What is a data strategy framework
Digitization is producing large volumes of data across industries. The only way to manage them is by designing and implementing a data strategy framework. It clarifies identifying essential data types and how they must be used and implemented. Data strategy definition can be as follows - It is a dependable roadmap to identify ways to leverage the power of data to drive growth and innovation. The framework can help standardize how data is interpreted and used across the organization. Most companies use data in silos which is highly ineffective; standardization through a framework can help in institutionalizing ways how data is consumed to drive decisions. It will also empower leadership teams to recognize business opportunities and de-risk a business to create value.
What is the need for a data strategy
The impact of not having a analytics data strategy is huge. The data volumes can get overpowering, and managing and using them would be impossible. This can weaken the value chain, resulting in lost business opportunities, poor decisions, and even significant data security and privacy risks, leading to non-compliance. While developing a framework, the inclusion of AI-ML is critical. AI-ML can analyze massive data volumes, learn from different data patterns, provide correlations, and identify any data irregularities in ways a data analyst cannot do. The main advantage is predicting futuristic business trends and tendencies by analyzing past data. By automating large data processing tasks, AI-ML can provide insights to understand target customers and optimize operations. This doesn’t mean human intervention isn’t needed; to unlock the power of data, the right combination of all three, AI, ML, and humans, is needed.
Steps for developing a strong data strategy framework
Here are a few considerations for developing a data strategy framework.
Start by defining the key objectives by engaging the stakeholders and understanding their priorities, identifying what type of data is required, and how it can help achieve these objectives. Next, evaluate the current status quo on how data is managed, evaluate the data infrastructure and its data sources. A data governance model must in place, along with data management policies, this is very critical along with deciding on the type of roles and responsibilities of users handling the data. Ensure data quality, define the data models, and workflows that can help manage data. Establish a roadmap to create a scalable, flexible, and secure data architecture and tools and techniques to analyze data. Finally, establish the structure to develop reports based on stakeholder needs and implement the framework. Monitor the framework’s effectiveness based on definite timelines and milestones and ensure it’s aligned with business objectives and meets stakeholder needs.
Data strategy implementation
While implementing the framework, it’s good to have a comprehensive set of intelligent solutions that can store, analyze and derive insights from large data sets with high accuracy. MS Azure is one of the data strategy solutions offering a broad range of services for analytics needs.
Data visualization: Azure Data Factory can be used to create data pipelines by integrating data from multiple sources and used for analysis and data visualization. Power BI is another feature that can be used for interactive visualizations and business intelligence. Custom dashboards and reports can be created and shared with various teams. Azure Stream Analytics can help visualize streaming data from IoT devices through charts, graphs, and maps. Azure Data Bricks has ML visualization capabilities, including built-in graphs and charts. It also has libraries such as MatPlotib and Bokeh for developing custom visualizations.
Data Integration: Azure Data Share has data integration features such as Azure Blob Storage, Azure Data Lake, and Azure SQL Database; it can also securely share data. Azure Data Factory can integrate data from on-prem databases and cloud databases. Azure Stream Analytics offers connectors and integration features such as Event Hubs, IoT Hubs, and Blob Storage to integrate streaming data sources. Azure Databrocks, with its Apache Spark-based Analytics, can integrate and analyze large data sets from various sources; it also has JDBC and ODBC connectors to integrate databases. Azure Synapse Analytics can integrate and analyze datasets from sources such as SQL servers, Oracle, and Hadoop.
Data Quality: Azure Data Catalog is a centralized location to store and manage the metadata about data sources. It can classify and tag data, track the data lineage, and document data quality needs. Azure Data Factory can do data profiling, data cleansing, and data validation which can fix data anomalies that can be either missing values, repeat records or conflicting data types. With its Python-based Pandas library and schema validation functions, Azure Databricks can clean and transform data.
Data Governance: Azure Purview can be used to get a holistic view of the data ecosystem. From discovery, classification, and data management from on-prem and cloud to SaaS environments, Purview can help define data policies, data discovery, and classification. Azure Policy can enable the creation, assigning, and management of policies across Azure resources; it can be used to enforce governance policies, including data retention policies, data classification, and access control policies.
Data Security & Privacy: Azure Security Center offers unified security management services to ensure advanced threat protection and continuously monitor data assets. It can mitigate risks of data leaks and malicious access attempts and provides recommendations on security best practices to meet compliance needs. Cryptographic keys can be protected using Azure Key Vault; it also offers secure storage and centralized management of keys and certificates, safeguarding sensitive data and blocking illegal access. Data can be classified, labeled, and protected with Azure Information Protection. This will allow only legitimate users to access the data. For ensuring role-based access to data, Azure Active Directory’s IAM capabilities can enforce access control policies, including role-based access controls and multi-factor authentication.
Data Strategy Framework Components and Tools
Let’s understand some key components of a data strategy. The first analytics strategy framework component is to have clear business objectives. The next best thing is to have a robust data governance model to establish policies and procedures for managing and securing data. Later, build a data architecture to store, process and analyze the data. Ensure data quality by confirming accuracy and reliability, then leverage analytics to derive insights from it. Lastly, establish a data-driven culture within the organization to make more informed decisions. This will also depend on how quickly employees can access this data and the tools they have to access them.
Build your Data Strategy with Ness
Considering Ness in developing a data strategy has its benefits, the most indispensable being its expertise. Ness is known for its experts, who are vastly experienced in handling data strategy tools, drafting data and analytics strategy framework, designing data collection and storage architectures, and capable of developing data infrastructure, transformation processes, and analysis methods. Every solution built is scalable, secure, and customized to unique business needs. They will meet data compliance regulations and are built to mitigate any privacy risks. Our digital analytics consultants have a firm grasp of tools, processes, and rules that define how to manage, analyze, and act upon your business data. We also have proven abilities in implementing data governance initiatives and developing solutions for data sharing and data democratization.