We have seen a lot of articles saying that strategy is crucial to a successful data analytics program. However, it is a very broad term and often thrown around without a lot of explanation of what it really means. Data strategies can be comprehensive or more limited in scope, however, it is important to have a strategy that can be monitored and refined as the organization’s data capabilities mature. In this blog post, we discuss a number of key focus areas required to formulate a successful data strategy. Our approach at Innovizo, looks at these areas to develop a customized strategy for each client that best meets their business needs.

Business Needs

Data is an enabler of the longer-term business vision, which defines the aspirational goals of the organization, and the shorter-term business objectives, which are actionable and tactical. The data strategy needs to be closely aligned with the overall business strategy and be able to adapt to changing business requirements.

For organizations embarking on any data projects it is important to have a solid baseline of the current state of the organization today in order to develop a data strategy that will align with the long-term business needs. Depending on the size and complexity of the project and organization it may be easier to break up the strategy on a functional or department level. However, for optimal performance the strategy needs to be generally aligned across the entire organization. An initial data baseline or data assessment should help answer the following questions:

  • What data is currently available?
  • How is the data being used?
  • What is the current data technology infrastructure?
  • Who are the users of the data within the organization?
  • What processes are being driven by the data analysis?

The results of the data baseline inform the development of the desired data strategy. When focusing on the business needs this strategy should consider:

  • Should data strategy be developed and implemented on an organization-wide or department/functional level?
  • How can the business needs of users across the organization be incorporated into the data strategy?
  • What business problems would be solved by better technology or availability of data?
  • How can data best support decision-making?
  • What does the organization need to do to make the results of analytics actionable?
  • Which employees would benefit from having more access to data?
  • How can data improve the customer experience?
  • How can stakeholders benefit from data?

Human Capital

Many organizations don’t have the in-house capabilities to evaluate big data technologies, implement technology tools, and conduct deep dive analytics. It is important to develop a strategy to address the skills gap and best allocate resources to meet business needs.

  • Do employees have the skills necessary to manage the desired data infrastructure?
  • Do employees have the analytics skills to use data analytics tools?
  • What is the nature of the resource requirements? Ongoing? Temporary?
  • What are key strategic functions that should be performed in-house and which functions can be effectively contracted or outsourced?
  • Which areas do we want to develop expertise in?
  • What are the highest value data/analytics activities for employees?

Financial Resources

Developing a data analytics capability requires as strong and sustained financial commitment. It requires an investment in technology infrastructure and analytics tools. It also requires the recruitment and training of highly sought after staff with advanced degrees and management to ensure a high performing team. For organizations just starting with their data analytics program there might not be a clear business case to support a large investment. The strategy should consider how to best allocate financial resources and how to build the business case for future investments.

  • What is the business case for investment?
  • What are potential risks of a data program?
  • Would the organization receive higher value from hiring consultants?
  • What is the timeline until the investment will start generating benefits and returns?


There are numerous technology choices available each of which comes with benefits and risks. An optimal technical solution is unique to each organizations business needs, regulatory requirements, and future plans. Some of the questions to consider include:

  • Would this complement our existing technology infrastructure?
  • Is there a clearly defined use case?
  • What is the scale that is needed?
  • Would this technology lock us in to that particular vendor?
  • Do we have the technical expertise to manage it in-house?
  • How much user training would be needed?
  • Can the solution be in the cloud or on premises?
  • What are the availability, failover, and disaster recovery requirements?

 In conclusion, there is no one size fits all data strategy. An effective data strategy needs to be comprehensive and reflect the unique strengths, weaknesses, and capabilities of the organization. Innovizo’s approach is based on the above-mentioned strategic planning and data assessment principles. We work closely with clients to identify their business needs, human capital capabilities, financial resources, and technology infrastructure to develop a highly customized solution. We are technology agnostic and aim to be a trusted advisor to our clients. If your organization is considering a data analytics project feel free to reach out for a free consultation.

 Gary Lipsky is a Managing Partner and co-founder of Innovizo. He helps clients understand how data, analytics, and technology can transform their business. Follow him on twitter @glipsky.





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