Developing a Data Strategy Roadmap

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A Data Strategy roadmap is a step-by-step guide to transform a business from its current state into the desired business organization. It is a more detailed, researched version of the Data Strategy and communicates when and how to implement specific advances to develop and upgrade a business’s data processes. A Data Strategy roadmap acts as a useful tool for aligning the organization’s processes with its desired business goals.

A successful roadmap will align the different solutions used to upgrade an organization and help to build a strong foundation for the business.  

A significant advantage in using a Data Strategy roadmap is the elimination of chaos and confusion. Time, money, and resources will be saved during the transformation process. The roadmap can also be used as a communication tool to convey the plans to stakeholders, staff, and management. A good roadmap should include: 

  • The specific goals: A list of what should be accomplished at the end of this project. 
  • The people: A description of who will be responsible for each step of the process.
  • Timeline: A schedule for implementing each phase or project. There should be an understanding of what needs to be done first.  
  • The finances: The budget needed for each phase of the Data Strategy.
  • The software: A description of software needed to achieve the specific goals listed on the Data Strategy roadmap.

The Key Phases for Developing a Data Strategy Roadmap

The first phase involves mapping the current data environment, defining business and data terms, and determining the business’s goals. It is important to develop a big-picture understanding of the data you have, where and how it’s stored, how the data moves through the organization, who is using it, and why.

Multiple graphs and charts should be used to provide a visual understanding. One chart within the Data Strategy roadmap should show the various pathways the data follows as it enters, is processed, and stored (and in some cases, where it exits). Knowing what you have makes it easier to understand and communicate what is needed. (Global Data Strategy offers a fairly good basic chart that can be used as a base, and expanded upon, here.)

Phase two involves identifying the basic components needed for the organization’s “future data usage.” (This could be a list rather than a chart.) The organization’s specific goals should be considered when planning data usage in the future. These components usually fall into four general categories: 

  • Analytics and business intelligence: A term that describes the applications, tools, and infrastructure used to access and analyze data. After the data has been analyzed, it should provide insights and useful business information for making better decisions. Artificial intelligence and machine learning solutions can be used to translate overwhelming amounts of raw data into usable business intelligence.
  • Data storage: The three most popular data storage systems are databases, data warehouses, and data lakes. A database stores limited amounts of structured data that can be accessed quickly and efficiently. A data warehouse stores large amounts of filtered, structured data that is normally used for research purposes. A data lake stores large amounts of raw data, in its original format, until it is needed. Data lakehouses are a fairly new concept – a merging of data warehouses and data lakes with the goal of combining their strengths – and are worth investigating.
  • Data sources: The sources you choose should support the business goals defined in the first phase. All modern data strategies include some mixture of the four basic kinds of data sources:
  • Internal structured data: Examples include a business’s or customer’s name, email address, and other useful information.
  • Internal unstructured data: Typically inhouse data that is difficult to integrate, such as contracts, which may exist in many versions, formats, and locations. 
  • External structured data: Data from outside sources that is made up of data types with patterns that make them easy to search and reformat. (PDFs, for example.)
  • External unstructured data: Typically collected from a variety of sources, such as social media, studies, news items, photographs, etc.
  • Providing access to customers and potential customers: This component is the interface layer to the website that is accessed by customers and visitors. When researching this component, consider specialized tools that will provide dashboards, making it easier to perform analytics, pull automated reports, and perform other self-service tasks.

Phase three places an emphasis on increasing overall efficiency, and supports the details needed for success. The following suggestions/goals should be incorporated into the Data Strategy roadmap to maximize efficiency:

  • Excellent data quality: Accurate, high-quality data is essential for gaining real-time insights. (If a Master Data Management platform and a Data Governance program are not yet in place, it’s time to make the investment. They are both necessary to guarantee high-quality data. Oh, and make sure the Master Data Management and Data Governance software are compatible.)
  • Good communications: Educating investors, stakeholders, management, and staff (and meeting the expectations you have created) will help in developing trust. As trust is gained, resistance to change is lowered. By delivering improvements on schedule, enthusiasm for the changes can be maintained. 
  • Getting the right cloud/software: In these modern times, chances are high the desired software will be accessed from a cloud. While this will significantly increase the chances software packages are compatible, do not automatically assume this is true of all software within a cloud. As the software needs of the organization are identified and evaluated, consider the following when seeking solutions:
  • Maturity of the solution: Software (or a cloud) that has been available for less than a year has an increased probability of bugs and kinks. Clouds and software that have been around for a while tend to have worked out their issues.
  • Availability of resources: A cloud with a broad array of tools and resources will be useful for generalized business. (Some clouds are tightly focused on apps development and have limited resources.)
  • Strength of the user community: A number of clouds and websites have begun the practice of having experienced customers answer complaints and provide solutions. A larger user-community will have more answers readily available, and a greater number of people responding to new questions.
  • Incremental development: A cloud that offers the ability to develop incrementally will be useful to a business that is deliberately evolving. A cloud that charges fines for adding services after a contract has been signed is not a partner you want to work with.
  • Use proven approaches: Rather than creating a brand-new system, it might be wiser to use an established architectural model, and adjust it to suit the organization’s purposes. Using proven architectures, frameworks, and processes will reduce the risk of failure and boost the probability of positive outcomes.
  • Security: Tedious as it might be, security is a crucial feature in any modern business. It is necessary to protect the business and its customers. Research any cloud or software that will be used for security weaknesses. 

The fourth phase focuses more on business and the ROI, or the “return on investment.” Stakeholders and investors will want to know the money is being used wisely. To maximize the returns on software and cloud investments, the following characteristics should be built into the Data Strategy roadmap:

  • Education: A necessary step in the transformation process is educating staff, management, stakeholders, and investors. Using a newsletter to inform and update everyone on a monthly or weekly basis can be remarkably useful. New vocabulary words and phrases that are appropriate for the transformed business should be included in the newsletter, with staff and management being encouraged to learn and use them. 
  • Repetitive processes: Some actions, perhaps several, supporting the transformation process, are repetitive. These processes should be standardized, speeding up development, completion, and rollout. The Data Strategy roadmap should include scheduled meetings with department heads and analysts to decide which processes are necessary, and which will no longer be needed. 
  • Automation: Data automation reduces human error and accomplishes tasks much more quickly than human beings can. It’s a good idea to automate whenever possible. Be sure to consider the organization’s automation maturity at present, and list areas and services that should be automated on the Data Strategy roadmap. (This might include repetitive processes.) 
  • Modularity: Breaking the Data Strategy roadmap’s phases and goals into smaller, logical projects will help to streamline the organization’s transformation process. The Agile philosophy and a DevOps approach should be included in the process to increase flexibility and speed up the process. The Agile and DevOps methods should be incorporated permanently and included in the “new words and phrases” vocabulary section of the newsletter. 
  • Effective staffing: There is an extremely high probability new staff with specialized skills will need to be hired to handle some of the new software and technologies. This should be included in the Data Strategy roadmap. In some cases, the business may provide additional training to current staff and add new responsibilities to their job description. The skills and resources of staff and management should be reviewed, with people being trained and/or hired, as needed.

In Closing

Developing a Data Strategy roadmap provides a valuable tool for implementing a Data Strategy and transforming the business into an effective and efficient organization. It will communicate a clear plan of the organization’s direction, and its expectations. Having these expectations outlined in the roadmap will help bring all members of the organization on this evolutionary journey. 

The Data Strategy roadmap keeps the business’s management and staff focused on the upcoming changes. 

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