How to build a Data Strategy? (Detailed Guide)

How to Build a Data Strategy

How to Build a Data Strategy?

Data is one of the most valuable assets for any organization. Every organization needs a good Data Strategy to run the business efficiently. Extracting the essential business insights from the data for your business growth begins with developing a good Data strategy.

In this article we will be discussing on Seven simple steps on Data strategy building by using steps and guidelines. We have covered everything you need to know and implement in order to create an apt data strategy for your business, right from the scratch, there are certain principles involved for a data strategy to work. Have a look.

Some Essential Data Strategy principles

There are some fundamental principles on which a data strategy are made. These are:

  • Quality and Availability: This means ensuring that your data is of high quality and is highly available at the required places. Having a high-quality data is the foundation for the data strategies, so it is very essential to have proper validation and management in the data.
  • Compliance: The data available should be safe to both possess and use. This means making laws and regulations for safe usage of the data like GDPR (General Data Protection Regulation). This ensures that sensitive data is available and accessible to only those required and at the same time without compromising on its security as well.
  • Operation: To ensure that data management and operations performed should be cost-effective and efficient.
  • Value: To Articulate how the data will deliver value to the organization’s vision and mission. Including things like collaboration of the multiple projects, extracting Data driven insights, answering business questions, etc.

Things discussed here are just briefly discussed and may not be understood clearly, they will be explained down the lines. If you are completely new to the topic then please do have a look on the topic Data Strategy first, it explains in depth about the data strategy definition, importance and its key elements. Let’s begin with the First and most important step: making or having your business strategy, with clear aims and objectives.

1. Business Strategy and Goals

It is the set of goals and statements for which your Data strategy is working. A data strategy should not be developed in isolation, but it should be developed by properly understanding all the aspects of the multiple departments and projects which can contribute to the mission and vision statement of the company. By identifying the overall business strategy’s needs and priorities, we can figure out how the data can help us achieve that.

To make things easy, answer these simple questions to define or refine your business strategy

i)  Define your Mission statement:

  • Why does your organization exist?
  • What do you want to achieve in 5 years?

ii) State deliverables and target personas: what you going to provide and to whom

iii) Define your core competencies: what is your unique signature Identity in business

iv) How data analytics can help you achieve, monitor and provide directions to your overall business.

Business Data Strategy and Goals

Also, it is equally important to know what are the goals that of it before building a data strategy, such as:

i) Using data to get the Insights:

  •  To know every aspects of your customers
  • Internal progress of the company
  • Future predictions of the products and services

ii) Offering a smarter and more customized product or experience to the customer

iii) To improve your internal processes, collaborations and operations of different projects in the organization

iv) To make or improvise the data management system, including privacy and security of the data.

v) Monetization aspects of the data: packaging and selling it to some potential buyers

Identifying the different use case scenarios of the data should be done with every intellectual in the business irrespective of the department or level. This not only helps us to explore the new dimensions of the data use case but also to earn Buy-in from company leadership, which is the next step.

2. Create a Buy-In Proposal

Buy-in proposal is created focusing on overall return on Investment and Growth of the company. It is meant to get the approvals and resources required for the implementation. It is the second step in our journey that could take time and may even need to be redrafted upon the leaders’ suggestions.

The best practice in this is to include the short-term adoption priorities. This means to identify 2-3 quick data wins, as a result is oriented in short term and economical as well, it is easier to showcase the potential of your data strategy through the quick wins.

Earning more buy-in from different company levels is also a crucial part of this step, which is required to get the amount of participation you need for the successful implementation. Ideally the culture of understanding the power and importance of data should be there at every level in the organization. So that data is being used and handled like an asset. It begins with the Leadership awareness and plans to go in one direction.

3. Identify the Data Requirements

This means identifying the type of data required and where it has to be collected. In this step, you need to answer these questions

What data you need? will it be structured, unstructured or semi structured?

From where is this data to be collected? Do you have enough internal resources for collecting the amount of data required? If No, from which external resources you need to collect the data? (Like Social Media, Online portals, etc.) Is the data you need is accessible and how you will collect it (Data Collection Methods)?

After we are done with identifying the data type and its sources, the next step is how we will store the data, process it, etc. For which we need the required technology and infrastructure.

4. Technology and Infrastructure Implications

Now what we will do is to full fill the technological and infrastructural implications of the data strategy? This means how we will be providing the hardware and software tools support required, Things for which we need to provide a solution are: –

  • Collection: Extracting the Data from different sources (Like Common Framework or tools, if data is structured)
  • Storage: Data Lakes or Data Warehouse or maybe a combination of them as per the requirements.
  • Organization: This means having a Master Data management system to improve the Data Quality and have proper meta data management system in the repositories to keep track of the data.
  • Analytics: Tools required to derive the insights from the data or to perform some other data transformations. (Like AI or Machine Learning prediction models or other analytical tools: Data analytics strategy)
  • Data Model: Delivering the insights using different data visualization models like Dash boards, Graphs, Reports, etc.

Master Data management system

After this step is done, we are almost done with the strategy, the only thing we need is proper planning and management, i.e., Governance and Management, which is our next step

5. Data Governance and Management

Data Analytics brings great returns, but it comes with the responsibility of its proper management and Governance. This step involves of planning and making guidelines for proper data quality, validation, security, privacy, etc. To make the things easy, you just have to find a suitable solution for below mentioned list-

  • Ensure that your Data is accurate, valid and complete.
  • Ensure that your Data is stored securely.
  • Accessibility of the Sensitive Data should be provided only to those are designated
  • How to minimize the data, where it is possible and beneficial
  • Ensure the ethical use of data across your organization

Data Governance and Management

Till now we have done everything we need for a well-Developed data Strategy, now it is the time to implement the data strategy, for which we need people with right skill set and a roadmap so that everyone involving the strategy knows it’s roles and responsibilities, and we can keep track of the progress we have made so far.

6. Team Building

This is the step in which you build your Data management teams and assign the data governance roles and responsibilities. In this process, you first need to identify the skill set you need for implementing the data strategy. If you don’t have the people with the required skill set, either you hire people with that skill set or train some in-house staff or what most organizations are doing is to partner with some data provider or acquire a company for external skills.

7. Build a Data Strategy Roadmap

This final step in your data strategy, making a Data Strategy is one thing, delivering on it is another. The roadmap is about laying out a plan in the time horizon so that we have a data strategy template or framework. We can manage, communicate and facilitate how we are going to achieve the goals that we have set.

Plans that we set here should be SMART (Specific, Measurable, Achievable, Relevant and Timebound) and include who owns the Goal. Having a Roadmap also provides trackability of the strategy and how much we have achieved so far. One more thing to be considered while making a Roadmap is to ensure that it is flexible to changes if we need to make it in the future.

Build a Data Strategy Roadmap

Wrap Up:

To conclude, following these simple steps will ensure you to have a well-developed data strategy right from scratch. As already discussed, a data strategy is a key for the J-Curve in your Business growth. But making a Data strategy is just the beginning of the implementation process. To empower its functioning, you need to ensure that your data strategy is well communicated and understood, failing which even a well-developed strategy fails to deliver.

WRITTEN BY

Himanshu Mishra

Technology Head
Himanshu is an entrepreneur with 17+ years of overall experience in strategic and advisory roles in Senior Management, IT program management, Quality Assurance, and ERP implementations. He has invested in companies focused on Digital Technologies and Healthcare industries. He has previously worked in domains like: Technology, Finance, Marketing… Read more

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