When leaders say they want to be a data-driven organization, a key objective is empowering business people to use data, predictive models, generative AI capabilities, and data visualizations to improve decision-making. Leaders seek smarter decisions that yield positive business benefits, faster decision-making to respond to opportunities, safer decisions that minimize risks, and change management disciplines to grow the number of employees using analytics tools across the organization. They also seek scalable solutions using the latest machine learning models, AI capabilities, and new data assets, ensuring that data is compliant, protected, and secure.
While many organizations have invested in data architectures, deployed analytics tools, built machine learning models, and rolled out data visualization capabilities, end-user adoption may lag, and business impacts may be disappointing. This article looks at seven steps to help address gaps between just deploying analytics versus end-user adoption of analytics for decision-making. The first four steps focus on how individual teams, departments, and businesses can improve their analytics development process, while the last three are about scaling them across larger businesses and enterprises.
1. Identify End-Users and Their Decision Processes
Conducting preliminary research around a new data set or an analytics domain is important, but it’s easy to take these efforts too far and deploy proof of concepts into production, leaving out key steps in defining the end-user personas, reviewing their workflows, and discussing the decisions and actions where analytics are needed. “Historically, the way analytics has been developed was to start with well-organized data, slap a bunch of well-thought-out algorithms to it, review what the data confesses, and expose recommendations in the form of visuals,” says Soumendra Mohanty, chief strategy officer at Tredence.
This approach misses capturing input from the end-user who will make decisions in their daily activity, whether it’s an inventory manager, a campaign director, or a factory warehouse foreperson, and is looking for real-time recommendations and directives on an hourly basis to put them into action. Engaging with end-users to understand how analytics fits into workflows and what integrations and automation are possible is key. Consider asking end-users questions such as how, when, and how frequently they make key decisions today, the impact of a wrong or slow decision versus the value of making faster and more accurate decisions, what data they use, and what steps they take to access it. Understanding how analytics fits into these workflows is critical.
2. Set Data Integrity Standards and Solutions
Of course, many end-users won’t be able to distinguish statistical analytics, machine learning, and generative AI solutions, but they can easily see when the data is wrong or solutions produce erroneous recommendations. Improving data quality is an iterative process, but if not addressed early enough in the development process, end-users will lose trust and return to how they previously worked. “Ready-to-use, high-quality business data is essential for ensuring accurate enterprise analytics and leveraging the benefits of generative AI,” says Irfan Khan, president and chief product officer at SAP HANA database and Analytics.
Top organizations for agile data science teams take on data integration and quality requirements to deliver analytics capabilities. They’ll define data quality metrics as non-functional requirements, publish improvement efforts, and update stakeholders as metrics improve. Ensuring data quality evolves iteratively and addressing it early helps maintain end-user trust. By embedding quality checkpoints throughout the data pipeline, businesses can build a strong foundation before scaling their analytics operations, always keeping end-user trust and usability in mind.
3. Hasten Data Accessibility and Decision-Making
Beyond data quality, teams should focus on two other analytics metrics related to speed. Time-to-data accounts for the delays in receiving and processing data, while time-to-decision accounts for the human factors, usability, integration, and level of automation going from when data is available to when end-users make decisions. “Time-to-data used to be the privilege of high-frequency trading platforms years ago,” says Nikolaos Vasiloglou, VP of research ML at RelationalAI. Now anyone can access cheap, infinite storage, computing, and software tools to consume data in real time.
While more organizations can acquire scalable infrastructure, optimizing data management and developing robust data pipelines requires architecture planning and design. Validating architecture performance with smaller-scoped analytics objectives before scaling usage, data, and capabilities is crucial. By starting with smaller goals, teams can fine-tune their approach and ensure they have a robust, adaptable infrastructure capable of growing with organizational needs. This validation process helps avoid pitfalls and ensures that the system can handle larger-scale data operations efficiently.
4. Incorporate Data Security At the Outset
The rush to prototype analytics solutions and ensure low-latency data pipelines can come at significant risk and cost if regulated data is compromised. Implementing necessary data protections early can prevent these issues. “All regulated data should be cryptographically protected (encrypted, masked, or tokenized) early in the data pipeline when the data is created or captured,” says Ameesh Divatia, CEO and co-founder at Baffle. Once this is done, downstream data usage for all use cases, including generative AI, could go much faster since no additional data discovery or review is necessary before using that data.
Implementing data protection early in the process also creates the opportunity to engage end-users and stakeholders on data security best practices. By addressing the risks associated with rapid prototyping and ensuring data security from the outset, organizations can safeguard their most sensitive information, avoid compliance issues, and build a culture of security awareness among their teams. This proactive approach to data protection ensures that analytics initiatives don’t just deliver valuable insights but also maintain the integrity and security of critical data assets.
5. Expand Data Governance Initiatives
Scaling analytics-driven decision-making to multiple businesses, departments, or domains requires evolving an analytics operating model and establishing data governance policies and practices. Felix Van de Maele, CEO of Collibra, shared how even very large enterprises can establish data governance practices quickly. “Data governance is the foundation for unlocking the true potential of AI,” he says. “McDonald’s, one of the world’s most recognizable brands, established a trusted data foundation in just 60 days with over 570 users across 21 countries already on board.”
A key data governance tool for scaling data-driven organizations is the data catalog, which helps implement access policies, configure authorizations, enable discovery, and maintain data dictionaries. Top data catalog and quality vendors include Alation, Collibra, Informatica, Google, Hitachi Vantara, IBM, Microsoft, Oracle, Precisely, SAP, SAS, and Talend. “Data catalogs that provide robust data governance and proactive quality monitoring drive confident business decisions,” says Emily Washington, SVP of product management at Precisely.
6. Set and Enhance Implementation Guidelines
Creating implementation standards sometimes falls under data governance, but the tools, development lifecycle, testing, deployment requirements, documentation, and usability standards cover a broader set of disciplines. Data-driven organizations create and evolve standards so that data science teams focus on the end-user and deliver benefits. A standards playbook helps accelerate delivery, scale best practices, and establish deployment requirements. Marty Andolino, VP of engineering at Capital One, shares recommendations regarding creating data standards and their benefits. “Data standards, such as metadata, quality, formats, SLAs, and observability, ensure integrity, ease of use, and security throughout the data lifecycle.”
Another best practice for smarter data visualizations is to define a style guide covering layouts, chart types, color schemes, naming conventions, and other usability considerations. Dashboards may be underutilized when they’re too slow, not oriented to solve specific problems, or where multiple dashboards lack usability standards. Larger enterprises with large-scale operational, analytical, and unstructured data sets should also define data management and architecture standards. Aislinn Wright, VP of product management at EDB, advises that “organizations should adopt a data platform that unifies transactional, analytical, and AI data and implement open and portable standards for deploying new analytics and data science projects rapidly.”
7. Foster a Data-Informed Culture
Technology capabilities, data governance, and analytics practice standards are the building blocks, but digital trailblazers must evolve the culture to truly transform into data-driven organizations. Improving communication and collaboration across all units of the organization to facilitate information sharing and decision-making is critical. “Companies need to focus on breaking down silos between business units, functions, and technologies that hinder information sharing and informed decision-making,” says John Castleman, CEO of Bridgenext.
Regularly scheduling demonstrations of new and enhanced analytics capabilities, highlighting their impact, and celebrating user success can promote the benefits of analytics adoption. While there may be some initial fears about using new tools and analytics for decision-making, showcasing successful and satisfied end-users helps promote adoption. By fostering a data-informed culture and promoting the adoption of analytics capabilities, organizations can unlock competitive business advantages and drive transformation. Starting with the end-user in mind, building trust in data, evolving data governance, and improving implementation standards are essential steps in this journey.
