Enterprise Data Analytics Strategies for Optimal Decision-Making

In today’s highly competitive and ever-changing business world, organizations cannot afford to overlook the potential of data analytics in deriving the benefits of data-driven decision making. In a 2023 survey by NewVantage Partners, the authors noted that “Data, and the ability to make sense of it, has been one of the greatest drivers of innovation in both business and society in recent decades, and a primary driver of economic success in the 21st century.” Enterprise data analytics is the key that empowers organizations to make sense of data.

What is Enterprise Data Analytics?

Enterprise Data Analytics is the process of analyzing enterprise data to uncover patterns and insights for gaining clarity on business activities and taking data-driven decisions. It involves collecting, cleaning, processing, analyzing, and visualizing data to extract and present business insights.

Enterprise data analytics can help decision makers identify bottlenecks, increase efficiency, and optimize operations. Its integration in business workflows across departments – be it finance, marketing, supply chain management, or sales – can propel organizations to convert opportunities and handle challenges confidently.

Key Benefits of Enterprise Data Analytics

Organizations are already realizing measurable benefits by implementing enterprise data analytics. In the NewVantage Partners survey, 91.9% of CDO/CDAOs and data leaders stated that their firms had delivered measurable business value from data and analytics investment. Here are some key benefits of enterprise data analytics:

Make faster and better decisions

With enterprise data analytics, decision makers gain clarity of business activities by getting easy access to instant insights. Enterprise data analytics platforms with self-service analytics capabilities go a step further to empower any decision maker to do their own analysis, without depending on analysts or going through a steep learning curve. This encourages a data-driven decision-making culture and reduces the possibilities of guesswork, bias, or error in human decisions.

Gain comprehensive visibility into business

Just collecting data is not enough. It is important to connect data and see the emerging patterns. Enterprise data analytics can help organizations get a unified view of their business activities. By analyzing data from multiple sources and formats, enterprise data analytics highlights the anomalies and analogies, identifies segments or clusters, monitors trends and patterns, and compares data over time periods. This helps build a clear and comprehensive picture of the business which becomes essential to implement action plans and create strategies for the future.

Accelerate time-to-insights from advanced analyses

Performing advanced analysis such as determining root causes, predicting trends, or forecasting demand can take a lot of time and effort. But enterprise data analytics removes the complexities from such analyses and accelerates the process of extracting insights. With time-to-insights reduced from weeks or days to minutes, decision makers can use real-time insights to take quick actions to convert opportunities or prevent losses.

Enrich customer experiences with data

Enterprise data analytics can help organizations craft enriching customer experiences that help retain and grow customers. By analyzing customer data, organizations can study buying behavior, purchase patterns, and preferences effectively. Equipped with these powerful insights, organizations can make customer journeys more engaging and enriching with targeted products and personalized recommendations.

Improve productivity and operational efficiency

With better access to insights, users across departments can work cohesively, minimize wastages, and improve efficiency in processes. Enterprise data analytics can provide organizations insights on areas that require attention in terms of productivity, resource allocations, budgets, and team performance. These insights can be used to improve overall operational efficiency and improve productivity across organizations.

Industry-wise Use Cases for Enterprise Data Analytics

As per the NewVantage Partners survey, 87.8% of the organizations surveyed from industries like financial services, retail, healthcare, entertainment, government entities, and others, reported that their investment in data and analytics increased in 2022, and 93.9% are planning to increase investments further in 2023. This shows the quantum of successful use cases and applications enterprise data analytics has found across industries.

Financial Services

The scope of a Chief Financial Officer’s (CFO) work is no longer limited to backward-looking number-crunching tasks. In addition to monitoring financial budgets, managing working capital, and planning future allocations, CFOs today are also involved in crucial activities like financial reporting on environmental, social and governance (ESG) performance, ensuring compliance with financial stipulations, and preventing policy violations. In a 2021 EY survey, 39% CFOs chose advanced analytics or predictive analytics as their main priority for technology investments over the next three years. With the help of enterprise data analytics, CFOs can perform financial analytics easily to monitor expenditures, identify frauds, manage risks, and work on accurate forecasting.

Financial institutions can use the wealth of customer data to derive useful insights and automate routine processes with enterprise data analytics. For example, a bank or a financial institution can use the data about a borrower’s financial standing such as credit ratings, past transactions, references, and background verification, to decide whether they can extend loan or credit limits to the borrower and to what extent. Such data-driven decision making helps them protect themselves from defaulters and losses.

Marketing and Customer Success

As customer buying behavior and preferences evolve, it becomes important for marketing teams to analyze the available for increasing awareness and creating conversion strategies. With the use of enterprise data analytics, marketing leaders can craft experience-led customer journeys to offer hyper-personalized products and services. Instant insights on customer success metrics such as churn rate, renewal rate, retention rate, and satisfaction scores guide them to implement strategies in time for increasing customer engagement and improving customer satisfaction. They can track the performance of their campaigns in real-time. By analyzing data such as click-through rates, conversion rates, and customer engagement, teams can identify which campaigns are working well and which are not.

The deep insights discovered from data can be used to optimize marketing operations. Marketing teams can track channel spends, compare leads generated from multiple sources, correlate sales with campaigns, and calculate customer acquisition costs. With such insights available on demand, organizations can optimize their marketing budgets and improve their ROI.

Supply Chain Management

Supply chain management is crucial for the cohesive and smooth functioning of various operations within an organization. Organizations need to anticipate not just the demands of customers but also plan where, when, and how many products or services need to be made available to fulfill the demand. With enterprise data analytics, insights can be useful in removing the roadblocks in the factory-to-shelf journey, making it more efficient and economical. For example, warehouse managers can get real-time visibility on stock levels, monitor inventory turnover, and identify potential shortages. Such insights can help them refill products with highest demand quickly, reschedule deliveries, manage logistics schedules, thereby reducing stockout costs and handling the end-to-end supply chain orchestration efficiently.

5 Steps to Set Up Enterprise Data Analytics

1. Identify business workflows that require data-driven insights

Though data-driven insights can prove valuable in every business process, start with prioritizing a few key areas for implementing enterprise data analytics. Identify the decision-making processes that are complex, time-consuming, recurrent, and need the latest data. Consider adopting data-driven decision making for such processes to expedite actions, minimize risk, and reduce delays. Use the takeaways and learnings from these processes to further apply analytics to other business workflows gradually.

2. Uncover data sources for every business function

Different departments use different stacks for collecting and storing data. Decisions made based on incomplete and outdated data can lead to incorrect actions and disastrous outcomes. Perform a thorough inventory of all the data sources. This exercise uncovers silos and provides clear visibility of data available for enterprise data analytics. Consider moving to the cloud, as cloud-based infrastructure is scalable, accessible, and can be easily integrated with modern enterprise data analytics platforms.

3. Ensure availability of high-quality data

Once you have consolidated all the data, the next important step is to measure its quality. Check the data for any missing or incomplete values, inconsistencies, and errors. If the data itself is incorrect, has duplicate or missing values, and is not consistent in nature, it loses its credibility to provide accurate insights. Decision makers need reliable and relevant data to make accurate business decisions. So verifying the quality of data is a must to get accurate insights. Put in place clear data governance policies to ensure transparency and accountability.

4. Create a data-driven culture for better adoption of analytics

Often, in the race to collect and analyze data, organizations focus only on technology and tend to neglect the human factor. The NewVantage Partners survey found that only 23.9% of organizations characterize themselves as data-driven, and only 20.6% say that they have developed a data culture within their organizations. Traditional data analytics tools require users to use syntax and SQL for querying, leading to a steep learning curve, especially for non-technical business users. Remove these technical barriers by offering natural language search, and conversational user interfaces for analytics, thus making users self-reliant in adopting analytics. Don’t restrict data-driven decision-making abilities only to high-level executives. Consider enabling it for the larger frontline workforce too such as account associates, sales representatives, marketing teams, customer success teams, or retail store managers.

5. Choose an enterprise data analytics platform that offers decision intelligence

When evaluating an enterprise data analytics platform, look for abilities such as self-service analytics, decision intelligence, natural language processing, interactive visualizations, easy data integrations, customizable machine learning models, configurable AI algorithms, and cloud-first architecture. Additionally, a simple and intuitive user interface that enables business users to interact with data naturally ensures that they can do their own analysis without learning new skills or depending on analysts.

Take Proactive Actions with MachEye’s Enterprise Data Analytics Platform

MachEye understands the diverse analytics needs of today's organizations and goes beyond point solutions to offer an enterprise-grade data analytics platform. MachEye enables decision makers to understand “What” happened in the past, “Why” business metrics are changing, and “How” to act based on insights. With its intuitive user interface, intelligent search, and interactive visualizations, MachEye empowers any user, regardless of skill level and industry, to search, analyze, and interact with complex data in seconds. Using its robust AI and machine learning models, users can receive actionable insights on anomalies, analogies, trends, and clusters, along with personalized recommendations. This way, MachEye enables organizations to make sense of their data, implement insight-based strategies, and ultimately offer optimized experiences to their customers.