The Manager's guide — Building a corporate data strategy.

datascience Feb 05, 2021
There are many ways that data can help a business but, and can be categorized into two; one is using data to improve existing business and decision-making, the second is using data to transform business operations.
 
Some companies start out wanting to improve their decision making and take it from there. But when using data, it must always start with a corporate data strategy. It is often overlooked how it can help set the objectives that will define what data the company needs to gathers and how you analyze it. Having a data strategy will also help the whole process run steadily while preparing you and your people for its journey ahead.
 
Moreover, creating a corporate data strategy is not for the faint of heart. Not only does it need the commitment of those at the top but also the acknowledgment that data is a corporate asset that must be managed and protected like any other asset.
 

What is a corporate data strategy?

A data strategy lays out a comprehensive vision across the company and sets the foundation for how to use data-related or data-dependent capability. Moreover, a corporate data strategy can provide guiding principles to do a data-driven vision, direct the company to specific business goals, and be a starting point for data-driven planning across the company.
 
It is can also be defined as a common reference of methods, services, architectures, usage patterns, and procedures for acquiring, integrating, storing, securing, managing, monitoring, analyzing, consuming, and operationalizing data in a company. It functions as well as a checklist for developing a roadmap toward a company's digital transformation journey and modernization efforts.
 
However, a corporate data strategy does not contain a detailed solution to use cases and specific technical problems, nor limited to high-level constructs intended only for senior leadership. It requires executive sponsorship and governance for alignment with corporate objectives and enforced adherence to adapt to technologies and related innovations that are maturing.
 
 

Four elements of a corporate data strategy

Defined goals and objectives

Start by outlining what the company wants to achieve with data, while considering either of these categories: improving business decision-making, improving business operations, and using data as an asset. It is recommended to focus on one category to have more focus in its direction. The outline should have a list of key questions that will help spot the company's priorities and should be answerable using data. This can be then used as a reference in defining short-term and/or long-term goals that will drive the data strategy and activities and help improve how data is being handled.

Discovery of right data

Data is initially “raw” regardless of origin, thus making it not ready for use. Choose a data infrastructure and architecture that will integrate and process the corporate data to a unified and consistent data view, and should help answer the following questions:
  • Which data is needed to achieve the defined goals?
  • What other sources will it need?
  • How will be the data collected, stored, analyzed, and processed?
Additionally, move and combine the corporate data residing in disparate systems to create a small set of homogeneous data sets that can be easily integrated by any data user based on specific goals or needs. Here, data catalogs will be useful in determining what datasets exist across the company and can help review which data sources are used and/or needed among different levels in the company.

Data governance

A successful data strategy should have a clear plan on how to unleash the value of data assets to serve business purposes. Data governance is the process of managing these datasets about ownership, integrity, compliance, quality, content, and relationship with other datasets. Without established governance, an enterprise would lack clarity and insight into its datasets, which could result in inconsistent or overlapping data strategies. Data can be an asset but it can also become a liability too if it doesn’t have good care for it. Furthermore, data should be safe from leakages, attacks, and even unwanted access by unnecessary people within the organization itself.

Strategy roadmap

A data strategy should have a roadmap that has a tactical short-term and long-term plan of initiatives to achieve the goals. The roadmap plan is used to articulate the phases and iterations for each of the key data strategy components above. All data-related initiatives should also align to the data strategy roadmap, the goals/objectives of the data strategy, and how an organization adapts and evolves. It is important to ensure that the first iterations of implementing the data strategy are achievable and deliver measurable value before pursuing higher maturity goals. Usually, it is enough to start defining and implementing the data strategy across its components, without driving any of them to their ultimate state of maturity.
 

Six steps to build a corporate data strategy

1. Get support

The support of executives and others at all levels of your organization is crucial for successful data strategy implementation. You gain support by creating a proposal or a report that would encourage their acceptance and willingness to actively take part in it and provide the resources you need to put in place for the strategy. It will show them also what are its business goals, how it will benefit the organization, and what are its economic logic (the positive returns over cost).

2. Manage the 3Ps around data

The 3Ps refers to People, Process, and Policy. You can start this by organizing a data management team, who will have direct involvement in the implementation of the strategy. Establish a team that understands the value of data, has the right business’ technological and organizational capabilities, and has data governance roles. This will ensure compliance in the data strategy with standards, deploying technologies, providing updates to employees about policy changes, and etc.

3. Identify source & types of data

Determining what data you’ll collect and how you’ll get it will help avoid avoidable factors (i.e. wrong, unreliable, or inaccurate data) that can impede the success of your data strategy. You can narrow down your search by basing it on the business goals of your strategy, then look from different sites (i.e. article groups, social media, and etc.) or buy it from second/third-party data. Also, while the strategy is in progress, this will secure quality information to support future decisions and insights.

4. Create an action plan

Outline a roadmap that will help achieve the short-term and long-term data strategy goals, step by step, and that can leverage and manage data for a strategic advantage. These plans should be specific and include what process and technology to use, how much it will cost, how long it will take, and the intended outcome. It should also be flexible so it can be adjusted if something will not work as expected or when circumstances change.

5. Coordinate & organize the process

It will be crucial to coordinate the steps of your data strategy to related departments, as they help determine how actionable and shareable your data is. Here, you'll also need to consider how data will influence data sharing, ease of access, and its usage for different departments. Different approaches may work best for different companies, but the overall goal is to create an accessible system that can help make the strategy's progress as steady as possible.

6. Earn approval & implement

Once you have set your goals and roadmap, it's ready to be packaged into a business plan and presented to company leadership's approval. The business plan should include other strategies you’ll use to achieve the company’s data goals and the resources you’ll need to implement the strategy (i.e. capital investments, new hires, new processes, or new organizational structures). Once you gain final approval, you're set for your data strategy's implementation and should be adjusted as needed.
 

Conclusion

Companies need a data strategy to know which data is available and in which quality. In this way, processes and measures can be initiated to improve the quality and availability of the data in making better and value-generating decisions in the long run. Don't forget to align the goals with the corporate goals and to ensure the full support of top management. Lastly, while data strategy has the same components as business strategy, it is still relatively new in the strategy world. Setting up the right data strategy requires first a business vision and alignment with a business strategy. Data strategy should also be considered as an enabler of the long-term vision, setting the cornerstone that future business strategies can rely on.
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