6 traits of data-driven enterprises

Companies have learned to thrive — and in some cases survive — by leveraging data for competitive advantage. But how many organizations are truly data-driven enterprises?

“Data is becoming increasingly valuable, especially from a business perspective,” says Lakshmanan Chidambaram, president of Americas strategic verticals at global IT consulting firm Tech Mahindra. “Afterall, 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.”

Here are some key traits of a data-driven enterprise, according to experts.

They operate with an organization-wide data strategy

To be a data-driven enterprise requires having a cohesive, comprehensive data strategy that applies across the organization. This encompasses technology and automation, including the use of artificial intelligence (AI). But it also includes culture, governance, cybersecurity, data privacy, skills, and other components.

“The market for data governance, storage, and analytical tools has grown considerably, yet enterprises are still struggling to wrap their arms around the scope of the challenge,” Chidambaram says. “CIOs, CTOs, and [chief administrative officers] must step back and establish an enterprise-wide strategy to harness the value of data for their enterprise and integrate AI to enable sales, marketing, and operational excellence.”

This includes ensuring that the data architecture provides both data professionals and non-technical decision-makers with the tools they need to move beyond instinctual and anecdotal decisions, Chidambaram says.

“Many corporate and government enterprises are leveraging data-driven insights for improving customer service, reducing operating expenses, creating new business streams, and achieving overall business efficiency,” Chidambaram says.

Getting an organizations’ leadership and workforce to commit to a data-driven approach is key to determining success, Chidambaram says. “Organizations must ensure that they [address] the following question to call themselves a truly data-driven organization: Is everybody willing to embrace data as part of the business culture?” he says.

They optimize resource allocation

It’s one thing to develop a data-driven strategy; it’s another thing entirely to effectively execute on the plan. That’s where having the right resources in place and updating them as needed is important.

“Once the strategy is defined, the people, process, and tools to support the strategy are critical for a data-driven organization,” says Kathy Rudy, partner and chief data and analytics officer at global technology research and advisory firm ISG.

For example, organizations need to have a process for building data catalogs; procedures and tools for data cleansing and data quality; defined data use cases and the right tools to support the use cases; effective and secure access to data for internal and external users; overall security to support the use cases; and a data center of excellence to support complex data requests

From a people perspective, being a data-driven organization means having a solid team of data analysts, data scientists, data engineers, and other professionals in place, and providing the necessary training when skills need to be updated.

They emphasize data governance

Data governance is another component of the overall data strategy that warrants extra attention. Governance encompasses data security, privacy, reliability, integrity, accuracy, and other areas. It’s essential to maintaining a data-driven operation.

“Without data governance, you cannot trust that the data you are using is of high quality, is synced across data sets by a common taxonomy, or is secure,” Rudy says. “Data governance also provides the foundation for access to the data.”

ISG is often faced with disparate databases with differing taxonomies and ways of maintaining the datasets, Rudy says. “Once we established a centralized data governance methodology — with people, processes, and tools — we were able to develop new ways to use our data internally and externally for client delivery, products, and data monetization.”

The centralized approach also established proper security protocols for data access inside the business, Rudy says. “Many people think data should be democratized, though I’m not convinced of that,” she says. “Unless you truly understand the source for the data, how it was collected, the context of the data, and [how to] analyze data, improper use can lead to bad decisions.”

For example, when the ISG sales teams asked for account information, the data team began pulling reports and discovered there were multiple names for the same client. “This made it very hard to pull together a snapshot of business over time, what was selling, by whom, etc.,” Rudy says. “Lack of governance over our data led to dirty data in our system, and an incomplete picture of a client that might have led us to incorrectly design an account strategy.”

Responsible data use is paramount for data-driven organizations, says Deepika Duggirala, senior vice president of global technology platforms at TransUnion, a provider of financial services, healthcare, and insurance technologies.

“This means securing all data within an enterprise’s data ecosystem — both in motion and at rest — while maintaining the privacy of associates and consumers,” Duggirala says. “An enterprise must be able to evolve alongside growing data protection regulations, doing so by educating all associates on US and international data privacy and protection regulations, and building security and compliance into the initial design of all data storage and consumption. This mindset is how TransUnion makes trust possible and protects our data ecosystem and its compliance.” 

They establish a broad data mindset

Building a data culture and mindset is part of the overall data strategy, but it bears special mention because it truly helps bring the strategy to life.

“All aspects of decision-making are influenced by data,” Duggirala says. “Associates are fluent in its interpretation to better understand the market and make sound decisions. This is the core of TransUnion’s product development process — product managers, customer experience designers, and developers all leverage a different facet of our data to identify solutions that solve specific needs, define launch timelines, and ensure simple, intuitive features.”

At companies that are data-driven, “there is an organization-wide acknowledgement that data is at the heart of decision-making,” Rudy says. “So, when challenges are posed, questions are asked or strategy is designed, people automatically reach for data to support decision-making.”

At ISG, “from marketing and sales materials that describe our credentials, to client deliverables where data is used to substantiate recommendations, industry briefings where we back up our knowledge and expertise with data and facts, data truly is at the heart of everything we do,” Rudy says. “Data gives businesses a competitive advantage. We view data as circular. We are continuously in the process of collecting, validating, managing, curating, and analyzing data to drive insights for all our stakeholders.”

Data-driven organizations have many drivers, says Theresa Kushner, head of the Innovation Center for North America at consulting firm NTT Data. “This means that no matter where you sit in the organization, you can have access to the data you need to do your job,” she says. “Non-data-driven organizations are usually siloed in their approach to data management.”

NTT Data research shows that a minority of organizations say data is shared seamlessly across the enterprise. “In a data-driven enterprise this is not the case,” Kushner says. “Because these groups are directed by their leadership to make decisions based on data and because they have teams that pay special attention to key data sets, they can move quickly and drive their businesses using accurate, readily available data.”

Regular collaboration is key to having a data mindset. “Data is nothing without people sharing and using it,” Kushner says. “Effective data-driven cultures depend on efficient collaboration and open communication between owners of the data and its users. This trait of a data-driven organization supersedes all others such as training, certification, data governance, regular process updates.”

They make data collection a primary concern

Many AI projects are shelved in short order because data scientists cannot find the data that is needed for a proposed model, Kushner says. “Oftentimes this is because the data was never collected,” she says. “Data-driven organizations do not have this problem. They know which data domains are important and necessary to the running of the business, and they ensure that these datasets are protected and curated.”

For example, most companies have customer relationship management (CRM) systems that are used by sales to record and track opportunities, Kushner says. But the data in these systems is often incomplete for customers and their transactions, especially if data entry is the responsibility of the salesperson, she says.

“This means that when data scientists want to create a customer model that identifies those customers who will buy at a particular time or from a specific channel, the data they need might not be available or complete enough to support the model,” Kushner says. “Data-driven organizations, however, understand that this data is primary to running the business and as a result ensure that data management practices are thorough for key areas.”

In many cases, to ensure that data is entered appropriately, these organizations automate sales entry processes to free sales from tedious entry tasks. “Depending on the business type or industry, key areas may change,” Kushner says. “For example, manufacturers may find that managing the information on their suppliers more closely is their key data domain. No matter what industry, data-driven organizations have a plan for collecting, managing, and using key data.”

They foster strong collaboration between IT and the business

Data-driven enterprises tend to feature good working relationships between IT and business leaders. For example, when the CIO works closely with the finance department, a company can maximize the value of financial data.

“Delivering the right information, at the right time, in the right format to executives and managers requires a close partnership between the CFO and CIO,” says Lynn Calhoun, CFO at professional services firm BDO.

“This includes getting the finance and IT teams together to define information requirements, collaborating on setting up the right IT systems and architecture to meet those requirements, and working closely to implement and support agile systems and processes that can keep pace with today’s rapidly changing business environment,” Calhoun says.

In BDO’s case, “we work closely together to understand what the business ‘needs,’ not just what they ask for, which is usually constrained by what they know,” Calhoun says. That constraint limits the ability of the business leaders to achieve their goals, he says.

Analytics, Data Management, Data Mining