Data seems to be the buzzword of the day. And not without good reason.
Statistics shared by CloudTalk illustrate why data in business is such a big deal:
- 80% of businesses have increased their revenue by leveraging real-time data
- 84% of customer experience (CX) leaders included customer analytics as their main source of insights to achieve business success
- More than 80% of companies achieved quicker product rollouts with real-time data
- Real-time data increased positive customer feedback by 90%.
In fact, according to their figures, 65% of organisations will be fully data-driven by 2026.
There are different types of analytics: descriptive analytics (for example, sales reports showing purchase history trends), diagnostic analytics (for example, feedback and transaction data), predictive analytics (for example, historical data and statistical models), and prescriptive analytics. Let’s find out how they’re being used to press competitive edge.
How are companies using big data and data analytics?
Big data analytics is revolutionising key aspects of global business and industry. It’s enhancing everything from CX personalisation, operations and supply chain management, and cybersecurity, to sales and marketing strategies, product and service innovation, and employee productivity. It’s helping financiers to detect fraudulent activity, enabling e-commerce platforms to predict buying behaviour, and supporting medical professionals to identify those most at risk of developing certain health conditions.
And one area in which it plays a particularly transformative role is decision making. Data-driven decision making (DDDM).
As McKinsey put it: ‘rapidly accelerating technology advances, the recognised value of data, and increasing data literacy are changing what it means to be “data driven”’. They highlight seven characteristics of tomorrow’s – and, in some cases, today’s – data-driven enterprises:
- Data embedded in every decision, interaction, and process
- Data processed and delivered in real time
- Flexible data stores enable integrated, ready-to-use data
- Data operating model treats data like a product
- The role of the chief data officer is expanded to generate value
- Data-ecosystem memberships are the norm
- Data management is prioritised and automated for privacy, security, and resiliency.
What is data-driven decision making?
IBM define data-driven decision making as an approach that emphasises using data and analysis to inform business decisions and strategic decisions. It relies on data instead of intuition, assumptions, biases, or personal experience, using rich data sources such as financial data, market trends, and customer data/feedback, using artificial intelligence and machine learning algorithms to derive meaning and insights. In this way, data analysts can help leaders can make informed decisions that are based on facts, statistics, and trends – improving accuracy, reliability, objectivity, and impact.
Additional benefits of data-driven decisions and actionable insights are numerous:
- Increased business intelligence
- Better risk management
- Alignment of business strategy
- Anticipation of customer needs, market trends, and future trends
- Optimisation of key performance indicators (KPIs)
- Operational efficiency (by identification of inefficiencies)
- Enhanced customer/user experience and customer satisfaction
- Embedded data-driven, collaborative, organisational culture
- Healthier financial performance and profitability
- Enhanced business growth
- Supports data visualisation efforts
- Improved competitive advantage.
How can data integration improve decision-making processes?
Data integration supports a unified view of important data, collected from different data sets and sources and stored within a data warehouse. This more-complete, holistic view equips decision-makers with more accurate information and meaningful insights on which to base strategies, decisions, and business activities.
Data integration relies on numerous types of data sets, from financial data to industry trend reports to customer behaviour/customer preference data. Intuitive, easy-to-use dashboards and data visualisation techniques mean data integration has never been more accessible to business professionals. Track key metrics and key performance indicators thanks to analytics tools such as Microsoft Power BI, SAP, Python, and Tableau.
How are healthcare businesses leveraging data analytics?
In an industry facing systemic challenges – chronic underfunding, huge caseloads, an ageing population, staffing shortages, health inequity, archaic infrastructure, chronic diseases – doing more with less is a gamechanger.
And, fortunately, the possibilities of how data analysis can be used within the healthcare industry are essentially limitless. Medical facilities, of all types, are using data-based insights and healthcare to drive innovation. Data scientists are using insights to transform diagnostics, treatment plans, precision medicine, preventative medicine, telemedicine, public and population health, medical research, and health service management.
Here are four key areas in which big data analytics is leveraged by healthcare providers, together with examples of each:
- Improving the quality of healthcare services. Designing personalised treatment plans, predicting disease incidence, detecting more medically effective and cost-effective ways to diagnose and treat patients, and highlighting trends that lead to improvements in societal health and lifestyles.
- Supporting the work of medical professionals. Detecting diseases at earlier stages (when they can be more easily and quickly cured), identifying patients who have the highest risk of life-threatening diseases, and predicting disease progression, determinants, and risk of complications.
- Supporting scientific and research activity. Identifying patients with specific biological features to take part in specialised clinical trials, using modelling and predictive analysis to design better drugs and medical devices, and improving work on new drugs and clinical trials by analysing ‘all data’ rather than a test sample.
- Business and management. Identifying unnecessary or ineffective medical activities and procedures (such as duplicate tests), improving cross-function communication and collaboration between different teams, and gaining quick insights and correlations related to changing health industry trends.
Leverage high-impact healthcare data in your decision-making processes
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