Modern businesses now collect more data than ever before to inform decision-making. Specifically, organizations rely on data analytics to boost operational efficiency, mitigate risks, and enhance strategic planning. With this in mind, the need for knowledgeable data analytics professionals continues to rise.
If you’re interested in advancing your own understanding of data analysis to improve decision-making and make more informed business moves, then it could even be time to elevate your education with a Master of Business Administration (MBA) in Data Analytics. Read on to learn more about the key data techniques, real-world applications, implementation steps, and career opportunities for data analytics professionals.
What Is Data-Driven Decision-Making?
In simplest terms, data-driven decision-making is a technique that more organizations are leveraging to make strategic business decisions that align as closely as possible with organizational goals and values. This process involves collecting and analyzing raw data in an effort to extract meaningful insights that will ultimately inform business strategy.
Key Techniques in Data Analytics
Within the realm of data analytics, four key techniques are commonly used — each contributing to decision-making in different ways.
Descriptive Analytics
With descriptive analytics, organizations rely on a combination of both current and historical data to identify patterns, pinpoint trends, and better understand recent or ongoing changes within a business.
Diagnostic Analytics
Another common type of data analytics used is known as diagnostic analytics, which aims to answer the question of why something has happened (or why something didn’t happen). Oftentimes, diagnostic analytics is used to pinpoint the source of business problems through techniques like correlation analysis and data drilling.
Predictive Analytics
Whereas diagnostic and descriptive analytics often look at what’s happened in the past, the aim of predictive analytics is to predict what might happen in the future based on historical trends and other factors. Businesses often use predictive analytics for risk mitigation and even to forecast demand for certain products.
Prescriptive Analytics
When organizations reach a crossroads and need to make a decision, they may turn to prescriptive analytics to help them decide how to move forward. Through the use of prescriptive techniques (like simulations and machine learning models), organizations can rely on prescriptive analytics to explore potential outcomes and support decision-making.
How to Make Data-Driven Decisions
Interested in using analytics to make better-informed decisions in your current role? The following few steps can help you think about decision-making through a data-driven lens.
Identify the Decision or Problem
Begin by identifying the specific problem or decision that needs to be made. By knowing what you want to achieve through data analysis, you’ll have an easier time deciding on the data analysis technique that will be most applicable to your situation.
Collect Relevant Data
Next, it’s time to start collecting data that will help you reach your decision. Remember that your data analysis outcomes can only be as good as your data, so it’s important to choose quality and relevant data sources here. This may include a mix of both internal and external data as well as qualitative and quantitative sources.
Analyze the Data
The next essential step in the process is to extract valuable insights from the raw data you have collected. Numerous tools can make data analysis easier and more streamlined than ever, ranging from business intelligence platforms to statistical modeling software and beyond. Deciding on the right tool for your data analysis goals may require you to refer back to your “why,” or the decision/problem you’re looking to solve.
Interpret Insights and Develop Strategies
As you interpret the results of your data analysis plan, you may begin to apply actionable insights to your own business operations. From there, you and your team can start strategizing and planning your next steps. In some cases, it may be necessary to account for alternative interpretations of your analysis in order to cover all of your bases.
Implement the Decision
Once you and your team have arrived at your data-driven decision, the next major step is to actually implement that decision at the organizational level.
Monitor Performance and Adjust as Needed
Even after the decision has been made and implemented, your data analysis work is not done. Ideally, you will continue to track and analyze the outcome of your decision and its effectiveness. Then, you can continue to make adjustments to optimize the outcome.
Real-World Applications of Data Analytics
These days, data analytics is integral to just about every industry imaginable — from retail and e-commerce to healthcare, finance, and beyond.
Retail and E-Commerce
In the competitive world of retail, businesses regularly collect and analyze data as a means of improving their marketing campaigns, optimizing their inventory, and even enhancing the overall customer experience. For example, predictive analytics may help forecast the demand for certain products, ensuring that they’re available to sell and ship when demand spikes.
Healthcare Industry
In healthcare, hospitals and other medical facilities rely heavily on data analytics to provide the best patient care, improve resource management, and even mitigate the spread of disease. In recent years, for instance, hospitals have begun to analyze patient health data to pinpoint individuals who may be at risk of certain diseases or medical conditions. From there, preventive measures can be taken to reduce risk.
Finance and Risk Management
In the world of finance, banks and other institutions often use data analytics for applications such as fraud detection, credit risk assessment, and optimizing investment strategies. More specifically, data analytics tools make it easier than ever to measure and track key performance indicators (KPIs) in finance, ranging from revenue to net income.
Manufacturing and Supply Chain
More often, manufacturers are using data analytics for predictive maintenance, inventory management, and logistics optimization. With the right data collection and analysis, for example, manufacturers can predict when machinery may need proactive maintenance to minimize downtime and reduce costs.
Benefits of Data-Driven Decision-Making
What do businesses stand to gain from applying data-driven decision-making in their everyday operations?
Enhanced Accuracy and Efficiency
When data analytics is used to drive decision-making, businesses can enjoy improved accuracy and efficiency in their everyday operations. That’s because decisions are made based on facts and objective information, which takes some of the guesswork out of decision-making.
Risk Mitigation
Data analytics can be especially useful when it comes to identifying potential risks so businesses can take proactive measures to prevent them. In this sense, data-driven decision-making can help businesses avoid potentially costly mistakes and keep operations moving smoothly.
Competitive Advantage
Using data science and analysis to make decisions, businesses can also gain a competitive advantage against businesses that are still relying solely on their own subjective experiences or instincts to make critical decisions. Likewise, businesses leveraging data have the potential to outperform competitors by identifying trends and readily adapting as needed.
Challenges in Implementing Data Analytics
Although data analytics can be highly beneficial to businesses across a wide range of industries, companies may face some common barriers when implementing data-driven strategies.
Data Quality and Integration Issues
Issues with data quality can present problems for businesses, especially when data is inconsistent or incomplete. With poor quality data, businesses may be erroneously led to the wrong decisions. Fortunately, strategies like data governance and data cleansing can be used to elevate the quality and reliability of data used for decision-making.
Skill Gaps and Talent Shortages
Throughout the industry, a shortage of data science and data analysis professionals continues to present challenges for businesses looking to improve data-driven decision-making. Businesses should be prepared to invest in their own training or make the leap to hire specialized data experts in order to get the most out of their data.
Data Security and Privacy Concerns
As businesses collect and store more data, there exists a growing concern over data security and privacy. Today, businesses must make cybersecurity a priority in order to maintain compliance with increasingly strict data protection regulations.
Career Opportunities in Data Analytics
There are a number of common career paths to explore within the field of data analytics.
Data Analyst
Data analysts are responsible for helping businesses collect, clean, analyze, and gain insights from raw data. They may also collaborate with stakeholders to present data in an accessible way using data visualization tools and reports.
Data Scientist
According to the BLS, “Data scientists use analytical tools and techniques to extract meaningful insights from data.” Similar to a data analyst, a data scientist sets themselves apart by looking at bigger-picture relationships between data sets.
Business Intelligence Analyst
Meanwhile, business intelligence (BI) analysts are responsible for gathering and analyzing data that is specifically used to make recommendations and offer insights to businesses as a means of informing their decision-making.
Database Architect
Last but not least, database architects are a crucial piece of the data analytics puzzle. These professionals are responsible for designing, building, and maintaining the databases used to collect, store, and organize raw data. This, in turn, can enhance data analysis and the decisions made as a result.
Harnessing Data for Smarter Decisions
Through data-driven decision-making, businesses can leverage raw data to achieve long-term success and gain a competitive advantage over other businesses. At the same time, the BLS projects more than 20,000 jobs for data scientists to open each year — while the supply of knowledgeable data analysts fails to meet this demand. For those who want to be part of the solution, now is the time to explore education and training options.
Learn More With an Online Graduate Degree in Data Analytics
Through the power of data-driven decision-making, organizations can use actionable insights to improve efficiency, mitigate risk, and gain a competitive advantage over other businesses. And with the right education and experience, you can leverage the latest data analysis, data visualization, and data management techniques to help businesses reach their full potential.
Ready to learn more? Johnson & Wales University Online offers an MBA in Data Analytics, which prepares business leaders to make data-driven strategic decisions, and a Master’s in Data Analytics focused on data science to pursue leadership tech roles in almost any industry. With courses in relevant topics such as big data analytics, research methods, and predictive modeling, you can delve into this exciting world while sharpening your business acumen and leadership skills. Get in touch to request more information about this program, or take the next step by applying today.
For more information about completing your degree online, complete the Request Info form, call 855-JWU-1881, or email [email protected].