Seven Attributes That Define the Data-driven Enterprise in 2025

Data is more than just a buzzword – it’s a fundamental force driving the present and shaping the future. Across vast swaths of industries, data is the lifeblood that fuels innovation, decision-making, and progress.

According to Mckinsey, data-driven organizations are not only 23x more likely to acquire customers, but they are also 6x as likely to retain customers and 19x more likely to be profitable. Every company must quickly assess its data analytics capabilities and chart a course for transformation to a data-driven enterprise. It is a crucial part of becoming more responsive to customers and to market opportunities, given the rapidly changing nature of technology and the marketplace.

McKinsey believes that by 2025, most companies will be using data to optimize their operations and empower better decision-making.  According to studies, the firm has listed the seven characteristics that will define the new data-driven enterprise.

1. Data needs to be embedded in every decision, interaction, and process  

Nearly all employees ‘naturally and regularly’ will have to leverage data to support their work. Rather than defaulting to solving problems by developing lengthy—sometimes multiyear—road maps, employees will have to use innovative data techniques to resolve challenges in hours, days, or weeks.

Embedding data in every decision, interaction, and process has the potential to provide valuable insights that can drive the growth and development of an organization. By incorporating data into every aspect of their operations, companies will not only save time, resources and increase efficiency but will also gain a competitive edge in the market.

2. Data needs to be processed and delivered in real-time

Real-time data processing and delivery will be a game-changer for businesses. This method is very important because it empowers leaders to make decisions based on the most current information available. This immediacy is vital in industries where markets shift rapidly, customer expectations evolve, and new opportunities or threats show up at any moment.

Examples of real-time data processing systems can be seen in everyday technology like bank ATMs, traffic control systems, personal computers, and mobile devices. All these applications are powered by a real-time processing architecture which forms the backbone of their operation.

3. Flexible data stores enable integrated, ready-to-use data

Teams will have to work with various database types, including time-series databases, graph databases, and NoSQL databases, facilitating the creation of more flexible pathways for organizing data. This will enable teams to easily and quickly query and understand relationships between unstructured and semi-structured data. 

Flexible data storage with advances in real-time technology and architecture will also enable organizations to develop data products, such as “customer 360” data platforms and digital twins—real-time-enabled data models of physical entities (such as a manufacturing facility, supply, or even the human body). This facilitates the creation of complex simulations and what-if scenarios using the power of machine learning or more sophisticated techniques like reinforcement learning.

4. Data operating model needs to treat data like a product

Data assets need to be categorized and supported as products, regardless of whether they are deployed by internal teams or external customers. These data products should have dedicated teams, or “squads,” aligned against them to embed data security, advance data engineering (for example, to transform data or continuously integrate new sources of data), and implement self-service access and analytics tools.

Teams using data products don’t have to waste time searching for data, processing it into the right format, and building bespoke data sets and data pipelines—an effort that ultimately creates an architectural mess and governance challenges.

5. The Chief Data Officer’s role needs to be expanded to generate value

Currently, many chief data officers are essentially responsible for compliance, policing data collection, and use – so that they meet regulatory guidelines and company policies. But eventually, their roles need to be extended.

They need to move from being a cost center to a profit center, encouraging the greater use of data within the organization, driving greater data literacy, and collaborating with business units to understand and meet their needs.  This allows organizations that are just getting started to centralize knowledge and involve fewer resources, if they were to set up cross-functional teams.

6. Making data-ecosystem memberships the norm

Data ecosystems, rather than siloed data sources, must become the norm.  Data siloes within the organization must be torn down, and ecosystems of shared data between organizations must emerge to improve the ability to make better decisions. This is particularly true when it comes to decisions pertaining to environmental and societal issues. On the whole, limitations in the exchange and combination of data will massively decrease, bringing together different data sources in a way that ensures greater value creation.

7. Data management needs to be prioritized and automated for privacy, security, and resiliency

Organizations must prioritize and automate data management for privacy, security, and resilience. Existing and emerging data privacy regulations, ongoing security threats, and the need for high-quality, timely data will require a greater level of data automation.

Benefits of the data-driven enterprise

The benefits of a data-driven enterprise aren’t limited to one aspect of the business – they boost the results in myriad ways, regardless of the industry or department.

A few of the benefits are listed below:

• The communication, products, and services are tailored specifically to the customer’s needs based on real-time data, leading to greater customer satisfaction.

• Since you always have data at your disposal, the key decision-making processes get faster and quicker.
• By gaining insights from the stored data, leaders and teams can develop new innovative business models
• Employees can automate time-consuming manual tasks to reduce costs while simultaneously freeing up time for more creative pursuits.
• Greater insight into the organization’s data won’t merely benefit just financially. It also helps identify other opportunities, like increasing diversity or pursuing sustainable business practices more effectively.

Example of data-driven organization

Netflix

The company constantly accumulates, analyzes, and relies on data input to boost its strategy and improve its customer satisfaction with new shows, correct suggestions, and better management of its customers. Netflix has achieved this top status because it gains insights from data that shows what triggers people to subscribe most, what makes them stay longer, and where to best invest the next. Also, by using the past and current data, Netflix is able to increase their subscribers and improve over all customer satisfaction.

Starbucks

The company uses customer’s data to gathers insights. It helps them in – creating a better customer experience, personalizing their strategy, improving service, gaining loyal customers, and maintaining popularity.

The right approach to transitioning into a data-driven enterprise

Although many organizations have adopted business intelligence and hired data scientists, they still have considerable work to do to become a data-driven enterprise.

CTOs and other business leaders need to recognize that building a data-driven culture requires a shift in behavior and a deeper understanding of how data can help in decision-making. Merely optimizing the data value chain and investing in data infrastructure is not enough. The real transformation happens when individuals throughout the organization embrace data as an enabler and an asset.

Getting both the leadership and the lower-mid-level employees to commit to a data-driven approach is the key to determining whether the transformation will be successful. Is everybody willing to embrace data as part of the business culture? Do leaders and the teams have the skills and know-how to derive insights from the data at your disposal? – These questions need to be addressed before paving the path ahead.

Moreover, orchestrating change, and doing it efficiently, requires executive advocacy, agility, data proficiency, and a broad, active community to ensure the mission, goals, and needs of the entire organization are met—in process and technology.

Leadership is a critical factor at the outset of any cultural transformation, and the data-driven enterprise is no different. Leaders need to lead from the front and set an inspiring example. Moreover, establishing a group of enthusiastic, data-savvy employees and IT specialists will help add momentum to the idea, bringing even the most skeptical people into the fold sooner or later.

The journey may be challenging, but the rewards of empowering everyone with data are immeasurable.

“Data is becoming increasingly valuable, especially from a business perspective,” says Lakshmanan Chidambaram ( President and Head of Americas Leadership Council at Tech Mahindra, and Americas Head, Mahindra Group). “After all, data can tell us a lot about a company’s processes and activities. It shows whether one is moving in the right direction, identifies areas of improvement, and suggests an appropriate process to make those improvements.”

In brief

The data-driven enterprise embodies a transformative shift in the way businesses operate. By embracing a data-centric culture, investing in tech infrastructure, and leveraging advanced skills, organizations can unlock their true potential.