A Shifting Mindset: How Continuous Data Is Reshaping Business

Innovation A Shifting Mindset: How Continuous Data Is Reshaping Business Arvind Prabhakar Forbes Councils Member Forbes Technology Council COUNCIL POST Expertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based) Jun 15, 2022, 09:30am EDT | Share to Facebook Share to Twitter Share to Linkedin CTO & Co-Founder of StreamSets , helping companies enable DataOps and deliver continuous data under constant change.

getty In today’s business world, decisions are driven by data. All departments—from marketing to engineering to sales to operations—rely on data to measure progress, understand customers and inform strategy. As more companies undergo digital transformations, the enormous volume of data available can be overwhelming and difficult to manage effectively.

As an Accenture report notes, 74% of employees report feeling overwhelmed or unhappy when working with data, and 60% to 73% of all enterprise data is never analyzed. To maximize the value of constant data, businesses must shift toward a continuous approach. When integrated organization-wide, a continuous approach allows teams to capture, view and analyze data in real time.

This improves business operations by boosting speed and accuracy, enabling proactive engineering and allowing access to immediately actionable insights. “Old” Data Is Holding Businesses Back For years, the best (and only) way to observe and analyze data was through periodic dashboards. Companies would pull in data to analyze semi-regularly (weekly, monthly), then data teams shared their findings with key stakeholders, influencing business decisions.

Many companies still use this model. However, for high-growth businesses, data becomes outdated and irrelevant so quickly that analyzing it periodically is preventing them from having an accurate understanding of where things stand in real time. MORE FOR YOU Google Issues Warning For 2 Billion Chrome Users Forget The MacBook Pro, Apple Has Bigger Plans Google Discounts Pixel 6, Nest & Pixel Buds In Limited-Time Sale Event Week-old or month-old data is insufficient for large-scale competitive projects, data-driven decision-making or machine learning (ML) implementation.

ML and AI models become more accurate with the more data they receive, making them stronger through continuous data delivery and better able to support other parts of the business. In short, the older a company’s data, the more likely it is that the business will move slowly and lose its competitive advantage. Companies still looking at data in terms of periodic cadences (i.

e. , dashboards updated monthly) can transform their businesses by shifting their mindset about how they work with data. Implementing continuous data systems allowing for real-time analysis will enable them to move with agility and remain on the cutting edge.

Moving Data Engineering From Reactive To Proactive Changing how and when business leaders and data providers analyze data also creates an opportunity for the role of data engineers to evolve in impactful ways. In the past, data engineering has primarily been reactive . Data engineers are charged with frantically keeping up with data requests and fixing broken data pipelines.

Data engineers are also brought in to help with major infrastructure changes and transitions, most commonly to modernize legacy data systems and practices. This is where the most significant data challenges often arise. When businesses feel that their technology and systems are behind the times, they can push to implement changes quickly without thinking of the implications, creating containment issues and sprawling, inconsistent data that can take data engineers years to clean up.

Continuous data circumvents these stumbling blocks by providing the foundational layer to digital transformation and modernization, allowing companies to build out as they scale. Because continuous data (and DataOps, which it supports) enables automation, transparent monitoring and real-time iteration, companies can move fast and be confident in the quality of their data. As a result, as more enterprises adopt continuous data systems, business leaders are realizing that the role of the data engineer is more valuable when it’s proactive.

Through a proactive approach, data engineers can build exciting new data frameworks and pipelines that contribute to business growth. Data will only continue to grow in importance, meaning proactive data engineering holds the key to outpacing competitors. How To Transition To Continuous Data Operations Once business leaders agree that operations could benefit from continuous data, they can make the transition successfully through the following actions: • Shift the company mindset.

There needs to be a core shift in the way companies approach and value data. The mindset of the company must transform to view data itself as a product that needs to be continuously delivered to internal and external customers. Processes must change to align with the mindset shift, moving from post-facto reporting to reactive, real-time analysis.

• Operationalize. A key part of transitioning to a continuous data strategy is operationalization. To accomplish this, the flow of data needs to be a tier-one process—something that top business leaders care about and use to inform core business decisions.

Operationalization requires a different level of rigor than a monthly reporting model because it requires data pipelines to be always on, always working and extremely resilient. Businesses must adopt precise systems and checks and balances. If continuous data processes break in an operationalized environment, businesses can lose money—and upset customers—instantly.

• Federate your data model. The fully centralized data model in which one central data team within an IT organization develops all reports and controls everything related to data is very slow and is no longer a practical option. To adapt to the speed at which data moves, businesses should move to a model that is more decentralized.

Fully decentralized models can hinder scalability and knowledge sharing. Many businesses can find a balance by working within a federated model in which a core group of experts works at the center while project teams work within decentralized divisions to stay on top of day-to-day business demands. • Iterate continuously.

Under the continuous data strategy, businesses need to change their approach to project and release cadence. Instead of working for three to six months at a time on “big bang” projects, companies should begin to iterate much faster. When a business is iterating continuously, operations are always up to date and aligned with business goals.

This can prevent businesses from having to undergo enormous, costly pivots due to external forces. A Key Driver Of Future Success Continuous data will soon reshape business as we know it. As corporate mindsets and approaches to data shift, businesses will move faster and improve strategic decision-making, increasing revenue in the long-term.

Those who adapt sooner rather than later should be well-positioned to succeed and grow in any industry. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify? Follow me on Twitter or LinkedIn .

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