Data Transformation
Mavengigs
Mavengigs is a global consulting firm providing consulting services for Mergers & Integrations (M&A) and Transformations. Through our network of independent resources and partners, we serve clients in USA and Europe. Mavengigs is a division of Panvisage Inc. (a holding company with interests in consulting, education, real estate and investments).
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Data and Generative Transformation
Data Enterprise
The concept of being “data driven” is undergoing a transformation as technology advances rapidly, the intrinsic value of data becomes more apparent, and data literacy continues to grow. Those who can advance most swiftly in this context are poised to reap the greatest benefits from data-driven capabilities. As per McKinsey’s findings, certain companies are currently witnessing artificial intelligence (AI) contributing to as much as 20 percent of their earnings before interest and taxes (EBIT).
The Enablers
Effective data management, characterized by the ability to share and collaborate on data both internally and externally while maintaining privacy, security, and resilience, is crucial for success. A well-defined data strategy and governance framework that identifies and prioritizes business needs are key drivers, given that data plays a vital role in every decision, process, and outcome.
Achieving success involves the development of a comprehensive enterprise data strategy that aligns with the overall business strategy. This entails implementing cultural and technological changes to modernize the data architecture, adopting a common data model for timely data processing and delivery, and providing insights from data generation to visualization. Cultivating data literacy within the organization is also essential, as it will enable better utilization of data assets, improve performance, enhance customer satisfaction, and drive monetization efforts.
Adopting and embarking on AI programs in an organization has its own nuances. How can organizations build trust in their AI programs to differentiate themselves and accelerate desired outcomes. It involves formulating the data strategy, understanding the current state, creating a roadmap, and defining the governance to ensure privacy, reliability, responsibility, accountability, security, and transparency.
Data Ecosystems
Data ecosystems include data strategy, data governance, data architecture, data quality, data security & privacy, data marketplaces, democratization (access with zero touch self-service) and data monetization. This is a long-term continuous process that requires executive commitment, change processes, data culture, and technology support.
Data Challenges
Challenges in the Data World
“A lot of organizations are talking about application modernization and bringing applications to the cloud, but they’re losing sight of the data itself”.
Typical Challenges as per Mckinsey’s Analysis
- Lack of front-office controls (e.g., poor quality of data entry at system of origin with limited validation).
- Inefficient data architecture (e.g., multiple data warehouses with no common data model, legacy systems, complex lineage).
- Lack of business buy-in for value of data transformation; not enough executive and senior management attention; data seen as an IT issue, not a business asset.
- Lack of central direction in driving transformation (e.g., disparate BU-led efforts). Ineffective governance model (e.g., unclear ownership of data, weak or unenforced policies).
- Insufficient funding/resource allocation for enterprise-level data transformation program.
Data transformation is driven primarily by regulatory compliance needs, with no focus on data quality. Manual effort required for reconciliation and remediation of data-quality issues.
Why Data Strategy and Data Governance
New Business Strategy
Organizations may be embarking on a new operating model to treat data as a product and or as a service towards a monetization goal and to empower employees in making well informed decisions. To accomplish these objectives, it is imperative to adopt a modern data architecture and generate actionable insights. This entails, but not limited to real time data processing, flexible and common data model, analytics and AI/ML. Additionally, data architecture on cloud like Lake Warehouse will support, accelerate development and deployment of new AI-driven capabilities and the discovery of new relationships in the data to drive innovation.
New Data repositories with minimal cooperation
The presence of multiple data repositories poses a significant challenge for organizations striving to obtain a comprehensive and precise view of their operations. These data silos typically arise when data is handled by various operational systems, a reflection of the organization’s structure. Overcoming these barriers and fostering data accessibility, data sharing, and collaborative efforts will be a crucial endeavor for organizations in the forthcoming years. To establish an effective data architecture or data framework that facilitates connecting and gaining insights from these silos, organizations must prioritize the communication and coordination of a well-thought-out data strategy and data governance program.
Improper Data Definitions
Data in its raw form, lacking precise business terminology and established rules, is susceptible to misinterpretation and ambiguity. Data Transformation, such as merging or aggregating information from various origins or sources, requires better understanding from both business perspective and physical formats. Associating data assets across multiple repositories for the purpose of obtaining enhanced data analytics and insights demands a harmonious approach and business alignment. This entails relationship of the data with master data, reference data, data lineage, and hierarchical structures. To establish and sustain these frameworks effectively, it is imperative to implement robust data governance policies and coordination.
Ensuring Data Security and Privacy
Managing the growing volume, utilization, and intricacy of datasets present significant challenges in the realms of data privacy and security. With the continual accumulation of personal and sensitive data in digital storage, the potential for data breaches and cyber-attacks escalates. To confront these issues and uphold ethical data management, organizations must allocate resources towards implementing solutions capable of safeguarding their data against unauthorized access and breaches
The ever-shifting regulatory environment and compliance requirements
In an environment where data governance regulations are in a constant state of flux, organizations must remain vigilant in keeping abreast of the most current requirements and directives. It is imperative for organizations to verify that their enterprise data governance procedures adhere to compliance standards. This necessitates the capability to (a) Monitor data-related issues (b) Assure data adherence to data quality standards (c) Define and administer business regulations, data standards, and industry mandates (d) Mitigate the risks associated with evolving data privacy regulations
Increasing Data Volume and Data complexity
Data generated and collected by organizations is continuously growing. The variety and complexity of these data are adding more challenges to manage and govern these data effectively. Efficiently managing and governing these data necessitates the adoption of novel technologies and data management protocols designed to cope with the expanding volume and intricacy of data. These technologies and processes must be adopted to work within the data governance sphere of influence.
Holistic View of Data
The perspective on data entails possessing a holistic comprehension of all the data within an organization, encompassing its format, origins, and utilization. Consider use cases like Consumer Insights, Product Insights etc. In the absence of these perspectives, organizations may encounter challenges in making data-informed business choices since they might not have access to the complete information required for a comprehensive understanding of their operations and the achievement of desired outcomes.
Post Covid Work Environment
In the era of remote work becoming the prevailing norm, organizations must discover effective means of overseeing data and ensuring compliance across various data sources and stakeholders. Even when employees are not physically present in the office, organizations must guarantee that data is accessed and utilized in an appropriate manner. This necessitates the implementation of a set of data governance best practices, encompassing policies, procedures, and technologies, to regulate and monitor data and system access.
Data Strategy Framework
Strategic Goal
- An offensive strategy focuses on growth, increasing revenue and customer satisfaction.
- A defensive strategy would focus on ensuring regulatory compliance and security of the infrastructure from potential threats, fraud detection and reducing risks.
- Technology strategy defines the On-Premises, Cloud, Multi-Cloud strategies, Open Source, Vendor lock-in, Pricing Model, Industry Specific Accelerators (Data Models)
- Insights Involve Advance Analytics/Data Science, Generative AI, AI/ML, Business Intelligence, and its underlying data architecture namely Data Lakehouse, Data Lake.
- Data as a Service or Data as a Product incubates the idea of a business unit, a new source of revenue by monetizing data services, data products, and data sharing.
Governance
- Managing the people, process, policies, and culture around data. The maturity of an organization at this level – or the lack of it – can determine the options for how data is used strategically, as well as the timeline for putting it into practice.
- Key Internal and External Stakeholders roles and responsibilities, their commitment and communication, and understanding of their goals and priorities.
- Data Literacy, preparedness for change, clear vision, defined KPIs, Training for Business User and Data Stewards.
- Delivery Model, Methodology Adoption, Best Practice Implementation, Documentation and Knowledge sharing.
Information Management
- Focuses on organizing, structuring, and labelling data effectively and sustainably.
- Managing and using information with Insight and Innovation
- Data Silos in a Hybrid or Multi-Cloud environment, Data Fabric, Data Mesh, Data Visualization.
- Data Sharing, Data Transformation and Data Availability
Data Management
- Acquiring Data Real Time and Batch (ETL/ELT) from various sources namely, transaction data, application, and system logs, IOT, social media, 3rd Party data, etc.
- Prioritizing data sources and processes based on business value, quality, and reliability.
- Data Access Rules, Authorization, Authentication, Data encryption.
- Collecting, compiling, and storing the data assets about different information, transactions or events that impact the business.
- Data Privacy Laws, Other Regulations, Localization Laws
Data Environment
- Defining disparate data sources: relational databases, big data, unstructured data, XML, documents, voice, and media etc
- Data Storage Management
- Data Storage Policies
- Data Lifecycle Management
Data Strategy Framework
Data Architecture
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