Get Your Data Strategy Right With Data Engineering

Data-driven abilities are at the core of 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 executives reported achieving transformational business outcomes with data. There are many underlying reasons—lack of business alignment, fragmented data, lack of talent, to name a few. A lot of stakeholders don’t have access to data, and when they do, they don’t fully trust it. This in turn results in data activities that are short term and poorly connected to the business strategy.

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

• Lack of data-driven culture: Designing data strategy is a tedious process, and with a piecemeal approach, the going only gets tougher. Organizations often lack a top-down push to build a data-driven culture. There are cases where analytics leaders have strongly advocated the focus on data analytics but drifted back to legacy methods due to a lack of support from the leadership or vice-versa.

• Lack of business alignment: Data strategy is not just technology implementation but has to be viewed from a business perspective. Oftentimes the data teams are not cognizant of the business requirements and end up investing a lot of energy and money on tasks that are not strategically aligned with business goals.

• Data silos: Data silos often result in lost business opportunities, increased operating costs and lower decision-making accuracy. Companies spend weeks in data mergers, taking away time from actual data analysis, delaying the times-to-insights.

• Data accessibility: Business teams require direct access to data. However, in most companies, either this process is not set up or 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 low-quality data and ensures data accessibility. A poor data governance framework slows down data management and results in inconsistencies in data usability, integrity and security.

• Scalability and performance: Companies must be able to create a sustainable data strategy to deal with the ever-growing data. Most companies keep revisiting their data strategy initiatives once every few months and never reach a point where they are completely ready. The use of dated technologies results in low performance and no cost-benefit.

• Data privacy: Data protection has become a lot more complicated with increasing data volumes and use cases such as cookies, personalization, etc. Businesses should 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.