90% of business analytical experts agree that a data-driven approach is pivotal to their organization's digital transformation goals. Yet, in a recent study by HBR, only 26.5% of organizations report having established a data-driven organization. Also, Gartner found that 65% of decisions are more complex (involving more stakeholders or choices) than they were two years ago. For example, conventional methods tend to have a ‘one size fits all’ approach. In modern decision-making, questioning the purpose of various data techniques brings a broader scope of potential improvements to the table.
In other words, mature, data-driven businesses utilize data to make better decisions and out-pace their competition. Here are a few use cases to illustrate this point:
- Commercial Real Estate (CRE) owners may leverage data from IoT devices to modify the design and construction of CRE buildings according to the changing consumption trends by employing tenants' and end-users' behavioral data. Data from IoT-enabled facilities help existing and prospective tenants make informed decisions for site selection.
- Retail mall investors can leverage traditional property data, such as footfalls, then combine it with alternative retail sales data from smartphone sensors, social media, and physical shop sales. They can then employ ML algorithms to study consumer purchasing behavior for a location or profile retail tenants. Brands also find it simpler to commit to spaces with convincing data surrounding key decision points, and the leasing process is greatly simplified.
- In healthcare, clinics and hospitals collect pools of data from patients: i.e., medical history, imaging, and lab results - a wealth of insight into patient health. In recent decades, this data has been digitized to create electronic health records, which allows practitioners to fetch patient history whenever required and ultimately to diagnose and make data-science-driven prescriptive decisions for individuals and larger cohorts of people.
The right data strategy will drive profitability for organizations that capitalize on it.
6 Characteristics of a Data-Driven Organization in 2023
7 of the top 10 most valuable companies actively use a data-driven strategy. And every company, from Apple to Amazon, relies on data to some extent to guide their most crucial decision-making procedures. Many businesses already exhibit at least some of these traits, and many more are starting to do so. In 2023, data-driven enterprises will be defined by these traits.
1. Data-Driven Decision-Making and Processes
Data-driven decision-making uses data to inform and evaluate an organization's decision-making process before committing to a course of action. This is one of the vital characteristics of data-driven organizations and avails the following benefits:
- Facilitates confident decisions
- Promotes proactiveness
- Helps save money
Organizations that use data for decision-making are 3X more likely to say that their decision-making has improved significantly. Big data users also experienced an 8% rise in profit and a 10% decrease in cost. Additional advantages that have resulted from the emphasis on data monitoring are:
- 69% of respondents acknowledged better strategic decisions
- 54% of respondents enhanced operational process control
- 52% of respondents increased consumer awareness as a result of big data
Implementing data-driven strategies across the organization can help avoid leaving money on the table. These strategies range from predictive systems to AI-driven automation.
For example, the HealthTech industry has seen extensive AI-based solutions based on the patient’s diagnostics and recovery data patterns. The traditional vs. value-based medical care model is a perfect example of artificial intelligence (AI) simplifying healthcare for all.
As per the widely adopted medical service model, patients pay for every consultation, test, scan, and prescription. As the name indicates, the fee-for-service approach incurs costs for each medical service rendered. This approach incentivizes providers with a lesser focus on the result of the treatment leading to higher expenses and lower accessibility to healthcare by individuals. A value-based medical care model emphasizes patients' clinical outcomes over the number of healthcare services. According to this paradigm, care providers are compensated according to the patient's treatment results deprioritizing the number of consultations, scans, etc. Health care requires a system that improves overall efficiency. And this transition from a traditional model to a data-driven one is facilitated by value-based care.
The role of AI in enhancing value-based care (VBC) is significant. AI creates algorithms that explore vast volumes of data for insights; over time, these algorithms can "self-learn" and "self-correct" to become more accurate. AI uses collaboration models, shared information, and technology solutions among stakeholders (physicians, clinics, other care providers, etc.) to promote better outcomes across the individual's lifespan. Automation and artificial intelligence can reduce the time in finding treatment options and augment many routine tasks. These elements work together to identify health concerns and forecast results. For example:
- AI can use unstructured and electronic health record (EHR) data to forecast a patient's health.
- AI can better screen for depression and dementia by identifying changes in voice patterns, and if a diagnosis is made, a more effective therapy may be suggested.
2. Easier and Quicker Data Integration and Accessibility
Data engineers often invest a great deal of time manually investigating and connecting different data sources. Furthermore, they often need to transform data from its organic, unstructured form into a structured format utilizing labor-intensive, non-scalable, and error-prone manual and customized methods.
To enable more adaptable methods of organizing data, practitioners are utilizing a variety of database types, including time-series databases, graph databases, and NoSQL databases. Teams can query and comprehend relationships between unstructured and semi-structured data more quickly and efficiently.
Integrating data from a myriad of sources to support accessibility and analytics is a significant challenge. Combining data from multiple platforms and putting it on a data integration platform to be converted into meaningful information expedites business goals. Done properly, data consolidation allows an organization to increase data accessibility, boost team communication, and get real-time work reports. When everyone in the organization has access to a consolidated data source, it becomes easier for them to evaluate the data for better decision-making.
3.Expanded Roles of a Chief Data Officer (CDO)
CDOs are becoming more commonplace and given more duties and responsibilities for generating measured business outcomes, with 80% of the most important KPIs being used to gauge their success in these areas. Some of the top KPIs for CDO success include:
- Innovation and revenue
- Productivity and capacity
- Operational efficiency and excellence
- Customer satisfaction and success
Chief Data Officers (CDOs) and their teams must develop rules, standards, and procedures to manage data and ensure its quality. The role of CDOs can be pivotal in transforming a company from being slow and unresponsive to quick and tech-savvy, from analog to digital, with the aid of advanced analytics and other technologies. Chief Digital Officers are often hired to lead digital transformation, establish P&L accountability, and develop a new profit center.
4.Improved Data Sharing Within and Between Organizations
The Sixth Annual Chief Data Officer Survey by Gartner found that respondents who successfully expanded data sharing led teams were 1.7 times more successful at showcasing the demonstrable, verifiable value to data and analytics stakeholders. Eliminating organizational silos, according to 86% of senior executives, is essential to increasing the use of data and analytics in decision-making.
Data marketplaces will make it possible to share, trade, and supplement data, allowing businesses to create original and proprietary data products and gain knowledge from them.
Large, complex companies will use data-sharing platforms to promote collaboration on data-driven projects within and between organizations. For example, Google introduced a data-sharing platform Analytics Hub in May 2021 to integrate datasets and share data, dashboards, and machine learning models. The company also introduced Datashare, built primarily for the financial sector, which was based on Analytics Hub. With Datashare, data publishers, aggregators, and consumers may collaborate to safely, swiftly, and conveniently exchange licensed datasets on Google Cloud.
Microsoft introduced Azure Data Share in 2019, allowing Azure users to share their data sets. Data sharing with Azure Data Explorer is now possible with Azure Data Share. Data providers may share data and manage their shares in one location by using Data Share. By defining the usage terms and conditions for their data sharing, they may maintain control over how their data is used. Before receiving the data, the consumer must agree to these conditions. Azure Data Share simplifies combining data from third parties to improve analytics and AI scenarios, improving insights.
Businesses leveraging these and similar data-sharing platforms will actively participate in a data economy that enables the pooling of data to produce more insightful information for all participants. For example, financial services companies can use data exchanges to develop new capabilities, such as giving publicly traded corporations environmental, social, and governance (ESG) scores to assist socially conscious investors.
5.Data-driven Decision-making around Data Security
With the Internet and cloud services, data resources are available anywhere in the modern digital world. While this presents amazing prospects for development and profitability for data-driven organizations, it also elevates the risk of data breaches.
True data-driven security-related decision-making will dramatically improve an organization's security posture. Clear and straightforward information about the following can help achieve data security.
- What data is readily available? Who created it? Who uses it and how?
- What are the highest risk areas to concentrate on (by vendor, system, or process)?
- Are hazards concentrated in specific areas of the organization?
Unfortunately, due to lack of necessary data or the abundance of irrelevant information, many businesses lack the ability to make intelligent decisions regarding data-related risks.
First, businesses must recognize a problem and evaluate their existing operations and processes. Then they should create a complete data roadmap. Using artificial intelligence tools can be part of an organization's data security approach because it is ideally suited to process large volumes of data. It will evaluate the company's current defense strategy, regularly validate it, and assist its security team in focusing on top priorities while considering financial and personnel limits specific to each organization.
Security Information and Event Management (SIEM) is an AI data security tool that employs rules and statistical correlations to generate actionable information on security events and enables security teams to deal with security events throughout the organizational environment. Security Operation Centers (SOC) specialists are better positioned to respond to data security risks in real time by using the information provided by SIEM. Security, Orchestration, Automation, and Response (SOAR) is another example of an AI-driven data security tool that detects low-level threats efficiently.
6.Enhanced Data Privacy:
As more people use digital technology, the generated data allows businesses to better connect with their customers while increasing the businesses responsibility to secure that data. For instance, many businesses utilize data to better understand their customers' demands and pain areas. Along with great insights, companies glean lots of personal data, including location tracking and other personally identifiable information. The smartest companies treat that PII very very carefully.
Leading organizations realize that data protection and privacy are not only government-mandated (in many cases) but can also add a point of differentiation as customers become more cautious about data sharing. Data leaders should, therefore, implement a comprehensive privacy program across the organization.
Proliferating breaches and consumer demands for privacy and control over their data have prompted governments to enact new legislation, such as Europe's General Data Protection Regulation (GDPR) and California's Consumer Privacy Act (CCPA). As per a study, since January 28, 2021, EU data protection authorities have penalized $1.25 billion for breaches of the bloc's General Data Protection Regulation (GDPR). Many more are following in their footsteps.
The GDPR is one of the toughest data protection legislation and has become a global model for data privacy rules in other places, such as California’s Consumer Privacy Act (CCPA). These data privacy regulations limit data collection from smart connected devices for data-driven organizations and, more broadly, on the collection, use, and storage of any PII.
Data-driven organizations empower people by securely and safely allowing employees easy access to data and providing interactive dashboards with actionable insights that drive success.
At Waterloo Data, we manage and resolve all things data. We focus exclusively on Data Warehousing, BI, Data Science, and Master Data Management. Our vendor-agnostic solutions integrate all the major tools and packages within the space. Our entire consulting practice is optimized to provide world-class customer data management services. Got complex data needs and challenges? We’re here to simplify and resolve them for you. Get in touch!
