what is data strategy?

What is data strategy and why is it important? [Framework & Examples]

If you run a business, you certainly have data. However, data on its own can’t help you to make data-driven choices to improve your business. You need a data strategy to turn data into value.

Data strategy refers to the tools, processes and rules that determine how you manage, analyze and process your data.

THE DATA
STRATEGY

THE DATA
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THE DATA
VISUALIZATION

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THE DATA
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THE DATA STRATEGY

THE DATA
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THE DATA VISUALIZATION

THE DATA
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THE DATA TRAINING

THE DATA SUPPORT

THE DATA STRATEGY

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Why is DATA STRATEGY important?

Almost every company collects data in a variety of forms. Data strategy helps to manage and explain data through the business. Not having a data strategy can result into the following:

Slow or ineffective business processes.
Data Privacy, Data Integration and Data Quality problems.
Inadequate data flow between different business sections.
Lack of understanding of important parts of your business such as:
- customers
- distribution chains
- competitiveness
- processes made

In short, a lack of data strategy can lead to ineffective performance and reduced profitability.

DATA STRATEGY goals

To overcome the problems that can occur in case no effective data strategy is implemented, you need to pursue the following goals:

Innovation: Every successful business creates new value or results through innovation. Innovation should be the main goal in creating and implementing data strategies.
Customer satisfaction: Data strategies should support and empower your users – anyone who can help you grow your business in your organization.
Handling Risks and Conditions: Effective data strategies should consider data security risks and compliance requirements for your business, which vary widely by business type.

Also, because your IT department is responsible for implementing and monitoring the tools and infrastructures that implement your data strategy, consider IT resources and capabilities when setting goals.

DATA STRATEGY: the 4 Key Components

Although no two data strategies are alike. All successful data strategies consist of 4 key components. Each of which is involved in implementing your data strategy.

1. Business strategy

First of all, your data strategy should strengthen and improve your overall business strategy. In order to achieve this, start by setting clear and scalable goals for your data strategy that will help your larger business strategy.

For example: your data strategy may include the goal of keeping storage costs below a certain threshold. To achieve this goal, the strategy may be to identify storage tools or services that meet your costing needs and help users find the best ways to improve storage costs. To help you achieve this goal, you need to set criteria such as the average cost of one gigabyte of storage space.

Set long-term and short-term goals. You can set a short-term goal to do monthly monitoring.

For example: The long-term goal may be to achieve a sustainable/continuous data checks.

Your business may already have a data strategy. However, the strategies of many companies are written many years ago. Periodically review your strategy to see if it meets your current business goals and available tools.

2. Organizational Roles

Secondly, your data strategy should focus on organizational roles, facilitating collaboration, avoiding duplication and documenting what people do with the data. Not everyone in the organization uses the same data. Their role in data collection, management and analysis is different. Three main types of users tend to implement a data strategy:

Data Engineer: Builds and maintains systems for data storage, processing and analysis. Ensuring data quality and integrity.
Data Scientist: Analyzes complex data sets, develops predictive models to derive insights and solve business problems.
Data Analyst: Analyses data to identify patterns and trends, creates visualizations and derives insights aligned with business goals. Collaborates with stakeholders to understand business needs.

Consider all the people of the company that use data, even if it is not a main part of their work responsibilities. For example, an account manager has a portion of data collection for customer information. A sales manager may need to analyze data to plan additional marketing campaigns. Your data strategy should document the role of each member of the team or group. In addition, when a business maintains multiple databases its strategy must determine which data is “owned,” that is, who is responsible for storing, storing and interpreting the various databases.

3. Data Architecture

Third, your data structure includes tools and processes that allow you to work and analyze data. These components may include various components and cloud hardware and software. The first step in determining the data structure is to determine what datasets exist in business units across the company. Data catalogs are useful tools for this purpose. If you do not have a data list, review the data sources and the users who work with the data. To analyze your data, you must store it in the same data store or data lake. You can also integrate or modify it into a design that is best for analysis. You need a database to retrieve source data from a structured data sources and copy it for storage and analysis. Data identification, input, storage and analysis are part of the data structure. Documenting and implementing your data structure is important for a consistent and predictable data strategy. It also makes it easy to measure data performance when your needs change.

4. Data Management

Lastly, data management. This encourages all team members to view data as a business asset rather than as an aid to business operations. Which will encourages employees to follow policies when working with data.

The basis of effective data management is one, which creates processes and responsibilities that ensure the quality and security of data used in the organization.

For example: if this is not a daily use, the data symbol may indicate that the administrator should archive the data in an “cold” storage location. Or the data privacy policy may require data encryption to increase security.

You need to update your data policy to reflect the changes your company needs. You can keep all of your data on premise today, but if you move your data to the cloud, you’ll need to update your data management policies to enable cloud-based data management.