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Components of Data strategy

Learn about the various components of Data strategy with explanation.
Submitted by IncludeHelp, on May 16, 2022

There are four fundamental components to a good data strategy. Each of the components plays a vital role to put our data strategy into action, and they are all included in every successful data strategy.

The following diagram illustrates key Components of the Data strategy -

Figure: Components of Data strategy

1. Business Strategy

A business strategy establishes a vision and a course of action for the entire corporation. It is critical for all employees in a firm to understand the organization's overall goals and mission statement. A strategy can provide this vision and also prevents individuals from losing sight of the goals of their own companies.

It is important that your data strategy supports and advances your entire business strategy, which refers to the techniques you employ to run and improve your company.

• Define specific goals and quantifiable targets for your data strategy that are aligned with your overall business plan.
• Consider the following example: your data strategy may contain a goal of maintaining data storage costs below a specific threshold.
• In order to reach this goal, the plan may specify:
• Storage tools or services that fulfill your cost criteria;
• Best practices that can assist users in reducing storage expenses.
• It should develop measurements, such as the average cost per gigabyte of storage space, to measure performance.

1.1 Long-term and short-term goals

Make a list of both long-term and short-term objectives. In contrast to a short-term aim of doing data quality reviews once a month, a long-term goal would be to attain continuous data quality, which means that you identify and address data quality concerns on a constant basis rather than depending just on periodic inspections. An effective business strategy provides a roadmap for the different parts to run a business, from hiring to organizational structure and acts as the fundamental unit for success. When the strategy aligns with the company's long-term vision, it makes it easier to ensure that everyone is working toward the same objectives.

1.2 Key elements of business strategy

The key elements of business strategy –

• Vision - business strategy should frame in such a way so that it can align the organizational vision or objectives.
• Values - The term "values" refers to ethical standards, legal obligations, and code of conduct. Often, these values can be found publicly displayed at the company's offices and other facilities, as well as on its website.
• SWOT analysis - SWOT stands for Strengths, Weaknesses, Opportunities, and Threats (SWOT) is a critical part of the strategy. The SWOT analysis helps to define the business landscape thoroughly.
• Tactics - Tactics are the business plan which defines how the company will get the necessary work done efficiently. Tactics are the effective plans to do the tasks within time limit.
• Resource allocation - resources allocation includes people, money, equipment, and materials. Resource allocation utilizes resources and maximizes profit for the organization.

1.3 How to execute important decisions?

In the business world, there is a biggest misperception that only higher management is responsible for developing an organization's business strategy. Planning and strategy can be beneficial to leaders at all levels and roles in the organization. If we want to take effective steps to improve our company's overall strategy, we must increase our problem-solving abilities.

2. Organizational roles and responsibilities

When developing a data strategy, it is important to consider organizational responsibilities and document who is responsible for what with the data in order to enable collaboration and avoid duplication.

Everyone in a company does not utilize data in the same manner, and their responsibilities in data collection, management, and analytics will differ as a result of this.

Three sorts of users commonly implement and enforce data strategy: administrators, data analysts, and data managers.

• Data engineers: data engineers are in charge of overseeing the data flow and are in charge of developing efficient and trustworthy data architecture. Work environments for data engineers are diverse, but they always revolve around the development of systems that gather, handle, and turn raw data into useable information which can be interpreted by data scientists and business analysts. They make data easily accessible so that enterprises may use it to evaluate and optimize their own performance and efficiency.
  Data engineers performs various job roles, some of them are listed below –

• • They take a methodical approach to the design, development, and maintenance of data architectures.
  • They collect information from valid sources.
  • They formulate set of dataset procedures; data engineers store the optimized data in a database.
  • To address challenges that may develop when attempting to solve a business problem, data engineers perform research in their field of expertise.
  • A data engineer uses practical database concepts. They continue enhance their abilities on a timely basis.
  • The descriptive data model is used by data engineers for data aggregation in order to derive historical insights.
• Data scientists: There are three types of data scientists who work with pipeline data: data scientists who work with data that the pipeline delivers; data analysts who specialize in analyzing and interpreting data; and business managers who assist with data operations and report review. Data scientists most widely apply the concepts of computer science, statistics, and mathematics. Their work involves data analysis, processing, and modeling, followed by interpretation of the results in order to develop actionable plans for businesses and other organizations.
  Data scientist performs various job roles, some of them are listed below –

• • Data scientist’s uses data mining, often known as data extraction, to obtain useful information from valuable data sources.
  • A data scientist uses machine learning tools to identify features, develop classifiers, and optimize them.
  • Preparing organized and unstructured data for analysis, as well as improving data gathering techniques to ensure that all necessary information is collected for the development of analytical systems
  • Data processing, cleansing, and integrity validation are all steps in preparing data for analysis.
  • The process of studying enormous amounts of data in order to discover patterns and solutions
  • Prediction systems and machine learning algorithms are being developed.
  • Presentation of findings in an understandable manner
  • Make recommendations for solutions and strategies to address company difficulties.
  • Collaborate with the business and information technology teams
• Data analysts: Data analyst is the experts in analyzing and interpreting data. When it comes to data analysis, it is the role of the data analyst to collect and analyze data in order to answer certain problems.
  Data Analyst performs various job roles, some of them are listed below –

• • Use of automated tools to extract information from primary and secondary sources.
  • Removing noisy data and correct them.
  • Arranging data in an understandable format.
  • Performing an analysis to determine the quality and significance of data.
  • Create reports and identify the performance indicators in order to detect and correct coding issues.
  • Identification, analysis, and interpretation of patterns and trends in complicated data sets that may be useful for diagnosis and prediction using statistical tools.
  • Assigning a numerical value to critical business functions in order to analyze and compare business performance across time.
  • Identifying and analyzing regional, national, and international trends that have an impact on the company and the industry.
  • Worked together with programmers, engineers, and management to identify process improvement possibilities and suggest them about system upgrades, as well as to develop data governance policies and strategies to maximize the profit.
• Business managers: Business managers who maintain customer’s information and do analysis. A sales manager may require data analytics to assist in the planning of the next marketing campaign. Business managers document the roles of each team member or group as data strategy.
  Business Managers performs various job roles, some of them are listed below –

• • Assessing and identifying new growth prospects in current and potential markets are essential.
  • Establish the goals and objectives of the organization.
  • Recruiting and training new personnel are important tasks.
  • Regular employee assessments should be conducted to identify areas for improvement.
  • Create business strategies and plans to help the organization achieve its objectives.
  • Make certain that the company has enough resources, such as staff, materials, and equipment, to meet its needs.

3. Data architecture

Over the last ten years, data has transformed our understanding of the world. Each of us generates roughly 2.5 quintillion bytes of data every day, which includes all of the emails, text messages, and YouTube movies we send and view with our friends and family. Businesses of all sizes, large and small, deal with vast amounts of data, and their ability to extract useful insights from that data is critical to their success. A data analyst is someone who does exactly that. They analyze statistical data and transform it into valuable information that businesses and organizations may utilize to make key decisions about their operations.

All industries are becoming increasingly reliant on data to make essential business decisions, such as which goods to develop, what markets or customers to target. Data is becoming increasingly important in making these decisions, as well as in other areas of life. Using data, they are also able to identify weak areas in the firm that require attention.

Data architecture refers to the tools and processes that enable you to deal with and evaluate data. The hardware and software components of this environment may include a variety of on-premises and cloud-based hardware and applications. The first stage in creating data architecture is determining which datasets are available throughout the organization's various business divisions.

3.1 Data catalogues

Data catalogues are used to maintain an inventory of data assets by discovering, describing, and organizing datasets," according to the definition.

• The utilization of data catalogues is a useful tool in this situation. If you do not have a data catalogue, discuss data sources with your team as well as the users who will be working with the information.
• In order to help data analysts, data scientists, data stewards, and other data consumers identify and understand important datasets for the purpose of extracting business value, the catalogue provides context.
• A data catalogue will provide a clear overview of your datasets, making your data systems more intelligent and allowing you to extract more value from your data.
• A contemporary data catalogue will serve as a single point of trust for all of your metadata, allowing you to focus on other aspects of your business.
• What does it mean to have a "contemporary" data catalogue?
• Take for example, the idea that your Amazon marketplace is linked to any shop, merchant, or even other e-commerce websites.
• However, unlike Amazon, we have the ability to shop and curate all of your data, as well as provide your data owners with the tools they need to curate, cleanse, and trust over time what's inside their datasets, allowing your catalogue to become a living marketplace for any valuable data within your organization.

3.2 Modern data catalogue

Modern data catalogues have an extensive range of powerful capabilities such as pattern detection, relationship discovery, pervasive profiling, automatic harvesting, and classification, allowing you to highlight data that is important to

• In today's world, a contemporary data catalogue will serve as a single point of trust for all of your metadata that can be shared inside your organization, facilitating cooperation.
• The data we collect from you must be stored in a central location, such as a data warehouse or data lake, so that we can analyze it. It's possible that you'll wish to integrate or change data in order to put it in a format that is more suitable for examination.
• Data pipelines are required to accept unstructured raw data from a variety of sources and replicate it to a central location for storage and analysis.

4. Data management and storage

When it comes to data management, all team members are encouraged to view data as a company asset rather than as a result of business processes.

It motivates everyone in your organization to adhere to policies while working with data, which is beneficial for everyone.

It is data governance that serves as the foundation for good data management because it defines the processes and responsibilities that ensure the quality and security of the data that is used throughout an organization.

A management may be required to archive data in an offline location if it is no longer in use on a daily basis, for example, according to data governance guidelines. Alternatively, a data governance policy may specify that data encryption be used to increase security.

As our business requirements evolve, we need revise our data governance policies.

We may be storing all of your data on-premises right now, but if we decide to move our data to the cloud, we may need to amend our data governance policies to account for cloud-based data storage and retrieval. For example, data kept on the cloud may necessitate the use of more stringent encryption standards.

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