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PLAN THE END GAME

Ido Biger
|
בינונית
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May 28, 2018
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להרשמה לניוזלטר

Don’t fall in love with the technology

Playing chess might be one of the most inspiring analogies of our real world. In his phenomenal movie – “Life of a king”, Jake Goldberger is taking us through the tough journey of poor kids living without a goal, without a planned end game, mainly focusing on neighborhood where kids are struggling and moving from one point to the other in a reactive mode without even thinking about where the path is going to. By opening a chess club and teaching those kids about focusing on the king as the target and seeing all the others as means to get to him, they now understand that each move needs to drive you one step closer to the goal, and if not – it is probably the wrong move.

A large Asian bank has just started a revised version of his Data Lake project after investing millions of dollars and over 18 months of planning and developing the Hadoop-based platform that was supposed to be his future enterprise central data hub. My assumption is – they didn’t clearly plan the end game. No one saw the king in that project. They planned to have a lot of great moves, but none of them would result in a checkmate.

For all of those who are planning the Next Gen of Data solutions throughout the enterprise, I would suggest one thing – don’t fall in love with the technology behind it. The Hadoop ecosystem comes with numerous components/capabilities that allows us a deep consumption and a far more cost-effective way of storing and processing the data that is gathered. BUT – the goal of the system/infrastructure is first and foremost – the business users. And via the operational systems - The customers themselves.

A known issue that a current CDO of a company is facing, is to look for a way to enhance the knowledge on his customers by gathering all the information that he can from their devices, the web\mobile apps, the operational systems and even social data. Sometimes this is referred as a 360 view of the customer data lake.

When planning such a project the design phase divides to two – dealing with the technical challenges – storing the data, securing it, govern and process it in an efficient way. The parallel path is dealing with the business cases and the operational outcomes.

Unfortunately, sometimes we’ve encountered some situations that the technical path overlooked the business path, not due to limitations or funds, but because the technical part of it was so loveable that the desire to have so many different data components, for very few understandable reasons caused the enterprise to burst out with an impressive data monument but not a business effective one.

One of the main fears that a big data solution architect should have, is to leave behind a beautiful white elephant. A magnificent data lake that will serve just a few. When searching for more and more technical skilled developers and hearing less about the end game of each scenario – the outcome won’t be a successful one.

Therefore, when deciding on starting a journey of moving from the traditional BI ecosystem to the Big Data world, one should keep his mind always on one thing – the end game of each business scenario.

The technology will be the assisting tool of the business need and not vice versa.

When the business purpose of the project is to know more about our customers – we should start with why? what would we do with that information? a direct marketing micro offers?

Having the ability to investigate and understand patterns of behavior are generic terms and should be changed to a more specific one such as an investigation of his credit card fraudulent events and patterns of location based entities. The big data journey should be planned Top bottom – from the business cases and all the way through the business flow and the different scenarios. Only when having the full picture of the needs/cases – one could plan and describe the technology that is needed behind the scenes.

As far as the development and deployment, if the design is done in a higher-level way, with the clear end game as a target - the cycle of the development/test/result/deploy should work in an agile methodology. Thus, having the deliverables achievable within each of the business users to consume. If there are 29 business scenarios covered in the HL design – then splitting the delivery boxes to an iterative process and having the first 3 delivered while developing the 4-7 ones – one should realize if there is a problem/issue way before we’re trying to deploy the entire ecosystem.

Big Data project requires patience but can be addressed better once the end game is clearer, getting there is sometimes a great challenge but as long as the goal is clear – the path might curve but won’t lose track.

About the writer:

Ido Biger is the Chief Data officer of yes Television. In charge of making yes Television a leading Data Driven organization. Managing 25 Data engineers alongside his technical responsibility for the analytical teams and the Data Science team in the company.

Reports to the CIO as the Head of BI and to the CMO as the Chief Data Officer.

International Speaker , BI professionals course senior Instructor, Big Data technologies and Data Visualization Lecturer at Tel-Aviv University's MBA.

Previous Posts:

Financial Intelligence Units in the Big Data Era

Top 7 keys to planning your Data Science / Intelligence team in the financial industry

Beware of the “Data Swamp”! Top key points when considering a Data Lake in the financial Industry.

Reviewing the Top 10 Big Data Trends in 2016 for Financial Services - 7 months later.

Magic and Data Science

ENTERPRISE’S BIG DATA ADOPTION IS FAR MORE THAN JUST TECHNOLOGY…

Sports, Compliance and Advanced Analytics.

Don’t fall in love with the technology

Playing chess might be one of the most inspiring analogies of our real world. In his phenomenal movie – “Life of a king”, Jake Goldberger is taking us through the tough journey of poor kids living without a goal, without a planned end game, mainly focusing on neighborhood where kids are struggling and moving from one point to the other in a reactive mode without even thinking about where the path is going to. By opening a chess club and teaching those kids about focusing on the king as the target and seeing all the others as means to get to him, they now understand that each move needs to drive you one step closer to the goal, and if not – it is probably the wrong move.

A large Asian bank has just started a revised version of his Data Lake project after investing millions of dollars and over 18 months of planning and developing the Hadoop-based platform that was supposed to be his future enterprise central data hub. My assumption is – they didn’t clearly plan the end game. No one saw the king in that project. They planned to have a lot of great moves, but none of them would result in a checkmate.

For all of those who are planning the Next Gen of Data solutions throughout the enterprise, I would suggest one thing – don’t fall in love with the technology behind it. The Hadoop ecosystem comes with numerous components/capabilities that allows us a deep consumption and a far more cost-effective way of storing and processing the data that is gathered. BUT – the goal of the system/infrastructure is first and foremost – the business users. And via the operational systems - The customers themselves.

A known issue that a current CDO of a company is facing, is to look for a way to enhance the knowledge on his customers by gathering all the information that he can from their devices, the web\mobile apps, the operational systems and even social data. Sometimes this is referred as a 360 view of the customer data lake.

When planning such a project the design phase divides to two – dealing with the technical challenges – storing the data, securing it, govern and process it in an efficient way. The parallel path is dealing with the business cases and the operational outcomes.

Unfortunately, sometimes we’ve encountered some situations that the technical path overlooked the business path, not due to limitations or funds, but because the technical part of it was so loveable that the desire to have so many different data components, for very few understandable reasons caused the enterprise to burst out with an impressive data monument but not a business effective one.

One of the main fears that a big data solution architect should have, is to leave behind a beautiful white elephant. A magnificent data lake that will serve just a few. When searching for more and more technical skilled developers and hearing less about the end game of each scenario – the outcome won’t be a successful one.

Therefore, when deciding on starting a journey of moving from the traditional BI ecosystem to the Big Data world, one should keep his mind always on one thing – the end game of each business scenario.

The technology will be the assisting tool of the business need and not vice versa.

When the business purpose of the project is to know more about our customers – we should start with why? what would we do with that information? a direct marketing micro offers?

Having the ability to investigate and understand patterns of behavior are generic terms and should be changed to a more specific one such as an investigation of his credit card fraudulent events and patterns of location based entities. The big data journey should be planned Top bottom – from the business cases and all the way through the business flow and the different scenarios. Only when having the full picture of the needs/cases – one could plan and describe the technology that is needed behind the scenes.

As far as the development and deployment, if the design is done in a higher-level way, with the clear end game as a target - the cycle of the development/test/result/deploy should work in an agile methodology. Thus, having the deliverables achievable within each of the business users to consume. If there are 29 business scenarios covered in the HL design – then splitting the delivery boxes to an iterative process and having the first 3 delivered while developing the 4-7 ones – one should realize if there is a problem/issue way before we’re trying to deploy the entire ecosystem.

Big Data project requires patience but can be addressed better once the end game is clearer, getting there is sometimes a great challenge but as long as the goal is clear – the path might curve but won’t lose track.

About the writer:

Ido Biger is the Chief Data officer of yes Television. In charge of making yes Television a leading Data Driven organization. Managing 25 Data engineers alongside his technical responsibility for the analytical teams and the Data Science team in the company.

Reports to the CIO as the Head of BI and to the CMO as the Chief Data Officer.

International Speaker , BI professionals course senior Instructor, Big Data technologies and Data Visualization Lecturer at Tel-Aviv University's MBA.

Previous Posts:

Financial Intelligence Units in the Big Data Era

Top 7 keys to planning your Data Science / Intelligence team in the financial industry

Beware of the “Data Swamp”! Top key points when considering a Data Lake in the financial Industry.

Reviewing the Top 10 Big Data Trends in 2016 for Financial Services - 7 months later.

Magic and Data Science

ENTERPRISE’S BIG DATA ADOPTION IS FAR MORE THAN JUST TECHNOLOGY…

Sports, Compliance and Advanced Analytics.

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