do you want to lower emissions and aim for target zero? How do you prove that you\u2019re making progress? Are you going to use the circle economy and aim to recycle? Look at your asset and decide which ones could be reused for carbon capture<\/a>, what condition are they in, and are they fit for purpose? If you\u2019re not recording your data well then this all becomes a problem during the transition, knowing what and where to move data. <\/p>\n\n\n\n\u201cManagement consultant and author, Peter Drucker, famously said, \u201cThere is surely nothing quite so useless as doing with great efficiency what should not be done at all\u201d<\/strong>. Before you jump into your data transformation\/transition, you must understand your change driver. Why is this change in your data needed? For example:<\/p>\n\n\n\n\n- Do you have a new asset?<\/li>\n\n\n\n
- Are you merging with other entities?<\/li>\n\n\n\n
- Is there an update to your existing CMMS system?<\/li>\n\n\n\n
- Are you going through a recommissioning process? <\/li>\n<\/ul>\n\n\n\n
\u201cWhen you move from one master data system to another, you want to ensure that you\u2019re in a\u00a0better position than you were before<\/a>.\u00a0In terms of the moving house analogy, you wouldn\u2019t bring old clothes that you never wear, so do you need to bring all of your outdated and obsolete data?\u201d<\/p>\n\n\n\n2. Compile your Data Standards<\/strong><\/h3>\n\n\n\n\u201cBegin with reviewing your data standards and be honest<\/strong> with yourself about whether you have a complete Data Standard that covers all your assets or not. With monumental amounts of data now available, it is easy to find yourself drowning in a data lake filled with rubbish, so try and understand what you really need. <\/p>\n\n\n\n“Write down how you are going to use your data both overall and within each department. This will not only help when conducting data standards creation, review, and issuing, but it will also give departments a sense of ownership and responsibility over the master data<\/strong>.\u201d<\/p>\n\n\n\n<\/figure>\n\n\n\n3. Find a way to manage the data change<\/strong><\/h3>\n\n\n\n\u201cThinking back to the moving house analogy, ask yourself who is going to do the moving? Will you hire a van or use your own car to do it yourself, or will you hire movers and packers and hand that responsibility across. One way to efficiently manage a data change is to find an asset management strategy deployment tool such as EffioTM<\/sup><\/a> that can deliver:<\/p>\n\n\n\n\n- Storage of the data standard\/rules \u200b<\/li>\n\n\n\n
- Linking of data across departments\u200b<\/li>\n\n\n\n
- Understanding of data quality and gaps\u200b<\/li>\n\n\n\n
- Means to close gaps and monitor progress\u200b<\/li>\n\n\n\n
- Visibility of data to users and transition team\u200b<\/li>\n\n\n\n
- Log of changes, ensuring auditable trail is in place\u200b<\/li>\n\n\n\n
- Process management and recorded sign-off <\/li>\n\n\n\n
- Accessibility for all stakeholders\u200b<\/li>\n\n\n\n
- Secure and managed access<\/li>\n<\/ul>\n\n\n\n
<\/p>\n\n\n\n
How to overcome these challenges <\/h1>\n\n\n\n1. Understand your current position and future aspirations<\/strong><\/h3>\n\n\n\n\u201cA good analogy to help clearly envision a data transition is to think of it like moving house. The first things you need to do are figure out what you have and what your aspirations are for the move<\/strong>. What are you going to take with you? Are you going to move yourself or pay for movers? From a business point of view, try to understand your business goals and what your current situation truly is. Do you want to lower emissions and aim for target zero? How do you prove that you\u2019re making progress? Are you going to use the circle economy and aim to recycle? Look at your asset and decide which ones could be reused for carbon capture<\/a>, what condition are they in, and are they fit for purpose? If you\u2019re not recording your data well then this all becomes a problem during the transition, knowing what and where to move data. <\/p>\n\n\n\n\u201cManagement consultant and author, Peter Drucker, famously said, \u201cThere is surely nothing quite so useless as doing with great efficiency what should not be done at all\u201d<\/strong>. Before you jump into your data transformation\/transition, you must understand your change driver. Why is this change in your data needed? For example:<\/p>\n\n\n\n\n- Do you have a new asset?<\/li>\n\n\n\n
- Are you merging with other entities?<\/li>\n\n\n\n
- Is there an update to your existing CMMS system?<\/li>\n\n\n\n
- Are you going through a recommissioning process? <\/li>\n<\/ul>\n\n\n\n
\u201cWhen you move from one master data system to another, you want to ensure that you\u2019re in a\u00a0better position than you were before<\/a>.\u00a0In terms of the moving house analogy, you wouldn\u2019t bring old clothes that you never wear, so do you need to bring all of your outdated and obsolete data?\u201d<\/p>\n\n\n\n2. Compile your Data Standards<\/strong><\/h3>\n\n\n\n\u201cBegin with reviewing your data standards and be honest<\/strong> with yourself about whether you have a complete Data Standard that covers all your assets or not. With monumental amounts of data now available, it is easy to find yourself drowning in a data lake filled with rubbish, so try and understand what you really need. <\/p>\n\n\n\n“Write down how you are going to use your data both overall and within each department. This will not only help when conducting data standards creation, review, and issuing, but it will also give departments a sense of ownership and responsibility over the master data<\/strong>.\u201d<\/p>\n\n\n\n<\/figure>\n\n\n\n3. Find a way to manage the data change<\/strong><\/h3>\n\n\n\n\u201cThinking back to the moving house analogy, ask yourself who is going to do the moving? Will you hire a van or use your own car to do it yourself, or will you hire movers and packers and hand that responsibility across. One way to efficiently manage a data change is to find an asset management strategy deployment tool such as EffioTM<\/sup><\/a> that can deliver:<\/p>\n\n\n\n\n- Storage of the data standard\/rules \u200b<\/li>\n\n\n\n
- Linking of data across departments\u200b<\/li>\n\n\n\n
- Understanding of data quality and gaps\u200b<\/li>\n\n\n\n
- Means to close gaps and monitor progress\u200b<\/li>\n\n\n\n
- Visibility of data to users and transition team\u200b<\/li>\n\n\n\n
- Log of changes, ensuring auditable trail is in place\u200b<\/li>\n\n\n\n
- Process management and recorded sign-off <\/li>\n\n\n\n
- Accessibility for all stakeholders\u200b<\/li>\n\n\n\n
- Secure and managed access<\/li>\n<\/ul>\n\n\n\n
\u201cA good analogy to help clearly envision a data transition is to think of it like moving house. The first things you need to do are figure out what you have and what your aspirations are for the move<\/strong>. What are you going to take with you? Are you going to move yourself or pay for movers? From a business point of view, try to understand your business goals and what your current situation truly is. Do you want to lower emissions and aim for target zero? How do you prove that you\u2019re making progress? Are you going to use the circle economy and aim to recycle? Look at your asset and decide which ones could be reused for carbon capture<\/a>, what condition are they in, and are they fit for purpose? If you\u2019re not recording your data well then this all becomes a problem during the transition, knowing what and where to move data. <\/p>\n\n\n\n \u201cManagement consultant and author, Peter Drucker, famously said, \u201cThere is surely nothing quite so useless as doing with great efficiency what should not be done at all\u201d<\/strong>. Before you jump into your data transformation\/transition, you must understand your change driver. Why is this change in your data needed? For example:<\/p>\n\n\n\n \u201cBegin with reviewing your data standards and be honest<\/strong> with yourself about whether you have a complete Data Standard that covers all your assets or not. With monumental amounts of data now available, it is easy to find yourself drowning in a data lake filled with rubbish, so try and understand what you really need. <\/p>\n\n\n\n “Write down how you are going to use your data both overall and within each department. This will not only help when conducting data standards creation, review, and issuing, but it will also give departments a sense of ownership and responsibility over the master data<\/strong>.\u201d<\/p>\n\n\n\n \u201cThinking back to the moving house analogy, ask yourself who is going to do the moving? Will you hire a van or use your own car to do it yourself, or will you hire movers and packers and hand that responsibility across. One way to efficiently manage a data change is to find an asset management strategy deployment tool such as EffioTM<\/sup><\/a> that can deliver:<\/p>\n\n\n\n\n
\u201cWhen you move from one master data system to another, you want to ensure that you\u2019re in a\u00a0better position than you were before<\/a>.\u00a0In terms of the moving house analogy, you wouldn\u2019t bring old clothes that you never wear, so do you need to bring all of your outdated and obsolete data?\u201d<\/p>\n\n\n\n2. Compile your Data Standards<\/strong><\/h3>\n\n\n\n
<\/figure>\n\n\n\n3. Find a way to manage the data change<\/strong><\/h3>\n\n\n\n
\n
![\"Online](\"https:\/\/3825857.fs1.hubspotusercontent-na1.net\/hub\/3825857\/hubfs\/Online%20data%20validation.jpg?width=347&name=Online%20data%20validation.jpg\")
<\/p>\n\n\n\n