This is the age of data-driven business. Organizations large and small are leveraging insights from data science to optimize operations and drive efficiency, productivity and growth.
Yet simply having access to big data sets isn’t the same as making the most of that information. Poorly used data can affect an organization in negative ways. Effective analytics is key to translating data into actionable insights.
This importance is clear in the design of Northern Kentucky University’s online Master of Business Administration degree program. In this MBA program, students choose two specialization course stacks from a number of options. The advanced study of data analytics is at the root of two of these options: 1) Data Analytics and 2) Innovation Through Competitive Intelligence. These studies prepare students to effectively use data to drive business decision-making and innovation.
Is Using Data Analytics a New Development in Business?
In many ways, businesses have always been analyzing and using data to inform their decision-making.
For instance, advertising companies in the early days of television would test ads and product placement in TV shows by judging the efficacy of those campaigns based on data (i.e. customer feedback, sales and revenue). The success of those marketing campaigns would inform future decisions in campaign strategy and design.
Rapid advancements in modern technology have spurred exponential increases in the potential of data analytics. With the birth of big data’s availability on digital platforms and the development of machine learning and other innovations, modern business leaders can leverage data analysis in unprecedented ways.
What Are the Basic Components of Modern Data Analytics?
Modern data analytics is the process of gathering a large amount of data, organizing and “cleaning” it and analyzing the final data to find patterns, correlations and the like. Insights come from drawing conclusions based on these patterns.
Data analytics breaks down into four basic types.
- Descriptive analytics illuminate what has happened.
- Diagnostic analytics explore why these things have happened.
- Predictive analytics forecast likely outcomes.
- Prescriptive analytics indicate beneficial courses of action based on all of the above.
Businesses use insights drawn from data analytics to make decisions, solve problems, address challenges and react to changes and disruptions in markets. All of this forms the basis for effective management, business intelligence (BI), innovation and strategic planning. Basically, data analytics helps businesses optimize operations, improve efficiency and increase productivity, all resulting in growth in organizational function and profits.
What Are the Benefits of Data Analytics for Today’s Businesses?
Data analysis can take place in real time with advanced computer algorithms and software based on technologies like machine learning. Modern business intelligence tools can render analysis into usable forms like data visualizations, allowing less data-savvy end users (e.g. management at various levels) to interact with information quickly and easily.
Business leaders can draw actionable insights from data analytics to inform rapid, iterative decision-making, allowing them to adjust strategy as needed. This can improve decision-making and leadership in many ways.
For example, data analytics can help supply chain and logistics professionals maximize efficiency in their department’s operations, cutting costs and expediting sourcing, manufacturing and distribution.
Marketing professionals can glean insights from multiple data sources (social media, web traffic, customer feedback, etc.). These insights improve campaign strategy and media design, budget allocation, high-value target market selection and other functions.
Human resources management can analyze data on employee productivity, incentive models, professional development programs, employee satisfaction and other factors to maximize effective hiring, job placement and employee performance and retention.
Executive leadership and top-level management can use broad scope data analytics tools to inform organizational restructuring, product selection and development, market expansion and strategic planning. All of this leads to improved competitive intelligence and data-driven decision-making, allowing businesses to develop competitive advantages, scale their operations and rapidly adapt to disruptions and market volatility.
Further, business decisions rooted in data on customer behavior and feedback can improve brand loyalty and retention. Today’s customers have high expectations for interaction with a company, so it makes sense to incorporate their feedback into product development and customer service. Poor data analysis and bad customer relationship management can have long-lasting negative effects on loyalty and retention.
What Are the Dangers of Poor Data Analytics?
Without proper cleaning, organization and other aspects of data quality management (DQM), low-quality data can misinform business decision-making through incorrect analysis and inaccurate predictions.
Poorly designed or executed data governance models can create data access problems. This may result in people within an organization accessing and potentially corrupting data.
Poor data security can lead to breaches, giving outside users access to sensitive personal information. This can erode the public’s trust and result in violations of privacy policies and data privacy laws and regulations.
Organizations can benefit greatly from effective data analytics. On the other hand, poor data analytics can be a liability for businesses. Developing advanced competencies in data-driven decision-making is essential for today’s business leaders.
Learn more about Northern Kentucky University’s online MBA program.
Sources:
Forbes: The Age of Analytics and the Importance of Data Quality
Entrepreneur: Why Data Analytics Can Help Drive Sales for Your Business
