Data Analytics- How Can Businesses Improve their Management?

In 2020, around 44 zettabytes of data were produced, 40 times more than the observable stars in the universe. In modern times, big data shows few signs of slowing down.

Studies show that approximately 463 exabytes of data will be created each day by 2025.

Enterprises today need professionals with one of the most demanded skills- data analytics to transform a bulk of unstructured data into valuable business insights. Professionals may use data analytics to improve their performance by assisting organizations in liberating data, identifying and understanding patterns, and leveraging the findings in real-world enterprise applications. Thus, it is evident that data analytics enhances business operations in different areas, from empowering human resource management to guiding marketing teams.

Learn more from business leaders like Voyager Partners to solve some of the most complex business challenges using powerful technologies.

How Does Data Analytics Improve Business Management?

It finds storage in a bulk repository like a data lake during the first data creation. However, the sad news is that enterprises drown in data lakes. To simplify, these repositories have enormous data, which they may need to recognize.

Business analytics experts know how to collect, maintain, and analyze huge chunks of data- right at the enterprise level. Resultantly, business analysts have the skills to help enterprises use data analytics to boost business performance.

Wondering what these are? Here are some ways in which businesses tend to improve outcomes using data analytics:

• Optimize Workflows for Better Engagement & Productivity:

Business organizations are battling with “The Great Resignation,” a movement where millions of employees have quit their jobs and left their companies for better options. Resultantly, business leaders wandered for ways of attracting and retaining the best of talent and improving productivity & employee engagement.

Big data provides sophisticated HR platforms for managing the use, enabling retention, and improving productivity.

Research states that HRMS (Human Resource Management System) integrates with enterprise data to ensure better engagement, employee performance, personnel, organization, recruitment, training, and salary management.

• Turn a Keen Eye to Data for Enhancing Cybersecurity:

Compliance and risk management are the top issues on the minds of today’s business leaders. The global pandemic saw a spike in account takeovers, frauds, and other cybersecurity threats.

Leaders scrambled to find workable solutions for mitigating risk, meeting the newly imposed regulatory standards, and mitigating risk.

Research states that embedding data analytics into a risk management process has become more crucial than ever as it helps with risk prioritization mitigation, identification, reporting, and monitoring.

Data scientists are building analytical models that can detect potential risks, assess their impact, and balance the financial and strategic implications against certain investments for managing the associated risks. The greater the data, the more the risk around privacy and data usage compliance. As a result, large companies have access to internal and third-party data for protecting their assets, reputation, and customers by installing risk management analytics.

By installing risk management analytics, large organizations can access internal and third-party data to protect assets, customers, and reputations.

• Streamline Operations for Greater Efficiency:

Business leaders employ data analytics to identify inefficient internal processes and develop new-fangled streamlined workflows that enhance operational efficiency.

Data analytics helps with business management improvements by assisting leaders in assessing the current workflow effectiveness, analyzing process outcomes, automating new workflows, and refining them over time.

Data also allows the leaders to determine if the processes have become burdensome, draining the budget, or have become challenging. The gradual transition of leaders from slow-moving manual workflows to more streamlined processes has accelerated digital efforts to altogether new levels.

• Track Customer Behaviors for Enriched Customer Experiences:

The customer service future depends on a solid data analytics strategy. An everyday use of data analytics involves business outcomes for tracking customer behavior and improving user experiences.

As per McKinsey & Company, organizations now have access to many data sets, including internal data on customer transactions, interactions, and profiles. These are more available to third-party data sets that cover customer purchase behaviors, attitudes, digital behaviors, preferences, and new data sets on customer sentiment, health, and the location rendered by the Internet of Things (IoT).

Therefore, enterprises have the requisite information for predicting customer satisfaction, personalizing experiences, and launching new products & services to know their customers better.

• Monitoring Market Trends for Launching New Products & Services:

Successful businesses are primarily agile and can launch new products to the market quickly. The enterprises that use data analytics to monitor market changes include evolving customer demands ideating new product & service concepts, prototype models, and testing market offerings.

For instance, retailers have the ability to measure when customers shift to new products to identify the distinctive and most redundant SKUs. These retailers employ data to improve outcomes and services. Still, top companies like Netflix, Uber, and Google also use customer data to track how people use their products to make visible changes.

• Measure Marketing Campaigns Performance:

Marketing campaigns necessarily have to be data-driven, right from their conception till their execution. For successfully launching a data-driven campaign, teams must establish the key performance indicators for determining success metrics.

Post that, marketing teams should gather descriptive data about distribution channels, target markets, trends, and more. Marketing teams must A/B test advertisements to determine which of all written and visual messages connect with their demographics.

Ultimately, the marketing teams monitor and review their campaign results to identify the areas of strengths and weaknesses. For instance, every year Spotify, the music streaming app, leverages user data to generate the Spotify Wrapped feature. Here, they pack and gift their customer with music insights, including some top songs, moods, and genres.

With this, the music listeners will be able to share their listening habits across numerous social media platforms and transform the feature into brilliant user-generated marketing campaigns.

• Use Data-driven Decision-Making for Leading Teams:

A business strategy remains good only if its data is good. Since data-driven business strategies employ past situations for predicting future possibilities, helping leaders prescribe the best path forward.

For instance, Netflix used Big Data and business intelligence to become one of the best brands. It is evident that Netflix uses predictive analysis to recommend new shows to users and create better recommendations.

After analyzing around 30 million streaming habits in a single day with over 4 million subscriber ratings, and 3 million searches, Netflix developed new content, which included super-hit shows like “House of Cards.”

Therefore, this is a shining example of how successful a company can become when working on a data-driven business model.

Final Words

To obtain the best possible results from data analytics, an organization needs to centralize the data for easy access in a data warehouse. Stitching is a simple data pipeline that helps replicate all of the business’s data to the warehouse of your choice.