Avoid Risks & Challenges while implementing an Effective Data Strategy

As we continue moving to digital transformation, as we are living this technological revolutions, the Data Value is the real strategic goal of a successful digital strategy.

Empowering the value of the data to powerful intelligence and transformative organizational performance.

Strength stakeholders relationships and optimize experience.

The Power of Data Value lead into new products and services that are developed to meet business needs

As we are responding to 4th industrial revolution of technology by data we increase business performance and reduce risks.

So Why do we need a data strategy?

• The data strategy is the vehicle for ensuring data is derived to support your organization’s strategic objectives.
• A Data leader in an organization should focus on establishing a robust and comprehensive data strategy as a toolkit to measure business value from data
• The data strategy serves the mechanism for making quality, trusted, and well-governed data available and accessible to deliver on your organizational mandate.

Business Strategy alignment with Data Strategy

• Business Strategy should be reflected to organization Strategy
• Most Organization are going to have a digital strategy to adapt the 4th industrial revolution
• Data Strategy is a main pillar in success of Digital Transformation Strategy

Align your data strategy with business strategy and data architecture

From your Data Strategy it is essential to translate this strategy into a road map that describes how it will fulfill the business strategy to drive it into a proper modern architecture that defines an effective work flow for your organization data. ?

Key components of effective data strategy

Defining a Data Strategy

????????Data Strategy should have Goals & Objectives with good Data Architecture

????????Modern Data Management capabilities should be existing in the organization

Understand the key components of the data strategy.

A data strategy is composed of four basic elements:

1. Goals and Objectives: Understanding the value of each data element, data is the organization assets
2. Data Architecture: A data mapping to all sources to be consolidated to single point of truth, all the applications
3. Data Management: A comprehensive understanding of where data is stored, processed, integrated and provisioned for analytics of the Organization
4. Data Governance: Defining a DG framework, data protection and retention strategy, accessibility matrix, comply regulations e.g. GDPR?

Data is the asset and must be governed

Without proper Data Governance it is hard to develop and implement it an effective Data Strategy

Data Strategy Components:

1. Identification

• Identify data and Understand its meaning
• without a data glossary and metadata, it might become hard to know our data.

2. Provision

• Share data
• Easy available and accessible
• Delivering Value of data

3. Storage

• Where the data go?
• Manage storing data
• Manage huge volume of various sources

4.?Integration

• Bring the data in one place
• Process and analyze the data
• Enable business intelligence for decision making

5.?Governance

• Data is the Organization’s Assets and it should be owned by business
• Data must be protected and secured

What is the current state and complexity?

Current State:

????????The volume and variety of data are growing rapidly with no slow down

????????Business needs and models are evolving.

????????Users and stakeholders are becoming more and more data centric, demanding are over expectations.

Complexity

????????Organizations struggle to develop a coherent business-driven strategy for effectively managing and leveraging their data assets.

????????The goal to reach a data- driven organization is not easy

????????Data architecture, models, and designs fail to deliver real and measurable business impact and value.

Risk & Challenges

Most of the organizations are suffering different challenges and risks that make it more complex to success data driven digital transformation

• Data Silos
• Lake of Information
• No Consistency, No integrity in the Data
• No Data Quality
• No data sharing, no data reuse,
• No business glossary
• No Data Dictionaries
• No proper data warehouse or data management process
• No Security

To be able to conquer this complexity and challenges so organizations have to enable a robust and comprehensive Data Strategy.

A comprehensive data strategy is essential for any organization in the digital era.

A comprehensive data strategy should first to be aligned with the organization’s business strategy. As we all know, all the organizations are establishing their digital transformation strategy in order to be aligned with the future revolution of technology in all the industrial domains. And Data is the core pillar of any digital strategy so it is very important to be derived with this organization strategy with defining vision and missions in a reliable road map.

Business Strategy usually starts with Organization Objectives & Goals, Business Drivers and Digitization

Data Strategy when it starts should include the following

-???????Vision & Mission with a real defined long term data value

-???????Data Strategy Roadmap

-???????Organizational Drivers Data Value

-???????Data Strategy Objectives & Principle

Then it has to focus on the Data Management, Tools / capabilities

Unlock The Value

-???????Transform with Data

-???????A Data Driven Culture

-???????Fuel Data Driven Decision Making

-???????Generate Changing Analytics

-???????A Foundation for Data Monetization

-???????Empower ML & AI

Current Culture

-???????Data Management Capabilities

-???????Data Culture and Data Literacy

-???????People: Roles and Organizational Structure

Start with people culture and mindsets, measure the capabilities of Data management in your organization, define risk and challenges and how mitigation can help drive the organization to reach the real goal of data value. Modern data management methodologies and proper data architecture should take place. Understand the data and it’s metadata. Integrate all the data in one single point of truth. Provision the data and make it easily accessible to the whole employees to increase analytics and fasten the decision making. Empower technology of Machine Learning and AI to transform data from descriptive analytics to predictive analytics and prescriptive analysis.

The culture side of the data strategy, and data literacy.?What does it look like?

• Does everybody know the data?
• Does everybody trust the data?
• Does everybody talk about the data?

A data-driven culture requires several elements:

• Trusted, single source of data from which the whole company can depend on.
• Business glossary & data dictionary: Users know what the data and its means.
• Access to data and analytics tools: Employees can leverage data immediately to resolve a situation, perform an activity, or make a decision, including frontline workers.
• Data literacy: Ability to collect, manage, evaluate, and apply data in a critical manner.
• Decision making: Data is integrated in decision-making processes.
• High-quality data: Ensure DQ measure and rules to gain more trust for highly quality of data.

Effective communication: Strength stakeholders relationships and optimize experience.

Data literacy is an essential part of a data-driven culture

• A data-driven culture:?Embraces the use of?data?in decision making
• Speak the same language
• A data-driven culture builds tools and skills, builds users’ trust in the condition and sources of data
• Building a data culture takes an ongoing investment of time, effort, and money. This investment will not achieve the transformation they want without data literacy

Effective Business Data Strategy

Concluding what was illustrated, an effective Business Data Strategy starts from Data Leadership & Partnership by strategic business alignment. Data Management and Data Governance capabilities should strongly exist. Data Culture & Literacy is very essential to continuous Data Evolution.

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