[{"innerBlocks":[],"name":"core\/freeform","postId":126219,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"Savvy entrepreneurs know that a successful business starts with identifying a real problem in the market. Only then do you build a team, develop a prototype and create a company, moving gradually against clear objectives to minimize risk.

We should think about data initiatives in the same way. If you embark on a \u201cdata project\u201d at your organization, you\u2019re setting yourself up to fail. That\u2019s because \u201cdata projects\u201d almost by definition are ill-defined and unfocused. In fact, Gartner has estimated that 60 percent of big data projects are abandoned before they get past the pilot stage, citing a lack of clear objectives, over-investment in complex tools, and a failure to get buy-in from stakeholders.

I\u2019d argue the real reason, however, is that we\u2019ve come to believe data has inherent value. Ever since The Economist <\/i>declared that data is \u201cthe world\u2019s most valuable resource,\u201d businesses have been scouring their organizations for every scrap of information that might be useful and throwing it in a massive data lake without thinking through how that data will actually drive the business forward.

Data is only valuable when it serves a business goal, and investing in a project not tied to a clear outcome will be costly. Those who ignore this will repeat the mistake businesses made in the 1990s when they built massive ERP systems that never delivered on their promise to move the business forward.

BI practitioners should approach data initiatives methodically and with a purpose. Here are some steps that will ensure your project delivers real value and that your users won\u2019t turn around when it\u2019s complete and tell you they wanted something else. These are obviously general points, but you can think about how they apply specifically to your business.

data project.jpg<\/bsp-image>Give the people what they want <\/b>
Start by sitting down with users in your organization to understand exactly what data they want and how they plan to use it. This is the reverse of what many projects attempt to do. Instead of starting at the beginning of the data pipeline and building a complex delivery infrastructure, give people the data they\u2019re asking for right away. This way, your users aren\u2019t banging down your door while you spend three months building the infrastructure--and your pivot costs will be much lower if they discover it\u2019s not what they wanted after all. In addition, by starting with your business users, your project will be tied to a concrete business goal from the outset.

Start manually<\/b>
Delivering data right away means you may be supplying it manually for a while, but that\u2019s better than building out a self-service platform, connecting it to various data sources and finding out later it\u2019s not useful. Feed data into your users\u2019 BI tools manually if you have to, and build out the automation technology afterwards. That way, you\u2019re not building infrastructure before you know it will provide immediate value.

Don\u2019t boil the ocean<\/b>
Data has transitioned over the past decade from a highly targeted asset to an abundant resource. It\u2019s streaming in from mobile apps, marketing tools, IoT devices, e-commerce sites and more. If you believe this data will someday provide value, by all means collect it now; storage is cheap, and future projects that leverage machine learning will benefit from an abundance of historical data. However, don\u2019t invest the time and resources to clean and prepare all this data for use until you know it will provide value.

It\u2019s one thing to store data, it\u2019s quite another to invest in data models and delivery pipelines before you know they\u2019ll be useful. Every bit of data you make available to end users should be tied to a business objective. If it isn\u2019t, leave it where it is.

Take a page out of the agile playbook<\/b>
Software teams learned early on that an iterative approach to development is faster, more efficient and leads to fewer errors. Business teams are now adopting this agile methodology, and those in charge of data initiatives should do the same.

An agile approach implies breaking a project into small parts that can be delivered as they\u2019re completed. This provides faster time to value and makes it easier to pivot if the data you\u2019re collecting isn\u2019t yielding the expected results. It\u2019s a cautious approach that minimizes risk and provides a faster return.

Trust your data<\/b>
The No. 1 objection I hear from organizations considering a new data initiative is that they don\u2019t trust their data. How can they allow users to make decisions affecting the future of the organization if they\u2019re not certain the data is 100 percent current and accurate?

Well, your employees are making decisions already, either based on no data at all or on fragments of the very same data that you\u2019re concerned about. Don\u2019t be paralyzed by inaction because you don\u2019t fully trust your data; it\u2019s better to get started and learn along the way.

Data is a valuable asset, but data initiatives can be costly so minimizing risk and maximizing returns is essential. These costs include not just the infrastructure and resources to build a data platform, but the opportunity costs of not getting it right.

If you spend three months building a data delivery system and your users still can\u2019t get the answers they want, they\u2019re unlikely to ask you to build it all over again. They simply won\u2019t ask those questions, and that could be a huge missed opportunity for the organization.

Building incrementally and validating your platform along the way is how you get it right first time. That way, your business project is far more likely to succeed.","saveContent":"Savvy entrepreneurs know that a successful business starts with identifying a real problem in the market. Only then do you build a team, develop a prototype and create a company, moving gradually against clear objectives to minimize risk.

We should think about data initiatives in the same way. If you embark on a \u201cdata project\u201d at your organization, you\u2019re setting yourself up to fail. That\u2019s because \u201cdata projects\u201d almost by definition are ill-defined and unfocused. In fact, Gartner has estimated that 60 percent of big data projects are abandoned before they get past the pilot stage, citing a lack of clear objectives, over-investment in complex tools, and a failure to get buy-in from stakeholders.

I\u2019d argue the real reason, however, is that we\u2019ve come to believe data has inherent value. Ever since The Economist <\/i>declared that data is \u201cthe world\u2019s most valuable resource,\u201d businesses have been scouring their organizations for every scrap of information that might be useful and throwing it in a massive data lake without thinking through how that data will actually drive the business forward.

Data is only valuable when it serves a business goal, and investing in a project not tied to a clear outcome will be costly. Those who ignore this will repeat the mistake businesses made in the 1990s when they built massive ERP systems that never delivered on their promise to move the business forward.

BI practitioners should approach data initiatives methodically and with a purpose. Here are some steps that will ensure your project delivers real value and that your users won\u2019t turn around when it\u2019s complete and tell you they wanted something else. These are obviously general points, but you can think about how they apply specifically to your business.

data project.jpg<\/bsp-image>Give the people what they want <\/b>
Start by sitting down with users in your organization to understand exactly what data they want and how they plan to use it. This is the reverse of what many projects attempt to do. Instead of starting at the beginning of the data pipeline and building a complex delivery infrastructure, give people the data they\u2019re asking for right away. This way, your users aren\u2019t banging down your door while you spend three months building the infrastructure--and your pivot costs will be much lower if they discover it\u2019s not what they wanted after all. In addition, by starting with your business users, your project will be tied to a concrete business goal from the outset.

Start manually<\/b>
Delivering data right away means you may be supplying it manually for a while, but that\u2019s better than building out a self-service platform, connecting it to various data sources and finding out later it\u2019s not useful. Feed data into your users\u2019 BI tools manually if you have to, and build out the automation technology afterwards. That way, you\u2019re not building infrastructure before you know it will provide immediate value.

Don\u2019t boil the ocean<\/b>
Data has transitioned over the past decade from a highly targeted asset to an abundant resource. It\u2019s streaming in from mobile apps, marketing tools, IoT devices, e-commerce sites and more. If you believe this data will someday provide value, by all means collect it now; storage is cheap, and future projects that leverage machine learning will benefit from an abundance of historical data. However, don\u2019t invest the time and resources to clean and prepare all this data for use until you know it will provide value.

It\u2019s one thing to store data, it\u2019s quite another to invest in data models and delivery pipelines before you know they\u2019ll be useful. Every bit of data you make available to end users should be tied to a business objective. If it isn\u2019t, leave it where it is.

Take a page out of the agile playbook<\/b>
Software teams learned early on that an iterative approach to development is faster, more efficient and leads to fewer errors. Business teams are now adopting this agile methodology, and those in charge of data initiatives should do the same.

An agile approach implies breaking a project into small parts that can be delivered as they\u2019re completed. This provides faster time to value and makes it easier to pivot if the data you\u2019re collecting isn\u2019t yielding the expected results. It\u2019s a cautious approach that minimizes risk and provides a faster return.

Trust your data<\/b>
The No. 1 objection I hear from organizations considering a new data initiative is that they don\u2019t trust their data. How can they allow users to make decisions affecting the future of the organization if they\u2019re not certain the data is 100 percent current and accurate?

Well, your employees are making decisions already, either based on no data at all or on fragments of the very same data that you\u2019re concerned about. Don\u2019t be paralyzed by inaction because you don\u2019t fully trust your data; it\u2019s better to get started and learn along the way.

Data is a valuable asset, but data initiatives can be costly so minimizing risk and maximizing returns is essential. These costs include not just the infrastructure and resources to build a data platform, but the opportunity costs of not getting it right.

If you spend three months building a data delivery system and your users still can\u2019t get the answers they want, they\u2019re unlikely to ask you to build it all over again. They simply won\u2019t ask those questions, and that could be a huge missed opportunity for the organization.

Building incrementally and validating your platform along the way is how you get it right first time. That way, your business project is far more likely to succeed.","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

How to turn a doomed data project into a digital delight

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