Get your data strategy with data engineering

Lokesh is the co-founder and CEO of Sigmoid, a leading data solutions company. He is a visionary and thought leader in the industry.

Data-driven capabilities are central to an organization’s digital transformation and AI journey. However, according to research, only 30% of companies have a well-articulated data strategy, and only 29.2% of leaders said they have achieved transformational business results through data. There are many underlying reasons: lack of business alignment, fragmented data, lack of talent, to name a few. Many stakeholders don’t have access to the data, and when they do, they don’t fully trust it. This in turn translates into data activities that are short-term and poorly connected to business strategy.

Challenges when building a robust data strategy

While organizations are eager to adopt data-centric initiatives, many are struggling to adopt technologies and processes to deploy models in production. One of the toughest challenges for AI and advanced analytics is managing data at scale. With the growing number of data sources, organizations need to ensure seamless data integration and accessibility. Some of the other challenges are:

• Lack of data-driven culture: Designing a data strategy is a tedious process, and with a piecemeal approach, things only get more complicated. Organizations often lack the top-down impetus to create a data-driven culture. There are instances where analytics leaders have strongly advocated focusing on data analytics but have reverted to legacy methods due to a lack of executive support or vice versa.

• Lack of business alignment: Data strategy is not just the implementation of technology, but must be considered from a business perspective. Often, data teams are unaware of business needs and end up investing a lot of energy and money in tasks that are not strategically aligned with business goals.

• Data silos: Data silos often lead to lost business opportunities, increased operating costs, and less accurate decision-making. Companies spend weeks merging data, taking time away from analyzing real data, delaying time to get insights.

• Data accessibility: Sales teams need direct access to data. However, in most companies this process is either not in place or it is not fast enough for them to take action. Getting the right data at the right time is a major challenge.

• Poor data governance: Data governance protects organizations from poor quality data and ensures data accessibility. A poor data governance framework slows down data management and leads to inconsistencies in data usability, integrity, and security.

• Scalability and performance: Businesses need to be able to create a sustainable data strategy to deal with ever-increasing data. Most companies keep revisiting their data strategy initiatives once every few months and never reach a point where they are completely ready. Using outdated technology results in poor performance and no cost-benefit ratio.

• Data confidentiality: Data protection has become much more complicated with the increase in data volumes and use cases such as cookies, personalization, etc. Businesses must follow industry-specific compliances such as HIPPA, PCI, and PII to meet privacy needs.

• Lack of specialized skills: Companies often rely on software engineering methods to build data strategies. While software engineers can maintain databases, take care of the backend, and write code, it requires specialized skills to efficiently support large volumes of data, build data pipelines, and ensure reliable data flow. and ready for consumption.

How Data Engineering Helps Address These Challenges

To establish a strong database, the first step is to instill a strong data culture and align business requirements with data initiatives. Everyone from the board of directors to CEOs and AI leaders should work to create a culture to take full advantage of data. Aligning efforts with data and keeping the extended team on the same page can seem daunting, but focusing on it early allows leaders to embed powerful data-driven decision-making into the bigger picture. the company.

One of the biggest cultural shifts towards data management is the transition from a legacy technology environment to a modern technology environment. A modern data architecture ensures that the organization’s data is scalable, accessible, and trustworthy. Adopting data engineering practices brings this modernization to the data infrastructure, which is future proof and data privacy compliant. It integrates data into centralized data lakes, data hubs, and data warehouses to make data accessible.

With robust data platforms, companies can break down silos and make it easier to collect data from multiple sources while ensuring accurate data is available to different stakeholders. ETL/ELT operations help organizations extract, cleanse, transform, load, and build reliable data pipelines that allow businesses faster, near real-time access to quality data and insights.

Unlike traditional approaches, where processing and analyzing large volumes of data carries risk, a data engineering practice ensures strong data governance. It establishes, manages and communicates information policies and mechanisms for the practical use of data and the maintenance of data pipelines. Data engineering can also facilitate the automation of data integration by infusing agility into the data management process and creating a friendly culture for the organization. Additionally, strong DataOps and MLOps practices operationalize data pipelines and analytical model management to ensure continuous delivery of results.

Regarding scalability, data engineering practices overhaul traditional software engineering practices and legacy technologies with data agile processes. Data engineering practices can scale stateful data systems and handle varying levels of complexity that traditional software engineering practices fail to address.

Finally, companies should not overlook the power of strong leadership and identify the right roles, such as a CDO, data analyst, data scientist, data engineers, and business leaders, to assign the right tasks. Facilitating open communication between the data engineering team and other stakeholders will boost the process of designing a strategy that is not only data-driven, but also aligned with growth goals.

Conclusion

In addition to modernizing the organization’s data and analytics environment, data engineering provides scalability, resiliency, reliability, and data management best practices. It enables companies to collect, store, transform and classify data and get the most out of their AI-ML projects by addressing their downstream application sets.

In the near future, organizations that intend to drive a successful data strategy will invariably need to integrate their data management strategy with a dedicated engineering team, either internally or through external specialists.


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