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Get Your Data Strategy Right with Data Engineering
Data-driven capabilities are at the core of a company’s AI and digital transformation journey. Starting a new data engineering role entails effectively handling new data sources, types, sets, and applications. This can be a challenging task in the era of Big Data.
During the early phase of your job transition, you need to excel by re-evaluating your data engineering challenges and strategy. In an environment where data is more vulnerable than ever and legislation over data collection is highly demanding, the right data engineering strategies help you excel within no time.
Data Engineering Challenges While Building a Robust Data Strategy
While companies plan to include data-centric solutions, several need help incorporating processes and technologies into their operations. Data management at scale is the biggest challenge for AI and advanced analytics. Some of the common data engineering challenges include:
- Lack of data-driven culture: Designing a data engineering strategy takes time and effort. Companies sometimes need a top-down push to support a data-driven culture. There are data engineering use cases where analytics leaders have strongly supported the data analytics focus but drifted back to legacy techniques. This is because of a lack of support from the leadership and vice-versa.
- Lack of business alignment: Data strategy extends beyond mere technology implementation; it demands a holistic understanding from a business view. Frequently, data teams need to pay more attention to the business requirements, resulting in wasted resources and efforts on endeavours that do not align with strategic business objectives.
- Data silos result in lost business opportunities, reduced decision-making ability, and enhanced operating costs. Businesses spend weeks in data mergers, moving away from fundamental data analysis and delaying time to seek valuable information.
- Data accessibility: Business teams need direct access to data. But for most companies, either the process needs to be faster or set up for them to take appropriate action. Having the correct information at the right time is a significant data engineering challenge.
- Poor data governance: Data governance safeguards companies from low-quality data and certifies data accessibility. A poor data governance architecture slows down data management and leads to inconsistency in data security, integrity, and usability.
- Scalability and performance: Organizations should design a sustainable data strategy to manage vast data. Several organizations keep refurnishing their data strategy practices once a month. Using outdated technologies results in no cost-benefit and low performance.
- Data privacy: Data protection complexity has escalated due to the surge in data volumes and the emergence of various data engineering use cases like cookies and personalization. To address privacy requirements adequately, businesses should adhere to industry-specific compliance standards such as HIPPA, PCI, and PII.
- Lack of specialized skills: Many companies use software engineering approaches when building data strategies. While software engineers excel at database maintenance, backend development, and coding, specialized skills are essential to effectively handle extensive data volumes, build data pipelines, and ensure the availability of reliable and readily consumable data streams.
Steps to Develop Your Data Engineering Strategy
1. Understand your business objectives
To align data and business priorities, you need a deep knowledge of the company’s and senior leadership’s aims. Discussion with business stakeholders and C-suite helps your company to reach its goals by supporting data as a true competitive advantage.
Ultimately, linking business and data strategies help merge the guidelines and frameworks throughout units for a unified view of the data landscape, which every individual agrees to. To help managers look at the strategic advantages of data and AI strategies, ensure data and business priorities are precise and agreed upon as your collaborative, data-driven surrounding takes shape.
You need to identify the most compelling use cases:
“If you had better access to high-quality data, which areas within your organization could you solve problems? When engaging with stakeholders, it is essential to identify data requirements related to various business objectives within or across different lines of business. This helps demonstrate the value of data as a strategic asset.
Explore the data landscape comprehensively. Imagine if you could decrease supply chain expenses by modernizing outdated applications. Alternatively, leveraging AI can enhance insights and expedite risk and compliance processes. You can obtain a more comprehensive overview of operations by gaining a deeper understanding of the data quality and its flow (or lack thereof) among departments such as finance, sales, and marketing. This approach reveals fresh opportunities to drive revenue growth, increase profitability, and minimize risks.”
2. Assess your current state
This step requires you to identify the barriers to building a data-first experience.
Organization silos sometimes underlie data management, integration, and workflow challenges. To offer the highest level of productivity, employees demand self-service data and AI-powered solutions or apps with the proper controls in place.
A design-thinking data strategy helps surface and identify organizational drawbacks; this brings strategic value across several use cases, lines of teams, or businesses. This mechanism helps produce attainable fixes in a consistent cycle of creation, reflection, and observation and looks out for issues and solutions as an ongoing conversation.
Focus on data elements for governance:
Maintaining control over critical and regulated data elements like names, addresses, social security numbers, and more is crucial to effectively operating various business systems while avoiding duplicate errors, unreliable searches, and privacy breaches. It is necessary to strike a careful balance between safeguarding data and encouraging innovation. Consider who are the current owners, managers, and policymakers responsible for data, and determine if their governance practices impact security, privacy, or compliance.
Ensure that the right individuals within your organization possess decision-making authority, an accountability framework, and external resources to promote responsible behaviour in data and analytics evaluation, creation, consumption, and control. Additionally, pay attention to the governance aspect when implementing AI technologies during this stage.
3. Map out data and AI strategy framework
This includes defining your data’s target state. Summarize your comprehensive vision such that data strategy conversations, and the leading business process reforms, are as helpful to business analysts and app developers as to HR and sales.
However, achieving digital transformation requires real-time decision-making capabilities facilitated by predictive models requiring data science environments. Operational data has increasingly become a vital component of your data ecosystem. To support a modern data architecture, it is essential to establish an integrated data ecosystem that can be effectively managed, governed, and secured. This ensures consistent data quality and allows adapting as digital channels evolve.
Having this level of detailed understanding makes altering business processes less challenging since you are prepared to address concerns with a comprehensive explanation of how solutions can enhance the user’s experience.
Capture your data engineering strategy highlights and share them:
This required you to make actionable plans for scalability and delivery. This also includes measures, objectives, and outcomes that keep you on track so you can share them with your company as the journey takes place. Some aspects you can include in your data strategy are:
- Data privacy and security needs
- Cross-functional data needs to assist several uses cases
- Measures, outcomes, and objectives
- Recommendations, challenges, and observations
4. Establish controls
Implementing a modern approach to data management involves incorporating a strong governance and privacy capability, helping organizations to thrive amidst the ever-increasing volume of data. Organizations can enhance visibility and foster collaboration throughout their entire structure, regardless of the data’s location, by establishing a metadata and governance layer that includes all data, analytics, and AI initiatives.
Crafting a comprehensive data governance policy will influence data quality, privacy, security, and management behaviours while demonstrating how AI can streamline compliance efforts. The policy you enforce must promote the standardization of terminology for structured and unstructured data, allowing everyone in the organization to communicate effectively. To ensure optimal protection, all these measures should be supported by designated applications tailored to specific environments, aligned with security and regulatory requirements, and implemented through a hybrid multi-cloud approach.
Standardize your nomenclature:
Several organizations implement a knowledge catalogue to use metadata to help standardize your nomenclature. This allows users to curate, access, share, and categorize data. The objective is to ensure everyone is on the same page, often related to data quality, governance, and compliance.
5. Create integrated solutions
For a data and AI strategy to occur, companies sometimes need to refurnish their whole culture across new environments and concepts. Initiate by knowing what you can attain in short. Integrate your cross-functional team against clear goals. Below is one of the standard processes followed by experts:
- Plan for one to two weeks through discovery webinars and data strategy planning sessions involving a data topology mapping exercise.
- Prove takes around six weeks using a user-based data engineering use case set with learnable and actionable milestones.
- Adopt and scale through a test product tracked around internal stakeholders to ensure conversion.
The last section is significant. To provide clear knowledge of the advantages of any data strategy, ensure the C-suite, business users, and tech teams all have the same finish line in their sights.
Promote adoption from all directions by supporting data consumers:
It is not only related to being heads down in data. New data management architecture is commonly used to support the implementation of the company’s data and AI techniques from all directions. In this manner, you will optimize security, enhance key workflows, influence how your business communicates, and unlock new market opportunities, business models, and operational efficiencies.
6. Scale your team and processes:
The talent shortage is undeniable, yet numerous organizations are at a loss when addressing this issue. Resolving the skills gap requires going beyond conventional approaches to hiring and training. With companies urgently striving to meet talent requirements, several are modifying their education and experience criteria to fill positions.
Build strong partnerships across the organization.
On the most basic level, your role as a data leader is to guide your organization in making the most informed choices regarding data collection, management, and utilization. As you forge and enhance partnerships at all levels, remain receptive to feedback and foster a collaborative environment while preparing for unforeseen developments.
An intriguing phenomenon unfolds as you grow a data-centric organization. The deeper your vision threads with the organization’s DNA, the more you can relinquish control and instead support a culture where individuals are motivated to learn and embrace new responsibilities. Throughout this journey, maintain clear communication of purpose and goals and an eye on the future.
Final thoughts
Besides modernizing the company’s data and analytics environment, data engineering offers reliability, resiliency, scalability, and top-rated data management strategies. It enables organizations to gather, store, classify, and transform data and imply AI/ML technologies by resolving their downstream sets of applications.
In the near future, companies that focus on driving successful data strategy with invariably have to incorporate their data engineering strategy with a dedicated engineering team- either in-house or external experts.
