The What, Why, How, Who, and When of Data Strategy | by Bahar Salehi | Jul, 2022
What is data strategy? Why is it important? Who is responsible for it? When should you start developing one, and how?
Strategy is referred to as planning to achieve a long-term goal! Historically strategy was the art of planning to win a military operation. However, in our modern life, strategy is more used in the context of business strategy and the art of planning to win your business war!
Data strategy is no different. It refers to planning to achieve your business’s long-term goals using data efficiently and effectively! In other words, data strategy is meaningless unless it is connected to business strategy.
Most companies believe that data is a strategic asset. However, raw data by itself does not bring value, nor does it empower business strategies.
Data becomes an important asset if:
- it helps individuals, especially non-tech employees, become more productive and make data-informed decisions rather than trusting their gut!
- it helps the business to generate revenue or reduce cost, for example by bringing a better customer experience by better understanding the customers’ needs in a b2c business.
- it encourages innovation with the help of AI and machine learning, which in turn helps with business growth and competitiveness in the market.
Having said that, bringing data into the heart and culture of a business is not easy. You may have encountered some or even all of these scenarios:
- Data silos: Different functions in the business collect or rely on their own data, and do not have trust in others.
- BAU vs data: There is a resistance to leaving BAU tasks and spending time to help with the data quality and process (including creating, maintaining, and analyzing)
- Lack of proper planning: plannings and decisions are not data-driven and are based on the loudest voice because there is no proper process for rapid value delivery using data.
- Data (il)literacy: There are a lot of skill gaps in both tech and non-tech functions to use data efficiently and effectively.
- Data is not fit for purpose: creating new value, especially for traditional old businesses is hard because all the easy problems are solved and new data proxies are needed.
- Data regulations: Data security, privacy, and other data regulations are questionable in the business.
- Infrastructure: Data infrastructure does not respond to the needs of technical and more importantly non-technical employees, brings a bad user experience, and does not provide a proper environment for innovation.
- And last but not least, data is not generating value!
All of the above challenges show the need for a guiding plan to define technology, processes, and people to help business achieve its goals using data. That guiding plan is called DATA STRATEGY.
Data strategy’s main purpose is to empower businesses to achieve their goals by extracting value from data. While there is not a unique guideline on how to develop a data strategy, there are a couple of topics that need to be considered when developing the data strategy:
- Data strategy should be aligned with business strategy, otherwise, the organization will not see a tangible ROI on data investment.
- After understanding the business strategy, along with the opportunities and challenges ahead of it, data&analytics use-cases are identified.
- Teams and infrastructure capabilities and skill gaps are identified.
- Easy wins and strategic initiatives are found.
- The roadmap is created.
I found this article useful on how to develop a data strategy with more details on the above 5 important steps.
Note that there is no defined prescription on how to develop data strategy. In a nutshell, it should be developed based on business strategy to extract value from data and it involves making decisions on technology, processes and people within the organisation. It is also important to note that strategy plan is different from operation plan, where the former is more focused on long term missions and goals rather than near-future plan.
Traditionally, business data was handled within the IT team. They would be the gatekeeper, making decisions on data architecture, data management, infrastructure, and who has access to what. This was against data and analytics democratization. Non-tech employees had to ask data analysts or alike for every question they had, which made the process too long. This all means that analytics was static and not scalable within the business. The data and analytics function has now moved from a traditional support function to enabling and accelerating data-driven decision-making within the entire organization either by data and analytics democratization or by bringing some degree of decision automation through AI and machine learning.
Data is an asset for the company and therefore it is not something that a specific team can argue they have full control on it. Similar to Busniess budget, every function should be responsible for the data asset that they bring to the table, maintained or developed.
However, the changes we mentioned above require a lot of changes within the business which cannot be achieved without executive sponsorship. It requires a top to bottom planning that includes making decisions on technology, data assets, business processes in the whole company, and identifying skill gaps within the business to bring data literacy and data democratization into the business. This all means that the decisions will and should impact the whole business culture. And not surprisingly there will be resistance and requires change management.
Recently a lot of companies have started bringing new senior executive roles such as a Chief Data Officer (CDO) responsible for strategic decisions on paving the road for using and governance of data across the whole organization.
You don’t always go for the most advanced infrastructure, hire the most talented people in the world and make friction by changing all the processes in the business if you cannot justify how your data strategy aligns with business strategies and help to achieve business goals.
Businesses usually start thinking about data strategy when they are in the scale phase (I previously wrote an article with more details on when a business should start investing in data), simply because data and analytics can enhance the business’s capability to scale with a proper data strategy in place.
What is data strategy? Why is it important? Who is responsible for it? When should you start developing one, and how?
Strategy is referred to as planning to achieve a long-term goal! Historically strategy was the art of planning to win a military operation. However, in our modern life, strategy is more used in the context of business strategy and the art of planning to win your business war!
Data strategy is no different. It refers to planning to achieve your business’s long-term goals using data efficiently and effectively! In other words, data strategy is meaningless unless it is connected to business strategy.
Most companies believe that data is a strategic asset. However, raw data by itself does not bring value, nor does it empower business strategies.
Data becomes an important asset if:
- it helps individuals, especially non-tech employees, become more productive and make data-informed decisions rather than trusting their gut!
- it helps the business to generate revenue or reduce cost, for example by bringing a better customer experience by better understanding the customers’ needs in a b2c business.
- it encourages innovation with the help of AI and machine learning, which in turn helps with business growth and competitiveness in the market.
Having said that, bringing data into the heart and culture of a business is not easy. You may have encountered some or even all of these scenarios:
- Data silos: Different functions in the business collect or rely on their own data, and do not have trust in others.
- BAU vs data: There is a resistance to leaving BAU tasks and spending time to help with the data quality and process (including creating, maintaining, and analyzing)
- Lack of proper planning: plannings and decisions are not data-driven and are based on the loudest voice because there is no proper process for rapid value delivery using data.
- Data (il)literacy: There are a lot of skill gaps in both tech and non-tech functions to use data efficiently and effectively.
- Data is not fit for purpose: creating new value, especially for traditional old businesses is hard because all the easy problems are solved and new data proxies are needed.
- Data regulations: Data security, privacy, and other data regulations are questionable in the business.
- Infrastructure: Data infrastructure does not respond to the needs of technical and more importantly non-technical employees, brings a bad user experience, and does not provide a proper environment for innovation.
- And last but not least, data is not generating value!
All of the above challenges show the need for a guiding plan to define technology, processes, and people to help business achieve its goals using data. That guiding plan is called DATA STRATEGY.
Data strategy’s main purpose is to empower businesses to achieve their goals by extracting value from data. While there is not a unique guideline on how to develop a data strategy, there are a couple of topics that need to be considered when developing the data strategy:
- Data strategy should be aligned with business strategy, otherwise, the organization will not see a tangible ROI on data investment.
- After understanding the business strategy, along with the opportunities and challenges ahead of it, data&analytics use-cases are identified.
- Teams and infrastructure capabilities and skill gaps are identified.
- Easy wins and strategic initiatives are found.
- The roadmap is created.
I found this article useful on how to develop a data strategy with more details on the above 5 important steps.
Note that there is no defined prescription on how to develop data strategy. In a nutshell, it should be developed based on business strategy to extract value from data and it involves making decisions on technology, processes and people within the organisation. It is also important to note that strategy plan is different from operation plan, where the former is more focused on long term missions and goals rather than near-future plan.
Traditionally, business data was handled within the IT team. They would be the gatekeeper, making decisions on data architecture, data management, infrastructure, and who has access to what. This was against data and analytics democratization. Non-tech employees had to ask data analysts or alike for every question they had, which made the process too long. This all means that analytics was static and not scalable within the business. The data and analytics function has now moved from a traditional support function to enabling and accelerating data-driven decision-making within the entire organization either by data and analytics democratization or by bringing some degree of decision automation through AI and machine learning.
Data is an asset for the company and therefore it is not something that a specific team can argue they have full control on it. Similar to Busniess budget, every function should be responsible for the data asset that they bring to the table, maintained or developed.
However, the changes we mentioned above require a lot of changes within the business which cannot be achieved without executive sponsorship. It requires a top to bottom planning that includes making decisions on technology, data assets, business processes in the whole company, and identifying skill gaps within the business to bring data literacy and data democratization into the business. This all means that the decisions will and should impact the whole business culture. And not surprisingly there will be resistance and requires change management.
Recently a lot of companies have started bringing new senior executive roles such as a Chief Data Officer (CDO) responsible for strategic decisions on paving the road for using and governance of data across the whole organization.
You don’t always go for the most advanced infrastructure, hire the most talented people in the world and make friction by changing all the processes in the business if you cannot justify how your data strategy aligns with business strategies and help to achieve business goals.
Businesses usually start thinking about data strategy when they are in the scale phase (I previously wrote an article with more details on when a business should start investing in data), simply because data and analytics can enhance the business’s capability to scale with a proper data strategy in place.
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