Although most businesses say that they are, or want to be a data-driven enterprise, turning this objective into a concrete, strategic plan depends upon a few key elements. To drive business success, a Chief Data Officer (CDO) or another data champion can start with the following guidelines, which must be aligned with the organization’s business objectives: 

• Foster a data-centric culture
• Prioritize use cases that further the chosen objectives 
• Invest in the technology that will yield the best results 
• Ensure data quality and governance by using the correct technology and ensuring adoption of company-wide best practices 
• Embrace collaboration so that decisions are based on a broad set of relevant data sources

However, a data driven approach is not an end, it’s a means to better processes and decisions that positively impact the business. These high-level guidelines are – well, high level. To implement a data analytics strategy, Gartner has defined its Data and Analytics Strategy and Operating Model (DASOM) framework. It is designed for D&A leaders to create a strategy that helps the organization become more data-driven, focusing primarily on business practices and less on the technological aspects. 

First, focus should be on creating value for customers. Onboarding such a strategy requires early buy-in from stakeholders, who can then influence the entire organization with the benefits of this framework. One of the best methods of getting this buy-in is by first examining each stakeholder’s business goals so that all stakeholders have a clear understanding of the “what’s in it for me?” Specifically, clarify how data analytics strategy can increase revenue for each stakeholder, instead of a broad, cross-company target. In addition, identify gaps and deficits in current data, culture, skills and analysis – not solely technologies –  across the enterprise. 

Gap analysis involves examining and evaluating the current methods of data collection, integration and management, data consistency, security governance and compliance. Organizations must also scrutinize their data architecture, technologies, and tools. 

At least half of the time spent on the data analytics strategy should be devoted to identifying stakeholders and their goals and linking these to measurable D&A initiatives and business outcomes. Interestingly, Gartner recommends not developing the D&A strategy’s vision, drivers and outcomes sequentially, as is usually done; instead create these components in parallel, since each element informs the others. When implementing this strategy, try to blend with the organization’s current mode – is it highly centralized or moving to a decentralized configuration or agile practices. This sensitivity can enhance the onboarding process. 

As noted by McKinsey, organizations can also adopt data democratization to enable all their employees to innovate and leverage data, as they will now have access to a fuller complement of company information. 

Learn more about Data Democratization here.

This data-first mindset begins at the top, with leadership and managers role modeling this approach. They should set a clear road map that prioritizes value and identify the sources of data that can power the necessary solutions. Carrying this mindset further, CDOs and their teams can be established as profit centers, to develop an enterprise data strategy, and incubate new revenue sources through data sharing and monetization of data services. 

On a practical level, organizations can implement self-service tools and training sessions. Here, new advances in technology can accelerate how quickly and effectively data is collected and integrated, leading to faster and more powerful insights. Generative AI, self-serve data, and low-code/no-code platforms are some of the technologies that expedite these processes. 

As noted in Garter’s “Creating a Modern, Actionable Data and Analytics Strategy That Delivers Business Outcomes,” improved automation for many tasks related to data and analytics means that managers have more independence and are now able to look at data and draw conclusions, without requiring the intervention of IT teams. This automation, combined with self-service and properly implemented data democratization enables the most effective decision making based on the widest knowledge possible. As with any other process, continuous evaluation, testing and reevaluation is required to measure the success of the data analytics strategy. 

Real-time Processing

For businesses to create a data and analytics strategy that results in tangible business outcomes; organizations must go far beyond collecting and analyzing data. Whereas monthly or weekly reports were once standard practice, now real time processing of data is required for many use cases. Users and consuming applications require accurate data within milliseconds, to be able to provide timely access that enables real-time decision making. 

A study from Unisphere Research of 2023 Modern Data Architecture Trends notes the top driver for considering and adopting a new data architecture is real-time operational analytics. Closely following are topics related to analytics performance, AI/ML and a need for broader analytics. Obtaining an integrated data management platform was next; closing the list was the need to reduce risk and improve security and compliance.  

An operational data hub is a proven way to power real-time operational analytics, utilizing an integrated data and application management platform. Organizations also need to ensure that they are delivering effective data solutions quickly and affordably without creating data silos, data bottlenecks, or conflicting data sets. Smart DIH, a leading operational data hub, offers a way to realize a data-driven enterprise, with fresh and complete data from enterprise systems of record (SoRs) that eliminates data silos and offers a single source of truth. Smart DIH delivers high performance, ultra-low latency, and an always-on digital experience that implements data-driven strategies.