How to Build an Enterprise Data Strategy

Data Is Valuable

The value of data is well understood nowadays. Most businesses recognize data as a valuable resource rather than a byproduct of different business activities. However, for a team, unlocking that value can be a challenge. They may not comprehend what data should be collected or how to gather it successfully. They must also put the data into a usable format, share it across the organization, and derive insights from it once it has been collected. A data strategy helps staff perform these tasks and ensures that they are done consistently throughout the organization.

Increasing Volumes of Data

The amount of data that exists in the world and is accessible to organizations is rapidly increasing. As the volume of data grows, managing it gets more complicated, and the demand for a data strategy grows. Some businesses may have been able to handle their data using common knowledge in the past. However, as the volume of data expands, it becomes hard for individuals to keep track of all relevant data. Instead, the organization must implement a data strategy. In today’s data-centric environment, relying on an informal approach might lead to inefficient data utilization, lost data, and incorrect outcomes.

Improve Data Management across Organizations

When a data-related issue arises, many businesses’ immediate instinct is to build a solution to address that specific problem. This point-by-point strategy may work in the short term, but it is not the most efficient way to resolve data-related difficulties. It also doesn’t handle concerns that cross department and project lines, as many things do when dealing with data. Data access and usage are company-wide needs that touch every group and level of management. That is why a company-wide data strategy is beneficial. Developing a company-wide plan enhances data management across the board and ensures that different divisions work together rather than against one another.

Use Resources Efficiently

Different departments and individuals will tackle data challenges independently if you don’t have a data strategy. For example, each department will format raw data in whatever way they see fit. Every department must devote time and resources to this task, which they might do in a format that does not fit another project. This strategy wastes a lot of money and time. With a data strategy implemented, every department and individual will be given directions regarding the format in which data should be submitted. While they may make changes as needed, the data should be in a usable format when it is retrieved. Because the data is in a standard format, it can be easily shared between departments. Having a company-wide strategy allows you to make better use of your resources and save money.

How to Build an Enterprise Data Strategy

1. Understand Your Data Architecture

The first step of a data strategy is understanding your data.

Ask yourself these questions:

• Where will your data live?
• What types of data will you be collecting?
• What sources will you use to collect the data?
• How will the data be organized?

These questions will help you understand your data structure; this is imperative to build a comprehensive plan to help you manage it.

2. Define Business Intelligence and Team Relationships

One of the most critical aspects of a data strategy is defining the teams engaged in the process and establishing business intelligence (BI) expectations.

In a large business that hasn’t considered data strategy before, you’ll typically discover that each team has its own model and relationship with BI, making it difficult for BI to operate in a streamlined and consistent manner.

The data analyst should be familiar with their team’s business logic as well as the structure of the data being collected. On the other hand, BI shouldn’t need to have specific knowledge on the operational area it is helping. Instead, it should focus on the data source and managing the platform to support the analyst.

When BI adjusts its approach frequently to match the team’s specific business logic, it slows things down and necessitates constant relearning.

To solve this, remove any business logic from the BI layer, focus on issues that affect as many teams as feasible, and develop a standard analyst profile and model for the relationship between BI and teams.

3. Assign Ownership

Following the establishment of your teams’ relationship with BI, the following stage is to determine who will be in charge of what.

It’s common to have a separate owner for each piece of data. One individual or team may own operating data while another owns reporting data, for example.

You may need to designate owners at different phases of the pipeline. For example, at one point, the BI team may own the data and then pass it on to the analysts.

Ownership starts with the teams that produce the data and each must feel some level of ownership over the data to take accountability if something is wrong.

4. Establish Data Governance

The increased usage of data and the expansion of your data infrastructure come with many benefits and a lot of responsibilities. Don’t cut corners when it comes to data governance and spend time developing and communicating policies and processes for proper data usage. Data governance refers to a system of policies and rules that control how data is collected and stored in order to assure accuracy and quality.

5. Reassess Regularly

Your data strategy will always need to be tweaked, no matter where you lie on the data maturity scale. For example, assume you’ve added a new function to your product or service and are now gathering more sensitive consumer information. This may necessitate a more defensive posture. Or, if your firm is growing exponentially, you may need to move away from a centralized strategy and toward a distributed one. Reviewing your plan every six months to a year is a good rule of thumb. Speak with business leaders, IT, and your team to get a sense of how everyone feels about your progress and what improvements are needed.

The steps you take to develop a data strategy will differ from one firm to the next, depending on your data maturity level, industry, and company size. You can build a data strategy that matches your firm’s specific needs by taking stock of where your company is now.